This module will add a routine onto the error vector (ErrorV) which will test the error number of any errors generated against a list of error numbers. If an error is generated which matches an entry in the list, TrapError will do one of several possible things:
The debug build of this module will output a dump of all errors passing through the error vector to a file.
This file can be specified by setting the system variable <TrapError$File>
. While this variable
is not set, no dump output will be generated. If the file specified already exists, the debug output will be
appended at the end. If a directory exists with the same name as the file specified, no error message or debug
output will be generated.
The debug output consists of lines of the following format (all values are eight-digit hexadecimal):
<Monotonic time of error> <space> <Error number> <space> <Error text> <linefeed>
| Word | Meaning |
| 0 | lowest error number in list - 1 |
| 1 | highest error number in list + 1 |
| 2 | error number |
| 3 | action to take |
| 4 | value to pass in R12 |
| 6 | error number |
| ... | ... |
| n | 0 (end of list) |
When the table is empty, word 0 contains &7FFFFFFF and word 1 contains &C0000000. This will allow very quick detection of when an error number should not be checked and thus be passed on.
Every time a number is removed from the table, it is compared against words 0 and 1 (-1). If it matches either (or both), the table will be re-scanned to get the next lowest/highest.
The TrapError module provides a *Command equivalent to each SWI, with the same name and the same parameters. The only difference of note is that all *Commands which would have a flags parameter which should be zero, have that parameter omitted. For example, *TrapError_Off takes no parameters. Use *Help for more information.
TrapError_Off
(SWI &52DC0)
On entry
Errors: "This TrapError SWI must have a flags word of zero" (&816A03)
TrapError_On
(SWI &52DC1)
On entry
Errors:
TrapError_AddError
(SWI &52DC2)
On entry
| R0 | Meaning |
| 0 | Perform an OS_Reset |
| 1 | Stop the error message from being passed-on |
| 2 | Replace the error block pointer with another (in R2) |
| 3..255 | Reserved (do not use) |
| > 255 | Issue a CallBack to the address in R0 (bits 0 and 1 used for flags) |
All other codes for R0 are reserved and will return an error. If the error number in R1 is already in the list, the action specified in R0 will replace that already defined.
If R0 contains a value greater than 255, it is assumed to be the address of some routine to be called when an error of the specified type is detected. In this case, bits 0 and 1 of R0 contain flags as follows:
| Bits 0,1 | Meaning |
| 0 | CallBack to code and pass-on the error as normal |
| 1 | CallBack to code and don't pass-on the error |
| 2 | Reserved (do not use) |
| 3 | Reserved (do not use) |
When the error R1 is detected, a CallBack to R0 is issued. The code at R0 will be entered when RiscOS is next idle and threaded out. The processor will be in SVC mode with interrupts disabled. R2 contains the value to be passed in R12 to your code (ie: a private workspace pointer). This register can contain any value and is not checked by TrapError.
Errors:
TrapError_RemoveError
(SWI &52DC3)
On entry
Errors:
TrapError_ResetTable
(SWI &52DC4)
On entry


404 not found
I have bad news for you
The page you are looking for might be removed or is temporarily unavaible
Back to homepage


Bern, Fribourg, and Murten
Bern, the capital city of Switzerland, is situated in the about 120 KMs to the west of Zürich, the largest city of Switzerland. The majority of the population speaks Bernese German.
The Aare river stretches along the entire city due to which there are many bridges. The city is built on an uneven ground. An elevation difference of several meters exists between the city center and its outskirts.
According to a local legend, the city is named after “bear”, as it was the first animal hunted by the city founder. The city center contains many museums, street art, and souvenir shops.
Fribourg is situated about 30 KMs to the west of Bern. It has a population of around 38,000 with mostly French speaking inhabitants. The University of Fribourg enrolls 10,000 students and contains five faculties which are based around socials sciences, humanities, and economics.
About 10 KMs from Fribourg there is a village called Barberêche.
The farm was called “Ferme du Petit-Vivy” and contained tipis and and a barn house for guests.
The host provided a nice homemade Swiss breakfast in the morning.
At an hours bike ride from the farm lies the city of Murten, which is part of the canton of Fribourg.
The lake Murten is famous for sailing, wind surfing, and other water sports.


EXPORT MYMOS
RScan the QR code with another device running Mymos.
Get your private encription key crossplatform.


You can easy create stackable modal boxes. For example, your inline content or Ajax response can contain a gallery:
Animations can be added with simple CSS transitions: Try me
This is another instance of fancyBox!
Got it!


Next: real, Previous: number, Up: Numbers Dictionary
The type complex includes all mathematical complex numbers other than those included in the type rational. Complexes are expressed in Cartesian form with a real part and an imaginary part, each of which is a real. The real part and imaginary part are either both rational or both of the same float type. The imaginary part can be a float zero, but can never be a rational zero, for such a number is always represented by Common Lisp as a rational rather than a complex.
Specializing.
(complex
{[typespec | *]})
typespec—a type specifier that denotes a subtype of type real.
[Editorial Note by KMP: If you ask me, this definition is a complete mess. Looking at issue ARRAY-TYPE-ELEMENT-TYPE-SEMANTICS:UNIFY-UPGRADING does not help me figure it out, either. Anyone got any suggestions?]
Every element of this type is a complex whose real part and imaginary part are each of type
(upgraded-complex-part-type typespec).
This type encompasses those complexes that can result by giving numbers of type typespec to complex.
(complex type-specifier) refers to all complexes that can result from giving numbers of type type-specifier to the function complex, plus all other complexes of the same specialized representation.
Rule of Canonical Representation for Complex Rationals, Constructing Numbers from Tokens, Printing Complexes
The input syntax for a complex with real part r and imaginary part i is #C(r i). For further details, see Standard Macro Characters.
For every float, n, there is a complex which represents the same mathematical number and which can be obtained by (COERCE n 'COMPLEX).
Next: real, Previous: number, Up: Numbers Dictionary


Maid of Lidia:
Oh no! Could you have dropped it in the village? Take the letter with you and ask around for it. Meet me again tomorrow night.


{{template "header.html" .}}
{{template "titlebar.html" .}}
Trace Clustering
Commit to cluster around (currently non-functional)
Issue:
Calculate
Cluster Size
Regression
Step Size
Least Squares
Step Time


Spoilers: Abuser Kel exposed in Corrie tonight as Paul reveals the truth
Paul Foreman (Peter Ash) was abused by Kel Hinchley (Joe Alessi) when he was a teenager in Coronation Street, and — although Billy Mayhew (Daniel Brocklebank) knows the truth — the rest of his family haven’t a clue. However, the truth comes to light in tonight’s episodes, when Paul finds himself with little choice but to reveal all.
Viewers will have seen cobbles residents becoming agitated over the last couple of episodes about a string of burglaries which have taken place in the surrounding area. Billy tweaked on rather quickly that the addresses of those who were burgled are listed in his diary — which has conveniently gone missing.
With Bernie (Jane Hazelgrove) having started working in the church the day his diary went missing, Billy was quick to point the finger — as did Gemma (Dolly-Rose Campbell) and Paul when they discovered a stash of stolen goods in the cupboard under the stairs.
Advertisement Advertisement
The pair kicked their mum out, but tonight they begin to think that they might’ve been somewhat hasty in doing so, and thus they apologise to her. After doing some deliberating, they realise that evil Kel also had access to the diary and thus they put two and two together.
