Where do the best baseball players come from?
Will a baseball player´s birth location or college location relate to salary or awards?
Attributes will be needed to describe the location each player is from, these will form independent variables.
These will give a variety of values about where the player came from. However, it boils down to two independent variables, college location and birth location. These variables have different scales i.e. country, state, city. The right granularity will need to be chosen.
To use more independent variables height and weight can also be investigated.
Salaries, AwardsPlayers, AllStarFull and/or HallofFame can be used to give an indication to the quality of the player. Any of these can be used as a dependent variable or a dependent variable could be created out of a combination of these variables.
The dataset has been processed using numpy and pandas to clean, create new variables and merge tables together. See further into the report for a section on data processing. See the preprocessing module for the code used.
An examination of the data will be described followed by data analysis and conclusions. The data analysis is not exhaustive so observations to do not lead to robust conclusions in this report. Any inference is tentative and would require further work to become robust.
from __future__ import print_function
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import os
# Use the top level of the repository
os.chdir(os.path.join("../.."))
# Helper functions made to create polished plots
from ballbase import figures
# Config the matplotlib backend as plotting inline in IPython
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import Baseball_data_investigation
df = Baseball_data_investigation.main()
Overall the dataset is well organised and good to use.
Some datasets display issues regarding how the values are populated
figsize = (15, 15)
# Needed to set up figure style
figures.common_set_up(figsize)
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=figsize)
fig.suptitle("Distributions of Weight", fontsize=16)
fig.subplots_adjust(hspace=0.18, top=0.95)
figures.univariate(df['weight'].dropna(), 'Weight', rug=False, bin_n=None, ax=ax1)
figures.univariate(df['weight'].dropna(), 'Weight', rug=False, ax=ax2)
figures.univariate(df['weight'].dropna(), 'Weight', rug=False,
x_truncation_upper=200, x_truncation_lower=150,
formatting_right=False, ax=ax3)
sns.despine(offset=2, trim=True, left=True, bottom=True)
Weight highlights this well. The three figures above are of the same dataset, by reducing the bin numbers to the number of unique values it can be seen that common weight measurements are taken every 5 pounds. The lowermost figure limits the x-axis to highlight this further. Some values are populated according to a higher granularity. A solution to this issue would be to bin weight or similar issues.
Another common theme is highly skewed datasets.
fig_a = figures.dist_transform_plot(df['mean_salary'].dropna(), 'Mean Salary', bin_n=None)
The example of salary shows lognormal distributions with a large dispersion of values towards the maximum value. The number of appearances in All Star matches, player awards and all forms of salary information display this style of data.
The majority of baseball players are born in the USA. This can be seen in a binary plot showing ratio of players born in the USA using the total data set
fig_c2 = figures.boolean_bar(df['birthCountry'].dropna()=='USA', 'USA as birth country', annotate=False)
fig_c3 = figures.boolean_bar(df['college_country'].dropna()=='USA', 'College in USA', annotate=False)
(df['college_country'].dropna()=='USA').describe()
This combined with the entire amount of college location information being sourced in the USA steers this investigation to primarily focus on the USA.
It is beyond the scope of this investigation to do a complete audit of all data in this database. Outliers will be assumed to be realistic, nan values will not be interpolated. Queries will ignore missing values.
The reason for this is to look for trends in players that contain the corresponding data rather than interpolating any salary or other information for this analysis.
Two key independent variables for this assessment are player´s birth state and college state. Both of these are categorical.
California is highlighted in both bar graphs below as the most common occurrence. There is a variety across the other states, the two count bar graphs do not give any information about how related a birth State and college State is.
# df where birthCountry == USA, sort on birthState then display birthState
fig_c4 = figures.count_bar((
df[ # From DataFrame
df['birthCountry'] == 'USA' # Select only USA as birthCountry
].sort_values(['birthState']) # Sort by birthState
['birthState']), # Display birthState
'Birth State of Players',
highlight=4
);
fig_c3 = figures.count_bar((df.sort_values(['college_state']) # Sort by birthState
['college_state']), # Display state of college
r"Player´s College State",
highlight=3);
The birth city has 2208 unique values in the investigation data set while college cities have 721, giving too much granularity to be considered of use at this stage of the investigation. The state is a more usable aggregated category for analysis.
The following section is a brief, preliminary data analysis. This is not a thorough exploratory data analysis or a more sophisticated data analysis to test any hypotheses in the data.
The question is related to the effect of geographic location on the quality of base ball players.
To begin a different independent variable can be compared to the dependent variables to see if there are no relationships. Height and weight would not be expected to have a strong correlation to dependent variables.
Height vs. weight, two independent shows a strong correlation.
sns.jointplot(x='weight', y='height', data=df[['weight', 'height']].dropna(),
s=40, alpha=0.1, color="grey", edgecolor="w", linewidth=1, size=8);
sns.despine(offset=2, trim=True, left=True, bottom=True)
When comparing one of these independent variables to a dependent variable like the mean career salary there is little correlation. The distribution reflects the normal distribution of the weight variable.
sns.jointplot(x='weight', y='mean_salary', data=df[['weight', 'mean_salary']].dropna(),
s=40, alpha=0.1, color="grey", edgecolor="w", linewidth=1, size=8);
sns.despine(offset=2, trim=True, left=True, bottom=True)
This is one example where there is not a strong relationship, the Pearson correlation coefficient is only 0.18.
Before investigating the geography of a player the dependent variables can be compared to each other. There are a few options here from salary, Allstar counts and award counts that can be compared to the independent variables.
The mean career salary and max ever career salary are compared in the next plot. These show a strong correlation. Note all these plots omit players who do not have both of the variables recorded.
sns.jointplot(x='mean_salary', y='max_salary', data=df[['mean_salary', 'max_salary']].dropna(),
s=40, alpha=0.1, color="grey", edgecolor="w", linewidth=1, size=8);
sns.despine(offset=2, trim=True, left=True, bottom=True)
The mean annually standardized career salary compared to the max standardized career, salary shows a similar relationship with a slight curve upwards towards max salary.
sns.jointplot(x='mean_salary_standardized_annually', y='max_salary_standardized_annually', data=df[['mean_salary_standardized_annually', 'max_salary_standardized_annually']].dropna(),
s=40, alpha=0.1, color="grey", edgecolor="w", linewidth=1, size=8);
sns.despine(offset=2, trim=True, left=True, bottom=True)
Comparing award count to Allstar count there is a correlation that scores 0.7 on the Pearson correlation coefficient.
sns.jointplot(x='allstar_count', y='award_count', data=df[['award_count', 'allstar_count']].dropna(),
s=40, alpha=0.1, color="grey", edgecolor="w", linewidth=1, size=8);
sns.despine(offset=2, trim=True, left=True, bottom=True)
The independent variables of a players birth state and college state are used to describe their geographic origins. The state is chosen as city is too granular.
The two bar charts show the mean of all players means career salary in a state. It also includes a 95% confidence interval.
figures.bar(df['max_salary'].dropna(),
"Mean of Max salary by birth State",
x_v=(
df[ # From DataFrame
df['birthCountry'] == 'USA' # Select only USA as birthCountry
].sort_values(['birthState']) # Sort by birthState
['birthState']),
ax_size=(28, 6),
highlight=0
)
figures.bar(df['max_salary'].dropna(),
"Mean of Max salary by College State",
x_v=(
df[ # From DataFrame
df['birthCountry'] == 'USA' # Select only USA as birthCountry
].sort_values(['college_state']) # Sort by CollegeState
['college_state']),
ax_size=(28, 6)
)
The next two images use box plots to describe the distribution of max salary standardized annually by birth state and college state. The limitation of this plot is that there are different numbers within each state making the distribution of values challenging to interpret.
sns.boxplot(x= (df[df['birthCountry'] == 'USA'].sort_values(['college_state'])['college_state']),
y=df['max_salary_standardized_annually'].dropna(), data=df, color="grey")
sns.despine(offset=2, trim=True, left=True, bottom=True)
sns.boxplot(x= (df[df['birthCountry'] == 'USA'].sort_values(['birthState'])['birthState']),
y=df['max_salary_standardized_annually'].dropna(), data=df, color="grey")
sns.despine(offset=2, trim=True, left=True, bottom=True)
To compare both categorical variables of college and birth state along with a third variable a heat map can be used. This allows for relationships and trends between the two independent variables to be studied. For example how many of those born in a state remains in the same state for college.
The first heat map shows only players who have been present in an Allstar match. The scale is fitted using quantiles so does not show the full range of occurrences but rather gives a feel for where there are more Allstar players. For each pair of states, the number of Allstar matches players have played in is summed.
The first thing that stands out is the diagonal marking the times when a player goes to college in the same state, they were born in.
# Make an arrays to be used for heatmaps
df_2d = df[df['birthCountry'] == 'USA'].groupby(['college_state', 'birthState']).sum().unstack('birthState')
df_2d = df_2d.fillna(0)
plt.figure(figsize=(20,15))
sns.heatmap(df_2d['allstar_count'], linewidths=.5, square=True, robust=True)
California (CA), Arizona (AZ) and Texas (TX) all show that they have colleges that have taken all star players born in other states. In the case of California because there is such a high count of players from this state, there are also a number of all star players being born in California and going on to study in different states. Arizona is an interesting state in which it has players attending college from other states but no Allstar players born in Arizona leave for other colleges in other states.
The final heat map shows a sum of all players annually standardised max salary compared to birth state and college state. A divergent colour scale is used to show if it is positive or negative, however, keep in mind this is is the sum of all players in the pair of states so states with very few players compared to pairs of states with a much higher number of players will act differently. An extremely high or low value may just be that there is only one player from that state. This is a flaw in the plot but at this stage in the investigation, the sum gives an impression if there are any general trends regarding baseball players from those states.
The figure highlights the same diagonal trend of players attending college in the same state they were born in.
For college state California (CA), Florida (FL), NC (North Carolina)and Texas (TX) each have players born in a wide range of states, each has players born in almost every state.
plt.figure(figsize=(20,15))
fig = sns.heatmap(df_2d['max_salary_standardized_annually'], center=0, linewidths=.5, square=True, robust=True)
North Carolina as a college state has more negative values than most other college states.
California has the most positive values compared to another college states.
This could be continued by taking samples of players from different states and designing a hypothesis test to investigate if there is a statistically significant difference between these groups.
This analysis is not thorough so any conclusions are tentative. The project asked the question where do the best base ball players come from.
The majority of base ball players come from within the USA. California is where the most base ball players have been born and where most have gone to college.
The majority of baseball players have attended college in the same state they were born in.
There are differences between states in the typical quality of baseball players, however, this also relates to the number of players coming from or studying in each state.
Height and weight to not show strong relationships to the quality of a player.
Quality of player could be defined as a combination of variables, this analysis could use formal statistical tests to take these ideas further.
Take the MASTER.csv table and use this as a central source for unique playerID, this will relate to many other tables.
Attributes will be needed to describe the location each player is from.
MASTER.csv also contains, birthCountry, birthState, birthCity which will be useful to investigate this question.
Schools and CollegePlaying are interesting candidates for positional information containing schoolCity, schoolState, schoolID are all values to explore.
CollegePlaying.csv gives the data, while Schools.csv gives the lookup to what the values in schoolID mean.
These will give a variety of values about where the player came from.
Salaries, AwardsPlayers, AllStarFull and/or HallofFame can be used to give an indication to the quality of the player.
Files are all csv files of high quality. playerID acts as a common key across different csv files.
Each file will be concatenated into the index of College Location, therefore reducing the data set only to areas where College information is available.
MASTER.csv is a key dataset to look up playerID along with a number of key attributes needed for investigating where a player is from.
CollegePlaying.csv and Schools.csv will need to be manipulated to give the location of the school. This will lead to city, state and country for each school.
One person can attend more than one school. For brevity, it is better to reduce this to one selection.
For simplicity, this is chosen alphabetically. So given a tie, tulane is selected over vandy. This will create a bias in the selection criteria but is good enough for the moment.
Each player´s selected school's city, state and country will be appended to the player from Schools.csv
Some errors occur when trying to match schoolID from CollegePlaying.csv to Schools.csv. At least one occurrence was found where it could not find a value from CollegePlaying.csv in Schools.csv. When this occurred the current solution is to replace the value with 'NAN'.
To see if there are any relationships between relatively better or worse players some form of an attribute will be needed to qualify quality.
These will form a variety of variables that could be dependent on other variables.
Salaries are one option, this data is delivered on a yearly basis. This would need to be manipulated into a single value, e.g. mean yearly salary. However, this will not compare well over time so would need to be compared to other salaries in that year.
Salary data has only been collected since 1985. Given that salary changes over time due to a combination of factors, it is difficult to compare one year to another year.
Processing includes standardizing salary annually to see which players earn more than others for each year.
To be able to make some simple comparisons each player´s career salary information needs to be compressed into single values. Min, max and mean have been chosen, for annually standardized and unstandardized.
AwardsPlayers gives a value that could be manipulated into a number of awards per player.
Processed to find the number of times a player has received an award.
Awards go back to 1877, however, the occurrence of awards varies over time as the number of awards given out per year changes.
AllStarFull could also give a number of times present in the All Star game classifier.
Processed to find the number of times a player has played in an all star game.
Data only begins at 1933. The highest number of occurrences in all star games is 25 by aaronha01.
HallofFame can also give a qualifier to compare to.
Processed to find all inducted members within the hall of fame.
Data only begins at 1933. There are 250 players in the hall of fame.
There are a number of limitations to these approaches related to how each of these has changed through time.
Performance statistics like Batting or Fielding could be used but will be left out for this analysis.
Pandas API
Seaborn Tutorial
How to change x and y limits with seaborn
matplotlib api
reStructeredText style guide.
StackOverFlow for number of times a value occurs in a column query - Link
StackOverflow how to transpose a dataset using groupby query - Link_
Code block for download_progress_hook() was taken from Udacity Tensorflow Example notebook.
Color choice for charts