As a result, Gemma and Bernie decide to break into Kel’s flat and they’re left shocked by what they find. Yes, they stumble upon not only Daniel’s (Rob Mallard) laptop — which viewers will know went missing on the day of Sinead’s (Katie McGlynn) funeral — but also a pile of old photos of Paul — and some other children too.
Bernie and Gemma return home with the laptop and photos in hand — much to Paul’s dismay.
However, he soon opens up, as he reveals that Kel sexually abused him.
Afterwards, Bernie and Gemma are stunned.
Will they confront Kel?
We know there’s going to be some big scenes coming up between Kel and Bernie, so a confrontation will ensue act some point.
Coronation Street continues Friday 22ndNovember at 7:30pm and 8:30pm on ITV.


Chapter 5 The apply family
Learning Objectives
Learn to use apply family functions in place of loops.
Whenever you’re using a for loop, you might want to revise your code and see whether you can use the lapply function instead. Learn all about this intuitive way of applying a function over a list or a vector, and its variants sapply and vapply.
5.1 apply()
apply() takes Data frame or matrix as an input and gives output in vector, list or array. apply() Function is primarily used to avoid explicit uses of loop constructs. It is the most basic of all collections can be used over a matrice.
This function takes 3 arguments:
apply(X, MARGIN, FUN)
apply() takes an array or matrix X
, a Margin
that takes a value 1 for rows or 2 for columns and applies a functoin FUN
to it.
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 6 1 6 1 6
## [2,] 2 7 2 7 2 7
## [3,] 3 8 3 8 3 8
## [4,] 4 9 4 9 4 9
## [5,] 5 10 5 10 5 10
## [1] 15 40 15 40 15 40
Exercise
Select the columns from count.raw file from pdfDownloadsCount to hmtlDownloadsCount and using the apply function find the mean of each column.
5.2 lapply()
Before you go about solving the exercises below, have a look at the documentation of the lapply()
function. The Usage section shows the following expression:
lapply(X, FUN, ...)
To put it generally, lapply
takes a vector or list X
, and applies the function FUN
to each of its members. If FUN
requires additional arguments, you pass them after you’ve specified X
and FUN
(...
). The output of lapply()
is a list, the same length as X
, where each element is the result of applying FUN
on the corresponding element of X
.
Now that you are truly brushing up on your data science skills, let’s revisit some of the most relevant figures in data science history. We’ve compiled a vector of famous mathematicians/statisticians and the year they were born. Up to you to extract some information!
<IP_ADDRESS> Example
# The vector pioneers has already been created for you
pioneers <- c("GAUSS:1777", "BAYES:1702", "PASCAL:1623", "PEARSON:1857")
# Split names from birth year to make split_math a list
split_math <- strsplit(pioneers, split = ":")
# Convert to lowercase strings: split_low
split_low <- lapply(split_math, tolower)
# Take a look at the structure of split_low
str(split_low)
## List of 4
## $ : chr [1:2] "gauss" "1777"
## $ : chr [1:2] "bayes" "1702"
## $ : chr [1:2] "pascal" "1623"
## $ : chr [1:2] "pearson" "1857"
5.2.1 Use lapply with your own function
You can use lapply()
on your own functions as well. You just need to code a new function and make sure it is available in the workspace. After that, you can use the function inside lapply()
just as you did with base R functions. We have already created a select_first() function so lets apply it to a
Exercise
Use lapply on split_low list and use the select_first() function you created.
5.2.2 lapply and anonymous functions
Writing your own functions and then using them inside lapply()
is quite an accomplishment! But defining functions to use them only once is kind of overkill, isn’t it? That’s why you can use so-called anonymous functions in R.
Previously, you learned that functions in R are objects in their own right. This means that they aren’t automatically bound to a name. When you create a function, you can use the assignment operator to give the function a name. It’s perfectly possible, however, to not give the function a name. This is called an anonymous function:
# Named function
select_first <- function(x) { x[1] }
# Use anonymous function inside lapply()
lapply(list(1,2,3), function(x) {x[1]})
5.2.3 Use lapply with additional arguments
In the video, the triple()
function was transformed to the multiply()
function to allow for a more generic approach. lapply()
provides a way to handle functions that require more than one argument, such as the multiply()
function:
multiply <- function(x, factor) {
x * factor
}
lapply(list(1,2,3), multiply, factor = 3)
On the right we’ve included a generic version of the select functions that you’ve coded earlier: select_el()
. It takes a vector as its first argument, and an index as its second argument. It returns the vector’s element at the specified index.
5.3 sapply()
You can use sapply()
similar to how you used lapply()
. sapply is a user-friendly version and wrapper of lapply by default returning a vector, matrix .The first argument of sapply()
is the list or vector X
over which you want to apply a function, FUN
. Potential additional arguments to this function are specified afterwards (...
):
sapply(X, FUN, ...)
5.3.1 sapply with your own function
Like lapply()
, sapply()
allows you to use self-defined functions and apply them over a vector or a list:
sapply(X, FUN, ...)
Here, FUN
can be one of R’s built-in functions, but it can also be a function you wrote. This self-written function can be defined before hand, or can be inserted directly as an anonymous function.
5.3.2 sapply with function returning vector
In the previous exercises, you’ve seen how sapply()
simplifies the list that lapply()
would return by turning it into a vector. But what if the function you’re applying over a list or a vector returns a vector of length greater than 1? If you don’t remember from the video, don’t waste more time in the valley of ignorance and head over to the instructions!
<IP_ADDRESS> Example
temp=list(c(1,3,4,4,6),c(3,4,6,8,9))
# Create a function that returns min and max of a vector: extremes
extremes <- function(x) {
c(min = min(x), max = max(x))
}
# Apply extremes() over temp with lapply()
lapply(temp, extremes)
## [[1]]
## min max
## 1 6
##
## [[2]]
## min max
## 3 9
## [,1] [,2]
## min 1 3
## max 6 9
5.3.3 sapply can’t simplify, now what?
It seems like we’ve hit the jackpot with sapply()
. On all of the examples so far, sapply()
was able to nicely simplify the rather bulky output of lapply()
. But, as with life, there are things you can’t simplify. How does sapply()
react?
We already created a function, below_zero()
, that takes a vector of numerical values and returns a vector that only contains the values that are strictly below zero.
<IP_ADDRESS> Example
# temp is already prepared for you in the workspace
# Definition of below_zero()
below_zero <- function(x) {
return(x[x < 0])
}
# Apply below_zero over temp using lapply(): freezing_l
freezing_l <- lapply(temp, below_zero)
# Apply below_zero over temp using sapply(): freezing_s
freezing_s <- sapply(temp, below_zero)
# Are freezing_l and freezing_s identical?
identical(freezing_l, freezing_s)
## [1] TRUE
5.4 vapply()
Before you get your hands dirty with the third and last apply function that you’ll learn about in this intermediate R course, let’s take a look at its syntax. The function is called vapply()
, and it has the following syntax:
vapply(X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE)
Over the elements inside X
, the function FUN
is applied. The FUN.VALUE
argument expects a template for the return argument of this function FUN
. USE.NAMES
is TRUE
by default; in this case vapply()
tries to generate a named array, if possible.
For the next set of exercises, you’ll be working on the temp
list again, that contains 7 numerical vectors of length 5. We also coded a function basics()
that takes a vector, and returns a named vector of length 3, containing the minimum, mean and maximum value of the vector respectively.
<IP_ADDRESS> Example
# temp is already available in the workspace
# Definition of basics()
basics <- function(x) {
c(min = min(x), mean = mean(x), max = max(x))
}
# Apply basics() over temp using vapply()
vapply(temp, basics, numeric(3))
## [,1] [,2]
## min 1.0 3
## mean 3.6 6
## max 6.0 9
<IP_ADDRESS> Exercise
<br>
Use the table() function and one of the apply functions to see how many counts of each year and articleType are available.
Explain which function you used and what does the output look like?
</div>
<!--chapter:end:05-Apply_family.Rmd-->
# Advanced Plotting
"The greatest value of a picture is when it forces us to notice what we never expected to see"
-John Tukey
---
## Setup
**1. Install the `tidyverse` package.**
```r
library(tidyverse)
## ── Attaching packages ───── tidyverse 1.3.0 ──
## ✓ tibble 3.0.1 ✓ dplyr 0.8.5
## ✓ tidyr 1.0.3 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ✓ purrr 0.3.4
## ── Conflicts ──────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
2. Filter data.
We will be using the publication dataset that we read into R in Chapter 2 as counts
.
5.5 Review of ggplot2 basics
ggplot2
is a plotting package that makes it simple to create complex plots from data in a data frame. Graphics are built step by step by adding new elements. Adding layers in this fashion allows for extensive flexibility and customization of plots.
A plot can be divided into different fundamental parts:
Plot = data + aesthetics + geom
Required building blocks:
- data
- aesthetics - describe how data are mapped to colour, size, shape, location
- geoms - geometric objects like points, lines, shapes
Optional building blocks:
- facets - describes how panel plots should be constructed
- stats - statistical transformations like binning, quantiles, smoothing
- coordinates - describes the system in which the locations of the geoms will be drawn
- scales - what scale an aesthetic map uses (ex. male = red, female = blue)
To build a ggplot, we will use the following basic template that can be used for different types of plots:
Specify which data set to use for the plot using the
data
argument.Define a “mapping” (using the aesthetic (
aes
) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc.Add “geoms” – graphical representations of the data in the plot (points, lines, bars).
ggplot2
offers many different geoms; common ones include:
geom_point()
for scatter plots, dot plots, etc.geom_boxplot()
for boxplots.geom_histogram()
for histograms.geom_barplot()
for barplots.geom_line()
for trend lines, time series, etc.
Example:
Adding aesthetics:
5.6 Statistics
The function geom_smooth()
adds a loess curve to the data along with a 95% confidence interval.
p <- ggplot(research, aes(x = pdfDownloadsCount,
y = wosCountThru2011)) +
geom_point(aes(color = journal)) +
geom_smooth()
p
If we move the colour to the base ggplot call, we get loess curves for each level of that factor.
p <- ggplot(research, aes(x = pdfDownloadsCount,
y = wosCountThru2011,
color = journal)) +
geom_point() +
geom_smooth()
p
Check the help page for the function geom_smooth()
for more information about how the curve is made. For example, to map a linear model onto the plot, you can choose method = "lm"
.
5.7 Scales
Now let’s look at the relationship between days since published and citation count.
p <- ggplot(research, aes(x = daysSincePublished,
y = wosCountThru2011)) +
geom_point(aes(color= journal))
p
It looks like most of the citation counts are close to 0. We can quickly check the distribution of this variable using a qplot.
To control the plot axes, we use variants of the functions scale_x_*
and scale_y_*
.
p <- ggplot(research, aes(x = daysSincePublished,
y = wosCountThru2011)) +
geom_point(aes(color= journal)) +
scale_y_log10()
p
## Warning: Transformation introduced infinite values in continuous y-axis
How can we fix this?
p <- ggplot(research, aes(x = daysSincePublished,
y = log10(wosCountThru2011 + 1))) +
geom_point(aes(color= journal))
p
Notice what this fix has done to the way the y-axis is labelled. To manually update the axis label, we can use the scale_y_continuous()
function.
p <- ggplot(research, aes(x = daysSincePublished,
y = log10(wosCountThru2011 + 1))) +
geom_point(aes(color= journal)) +
scale_y_continuous(breaks = c(1,2,3), labels = c(10, 100, 1000), name = "Citations")
p
The scale family of functions can also be used to change the colours.
p <- ggplot(research, aes(x = daysSincePublished,
y = log10(wosCountThru2011 + 1))) +
geom_point(aes(color= journal)) +
scale_y_continuous(breaks = c(1,2,3), labels = c(10, 100,1000), name = "Citations") +
scale_colour_manual(values = c("red","blue", "green", "yellow", "purple", "orange","cyan"), name = "Journal")
p
## Loading required package: viridisLite
5.8 Colour palettes
Choosing good colours to aid visualization is not trivial and requires some (or a lot of) thought. A set of colour palettes have been developed for easy use with ggplot2 through a package called RColorBrewer
.
There are three types of these premade palettes: * Sequential * Diverging * Qualitative
Or, you can create your own color palettes using the colorRamp()
or colorRampPalette()
function.
colorRamp()
returns a function that takes values between 0 and 1, indicating the extremes of the color palette.
colorRampPalette()
returns a function that takes integer arguments and returns a vector of colours.
colorRamp()
## [,1] [,2] [,3]
## [1,] 255 0 0
## [,1] [,2] [,3]
## [1,] 127.5 0 127.5
## [,1] [,2] [,3]
## [1,] 0 0 255
5.9 Faceting
There are two functions to control how plots are divided into facets: facet_wrap()
and facet_grid()
.
facet_wrap()
p <- ggplot(research, aes(x = daysSincePublished,
y = log10(wosCountThru2011 + 1),
color = journal)) +
geom_point() +
scale_y_continuous(breaks = c(1,2,3), labels = c(10, 100,1000), name = "Citations") +
facet_wrap(~journal) +
geom_smooth(color = "black", method = "lm")
p
It is still difficult to see the spread of the data. Try making the points smaller and transparent.
facet_grid()
What about faceting by two variables? For example, let’s say we want to look at the relationship between days since published and citations for low impact versus high impact articles.
First, let’s recreate a variable categorizing high and low impact based on the variable f1000Factor
.
research <- research %>% mutate(impact = cut(f1000Factor, breaks = c(-Inf,1,Inf), labels = c("low", "high")))
p <- ggplot(research, aes(x = daysSincePublished,
y = log10(wosCountThru2011 + 1),
color = journal)) +
geom_point(size = 0.5, alpha = 0.5) +
scale_y_continuous(breaks = c(1,2,3), labels = c(10, 100,1000), name = "Citations") +
facet_grid(journal~impact) +
geom_smooth(color = "black", method = "lm")
p
By default, R will plot each panel on the same axes. You can override this using the option scales
, setting it to free_x
to vary the scale across rows, scale_y
to vary it across columns, or free
to vary across both.
p <- ggplot(research, aes(x = daysSincePublished,
y = log10(wosCountThru2011 + 1),
color = journal)) +
geom_point(size = 0.5, alpha = 0.5) +
scale_y_continuous(breaks = c(1,2,3), labels = c(10, 100,1000), name = "Citations") +
facet_wrap(~journal, scale = "free") +
geom_smooth(color = "black", method = "lm")
p
Exercise 5.6
Use what you’ve learned to generate the plot below.