DBs4xKeyboard Instructions
-
Basic Rules
- Device Intention:
( iPhone 3G, 3GS, 4, iPod 2 - 4, iPad 1-2? on iOS 4.0.1 higher ). - Must be Installed:
( Winterboard ), ( iAcces4China, for the device and firmware version ). - How to:
( With iAcces installed ). Goto Winterboard / Select Themes, select DBs4x_Keyboard, Respring device, select sms to start the keyboard. click the globe to change keyboard views.
- Device Intention:
- *There are a few ways to get this
to work, DirtyBeans has created a theme to go along with
iAcces, and not iAcces ! For further info on how to use
iAcces try Youtube.*
- Incompatable Devices:
( iPhone 1stGen ), ( iPod 1stGen ). - Still Testing:
( iPod 2, 3, 4 ) ( iPad 1, 2 ).


Starlets spank Burundi to reach CECAFA final
by Capital SportNAIROBI, Kenya, Nov 23 – Kenya’s Harambee Starlets spanked Burundi 5-0 to romp into the CECAFA Women’s Challenge Cup final to be played on Monday in Dar es Salaam, Tanzania.
The Kenya Women’s team got their goals from Gentrix Shikangwa who scored a brace to lead the top scorers chat while skipper Dorcas Shikobe and Mwanalima Adam netted a goal each.
“I am happy with not only the result but also the performance of the team. Our focus is now on the final, and I am confident that we will come home with the trophy,” Head coach David Ouma said after the match.
Starlets will now face the winner between hosts Tanzania and Uganda who are set to battle in the second semifinal.
Harambee Starting XI
3.Annette Kundu (GK), 2. Mwanalima Jereko, 11.Nelly Sawe, 7.Janet Bundi, 16.Siliya Lumasia, 5.Dorcas Shikobe (C), 6.Sheril Andiba, 20. Ruth Ingosi, 4. Cynthia Shilwatso, 3. Vivian Makokha, 17. Corazone Aquino.
Substitutes
12.Lydia Akoth, 19.Monica Odato (GK), 10. Jentrix Shikangwa, 9. Situma Topister, 18.Judith Osimbo (GK), 13. Lucy Akoth.and Corazone Aquino, saw the Starlets express their dominance ahead of the high stake final.
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Random generator for various text values like names, addresses or phone numbers.
More...
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| static string | Color () |
| | Generates a random color name. The result value is capitalized. More...
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| |
| static string | Stuff () |
| | Generates a random noun. The result value is capitalized. More...
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| |
| static string | Adjective () |
| | Generates a random adjective. The result value is capitalized. More...
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| |
| static string | Verb () |
| | Generates a random verb. The result value is capitalized. More...
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| |
| static string | Phrase (int size) |
| | Generates a random phrase which consists of few words separated by spaces. The first word is capitalized, others are not. More...
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| |
| static string | Phrase (int minSize, int maxSize) |
| | Generates a random phrase which consists of few words separated by spaces. The first word is capitalized, others are not. More...
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| |
| static string | Name () |
| | Generates a random person's name which has the following structure optional prefix - first name - second name - optional suffix More...
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| |
| static string | Word () |
| | Generates a random word from available first names, last names, colors, stuffs, adjectives, or verbs. More...
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| |
| static string | Words (int min, int max) |
| | Generates a random text that consists of random number of random words separated by spaces. More...
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| |
| static string | Phone () |
| | Generates a random phone number. The phone number has the format: (XXX) XXX-YYYY More...
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| |
| static string | Email () |
| | Generates a random email address. More...
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| |
| static string | Text (int size) |
| | Generates a random text, consisting of first names, last names, colors, stuffs, adjectives, verbs, and punctuation marks. More...
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| |
| static string | Text (int minSize, int maxSize) |
| | Generates a random text, consisting of first names, last names, colors, stuffs, adjectives, verbs, and punctuation marks. More...
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static readonly string [] | _namePrefixes = new string[] { "Dr.", "Mr.", "Mrs" } |
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static readonly string [] | _nameSuffixes = new string[] { "Jr.", "Sr.", "II", "III" } |
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| static readonly string [] | _firstNames |
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| static readonly string [] | _lastNames |
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| static readonly string [] | _colors |
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| static readonly string [] | _stuffs |
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| static readonly string [] | _adjectives |
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| static readonly string [] | _verbs |
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| static readonly string [] | _allWords |
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Random generator for various text values like names, addresses or phone numbers.
var value1 = RandomText.Name();
var value2 = RandomText.Verb();
var value3 = RandomText.Text(50);
◆ Adjective()
| static string PipServices3.Commons.Random.RandomText.Adjective |
( |
| ) |
|
|
static |
Generates a random adjective. The result value is capitalized.
- Returns
- a random adjective.
◆ Color()
| static string PipServices3.Commons.Random.RandomText.Color |
( |
| ) |
|
|
static |
Generates a random color name. The result value is capitalized.
- Returns
- a random color name.
◆ Email()
| static string PipServices3.Commons.Random.RandomText.Email |
( |
| ) |
|
|
static |
Generates a random email address.
- Returns
- a random email address.
◆ Name()
| static string PipServices3.Commons.Random.RandomText.Name |
( |
| ) |
|
|
static |
Generates a random person's name which has the following structure optional prefix - first name - second name - optional suffix
- Returns
- a random name.
◆ Phone()
| static string PipServices3.Commons.Random.RandomText.Phone |
( |
| ) |
|
|
static |
Generates a random phone number. The phone number has the format: (XXX) XXX-YYYY
- Returns
- a random phone number.
◆ Phrase() [1/2]
| static string PipServices3.Commons.Random.RandomText.Phrase |
( |
int |
size | ) |
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|
static |
Generates a random phrase which consists of few words separated by spaces. The first word is capitalized, others are not.
- Parameters
-
- Returns
- a random phrase.
◆ Phrase() [2/2]
| static string PipServices3.Commons.Random.RandomText.Phrase |
( |
int |
minSize, |
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|
int |
maxSize |
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) |
| |
|
static |
Generates a random phrase which consists of few words separated by spaces. The first word is capitalized, others are not.
- Parameters
-
| minSize | (optional) minimum string length. |
| maxSize | maximum string length. |
- Returns
- a random phrase.
◆ Stuff()
| static string PipServices3.Commons.Random.RandomText.Stuff |
( |
| ) |
|
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static |
Generates a random noun. The result value is capitalized.
- Returns
- a random noun.
◆ Text() [1/2]
| static string PipServices3.Commons.Random.RandomText.Text |
( |
int |
size | ) |
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static |
Generates a random text, consisting of first names, last names, colors, stuffs, adjectives, verbs, and punctuation marks.
- Parameters
-
- Returns
- a random text.
◆ Text() [2/2]
| static string PipServices3.Commons.Random.RandomText.Text |
( |
int |
minSize, |
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int |
maxSize |
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) |
| |
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static |
Generates a random text, consisting of first names, last names, colors, stuffs, adjectives, verbs, and punctuation marks.
- Parameters
-
| minSize | minimum amount of words to generate. Text will contain 'minSize' words if 'maxSize' is omitted. |
| maxSize | (optional) maximum amount of words to generate. |
- Returns
- a random text.
◆ Verb()
| static string PipServices3.Commons.Random.RandomText.Verb |
( |
| ) |
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static |
Generates a random verb. The result value is capitalized.
- Returns
- a random verb.
◆ Word()
| static string PipServices3.Commons.Random.RandomText.Word |
( |
| ) |
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static |
Generates a random word from available first names, last names, colors, stuffs, adjectives, or verbs.
- Returns
- a random word.
◆ Words()
| static string PipServices3.Commons.Random.RandomText.Words |
( |
int |
min, |
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|
int |
max |
|
) |
| |
|
static |
Generates a random text that consists of random number of random words separated by spaces.
- Parameters
-
| min | (optional) a minimum number of words. |
| max | a maximum number of words. |
- Returns
- a random text.
◆ _adjectives
| readonly string [] PipServices3.Commons.Random.RandomText._adjectives |
|
staticprivate |
Initial value:= new string[] {
"Large", "Small", "High", "Low", "Certain", "Fuzzy", "Modern", "Faster", "Slower"
}
◆ _allWords
| readonly string [] PipServices3.Commons.Random.RandomText._allWords |
|
staticprivate |
Initial value:= _firstNames.Concat(_lastNames).Concat(_colors).Concat(_stuffs).Concat(_adjectives)
.Concat(_verbs).ToArray()
◆ _colors
| readonly string [] PipServices3.Commons.Random.RandomText._colors |
|
staticprivate |
Initial value:= new string[] {
"Black", "White", "Red", "Blue", "Green", "Yellow", "Purple", "Grey", "Magenta", "Cian"
}
◆ _firstNames
| readonly string [] PipServices3.Commons.Random.RandomText._firstNames |
|
staticprivate |
Initial value:= new string[] {
"John", "Bill", "Andrew", "Nick", "Pamela", "Bela", "Sergio", "George", "Hurry", "Cecilia", "Vesta", "Terry", "Patrick"
}
◆ _lastNames
| readonly string [] PipServices3.Commons.Random.RandomText._lastNames |
|
staticprivate |
Initial value:= new string[] {
"Doe", "Smith", "Johns", "Gates", "Carmack", "Zontak", "Clinton", "Adams", "First", "Lopez", "Due", "White", "Black"
}
◆ _stuffs
| readonly string [] PipServices3.Commons.Random.RandomText._stuffs |
|
staticprivate |
Initial value:= new string[] {
"Game", "Ball", "Home", "Board", "Car", "Plane", "Hotel", "Wine", "Pants", "Boots", "Table", "Chair"
}
◆ _verbs
| readonly string [] PipServices3.Commons.Random.RandomText._verbs |
|
staticprivate |
Initial value:= new string[] {
"Run", "Stay", "Breeze", "Fly", "Lay", "Write", "Draw", "Scream"
}
The documentation for this class was generated from the following file:


It took a few days for me to think of a project that I wanted to create. I finally landed on a dog food dispenser.
First, I make a cardboard versioin, to test and see if this is something that would actually work. I am glad that I made
a test model, because it allowed me to make some changes to the design, in order for the food to acutally come out.
I used maker case, so the sides would lock together. The walls are 12x7.5x7.5. And the funnel inside that holds the
food is a trapezoid. The base lengths are 7.45 and 5. And it is 5 high. (All of these units are in inches).
In one of the sides of my walls I have a 0.75 circle cut out, so I can put a dowel rod through it and a 5x2 rectangle
cut out for the slide. The base of the dispenser is 8x8. I also created a 4.5x4.5 square so the jar can rest on it.
I designed the structure of the dispenser on Corel Draw. The design took a class period to make. The following class,I
cut out the pieces and I started designing my slide. I additionally want to make a sticker that says my dog’s name to
put on the front. I ran into a lot of bumps during this project. It took longer than I thought it would. I had to
carefully take measurments. I took a weekend to build my dispenser,and a class period to design the slide. Unfortunately
the slide I desigend was too large to print, so it is not the correct size. If I want to use this at home, I will
probably design one out of wood. It was difficult putting Charlie's name on the front, becuaseit would not release
from the contact paper, and it doesn's stick to wood very well.It is hard to explain in words how this is assembled, so
please look at the pictures below!


A "smart information" service. Chat with others, view word/phrase sentiment analysis, and watch videos, all from one ultra-lightweight application.
Christopher Raley, Johnny Wong, William Wong, and Dennis Yang.
The holy trinity of HTML/CSS/JavaScript, as well as:


---
layout: post
title: 'BucketWise: Preview #2'
post_id: 36
categories:
- projects
date: 2009-04-08 18:46:00.000000000 -06:00
author: Jamis
permalink: 2009/4/8/bucketwise-preview-2.html
---
“Buckets” is now “BucketWise”. The name was more unique, easier to identify as an application, and just felt better than “Buckets”.
To celebrate the new name, I’ve also made another screencast, this one demonstrating BucketWise’s anti-debt features. It’s a bit more long-winded than the first one: five and a half minutes of me talk-talk-talking:
BucketWise Preview #2 (QuickTime, 5:30, 10MB)
I’m slowly working my way through the list of things that I want done before I release the code. For the curious, here’s what I’m wanting done before I open it up:
Existing bugs:
- after saving a reallocation event, the line items don’t reset
- green “your transaction has been recorded” notice doesn’t disappear when starting a new event
- date column for event row is squashed in safari
- “Add a new bucket” should set focus on new bucket line
- check number and “select repayment options” links are out of whack when editing an event
- deleting event from account perma results in ajax error (maybe only when there is only a single event in the account?)
- Reallocating a bucket doesn’t change the updated_at field for the bucket?
- “move in” and “move out” links on dashboard don’t use existing form—they go to events/new
- make all autocompleting fields refresh their source array after event creation
Remaining features:
- tag delete (optionally move transactions to different tag, e.g. “tag merge”)
- tag rename
- API
- validations
- rake tasks for user and subscription creation
- change style for event detail view: “window shade” style isn’t really working
- attr_protected everywhere (perhaps implement an update_override method or something for places where it would be useful to do mass updates despite protected attrs?)
- spinner for ajax actions
- user info page (for password change)
- password reset from login page
- cache balances on events (?)
-
README with installation instructions
- CHANGELOG
Those lists are straight from my TODO file. But for those who think I’m waiting for too much to be finished, never fear. I’ve got plenty that I want to implement, but which I’m willing to wait until post-release to work on:
- signup process
- exception notifier (?)
- /subscriptions index
- autocomplete actor field in event form
- better 404 and 500 error pages
- searching & reporting
- account reconciliation
- transaction templates (“saved” or “memorized” transactions)
- scheduled transactions (occur automatically at specified intervals)
- show check numbers in event detail view
- add a memo field for events
- print stylesheet
- oauth authentication for API
- filter event views to show only events with a non-zero balance for the account in question
- filter event views to show only deposits, or only expenses
- normalize ‘actor’ so we can do actor-centric queries more efficiently
- full-text searching on the memo field
- show bucket and account balances next to names in drop-downs
- make bucket reallocation work from bucket perma
Depending on how soon my bank sends the next statement for my checking account, I may or may not need to have “account reconciliation” done before launch, too.
As before, let me state: I’m writing this application for myself, and really only for myself. If other people like it, that’s cool. I’m more than happy to share and let other people hack on it. I may even release it into the public domain (I haven’t yet decided what license I’ll use). But BucketWise will always be very opinionated. If you prefer having your finance app download your transactions from your bank, you’ll be disappointed by BucketWise. If you need currencies other than the US dollar, you’ll be disappointed by BucketWise. If you need pretty charts and graphs to analyze your cash flow, or if you want to track investments, or any number of other things, you’ll be disappointed by BucketWise.
Just FYI. :)