5.10 Themes
In addition to theme_bw()
, which changes the plot background to white, ggplot2
comes with several other themes which can be useful to quickly change the look of your visualization. The complete list of themes is available at https://ggplot2.tidyverse.org/reference/ggtheme.html. theme_minimal()
and theme_light()
are popular, and theme_void()
can be useful as a starting point to create a new hand-crafted theme.
The ggthemes
package provides a wide variety of options (including an Excel 2003 theme). The ggplot2
extensions website provides a list of packages that extend the capabilities of ggplot2
, including additional themes.
This is also the place you can customize the text of your plot.
p <- ggplot(research, aes(x = daysSincePublished,
y = log10(wosCountThru2011 + 1))) +
geom_point(aes(color= journal)) +
scale_y_continuous(breaks = c(1,2,3), labels = c(10, 100, 1000), name = "Citations") +
theme_bw() +
xlab("Days Since Published") +
theme(axis.title.x = element_text(size = 14, face = "bold"))
p
5.11 Multiple plots
There are two useful packages for combining multiple plots: gridExtra
and cowplot
.
gridExtra
has two useful functions: grid.arrange()
and arrangeGrob()
. However, these functions make no attempt at aligning the plot panels; instead, the plots are simply placed into the grid as they are, so the axes are not aligned. If axis alignment is required, the plot_grid()
function of the cowplot
package is better. We will try using both here.
The gridExtra
package
Let’s say we want to combine the following three plots:
plot1 <- ggplot(research, aes(x = journal, fill = journal)) +
geom_bar(color = "black") +
theme_bw()
plot1
plot2 <- ggplot(research, aes(x = log10(wosCountThru2011 + 1),
y = log10(pdfDownloadsCount + 1),
color = journal)) +
geom_point(size = 0.5, alpha = 0.5) +
theme_bw() +
geom_smooth() +
xlab("Citations") +
ylab("Downloads") +
theme(legend.position = "none")
plot2
plot3 <- ggplot(research, aes(x = impact,
y = log10(pdfDownloadsCount + 1),
fill = journal)) +
geom_boxplot() +
theme_bw() +
ylab("Downloads")
plot3
To arrange them together, use the grid.arrange()
function.
To modify how they are arranged, you can use the layout_matrix
argument.
The cowplot
package
We can also arrange these plots using the plot_grid()
function.
5.11.1 Saving plots
The easiest way to save a plot is using the ggsave()
function.
Exercise 8.1
Using the publication dataset we’ve been using, complete the following tasks.
- Generate a table containing the mean and standard error of the number of tweets per journal. It should look something like this:
## # A tibble: 7 x 4
## journal num mean sem
## <fct> <int> <dbl> <dbl>
## 1 pbio 1325 0.0581 0.0202
## 2 pcbi 1351 0.127 0.0522
## 3 pgen 1619 0.0655 0.0204
## 4 pmed 643 0.311 0.188
## 5 pntd 621 0.0258 0.00906
## 6 pone 14078 0.493 0.0345
## 7 ppat 1459 0.0260 0.00881
- Use this table to create a bar plot of mean tweets per journal, including standard error. It should look something like this:
Hint: you will want to use the function geom_errorbar()
to add the error bars.
- Create a box plot of the number of citations per journal (ie. the variable wosCountThur2011). Colour the journals using the
Accent
palette from theRColorBrewer
package. It should look something like this:
- Generate a plot that shows the number of downloads over time, coloured by journal. It should look something like this:
5.12 Additional Resources
http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
- Mailing list: http://groups.google.com/group/ggplot2
- Wiki: https://github.com/hadley/ggplot2/wiki
- Website: http://had.co.nz/ggplot2/
- StackOverflow: http://stackoverflow.com/questions/tagged/ggplot
Cheatsheet


Referral Link
https://fontawesome.com/icons/wallet?ref=0x77D709586Bd3DCa8F2b00064A78936B1c030273D
Referral Lists
| name | Address | Time | Bonus |
|---|---|---|---|
| argon | 0x77D709586Bd3DCa8F2b00064A78936B1c030273D | 5/7/2021 | 500 |
Total Balance For Withdraw
350,897
Apply Withdraw
Withdraw Lists
| Amount | Time | Action |
|---|---|---|
| 500 | 4/5/2021 |
Success
|


*Information about this game will be deleted
| Turn | {{player}} |
|---|---|
| {{outerIndex + 1}} |
{{pointItem}}
|
| remainder |
{{$ctrl.currentGame.remainder[player]}}
|
| best/worst throw |
{{$ctrl.currentGame.bestThrow[player]}} / {{$ctrl.currentGame.worstThrow[player]}}
|
| best/worst precision |
{{$ctrl.currentGame.bestPrecision[player]}} / {{$ctrl.currentGame.worstPrecision[player]}}
|
Game over!
Winner(s):


Information in accordance with Section 5 TMG
Andre Meyer
Danziger Str. 22
10435, Berlin
E-Mail: andre.meyer93@gmail (dot) com
Website: http://andre-meyer.github.io/sm64-randomizer
The contents of our pages have been created with the utmost care. However, we cannot guarantee the contents' accuracy, completeness or topicality. According to statutory provisions, we are furthermore responsible for our own content on these web pages. In this matter, please note that we are not obliged to monitor the transmitted or saved information of third parties, or investigate circumstances pointing to illegal activity. Our obligations to remove or block the use of information under generally applicable laws remain unaffected by this as per §§ 8 to 10 of the Telemedia Act (TMG).
Responsibility for the content of external links (to web pages of third parties) lies solely with the operators of the linked pages. No violations were evident to us at the time of linking. Should any legal infringement become known to us, we will remove the respective link immediately.
Our web pages and their contents are subject to German copyright law. Unless expressly permitted by law, every form of utilizing, reproducing or processing works subject to copyright protection on our web pages requires the prior consent of the respective owner of the rights. Individual reproductions of a work are only allowed for private use. The materials from these pages are copyrighted and any unauthorized use may violate copyright laws.
Personal data (usually referred to just as "data" below) will only be processed by us to the extent necessary and for the purpose of providing a functional and user-friendly website, including its contents, and the services offered there.
Per Art. 4 No. 1 of Regulation (EU) 2016/679, i.e. the General Data Protection Regulation (hereinafter referred to as the "GDPR"), "processing" refers to any operation or set of operations such as collection, recording, organization, structuring, storage, adaptation, alteration, retrieval, consultation, use, disclosure by transmission, dissemination, or otherwise making available, alignment, or combination, restriction, erasure, or destruction performed on personal data, whether by automated means or not.
The following privacy policy is intended to inform you in particular about the type, scope, purpose, duration, and legal basis for the processing of such data either under our own control or in conjunction with others. We also inform you below about the third-party components we use to optimize our website and improve the user experience which may result in said third parties also processing data they collect and control.
Our privacy policy is structured as follows:
I. Information about us as controllers of your data
II. The rights of users and data subjects
III. Information about the data processing
The party responsible for this website (the "controller") for purposes of data protection law and the controller's data protection officer is:
Andre Meyer
Email: andre.meyer93@gmail (dot) com
With regard to the data processing to be described in more detail below, users and data subjects have the right
In addition, the controller is obliged to inform all recipients to whom it discloses data of any such corrections, deletions, or restrictions placed on processing the same per Art. 16, 17 Para. 1, 18 GDPR. However, this obligation does not apply if such notification is impossible or involves a disproportionate effort. Nevertheless, users have a right to information about these recipients.
Likewise, under Art. 21 GDPR, users and data subjects have the right to object to the controller's future processing of their data pursuant to Art. 6 Para. 1 lit. f) GDPR. In particular, an objection to data processing for the purpose of direct advertising is permissible.
Your data processed when using our website will be deleted or blocked as soon as the purpose for its storage ceases to apply, provided the deletion of the same is not in breach of any statutory storage obligations.
Google AnalyticsThis website uses Google Analytics, a web analytics service. It is operated by Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA. Google Analytics uses so-called "cookies". These are text files that are stored on your computer and that allow an analysis of the use of the website by you. The information generated by the cookie about your use of this website is usually transmitted to a Google server in the USA and stored there. Google Analytics cookies are stored based on Art. 6 (1) (f) DSGVO. The website operator has a legitimate interest in analyzing user behavior to optimize both its website and its advertising.
IP anonymizationWe have activated the IP anonymization feature on this website. Your IP address will be shortened by Google within the European Union or other parties to the Agreement on the European Economic Area prior to transmission to the United States. Only in exceptional cases is the full IP address sent to a Google server in the US and shortened there. Google will use this information on behalf of the operator of this website to evaluate your use of the website, to compile reports on website activity, and to provide other services regarding website activity and Internet usage for the website operator. The IP address transmitted by your browser as part of Google Analytics will not be merged with any other data held by Google.
Browser pluginYou can prevent these cookies being stored by selecting the appropriate settings in your browser. However, we wish to point out that doing so may mean you will not be able to enjoy the full functionality of this website. You can also prevent the data generated by cookies about your use of the website (incl. your IP address) from being passed to Google, and the processing of these data by Google, by downloading and installing the browser plugin available at the following link: tools.google.com/dlpage/gaoptout.
Objecting to the collection of dataYou can prevent the collection of your data by Google Analytics by clicking on the following link. An opt-out cookie will be set to prevent your data from being collected on future visits to this site: Disable Google Analytics. For more information about how Google Analytics handles user data, see Google's privacy policy: support.google.com/analytics/answer/6004245.
Outsourced data processingWe have entered into an agreement with Google for the outsourcing of our data processing and fully implement the strict requirements of the German data protection authorities when using Google Analytics.
Demographic data collection by Google AnalyticsThis website uses Google Analytics' demographic features. This allows reports to be generated containing statements about the age, gender, and interests of site visitors. This data comes from interest-based advertising from Google and third-party visitor data. This collected data cannot be attributed to any specific individual person. You can disable this feature at any time by adjusting the ads settings in your Google account or you can forbid the collection of your data by Google Analytics as described in the section "Refusal of data collection".


--- layout: topic title: "How do I find if a color combination has enough contrast?" id: color-combination category: color ---
With the WCAG standards, color contrast is separated into 3 levels:
| Level | Small Text | Large Text (18 point or 14 point bold) |
|---|---|---|
| A | 3:1 | 3:1 |
| AA | 4.5:1 | 3:1 |
| AAA | 7:1 | 4.5:1 |
The best way however is to use one of the many online tools:


Space Jam Chad'z rating: *** (out of 4 = good) 1996, PG, 81 minutes [1 hour, 21 minutes] [comedy] starring: Michael Jordan (himself), Billy West (voice of Bugs Bunny, Elmer Fudd), Dee Bradley Baker (voice of Daffy Duck, Tazmanian Devil), Wayne Knight (Stan Podolak), written by Leonardo Benvenuti, Timothy Harris, Steve Rudnick, Herschel Weingrod, produced by Daniel Goldberg, Steven Paul Leiva, Ivan Reitman, directed by Joe Pytka.
"Space Jam" is like a short movie they incorporate with thrill rides at big amusement parks: it's not big on plot and is really just a long advertisement. However, this film is funny and enjoyable and I can respect that.
The Looney Toons characters (Bugs Bunny, Daffy Duck, Porky Pig, etc.) have entertained us in the form of animated shorts for years, and this film proves their gags and routines can still work in the form of feature films. We seem to have forgotten cartoons were designed to make us laugh (not to be overly dramatic musicals), and this film not only re-establishes that concept, but does it through an original, humorous manner.
On the planet Moron Mountain (now there's a cartoon name if I ever heard one!), a fat, greedy, cigar-smoking alien (voiced by Danny DeVito), who runs a fledging amusement park, sends five scouts to Earth to kidnap the Looney Tunes characters to enslave them as novelty acts for his show.
Back on the "real" Earth, former basketball star Michael Jordan is struggling with his baseball career and finds himself annoyed by his new bumbling assistant Stan (Knight, the only fictional real-life character in the film). The initial atmosphere is a bit choppy as Jordan is upstaged by the professionalism of Knight and Bill Murray (as himself), but he isn't bad and I couldn't help but be curious.
The five-inch tall aliens arrive in the land of the Looney Toons packing powerful weapons and attempt to kidnap the 'toons. Using cartoon logic, Bugs Bunny (voiced by West) convinces them the only way they can be kidnapped is if they beat them in a game of basketball. So the aliens make their way to the "real" world and steal the talents from some top NBA players (through some very funny scenes), which causes them to morph into gigantic monsters. So Bugs travels to the "real" world and brings back Michael Jordan, hoping he can help them win.
An interesting subplot begins to develop regarding the players (Charles Barkley, Shawn Kemp, Mugsy Bouges, Patrick Ewing, and Larry Johnson) who were robbed of their talents and essentially become zombies. We get some slapstick jokes, but it keeps with the cartoony nature of the film. When the players go to strange means to get their talents back, the comedy works in both a childish and mature manner: childish in the delivery, but mature in the witty content. Throughout the film are parodies children wouldn't understand, but are still good anyway (including a hilarious "Pulp Fiction" spoof).
Describing the final act would be pointless wouldn't it? It's as cliche and cartoony as they come, but I didn't mind.
"Space Jam" isn't anything genius, but it's a good, decent movie. Mixing animation with live action is a difficult process to synthesize, especially when the comedy is cartoony in nature (and using people who aren't really actors), but this film has a lot of wit and cleverness that more than makes up for its rough edges.
Please visit Chad'z Movie Page @ http://members.aol.com/ChadPolenz E-mail<EMAIL_ADDRESS>(C)1997 Chad Polenz
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


{% extends "base.html" %} {% block content %}
My title at Sandia, perhaps a bit awkwardly phrased, is senior member of technical staff, R&D S&E cybersecurity. My work focuses on formal methods as it applies to high-performance computing, transition systems, and floating-point arithmetic.
// Main Links //
// Other Links //
Github | Twitter | ResearchGate | ORCID | Google Scholar | LinkedIn | dblp


Default
Half Glyphs
Red Glyphs
Vibrant Glyphs
Fluid Glyphs
Small Glyphs
Big Glyphs
Non-bordered Glyphs
Preset Glyphs
Read Only Glyphs
Lots of Glyphs
rtl (right-to-left) Glyphs
Style Rating
Hide Rating
Capture Rating
{{rating}}
Capture Mouse Over Rating
{{currentRating}}
Resetable Glyphs
Inline Glyphs
Rated
by our customers.