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Drought impact study shows new issues for plants and carbon dioxide
Multiple Earth Systems computer models assessed potential drought levels
| Source: | DOE/Los Alamos National Laboratory |
| Summary: | Extreme drought's impact on plants will become more dominant under future climate change, as noted in a new article. |
Extreme drought's impact on plants will become more dominant under future climate change, as noted in a paper out today in the journal Nature Climate Change. Analysis shows that not only will droughts become more frequent under future climates, but more of those events will be extreme, adding to the reduction of plant production essential to human and animal populations.
"Even though plants can, in many cases, benefit from increased levels of carbon dioxide that are predicted for the future atmosphere, the impact of severe drought on destroying these plants will be extreme, especially in the Amazon, South Africa, Mediterranean, Australia, and southwest USA," said lead study author Chonggang Xu of Los Alamos National Laboratory. Future drought events are typically associated with low humidity, low precipitation, high temperature, and changes in carbon released from fire disturbances.
The frequency of extreme droughts (defined by low plant-accessible soil water) per year is predicted to increase by a factor of ~3.8 under a high greenhouse-gas emission scenario and by a factor of ~3.1 under an intermediate greenhouse-gas emission scenario during 2075-2099, compared to the historical period of 1850-1999.
Drought is already the most widespread factor affecting plant production via direct physiological impacts such as water limitation and heat stress. But indirectly it also can have devastating effect, through increased frequency and intensity of disturbances such as fire and insect outbreaks that release large amounts of carbon back into the atmosphere.
Plants fix carbon dioxide into an ecosystem through photosynthesis, and this process plays a key role in the net carbon balance of the terrestrial biosphere that contributed to its regulation of atmospheric carbon dioxide. And even though higher carbon dioxide concentrations in future decades can help increase plant production, the combination of low soil water availability, heat stress, and disturbances associated with droughts could negate the benefits of such fertilization.
"Future plant production under elevated carbon dioxide levels remains highly uncertain despite our knowledge on carbon dioxide fertilization effects on plant productivity," Xu said.
The research team analyzed the outputs from 13 Earth System Models (ESMs) and the results show that due to a dramatic increase in the frequency of extreme droughts, the magnitude of globally-averaged reductions in plant production will be nearly tripled by the last quarter of this century relative to that of the study's historical period (1850-1999).
For plants living through mild or moderate droughts, the situation is not as dire. The problem is that more of the droughts that come will be the extreme ones. "Our analysis indicates a high risk of increasing impacts of extreme droughts on the global carbon cycle with atmospheric warming," Xu said, "At the same time though, this drought risk will be potentially mitigated by positive anomalies of plant production associated with favorable environmental conditions."
make a difference: sponsored opportunity
Story Source:
Materials provided by DOE/Los Alamos National Laboratory.
Note: Content may be edited for style and length.
Journal Reference:
- Chonggang Xu, Nate G. McDowell, Rosie A. Fisher, Liang Wei, Sanna Sevanto, Bradley O. Christoffersen, Ensheng Weng, Richard S. Middleton. Increasing impacts of extreme droughts on vegetation productivity under climate change. Nature Climate Change, 2019; DOI: 10.1038/s41558-019-0630-6
Cite This Page:
| MLA | DOE/Los Alamos National Laboratory. "Drought impact study shows new issues for plants and carbon dioxide: Multiple Earth Systems computer models assessed potential drought levels." ScienceDaily. ScienceDaily, 25 November 2019. <www.sciencedaily.com/releases/2019/11/191125145549.htm>. |
| APA | DOE/Los Alamos National Laboratory. (2019, November 25). Drought impact study shows new issues for plants and carbon dioxide: Multiple Earth Systems computer models assessed potential drought levels. ScienceDaily. Retrieved November 25, 2019 from www.sciencedaily.com/releases/2019/11/191125145549.htm |
| Chicago | DOE/Los Alamos National Laboratory. "Drought impact study shows new issues for plants and carbon dioxide: Multiple Earth Systems computer models assessed potential drought levels." ScienceDaily. www.sciencedaily.com/releases/2019/11/191125145549.htm (accessed November 25, 2019). |


Whole Grain Bread
Fresh, whole grain loaves of bread baked in the My Market
Bakery
Price: $5.99 each
Organic Eggs
One dozen organic, cage-free eggs
Price: $2.99
Locally Grown Apples
Crisp apples harvested from a local farm.
Price: $1.85 per pound
2% Milk
One gallon of 2% milk.
Price: $2.89
Chocolate Chip Cookies
Fresh baked chocolate chip cookies from the My Market Bakery.
Price: $4.99 per dozen
Frozen Pizza
Frozen cheese or pepperoni pizza for those busy school nights!
Price: $3.99 each


--- title: Operations Crystal Directions sidebar: documentation_sidebar permalink: CrystalOperations.html folder: documentation toc: false ---
In this section we discuss basic operations with crystal directions. Therefore, lets start by importing the trigonal Quartz crystal symmetry
{% highlight matlab %} cs = loadCIF('quartz') {% endhighlight %} {% highlight plaintext %} cs = crystalSymmetry mineral : Quartz symmetry : 321 elements : 6 a, b, c : 4.9, 4.9, 5.4 reference frame: X||a*, Y||b, Z||c* {% endhighlight %}and consider two hexgonal prism normal
{% highlight matlab %} m1 = Miller(1,-1,0,0,cs) m2 = Miller(1,0,-1,0,cs) plot(m1,'upper','labeled','backgroundColor','w') hold on plot(m2,'labeled','backgroundColor','w') hold off {% endhighlight %} {% highlight plaintext %} m1 = Miller (Quartz) h k i l 1 -1 0 0 m2 = Miller (Quartz) h k i l 1 0 -1 0 {% endhighlight %}Both prism planes intersects in a common zone axis which is orthogonal to both plane normals can is computed by
{% highlight matlab %} d = round(cross(m1,m2)) {% endhighlight %} {% highlight plaintext %} d = Miller (Quartz) U V T W 0 0 0 1 {% endhighlight %}Note that MTEX automatically switches from reciprocal to direct coordinates for displaying the zone axis.
The other way round two, not paralllel, zone axes
{% highlight matlab %} d1 = Miller(0,0,0,1,cs,'UVTW'); d2 = Miller(1,-2,1,3,cs,'UVTW'); {% endhighlight %}span a lattice plane with normal vector
{% highlight matlab %} round(cross(d1,d2)) {% endhighlight %} {% highlight plaintext %} ans = Miller (Quartz) h k i l 1 0 -1 0 {% endhighlight %}Since crystal lattices are symmetric lattice directions can be grouped into classes of symmetrically equivalent directions. Those groups can be derived by permuting the Miller indeces (uvw). The class of all directions symmetrically equivalent to (uvw) is commonly denoted by uvw, while the class of all lattice planes symmetrically equivalent to the plane (hkl) is denoted by {hkl}.  Lets apply this to a list of lattice planes
{% highlight matlab %} h = Miller({1,0,-1,0},{1,1,-2,0},{1,0,-1,1},{1,1,-2,1},{0,0,0,1},cs); for i = 1:length(h) plot(h(i),'symmetrised','labeled','backgroundColor','w','grid','upper','doNotDraw') hold all end hold off drawNow(gcm,'figSize','normal') {% endhighlight %}The command <vector3d.eq.html eq or == > can be used to check whether two crystal directions are symmetrically equivalent. Compare
{% highlight matlab %} Miller(1,1,-2,0,cs) == Miller(-1,-1,2,0,cs) {% endhighlight %} {% highlight plaintext %} ans = logical 0 {% endhighlight %}and
{% highlight matlab %} eq(Miller(1,1,-2,0,cs),Miller(-1,-1,2,0,cs),'antipodal') {% endhighlight %} {% highlight plaintext %} ans = logical 1 {% endhighlight %}The angle between two crystal directions m1 and m2 is defined as the smallest angle between m1 and all symmetrically equivalent directions to m2. This angle is in radians and it is calculated by the function angle
{% highlight matlab %} angle(Miller(1,1,-2,0,cs),Miller(-1,-1,2,0,cs)) / degree {% endhighlight %} {% highlight plaintext %} ans = 60.0000 {% endhighlight %}As always the keyword antipodal adds antipodal symmetry to this computation
{% highlight matlab %} angle(Miller(1,1,-2,0,cs),Miller(-1,-1,2,0,cs),'antipodal') / degree {% endhighlight %} {% highlight plaintext %} ans = 0 {% endhighlight %}In order to ignore the crystal symmetry, i.e., to compute the actual angle between two directions use the option noSymmetry
{% highlight matlab %} angle(Miller(1,1,-2,0,cs),Miller(-1,-1,2,0,cs),'noSymmetry') / degree {% endhighlight %} {% highlight plaintext %} ans = 180 {% endhighlight %}This option is available for many other functions involving crystal directions and crystal orientations.
Essentially all the operations defined for general directions, i.e. for variables of type vector3d are also available for Miller indices. In addition Miller indices interact with crystal orientations. Consider the crystal orientation
{% highlight matlab %} ori = orientation.byEuler(10*degree,20*degree,30*degree,cs) {% endhighlight %} {% highlight plaintext %} ori = orientation (Quartz → xyz) Bunge Euler angles in degree phi1 Phi phi2 10 20 30 {% endhighlight %}Then one can apply it to a crystal direction to find its coordinates with respect to the specimen coordinate system
{% highlight matlab %} ori * m1 {% endhighlight %} {% highlight plaintext %} ans = vector3d {% endhighlight %}By applying a crystal symmetry one obtains the coordinates with respect to the specimen coordinate system of all crystallographically equivalent specimen directions.
{% highlight matlab %} p = ori * symmetrise(m1); plot(p,'grid') % % The above plot is essentialy the pole figure representation of the % orientation *ori*. % {% endhighlight %}Converting a crystal direction which is represented by its coordinates with respect to the crystal coordinate system a, b, c into a representation with respect to the associated Euclidean coordinate system is done by the command vectord3d.
{% highlight matlab %} vector3d(m1) {% endhighlight %} {% highlight plaintext %} ans = vector3d {% endhighlight %}Conversion into spherical coordinates requires the function polar
{% highlight matlab %} [theta,rho] = polar(m1) {% endhighlight %} {% highlight plaintext %} theta = 1.5708 rho = 5.2360 {% endhighlight %}


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---
layout: index-layout
subject: My Email Templates
---
Hi there!
Thanks for downloading Foundation for Emails!  You’ll soon be cranking out some slick, responsive emails in no time! To help get you up and running, we've put together 11 templates for the most common email use cases including newsletters, transactional emails, and marketing blasts. Feel free to customize them to your hearts content.
Happy Coding,
The Foundation Team
Keep on keepin' on. <3 ZURB


Actively involved in CI/CD for different clients. Containerise apps, deliver them via IaaC. Run and monitor the clouds specially AWS. Automate with Python and bash. Deliver software with a mix of products Atlassian, Jenkins, Spinnaker.
In short, I released the first BPMS as a Service for a health and nutrition client in Colombia, South America.
As member of the ESB team I monitored
production, managed dev and Prod env, delivered many automated web services to ensure integrity
of the system.
I contributed an automated reporting system that encrypts reports in an SFTP.
Worked as part of 2 companies for the same client
Wipro and SQDM
Sopohos bankings solutions hired me to be full time at client location. Developer for the web portal and Integration service Bus. Achieved the creation of a service to transfer data from mainframe database to the transactional system DB.
MSOC masters: built on my professional experience in the information technology industry. Cloud computing, service science and service-oriented computing. latest trends in practice and research. Research on BPMS with IoT in the cloud.
A 5 year super cool degree. Thesis in learning software for children in a SOA Service Oriented Architecture
One of the greatest experiences of my life up to that point. I was selected to travel to India with 40 other colombian students to be trained in Infosys Campus. I learned and experience a software Develepment lifecycle and software engineering methodologies with a real project.
Proficiency in the mechanics of SOA through the design of service-oriented technology architectures, service-oriented solutions, and related infrastructure.
Content manager and web developer for the website.
I started Volunteering in 2014 for one school in a slum from Colombia. The experience encouraged me to proceed.
See all my recommendations in the Recommendations section in my LinkedIn Profile
Linkedin Recommendations