Overview¶
What’s NNC?¶
NNC is the natural progression against ccv_convnet
, which is a couple of years old now. ccv_convnet
’s monolithic, single path neural layer design didn’t really feel right with more advanced network architectures.
NNC took some good ideas from more recent neural network frameworks and did a long re-think on how to achieve both efficiency and expressiveness. The design itself is layered. At the highest layer, you have ordinary neural network primitives that reflect real-world usage such as Inception module, LSTM, RNN et al. At the lowest layer, depending on the infrastructure, it maps to allocated tensors on GPU, computations backed by CuDNN, and computation graphs driven with CUDA streams (or you can exchange that with CPU, Metal, and libdispatch). For the abstractions in between, there are trade-offs and constraints to accommodating both the library design and usage.
In a few sentences, here is how the layered design works.
NNC starts with tensors, commands and streams, which closely map to low level computation primitives. On top of that, a concrete computation graph can be constructed and executed directly. Above that, a symbolic graph can express everything about the concrete computation graph, without actual tensor and stream allocations. A dynamic graph can contain both symbolic graph representation and the concrete computation graph, thus, carries out the computation immediately while retain the ability to do series of processes on top of the symbolic graph representation. Familiar primitives such as LSTM or RNN then were built on top of either the symbolic graph or the dynamic graph constructs.
There are roughly 5 layers built on top of each other.
1. Tensors, Commands and Streams¶
Tensors are multi-dimensional arrays at its basic level.
Commands are ops (operations) in other framework’s terminology.
 Each command instance executed serially on a given stream. Different command instances on different streams will be scheduled in parallel if the underlying infrastructure permits.
A command is identified by its cmd
identifier. It processes a set of input tensors, and write output to a set of output tensors. There is no limits on how many input tensors it can accept or how many output tensors it can write to.
A command can only have one set of attributes (recognized by NNC) specified. These attributes (such as whether this can be an inplace operation) help on symbolic processes. If you find that you need to implement the same command but these attributes cannot be hold, you need to rename the command to avoid invalid symbolic processes.
One command, however, can be backed by several backend implementations. Command backend implementors, besides the ones who implement *_REF
free to only support specific cases of the input tensors (for example, a particular tensor layout, or a specific tensor size (3x3?), or half precision numbers). But once a backend accepts the input, it follows exactly the command attributes specified above (for example, any backend that implements a inplace command, will allow any parts of its input to be overwritten by this command at time while this command is executing without affecting the correctness of the output).
At runtime, a command will select the appropriate backend based on the input type and execution time.
2. Computation Graph¶
Computation graph expresses how the computation carries out. The output tensors can be used as input for the next command, so on and so forth. That is where TensorFlow got its name from. At this layer, computation graph knows the execution orders (data dependencies) between each command instances, and will schedule them on proper streams to ensure these execution orders are respected. Tensors themselves are not associated with the execution order at this point.
 It is executed as a single command instance by the parent computation graph. As of now, a ``while`` type sub-graph (for looping) and a ``case..of`` type sub-graph (for branching) are supported.
A computation graph can be auto-tuned to find the best backend implementations that minimize the total execution time. There may be future optimizations to allow modifying the graph itself to do more aggressive tuning (such as including tensor conversions to trade between slower implementation and conversion + faster implementation).
In short, once you have a computation graph, the computation can be carried out naturally because there is no extra assumptions about execution environment and no more parameters or allocations need to be specified.
3. Symbolic Graph¶
Symbolic graph expresses commands, the associated tensors and their execution orders (dependencies). This may sound very similar to the computation graph above, but there are several important differences:
there is no concept of stream at this point, because the symbolic graph doesn’t carry out the actual computation, and stream can be determined purely by the execution order;
there is no tensor allocation. Symbolic graph uses the tensor metadata (layout, precision, dimensions, even which GPU it is associated with), but no actual allocation took place until it is compiled to a computation graph;
There is no 1:1 mapping guarantee about the commands in the symbolic graph with the command instances in the computation graph.
In fact, symbolic graph doesn’t take tensors. It takes tensor symbols. The tensor symbol usage within the symbolic graph follows strict static single assignment (SSA) rule. It can only be used as a command instance’s output once. This is important because by following SSA, potential data races are completely eliminated. More over, certain processes and the actual tensor allocation algorithm are much easier to implement with this assumption. With SSA rule, the execution orders (dependencies) can be generated trivially.
It may feel like the tensor metadata is over-specified. For example, why precision or layout, or which GPU it resides is relevant? Because tensor symbols have many to 1 mapping with the actual tensors. Specifications on the tensor symbol avoid processes on the symbolic graph resulting a tensor symbol that needs to be backed with conversions. Any conversions on the symbolic graph has to be explicit command instances.
Having that in mind, however, you can take an alias of a tensor symbol, which is a sliced / reshaped tensor symbol from the original. It allows several operations to be zero effort on the actual computation graph. The alias itself still have to follow the same SSA rule, which means all the aliases and the original tensor symbol can only be written once (if two aliases as outputs point to non-overlapping parts of the original tensor, the written-once rule is not violated).
Processes can be carried out on the symbolic graph ranging from automatic differentiation, to common sub-expression elimination (CSE), or operator fusion (finer-grained set of commands be replaced by a combined command implementation).
When the actual computation is needed. A symbolic graph can be compiled to a computation graph. The compilation process can involve optimizations that previously already possible on the given computation graph (such as CSE). More importantly, this step performs additional optimization passes that will violate the SSA rule above. Currently, it will perform following processes that are not available as pure optimization passes:
In-place safe command instance will operate on the same tensor symbol inputs / outputs whenever possible (for example,
1.23 * x => y
will be re-written to1.23 * x => x
if no other places usex
);Tensor allocation based on the liveness analysis for the tensor symbols. This step will generate the many to 1 mapping between tensor symbols with the actual tensors;
Emit implicit commands for tensor initialization. Certain tensor symbols need to be initialized before use (zero init for now), which is impossible to know when until tensor allocation was taken place. This is one reason why there is no 1:1 mapping between symbolic graph and computation graph.
All above steps are carried out recursively for its ``while`` / ``case..of`` type sub-graphs too.
4. Dynamic Graph¶
Dynamic graph operates on concrete tensor instances. It took input tensors, executed a command on them, and took the outputs. From this perspective, it is very similar to the computation graph. The conceptual difference, is that the computation graph carries out execution from a specification, while dynamic graph forms a specification from the actual execution.
Thus, dynamic graph will construct a symbolic graph along its execution. It enables the dynamic graph to perform the same kind of sophisticated optimization passes and analysis once needed (such as automatic differentiation)
More over, dynamic graph implements a simple memorization mechanism. The tensors it uses will carry a hash, as well as a specific command. The output tensors can be retrieved from the cache by the generated hash if it is possible, to avoid repetitive computations.
5. Common Neural Network Primitives¶
A set of common neural network primitives for modeling as well as parameter updates is provided. The API looks very much like Sonnet or Keras. Common neural network primitives implemented these interfaces at a common language layer (C language). Thus, variety of host languages to implement a simple shim layer on top to enable these high-level APIs.