The autocatalytic reaction involves the collision of two particles, A and catalyst B.
In this reaction, product B also works as a catalyst, so it requires B to proceed the reaction.
- Pairs of balls are drawn simultaneously
- If a drawn pair consists of A and B, then it is changed to BB and then remixed
- In all other events (AA,AB) the wrong pair is remixed without changing the colors


User Name : {{ y.userName }} User Id : {{ y.userId }} Email : {{ y.email }} Account active: {{ y.enabled }} User Role : {{ y.role }} Confirm Delete ? YesNo


Patrick Sheridan
"A nobel spirit
enbiggens
the smallest man"
Top Ten Favorite Pizza Toppings
Pepperoni
Sausage
Mushrooms
Peppers
Extra Cheese
Bacon
Onions
Anchovies
BBQ Chicken
Double Pepperoni
Bio
Education
College: Western Washington University,
BS in Environmental Science
Stanwood High School
Job Experience
US Forest Service
LabCorp
Goals
Learn Code
Win Olympic Gold Medal
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Terms of Service
The following terms and conditions govern the use of the services provided on www.phostream.com (hereafter referred to as “phonostream”).
- Your access to and use of phonostream is conditioned on your acceptance of and compliance with these Terms. These Terms apply to all visitors, users and others who access or use the Service. By accessing or using the Service you agree to be bound by these Terms. If you disagree with any part of the terms then you may not access phonostream.
- The use of phonostream is governed by the law of Switzerland with the following modifications and explanations to be considered.
- Phonostream offers a technical service on a voluntary basis. Hence, phonostream does not take liability for the service working unimpeded or for permanent availability of the service.
- Phonostream must only be used for private purposes. Any commercial use of phonostream is strictly forbidden and will be pursued in a court of law.
- Phonostream is only to be used to access content which was posted onto the Soundcloud platform by an authorised third party. The user is obliged to check respective rights concerning the content.
- Accessing pornographic content or such content that infringes any right in any other way is strictly forbidden. In case a third party right is already violated by downloading the content, the user bears complete responsibility for this as he started the processing of the data in question.
- Accessing the systems or services of phonostream in any way other than the intended use is forbidden.
- It is the only purpose of phonostream to create a copy of downloadable online-content for the private use of the user ("fair use"). Any further use of the content transmitted by phonostream, particularly but not exclusively making the content publicly accessible or using it commercially, must be agreed upon with the holder of the rights of the respective downloaded content. The user bears full responsibility for all actions related to the data transmitted by phonostream. Phonostream does not grant any rights to the contents, as it only acts as a technical service provider.
- The user carries full responsibility for checking the legitimacy of his use of phonostream. Phonostream only provides the technical service. Hence, phonostream does not take liability towards the user or any third party for the permissibility of downloading content through phonostream.
- Our platform contains links to external websites of third parties, the contents of which cannot be influenced by us. Thus, we cannot take responsibility for this content. The responsibility lies with the respective provider or host of the websites in question. All linked websites were checked for violations of rights at the time of establishing the link. There was no suspicion of illegal content at that time. Continuous checking of the linked websites, however, is not reasonable without any concrete evidence of a rights violation. Should we be informed about any rights violation, we will immediately delete such links (DMCA).
- Should individual provisions of these present terms and conditions be or become ineffective, this shall not affect the effectiveness of the remaining provisions. The ineffective provision shall be replaced by a substitute provision regulating the intended purpose of the ineffective provision as precisely as possible.
- The legal venue shall, if admissible, be Zurich, Switzerland.
- We reserve the right, at our sole discretion, to modify or replace these Terms at any time. If a revision is material we will try to provide at least 30 days notice prior to any new terms taking effect. What constitutes a material change will be determined at our sole discretion. By continuing to access or use our Service after those revisions become effective, you agree to be bound by the revised terms. If you do not agree to the new terms, please stop using the Service.
- Every request made on phonostream is requested by a user himself. Therefore, we are only providing a service and never permanently store any files on our servers. Hence, we will only be able to block third-party links violating DMCA, as phonostream is not the owner (“Soundcloud Inc.”) of the requested files.


 I suppose next you will be asking me about "Carl Max". Or maybe you would like to expound on Petyr Crapitkon?
Please learn how to spell before you waste people's time.
"The most violent element in society is ignorance." - Elmo Gultmon


I am a seasoned data scientist with extensive experience in academic research and data analytics. My expertise lies in proficiently managing data and utilizing statistical packages such as Javascript, Python, R, Stata, MatLab, E-View, SQL, and VBA. In 2015, I earned an M.S. in Quantitative Economics with a specialization in Health Economics, Industrial Organization, and Behavioral Economics. Additionally, I completed the U.C. Berkeley Data Analytic boot camp in 2019, enabling me to stay abreast of the latest technologies and industry best practices. In 2020, I achieved certification as a Tableau Desktop Specialist. With a solid foundation in academic research, I am driven to leverage my abilities in constructing diverse statistical models, accompanied by visualization techniques, to empower decision-making processes and facilitate the achievement of company objectives.
A 24-week intensive program focused on gaining technical programing skills in Excel, VBA, Python, R, JavaScript, SQL Databases, Tableau, Big Data, and Machine Learning, 2019.
Master in Quantitative Economics, 2015
Bachelor in Economics, 2004


2 California murder suspects escape from jail prompting frantic search
by Frank MilesTwo murder suspects, considered dangerous, escaped Sunday from a central California jail sparking a frantic manhunt, investigators said.
Santos Fonseca, 21, and Jonathan Salazar, 20, escaped from the Adult Detention Facility in the early morning, the Monterey County Sheriff’s Office announced.
Both men are five-feet-seven-inches tall, and they each have black hair and brown eyes, The Associated Press reported; Fonseca weighs 150 pounds and Salazar weighs 170 pounds.
MOM IN VIRAL OVERDOSE PHOTO CELEBRATES 3 YEARS OF SOBRIETY
The men had been jailed since 2018 and were awaiting trial on unrelated murder counts “along with numerous other felony charges,” a sheriff’s statement said.
Fonseca was being held on two counts of murder and attempted robbery on a $2.25 million bail, Fox 2 reported. He was arrested for a June 2018 shooting, and another deadly shooting three days earlier.
Salazar, the news outlet reported, was arrested along with a 15-year-old boy in July 2018 for a deadly October 2017 shooting.
CLICK HERE FOR THE FOX NEWS APP
“An investigation is underway to determine their means of escape but, at this time, we can provide no details on that topic,” the sheriff’s statement added. “Our focus, and resources, are dedicated to locating and returning Fonseca and Salazar to custody.”
The Associated Press contributed to this report.


Weekly news quiz: Smashed glass, commemorative coins and gigs on hold
by NewstalkHave you been keeping up with this week's headlines? Test your knowledge of the latest news stories - Irish and international - with our weekly news quiz...
 Test your knowledge of the latest news stories - Irish and international - with our weekly news quiz...


BusMall™ Data Collection Survey
Welcome to BusMall! The one and only in-transit catalogue for the Puget Sound regional transit system.
We're well on the way to making our catalogue the absolute best it can possibly be. All we need now is the input of riders like you! To participate in our data collection survey, just follow the instructions below. It's easy!
You'll be shown three images at a time.
Just click on whichever one you'd be most intersted in seeing as a part of the BusMall catalogue!
Thanks!
-The BusMall Team


© Jeevanandam M., All rights reserved.
THUMBAI is released under Apache License 2.0, unless otherwise noted Content is released under CC BY 4.0 License
Made with using aah framework


This plugin adds "Open Project Fast [...]" menu item in the "File" menu. This menu opens the project currently selected in the clipboard, and thereby allowing the following workflow that lets you open a project more quickly.
This plugin is particularly effective when you have a small shell script that lets you send arbitrary file name into clipboard. On Unix, such script would look something like:
#!/bin/sh D=`dirname "$1"` B=`basename "$1"` abspath="`cd \"$D\" 2>/dev/null && pwd || echo \"$D\"`/$B" echo $abspath | xclip -selection clipboard
On Windows it will look something like this:
@echo off echo %~f1 | clipboard -
In this way, you can pick a project easily from a shell, by taking advantage of a file name completion and other smart features in shell like this.
This plugin also opens a TCP port 39524 and waits for a connection. You can have the plugin open a project by simply sending a directory name that contains .ipr or pom.xml, or a name of those files. For example, on Unix, such script could be:
#!/bin/sh D=`dirname "$1"` B=`basename "$1"` abspath="`cd \"$D\" 2>/dev/null && pwd || echo \"$D\"`/$B" echo $abspath | nc -q 1 localhost 39524
I haven't tried this on Windows, but I suppose telnet command would do.


Externalizable
classes which define readObject()
or writeObject()
methods. These methods are not called for serialization of
Externalizable
objects.
Example:
abstract class Crucial implements Externalizable {
int value;
private void readObject(ObjectInputStream in) {
value = in.readInt();
}
}


Scandio.timing = {};
Register timinig namespace on scandiojs object
Scandio.timing = {};
Puts the breaks on a function which may be called to often
such as scrolling or resizing callbacks.
The function will actually be called after release
-milliseconds elapsed
Scandio.timing.breaks = function(on, release) {
Initialize the context, arg, timeout and previous local variables
var
context = null,
args = null,
result = null,
timeout = null,
previous = 0,
remaining = null,
Wrapper to be called if release
has not elapsed yet
toBeCalled = function() {
Timestamp of call
previous = new Date();
timeout = null;
Result being function on which brakes have been put
result = on.apply(context, args);
};
Returning a function which returns the execution’s result
return function() {
Where the function is in time
var now = new Date();
If a previous call hasnt been made its now
if (!previous) {
previous = now;
}
How much time remains until final execution of on
remaining = release - (now - previous);
The function context and arguments
context = this;
args = arguments;
Time has elapsed, on
can be called
if (remaining <= 0) {
Clears all state
clearTimeout(timeout);
This will break it on next call
timeout = null;
previous = now;
result = on.apply(context, args);
} else if (!timeout) {
Next execution round
timeout = setTimeout(toBeCalled, remaining);
}
return result;
};
};
Delays a function execution ms
milliseconds.
Scandio.timing.delay = function(fn, ms) {
Arguments are anything after fn
and ms
var args = slice.call(arguments, 2);
Return the result of setTimeout
return setTimeout(function() {
return fn.apply(null, args);
}, ms);
};


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 8
Bring a very large pan of water to the boil and add the salt and the white wine vinegar.
Plunge the lobsters into the boiling wate. Return to the boil and cook for five minutes for lobsters of this size. (You will probably need to cook the lobsters in two or three batches, according to the size of your pan.) Remove the lobsters from the water and leave to cool.
Meanwhile, in a clean pan, boil the white wine with the shallots until the wine has reduced to about two teaspoons.
Add the fish stock and boil again until the liquid has reduced to almost nothing.
Add 200ml/7fl oz of the cream and the mustard, bring to the boil and reduce until the sauce is thickened.
Add the cheddar and whisk until the cheese has melted and the sauce is smooth. Season, to taste, with salt and freshly ground black pepper, then leave to cool.
Preheat the oven to 230C/445F/Gas 8 (or heat the grill to its highest setting).
Add the egg yolks to the cooled cheese sauce and mix well.
Whip the remaining 100ml/3½fl oz of cream in a clean bowl, until soft peaks form when the whisk is removed.
Fold the cream into the thermidor sauce.
Remove the claws from each cooled lobster, crack them with a sharp, heavy knife and take out the claw meat, preferably in one piece.
Cut each body in half lengthways by inserting the point of a knife in the head and pushing it with the palm of your hand down the length of each lobster. Remove the meat from the tails and cut it into four pieces.
Place the tail meat into a bowl and add a little of the thermidor sauce. Mix well, then spoon the tail meat and thermidor sauce mixture into the empty tails of the lobster halves.
Place the meat from the claws into the head section of each lobster half.
Spoon equal amounts of the remaining thermidor sauce over the lobster meat, then place the lobster halves onto baking sheets. Transfer to the oven to roast for 10-15 minutes, until the sauce topping is golden-brown and bubbling. (Alternatively you can place the sauce-coated lobsters under the grill to cook for 4-5 minutes, or until bubbling and golden-brown.)
Serve immediately.


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Tag Name Quiz
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21 September 2000
Disease Outbreak Reported
Four cases of leptospirosis associated with the Eco-Challenge sports event (see previous report) have now been reported in France. Of the 4 cases reported, 1 has been laboratory-confirmed.
WHO is collaborating in case-finding activities.


A permutation is a change of places. Thus, 'lolhe' is a permuted 'hello' (commonly referred to as 'scrambled text').
I wish to present an SQL solution for checking if two strings are permutations of the same text.
About permutations
So, if 'lolhe' is a permutation of 'hello', then 'hello' is a permutation of 'lolhe', as well; and both are permutations of 'elloh'. The REVERSE() of a text is an example of permutation. Mathematically, string permutation is an equivalence relation, and divides all strings to equivalence classes.
Use cases
- We may be interested in permutations when a user chooses a password. We may disallow a password which is identical to the login name; but we may also disallow upper-lower-case-only transformations of the text. We may still disallow a permutation of the text.
- On a slightly different scale, the two queries: SELECT * FROM City WHERE id IN (5, 21, 13) and SELECT * FROM City WHERE id IN (13, 5, 21) are identical. Here, the permutation is not with string characters, but with string tokens. While the solution discussed is targeted at string characters, it can be easily converted to work with string tokens.
Checking for permutation
The solution I'm suggesting checks for permutation between 2 strings by permuting both to a third, normal form. The two string are permutations of each other if both have the same normal form.
What exactly is a normal form? Well, anything you like, really, as long as it's deterministic and works the same for all elements in the equivalence class (mathematically speaking, this was a really bad definition, I know).
Enough of mathematical notions: on the practical side, I'll normalize 'CAB' to 'ABC', and 'DOG' to 'DGO'. Can you see what normalization means here? I'm merely rearranging the characters in alphabetical order. This rearrangement is in itself a permutation of the string (hence, one last mathematical statement, it can be seen as a representative of the equivalence class).
So, to see if two strings are permutations of each other, we rearrange both in alphabetical order, and see if we got the same text. 'hello' and 'lolhe' both normalize to 'ehllo', hence are permutations.
Normalizing the text
Down to business: how do we normalize a text using SQL? Well, once again, string walking and string unwalking to the rescue. The trick is to break the string apart (to distinct characters), then re-combine the characters, but instead of in original order, do that in ascending order.
Breaking of the word 'hello' is as follows:
SELECT SUBSTRING(s, value+1, 1) AS c FROM tinyint_asc, (SELECT 'hello' AS s) sel1 WHERE value < char_length(s);
Recombining it in ascending character order is as follows:
SELECT GROUP_CONCAT(c ORDER BY c SEPARATOR '') AS normalized FROM ( SELECT SUBSTRING(s, value+1, 1) AS c FROM tinyint_asc, (SELECT 'hello' AS s) sel1 WHERE value < char_length(s) ) walked_string_select; +------------+ | normalized | +------------+ | ehllo | +------------+ 1 row in set (0.00 sec)
We can write a stored function to do that more conveniently (and, while at it, also normalize character case):
DELIMITER $$ DROP FUNCTION IF EXISTS `normalize_text`$$ CREATE FUNCTION `normalize_text` (input_text VARCHAR(255) CHARSET utf8) RETURNS VARCHAR(255) CHARSET utf8 DETERMINISTIC READS SQL DATA BEGIN SELECT GROUP_CONCAT(c ORDER BY c SEPARATOR '') INTO @normalized FROM ( SELECT SUBSTRING(s, value+1, 1) AS c FROM tinyint_asc, (SELECT input_text AS s) sel1 WHERE value < char_length(s) ) walked_string_select; RETURN LOWER(@normalized); END$$ DELIMITER ;
And use like this:
mysql> SELECT normalize_text('Smith'); +-------------------------+ | normalize_text('Smith') | +-------------------------+ | himst | +-------------------------+ 1 row in set (0.00 sec) mysql> SELECT normalize_text('Smith') = normalize_text('Thims'); +---------------------------------------------------+ | normalize_text('Smith') = normalize_text('Thims') | +---------------------------------------------------+ | 1 | +---------------------------------------------------+ 1 row in set (0.00 sec) mysql> SELECT normalize_text('Smith') = normalize_text('Sniff'); +---------------------------------------------------+ | normalize_text('Smith') = normalize_text('Sniff') | +---------------------------------------------------+ | 0 | +---------------------------------------------------+ 1 row in set (0.00 sec)
To compare tokenized strings (like '21,5,13' vs. '5,13,21'), we can use the SUBSTRING_INDEX() function to break the string apart, instead of SUBSTRING().
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Copyright (c) 2013-2015 Delft University of Technology, PO Box 5, 2600 AA, Delft, the Netherlands. All rights reserved.
See for project information djunits.org.
The DJUNITS project is distributed under the following BSD-style license:
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright holder or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.


Adding Icons to Links
Here's a list of downloadable files:
Presentation slides
Video of presentation
Notes for presentation
GIF Icon sprite
PNG Icon sprite
PNG Icon sprite
PNG Icon sprite
Presentation slides
Video of presentation
Notes for presentation
GIF Icon sprite
PNG Icon sprite
PNG Icon sprite
PNG Icon sprite


The Siege * 1/2 (out of * * * * )
Directed by Edward Zwick. Cast: Denzel Washington, Annette Bening, Tony Shalhoub, Bruce Willis, Sami Bouajila, David Proval, Helmi Kassim. 1998 - 112 minutes.  Reviewed November 7, 1998.
By Dustin Putman < Dustin Putman's Film Reviews >
In 1989, director Edward Zwick began his career with the powerful Civil War drama, "Glory," but since then, he has made continuous disappointments, to me at least, with 1994's "Legends of the Fall," and 1996's "Courage Under Fire." Those two films weren't bad, just not very good, but with Zwick's latest film, "The Siege," he has finally made one.
"The Siege," is a modern-day action-thriller that focuses on terrorism that is sweeping through New York City. Investigating the matter is FBI agents Anthony Hubbard (Denzel Washington) and Frank Haddad (Tony Shalhoub), who are first hit by the ordeal when a city bus explodes with several innocent people on it.  They soon meet CIA operative Elise Kraft (Annette Bening), who may very well hold the key to the identity of the Arab terrorist.
There are some movies that simply don't need to be made, and, "The Siege" may very well be one of them. Oh, sure, Zwick thinks he is making a "meaningful" action film, since he has added lots of exposition scenes in which the audience is basically lectured on the horrors of terrorism, but what he really has made is actually no more substantial than, say, "Die Hard," which also starred Bruce Willis. This time, Willis is horribly wasted as a General of the U.S. Army, who puts a state of martial law over NYC during the climax. Whatever points, "The Siege," earnestly tried to make, were obviously lost in the translation from page to screen, or maybe, Zwick never really had anything serious to say in the first place. The role of the Arab community is offensively stereotypical, just as women were in the recent, "John Caprenter's Vampires," by placing them all in the category of "bad guys."
"The Siege," contains three fine actors---Washington, Bening, and Shalhoub---but Washington is stuck with a character that contains not an ounce of even remote development. Bening has slightly more to do, and has an intriguing character to work with, while Shalhoub pretty much steals the show, even though, again, he has no real character.
By the climax of, "The Siege," with Washington giving a sermon to Willis on the so-called "message" of the story, I felt like I had wandered into a sequel to, "On Deadly Ground," in which Steven Seagal gave a 10-minute speech at the end about preserving the environment. "The Siege" is constantly heavy-handed, not entertaining or insightful, and just goes to prove that not even the best actors can save a film that lacks a satisfactory screenplay.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


package helper
Ordering
- Alphabetic
Visibility
- Public
- All
Value Members
-
object
DictionaryExtractor
Dictionary Extractor Object
Dictionary Extractor Object
Execution of this object is required before system builds, because there is a deserialization issue in SEunjeon, caused by various scala versions. We need to extract and save dictionary objects differently.