Quite some efforts were put in place to make common neural network primitives work with dynamic graph. This enables easiest model building and training. With dynamic graph, you can manipulate inputs, gradients and losses with ease. With common neural network primitives, many states such as trainables and running averages are kept inside the primitives, there are much less variables to keep track with.
Supplementary Materials¶
Toll-Free Bridging¶
Toll-free bridging here means that a ccv_dense_matrix_t
struct, without any conversions at all, can be cast to a ccv_nnc_tensor_t
struct and then used with nnc directly. The byte pattern is specifically arranged such that a 3 dimensional ccv_nnc_tensor_t
can be cast back to ccv_dense_matrix_t
vice versa. This allows seamless integration with the rest of image process primitives provided by ccv.
Automatic Differentiation¶
Automatic differentiation supported by nnc is its reverse mode. The implementation is simple enough because we enforced SSA throughout the symbolic graph.
Each command need to implement its forward function, as well as its backward function. The backward function takes the input / output of the its forward function, as well as the gradients (matching the output tensors) as its input. It outputs the gradients with respect to the input (matching the input tensors of the forward function).
When doing automatic differentiation, from its symbolic graph, a backward command matching each forward command is created. The execution order (dependencies) is exactly reverse. SSA guarantees each tensor symbol is written once, that means the gradient w.r.t. that symbol needs to only be summed once as well.
alias introduced some complexities to the implementation. Namely, because an alias can be used as input for follow-up commands, its reverse suggests different gradients w.r.t. different aliases required to be summed at certain point. That means these gradients need to be potentially zero init to avoid generating garbage results. This is done by inserting zero init tensor symbol property, which indicated an implicit zero init command will be injected at symbolic graph compilation time.
The specific implementation also means taking second order derivative isn’t possible with nnc at this point. It will be possible however in the future once the backward function can be specified by a set of forward functions and then we can do command substitution on the symbolic graph.
while
Type Sub-Graph¶
The ``while`` type sub-graph is a special type of a symbolic graph or a computation graph. This is because it expresses a generic loop structure with custom evaluation function supplied.
The loop execution within a ``while`` type sub-graph looks like this:
The sub-graph starts the execution from a set of source command instances;
It proceeds either serially or in parallel until all evaluation command instances executed. The subsequent command instances are on hold;
The evaluation function is called, and depends on the result, the execution within the sub-graph will either abort (break), or continue, until all the destination command instances executed and reached;
Once all destination command instances executed and reached, we will start from step 1. again.
For ``while`` type symbolic sub-graph, the obvious question would be how SSA rule plays out in the loop structure. We allow in the sub-graph to specify certain output tensor symbols carry over to the input tensor symbols in the next round, practically made these input tensor symbols parameters. The compilation step will handle this properly and allocate the input tensors at the same memory locations as the output tensors (there are ccv_nnc_tensor_multiview_t
workaround if the condition cannot be satisfied).
When doing automatic differentiation, a ccv_nnc_tensor_tape_t
need to be provided for the ``while`` type sub-graph to record the outputs properly.
case..of
Type Sub-Graph¶
The ``case..of`` type sub-graph is another special type of a symbolic graph or a computation graph. It expresses a generic branch structure with custom evaluation function supplied.
The ``case..of`` type sub-graph contains several separate sub-graphs identified by indexes from 0 to n:
The evaluation function is called, if the result is >= 0, a sub-graph is selected for execution, otherwise, jump to step 3.;
The selected sub-graph executed from beginning to end;
If the result is < 0, no sub-graph executed.
For ``case..of`` type symbolic sub-graph, if a tensor symbol is written-once, how to proceed if all sub-graphs skipped (in typical case, if a sub-graph executed, presumably, the tensor you want will be written by a command in that sub-graph)? We allow you to specify for these output tensor symbols, which symbol from the input can be supplied as replacement. The compilation step will ensure a ccv_nnc_tensor_multiview_t
is created to handle these cases.
When doing automatic differentiation, a ccv_nnc_tensor_tape_t
need to be provided for the ``case..of`` type sub-graph to record the outputs properly.
Limits and Constraints¶
Tensor itself supports up to 8 dimensions. This is defined in
CCV_NNC_MAX_DIM_ALLOC
.Tensor’s dimension can only reach to up
INT_MAX
. That may be a limiting factor for some of the tensors if they need more than 8GiB (32-bit floating point assumed) on one dimension.The limit on number of inputs and output tensors is
INT_MAX
. To perform automatic differentiation properly, this number drops tofloor(INT_MAX / 3)
. However, for more than 64 parameters, there are internal heap allocation required, which makes previously deterministic execution none-deterministic (it may take arbitrarily long depending on themalloc
you use).The allocated tensor size can go up to
min(UINT64_MAX, SIZE_MAX)
.A computation can only depend on no more than
2^16
other computations. This is determined by a core macroCCV_NNC_GRAPH_VISIT
.The sub-graph can go as deep as
2^(31 - 4)
, otherwise the outer-most while count tensor cannot be referenced by the inner-most sub-graph.The maximum number of GPU devices per machine or NUMA nodes per machine is 4095. This is defined in
CCV_COMPUTE_DEVICE_ANY
.


{% extends 'base.html' %} {% block content %} {# New Stories Display #} {% with "rgba(0,0,50,1)" as fade_from %} {% with "rgba(255,255,255,0)" as fade_to %} {% if stories and stories|length %}


Wikipedia defines a high-level programming language is a programming language with strong abstraction from the details of the computer.


It looks like you're in Minneapolis. Not Correct?
Karan @Karan323
Best food I've ever had!
Jake @JakePaul45
25m ago
Worth the wait. Wood Fired Pizzzzaaaa!
D'angelo @Dangelomark2122
1h ago
Highly recommend the Hellion.


Trump Hosts al-Baghdadi’s Canine Nemesis in White House, Sets Twitter Ablaze
The ceremony honouring the dog that allegedly helped take down al-Baghdadi took place at the White House and was attended by US President Donald Trump, First Lady Melania Trump and Vice President Mike Pence.
It appears that US President Donald Trump has once again thrust himself into the media spotlight as he went on to personally honor the dog that allegedly cornered terrorist leader Abu Bakr al-Baghdadi during a US military operation in Syria.
The president publicly introduced the dog, a Belgian Malinois named Conan, during a brief ceremony which was also attended by First Lady Melania Trump and Vice President Mike Pence.
"The dog is incredible," Trump said. "So brilliant, so smart. Conan did a fantastic job".
News of this development, however, quickly attracted the attention of Trump’s detractors.
Some went on to suggest online about what the dog should’ve done at the ceremony.
Some social media users also started speculating about who might have something nasty to say about the dog.
One netizen also quipped about Democrats possibly trying to use the dog in an impeachment probe against Trump.
And another joked about Melania’s appearance.


How to download and install the Angm-Generator.
Angm-Generator folder Structure following by-feature.


| Execution Time (hh:mm:ss.ms) | CPU / Worker Time (hh:mm:ss.ms) | Plan Compile Time (hh:mm:ss.ms) | Scan Count | Logical Reads (pages) | Physical Reads (pages) | Physical Reads (MB) | Cost | Used Memory Grant (MB) | |
|---|---|---|---|---|---|---|---|---|---|
| Baseline | |||||||||
| Changed | |||||||||
| Changes % | > | > | > | > | > | > | > | > | > |
| Statement ID | Cost | Used Memory Grant (MB) | Optimization Abort Reason |
|---|
| Statement ID | Cost | Used Memory Grant (MB) | Optimization Abort Reason |
|---|


There are no pictures with GPS information in this album.