---
layout: post
title: Thinking outside of the box
tags: []
status: publish
type: post
published: true
meta: {}
---
While reading through the Slashdot newsletter I stumbled on an entry about hacking the Linksys NSLU2, which was a two part article on how to access a backdoor on the network storage device and modify it to support ssh, nfs and pretty much anything else you can think of as it runs a Linux OS. A real interesting read and an insight into the approach taken to get around the security of this device. You can read up on even more information here.


SportsPal
About Our Team
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About Us
Emma Nguyen
Summer Feelz: sunshine, watermelon, and shorts
Aspirational Hobby: scuba diving
Ask me About: novels, beauty, and maybe fashion
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Summer Feelz: Sun, No School, Beach
Aspirational Hobby: Playing Guitar, Acting, Travelling
Ask me About: Football, Game Of Thrones, Madagascar
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-
Initial Patient Population =
- AND: "Patient Characteristic Birthdate: birth date" >= 3 month(s) starts before start of "Measurement Period"
- AND: "Patient Characteristic Birthdate: birth date" <= 18 year(s) starts before start of "Measurement Period"
- AND:
- OR: "Occurrence A of Diagnosis, Active: Upper Respiratory Infection" starts during
- OR: "Encounter, Performed: Office Visit"
- OR: "Encounter, Performed: Emergency Department Visit"
- OR: "Encounter, Performed: Preventive Care- Initial Office Visit, 0 to 17"
- OR: "Encounter, Performed: Preventive Care - Established Office Visit, 0 to 17"
- OR: "Encounter, Performed: Hospital Observation Care - Initial"
- OR: "Encounter, Performed: Face-to-Face Interaction"
- during "Measurement Period"
- OR:
- OR: "Encounter, Performed: Office Visit"
- OR: "Encounter, Performed: Emergency Department Visit"
- OR: "Encounter, Performed: Preventive Care- Initial Office Visit, 0 to 17"
- OR: "Encounter, Performed: Preventive Care - Established Office Visit, 0 to 17"
- OR: "Encounter, Performed: Hospital Observation Care - Initial"
- OR: "Encounter, Performed: Face-to-Face Interaction"
- during ("Occurrence A of Diagnosis, Active: Upper Respiratory Infection" during "Measurement Period")
-
Denominator =
- AND: "Initial Patient Population"
-
Denominator Exclusions =
- AND:
- OR: "Diagnosis, Active: Competing Conditions for Respiratory Conditions" <= 3 day(s) starts after start of "Occurrence A
of Diagnosis, Active: Upper Respiratory Infection"
- OR: "Medication, Active: Antibiotic Medications" <= 30 day(s) starts before or during "Occurrence A of Diagnosis, Active:
Upper Respiratory Infection"
-
Numerator =
- AND NOT: "Medication, Order: Antibiotic Medications" <= 3 day(s) starts after start of "Occurrence A of Diagnosis, Active:
Upper Respiratory Infection"
-
Denominator Exceptions =
-
"Diagnosis, Active: Competing Conditions for Respiratory Conditions" using "Competing Conditions for Respiratory Conditions Grouping Value Set (2.16.840.1.113883.3.464.1<IP_ADDRESS>7)"
-
"Diagnosis, Active: Upper Respiratory Infection" using "Upper Respiratory Infection Grouping Value Set (2.16.840.1.113883.3.464.1<IP_ADDRESS>2)"
-
"Encounter, Performed: Emergency Department Visit" using "Emergency Department Visit Grouping Value Set (2.16.840.1.113883.3.464.1<IP_ADDRESS>0)"
-
"Encounter, Performed: Face-to-Face Interaction" using "Face-to-Face Interaction Grouping Value Set (2.16.840.1.113883.3.464.1<IP_ADDRESS>8)"
-
"Encounter, Performed: Hospital Observation Care - Initial" using "Hospital Observation Care - Initial Grouping Value Set (2.16.840.1.113883.3.464.1<IP_ADDRESS>2)"
-
"Encounter, Performed: Office Visit" using "Office Visit Grouping Value Set (2.16.840.1.113883.3.464.1<IP_ADDRESS>1)"
-
"Encounter, Performed: Preventive Care - Established Office Visit, 0 to 17" using "Preventive Care - Established Office Visit, 0 to 17 Grouping Value Set (2.16.840.1.113883.3.464.1<IP_ADDRESS>4)"
-
"Encounter, Performed: Preventive Care- Initial Office Visit, 0 to 17" using "Preventive Care- Initial Office Visit, 0 to 17 Grouping Value Set (2.16.840.1.113883.3.464.1<IP_ADDRESS>2)"
-
"Medication, Active: Antibiotic Medications" using "Antibiotic Medications Grouping Value Set (2.16.840.1.113883.3.464.1<IP_ADDRESS>1)"
-
"Medication, Order: Antibiotic Medications" using "Antibiotic Medications Grouping Value Set (2.16.840.1.113883.3.464.1<IP_ADDRESS>1)"
-
"Patient Characteristic Birthdate: birth date" using "birth date LOINC Value Set (2.16.840.1.113883.3.560.100.4)"
-
"Patient Characteristic Ethnicity: Ethnicity" using "Ethnicity CDC Value Set (2.16.840.1.114<IP_ADDRESS>7)"
-
"Patient Characteristic Payer: Payer" using "Payer Source of Payment Typology Value Set (2.16.840.1.114<IP_ADDRESS>91)"
-
"Patient Characteristic Race: Race" using "Race CDC Value Set (2.16.840.1.114<IP_ADDRESS>6)"
-
"Patient Characteristic Sex: ONC Administrative Sex" using "ONC Administrative Sex Administrative Sex Value Set (2.16.840.1.1137<IP_ADDRESS>)"


Match Play. Red leads 3-2 to 7. Should Red double? If doubled, should White take?
pips Red=137 White=147
(CorrectAnswer= 0/0, ErrorScore= 0)
A classic volatile middle game position. White’s four blots invite a double which is perfectly correct and not doubling is a blunder.
The take is very clear as White has so many ways to win the game from here, especially as Red is on the bar and fans 11% of the time. Passing this would be worse than a quadruple blunder.
Interesting things happen when you play around with moving the checkers in Red’s home board. Give him his 5-pt and 4-pt instead of his 3-pt and 2-pt and the position becomes double and big pass at this score but double and an easy take for money.
The take is very clear as White has so many ways to win the game from here, especially as Red is on the bar and fans 11% of the time. Passing this would be worse than a quadruple blunder.
Interesting things happen when you play around with moving the checkers in Red’s home board. Give him his 5-pt and 4-pt instead of his 3-pt and 2-pt and the position becomes double and big pass at this score but double and an easy take for money.
Cube analysis 2-ply cubeless equity +0.470 (Money: +0.370) 0.581 0.305 0.027 - 0.419 0.118 0.007 Cubeful equities: 1. Double, take +0.577 2. Double, pass +1.000 (+0.423) 3. No double +0.515 (-0.062) Proper cube action: Double, take


Pipeline Rasterisation, clipping and culling state configuration. Culling, winding order, depth clipping, raster discard, point size, fill mode, provoking vertex.
More...
|
| | RasterStateCreateParam (types::Face cullFace=types::Face::None, types::PolygonWindingOrder frontFaceWinding=types::PolygonWindingOrder::FrontFaceCCW, bool enableDepthClip=true, bool enableRasterizerDiscard=false, bool enableProgramPointSize=false, types::FillMode fillMode=types::FillMode::Fill, types::ProvokingVertex provokingVertex=types::ProvokingVertex::First, float32 lineWidth=1.0f, bool enableDepthBias=false, float32 depthBiasClamp=0.f, float32 depthBiasConstantFactor=0.f, float32 depthBiasSlopeFactor=0.f) |
| | Create a rasterization and polygon state configuration.  (If enabled, clamp, constant factor and slope factor can be checked) More...
|
| |
| bool | isDepthClipEnabled () const |
| | Check if depth clipping is enabled (i.e. depth clamping disabled). If disabled, polygons will not be clipped against the front and back clipping plane clipping planes: instead, the primitives' depth will be clamped to min and max depth instead. More...
|
| |
| bool | isProgramPointSizeEnabled () const |
| | Check if program point size is enabled More...
|
| |
| bool | isRasterizerDiscardEnabled () const |
| | Check if rasterization is skipped (so all parts of the pipeline after transform feedback) More...
|
| |
| RasterStateCreateParam & | setCullFace (types::Face face) |
| | Set the face that will be culled (front/back/both/none). More...
|
| |
| RasterStateCreateParam & | setDepthBias (bool enableDepthBias, bool depthBiasClamp=0.f, bool depthBiasConstantFactor=0.f, bool depthBiasSlopeFactor=0.f) |
| | Enable depth bias (add a value to the calculated fragment depth) More...
|
| |
| RasterStateCreateParam & | setDepthClip (bool enableDepthClip) |
| | Select between depth Clipping and depth Clamping More...
|
| |
| RasterStateCreateParam & | setFillMode (types::FillMode mode) |
| | Set polygon fill mode. More...
|
| |
| RasterStateCreateParam & | setFrontFaceWinding (types::PolygonWindingOrder frontFaceWinding) |
| | Set which polygon winding order is considered the "front" face (The opposite order is considered back face). More...
|
| |
| RasterStateCreateParam & | setLineWidth (float32 lineWidth) |
| | Set the line width More...
|
| |
| RasterStateCreateParam & | setProgramPointSize (bool enable) |
| | Enable/disable Program Point Size. More...
|
| |
| RasterStateCreateParam & | setRasterizerDiscard (bool enable) |
| | Disable all phases after transform feedback (rasterization and later) More...
|
| |
Pipeline Rasterisation, clipping and culling state configuration. Culling, winding order, depth clipping, raster discard, point size, fill mode, provoking vertex.
Defaults: Cull face: back, Front face: CounterClockWise, Depth Clipping: true, Rasterizer Discard: false, Program Point Size: false, Point Origin: Lower left, Fill Mode: Front&Back, Provoking Vertex: First
| pvr::api::pipelineCreation::RasterStateCreateParam::RasterStateCreateParam |
( |
types::Face |
cullFace = types::Face::None , |
|
|
types::PolygonWindingOrder |
frontFaceWinding = types::PolygonWindingOrder::FrontFaceCCW , |
|
|
bool |
enableDepthClip = true , |
|
|
bool |
enableRasterizerDiscard = false , |
|
|
bool |
enableProgramPointSize = false , |
|
|
types::FillMode |
fillMode = types::FillMode::Fill , |
|
|
types::ProvokingVertex |
provokingVertex = types::ProvokingVertex::First , |
|
|
float32 |
lineWidth = 1.0f , |
|
|
bool |
enableDepthBias = false , |
|
|
float32 |
depthBiasClamp = 0.f , |
|
|
float32 |
depthBiasConstantFactor = 0.f , |
|
|
float32 |
depthBiasSlopeFactor = 0.f |
|
) |
| |
|
inline |
Create a rasterization and polygon state configuration.
- Parameters
-
| cullFace | Face culling (default: Back) |
| frontFaceWinding | The polygon winding order (default: Front face is counterclockwise) |
| enableDepthClip | Enable depth clipping. If set to false , depth Clamping happens instead of clipping (default: true) |
| enableRasterizerDiscard | Enable rasterizer discard (default:false) |
| enableProgramPointSize | Enable program point size (default:true) |
| fillMode | Polygon fill mode (default: Front and Back) |
| lineWidth | Width of rendered lines (default: One) |
| enableDepthBias | Enable depth bias (default: false) |
| provokingVertex | The provoking vertex to use |
<param name="name="enableDepthBias">Enable">depth bias (default: false)
<param name="name="depthBiasClamp">If">depth bias is enabled, the clamping value for depth bias (default:0)
<param name="name="depthBiasConstantFactor">If">depth bias is enabled, the constant value by which to bias depth(default:0)
<param name="name="depthBiasSlopeFactor">If">depth bias is enabled, the slope value by which to bias depth(default:0)


|
ViennaCL - The Vienna Computing Library
1.5.2
|
Internal helper class representing a row-major dense matrix used for the QR method for the purpose of computing eigenvalues. More...
#include <qr-method.hpp>
Public Member Functions | |
| FastMatrix () | |
| FastMatrix (vcl_size_t sz, vcl_size_t internal_size) | |
| SCALARTYPE & | operator() (int i, int j) |
| SCALARTYPE * | row (int i) |
| SCALARTYPE * | begin () |
| SCALARTYPE * | end () |
Data Fields | |
| std::vector< SCALARTYPE > | data |
Internal helper class representing a row-major dense matrix used for the QR method for the purpose of computing eigenvalues.
| FastMatrix | ( | ) | [inline] |
| FastMatrix | ( | vcl_size_t | sz, |
| vcl_size_t | internal_size | ||
| ) | [inline] |
| SCALARTYPE* begin | ( | ) | [inline] |
| SCALARTYPE* end | ( | ) | [inline] |
| SCALARTYPE& operator() | ( | int | i, |
| int | j | ||
| ) | [inline] |
| SCALARTYPE* row | ( | int | i | ) | [inline] |
| std::vector<SCALARTYPE> data |


- Z - Static variable in class com.sri.ai.expresso.helper.Expressions
-
- ZERO - Static variable in class com.sri.ai.expresso.helper.Expressions
-
- ZERO - Static variable in class com.sri.ai.grinder.polynomial.core.DefaultMonomial
-
- ZERO - Static variable in class com.sri.ai.util.math.Rational
-
The constant zero (0).
- ZERO_POINT_FIVE - Static variable in class com.sri.ai.expresso.helper.Expressions
-
- zip3With(TernaryFunction<A1, A2, A3, R>, List<A1>, List<A2>, List<A3>) - Static method in class com.sri.ai.util.Util
-
Returns list of results of application of a function to triples of
elements with same indices on three given lists.
- zipApply(String, List<Iterator<?>>) - Static method in class com.sri.ai.expresso.helper.Expressions
-
Returns a list of applications of a functor to successive lists of arguments, formed from the next elements of a list of iterators.
- ZipIterator - Class in com.sri.ai.util.collect
-
A ZipIterator is constructed on an array of iterators, and iterates over
lists of their next elements until one of them runs out of elements.
- ZipIterator(Iterator<?>...) - Constructor for class com.sri.ai.util.collect.ZipIterator
-
- ZipIterator(List<Iterator<?>>) - Constructor for class com.sri.ai.util.collect.ZipIterator
-
- zipWith(BinaryFunction<A1, A2, R>, List<A1>, List<A2>) - Static method in class com.sri.ai.util.Util
-
Returns list of results of application of a function to pairs of elements
with same indices on two given lists.


@(courseName: String, teacherName: String, students: Seq[controllers.storage.StorageController.StudentViewModel])


Social and Technical Systems
Home automation is the intersection of social and technical systems. It strives to accomplish the same general goal that Facebook does: o change the way you collaborate and connect with others. Instead of other human beings, home automation changes the way you collaborate with your home, as well as how items in your home interact with one another.  The home automation system itself will potentially interface with the home’s utilities (water, electricity, phone/internet) as well as individual appliances and heating/cooling systems. Therefore, the design should minimize the possibility for damage to these and not increase the likelihood of a safety hazard.
Design Implications
Users: As mentioned above in our initial success criteria, our user’s economic and social factors will bring an influence to our design. Their known technical abilities will allow us to add more functionality into the system without the fear of it becoming too advanced. Their economic awareness and concerns will force our design to push beyond the standard set of home automation features and incorporate money savings rather than just time savings. Environment: Our design must be sensitive to a user’s connection with the objects/ tasks we are considering in the home automation space. While some tasks such as ‘watering the plants’ or ‘making coffee’ are great examples for incorporating into a home automation design, it’s important to remember that these may be enjoyable task for users to complete, when time allows. Home automation should allow users to continue to perform tasks manually, without disturbing the automation schedule.
Design Space Justification
Justification for this effort is to build upon the modern-day innovations we see every day. We live in a world driven by “convenience.” Technology is creating positive disruptions within everyday industries that haven’t seen change in decades. These disruptions are made up of the Internet of Things (IoT) and should not be limited; therefore, exploring the opportunities and possibilities available within in-home automation is just the beginning in bringing the benefits of technology to everyone, all around the world. To begin, we must understand how tasks are performed today, the pain points and frustrations. Only then, can we expose opportunities to be leveraged by technology.


Bank merger | Unions to stage dharna before Parliament on December 10
In August, the government announced its plan to merge 10 public sector lenders into four to create fewer and stronger global-sized banks.
by PTIVarious bank unions plan to stage dharna on December 10 in front of the Parliament to protest against the government decision to merger 10 public sector banks.
In August, the government announced its plan to merge 10 public sector lenders into four to create fewer and stronger global-sized banks.
"Government has been pursuing the policy of merger of public sector banks despite the adverse implications.
Experience of mergers in the country and elsewhere has clearly shown that no benefit accrues to any of the stake holder," bank union said in a statement.
The unions will stage dharna on December 10 in front of the Parliament, it said.


Welcome – enhance.x0r.sh is an online service for Super Resolution of images using deep learning, based on Alex J. Champandard's neural enhance.
Start by uploading a PNG image. Right now, only images up to 100kb are supported.
Server Resources are limited. Your upload can take several minutes depending on the amount of users currently using the service.
Loading...


Main Website
My personal website, serves as the Portal for anything and everything going on in my life.
ActiveSecondary Website
Main website for my online activities, whether that is youtube or my service shop.
ActiveMy personal website, serves as the Portal for anything and everything going on in my life.
ActiveMain website for my online activities, whether that is youtube or my service shop.
Active


Shiny is a package from RStudio that can be used to build interactive web apps with R.
You can host standalone apps on a webpage or embed them in R Markdown documents or build [dashboards](http://rstudio.github.io/shinydashboard/. You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions.
Shiny combines the computational power of R with the interactivity of the modern web.
Shiny apps are easy to write. No web development skills are required.
You can download the GitHub repo created for this tutorial which contains the xaringan presentation slides and the app used in the presentation from: https://github.com/TanjaKec/LearnShiny
Do you remember our dplyr
challenge using group_by
function to compute the median life expectancy for year 2007 by continent for gapminder
data that we had to visualise?
Can you create a Shiny app for this visualisation?
Have a look at this 👉😎
useful links:
Look for inspiration at rstudio shiny gallery
Getting Started with Shiny by CODING CLUB https://ourcodingclub.github.io/2017/03/07/shiny.html
Follow Dean’s blog: https://deanattali.com
An example: Cancer Research UK Cambridge Institute
Go further: Shiny Dashboard
© 2019 Tatjana Kecojevic


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From: Top 100 Universities And Colleges in North America
This is an absolute positioned div with an onchange handler on the original select element.


A/a
RegEx
/* This a sample MDX editor based on CodeMirror */ WITH SET [Range] as '{[Time].[Year].[2010], [Time].[Year].[2011]}' // this the timeframe to show MEMBER [Time].[Range Total] as 'Aggregate([Range])' -- this member aggregates depending on measure , FORMAT_STRING = "#,##0.00" , SOLVE_ORDER = 1 SELECT { [Measures].AllMembers } ON COLUMNS, Union([Range], {[Time].[Range Total]}) ON ROWS FROM [Sales] WHERE ( [Store].[All Stores] )