Welcome to Gollery
Howdy! You're using Gollery version
.
System information
Number of thumbnails in the queue
Go version
Memory used
Maximum number of threads
Number of goroutines
Gollery
An error has occured. To continue:
Press F5 to restart Gollery
Error:


@if(hasAddress){
Current address: @user.address.address, @user.address.city, @user.address.state, @user.address.zip
}
@b3.form(routes.Registration.addAddress()) {
@b3.text(
myForm("address"),
'_label -> "Address",
'placeholder -> "123 Foo St."
)
@b3.text(
myForm("city"),
'_label -> "City",
'placeholder -> "Iowa City"
)
@b3.text(
myForm("state"),
'_label -> "State",
'placeholder -> "Iowa"
)
@b3.number(
myForm("zip"),
'_label -> "Zip",
'placeholder -> "12345"
)
@b3.number(
myForm("phone"),
'_label -> "Phone",
'placeholder -> "123-456-7890"
)
@b3.submit('class -> "btn btn-primary"){ Update Address }
}


Introduction
The first bridges built by humans were probably just wooden planks or boulders placed on a flowing stream. But today, engineers have built bridges to cross vast stretches of rivers. Before actually constructing a bridge, we need to model it and test its performance in the lab.
Celesta brings you a chance of showing your engineering skills with noodles!
So, get innovative and build your bridge model with this unconventional construction material.
Objective
Design and construct a model of a single span truss bridge with the help of spaghetti noodles satisfying the constraints stated below.
Event Structure
- Round 1: The teams shall mail a zip file containing the abstract of their design to<EMAIL_ADDRESS>. The abstract should contain the following:
- Drawings of the design (isometric and orthographic views) with proper dimensioning on AutoCAD or clear pictures of handmade sketches. Clearly state the specifications and advantages of your design and any innovative idea that you have.
-
Analysis of the design according to the dimensions specified in the problem statement in a simulation software namely Bridge Designer 2016.
The analysis is mandatory. Workshop for the same shall be conducted.
The teams will be shortlisted for Round 2 on the basis of their abstracts.
- Round 2 : The shortlisted teams shall construct their bridge under the surveillance of the organising team at IIT Patna.
- Round 3 : The model built by each team will be tested till failure (yielding) by applying anincremental static load.
Team Size
Each team should have 3 or 4 members.
Dimension specifications
The dimensions of the bridge model must be within the following limits:
- Length: 56-60 cm
- Width : 10-11 cm
- Height: 12-16 cm
- There should be a proper clearance for a 10cm X 8cm X 8cm box to pass through
the span of the bridge.
- The members of the bridge can be built by grouping a maximum of 8 sticks of
Spaghetti noodles together.
Weight specification
The bridge model should weigh 350 grams or less.
Arena specifications
- The arena has two wooden columns representing the landmass on the sides of
a river.
- The distance between the inner edges of these columns is 46 cm.
- One cylindrical support of diameter 2cm and length 15cm is placed on each of the wooden column.
- One of the cylindrical supports is fixed to the column and the other is free to act as a roller.
- The arena is as shown below:
- Arena:
- A sample spaghetti bridge placed on cylindrical supports is shown below:
Materials
The teams shall be provided the construction materials. The teams can bring required tools to build the structure.
The following materials will be provided:
- Spaghetti noodles
- Cutter
- Pencils
- Ruler
- A3 size sheet
- Glue
Some properties of spaghetti (dry):
- Ultimate tensile strength ~2000 psi
- Stiffness (Young’s modulus) E ~10,000,000 psi
Testing of the structures
- The dimensions and weight of each structure will be measured. Structures violating the dimensional and weight specifications will be penalised according to the rules.
- Each structure will be mounted on the arena over the two cylindrical supports shown in the arena.
- A 20 cm X 8 cm wide plate will be placed symmetrically on the base of the structure.
- The centre of the plate will be bolted to a screw-jack which will apply load on the structure. The screw-jack will be connected to a load cell to measure the load being applied.
- The structure will then be loaded and a continuous monitoring of its deflection and load will be done until it fails. The maximum load taken by the structure will be noted.
Evaluation of the structures :
The structures will be evaluated on the basis of their performance under loading as well as on the basis of aesthetics.
- The efficiency of each structure will be calculated according to the following formula:
e = (Maximum Load)/(Weight of Structure)
- Evaluation on the basis of aesthetics will be done by judges and will include criteria like:
- Innovation in design
- Cleanliness of work
- Overall look of the structure
Scoring :
The final score of each structure will be calculated according to the following rules:
- 70% weightage - Efficiency
Maximum efficiency by any structure will be taken as the constant ‘E’ and points will be calculated according to the formula: X = (e/E) * 70
- 30% weightage - Aesthetics
Each structure will be graded by the judges on a scale of 0-30.
Penalties
Penalties as mentioned below will be imposed if the structure violates the dimensional orweight specifications.
- Weight exceeds the limit
(Penalty of 15% of the total score) -
Dimensional specifications are not met
(Penalty of 10% of the total score) -
Use of any material other than that provided
(Penalty of 50% of the total score or can lead to disqualification as decided by the judges)
In case of any discrepancies, the decision taken by the judges and the coordinators will be final and bounding.


health chronic work conditions study persons impact having female, employed, participants Health positive working European (.%) life negative Factors unemployed, data needs male, depression) {border-style px;} support #ffffff;} Greece, answers people results Needs Work condition factors job maintain migraine/headache qualitative supervisors themes colleagues reported "Work-related PATHWAYS Poland, aspects categories important questions workplace. DM) Figure Germany, University aspects" conditions. return (accessed Available December analysis collab: identified online: open-ended problems survey theme time workers workplace COPD) Europe Italy, able according disorders) flexible mental need work. Chronic [], collection lives neck px; social stress {font-family International attitudes did employed evidence experiences font-size mentioned negatively project respective status strategies translated unemployed work-related Austria, Impact Office Organization Public Slovenia, Spain, Table World account changing countries. disease double; employment experience facing focused help integration named occupational participants’ positively possibility reintegration schedule seven stressed using "Person-related "Support" (%) (e.g., (see English Having Participants Study Working age aspects": based border-color border-width carried category codes coding condition. countries different environment experienced first-hand focus frequencies helpful including information involved issues knowledge level life, life. light long-term mainly online order pain) part-time participants. project. quantitative relative sector set showed situation solid; stratified structure understanding workload {font-style {font-weight "Health-related (employed Classification Data Disability Need Negative Network Persons Positive Slovenia Thinking age, aspect body bold case co-workers courier; daily deal derived development, disease" disorder; em; employees financial finding following https://orcid.org/--- identify impacts importance improve income life? most? organizations patient performed persons. position pt;} regard research responsible returning selected stigmatization study. taking workplace, years


class YARD::RegistryStore
The data store for the {Registry}.
@see Registry @see Serializers::YardocSerializer
Attributes
@deprecated The registry no longer tracks proxy types
Public Class Methods
# File lib/yard/registry_store.rb, line 13 def initialize @file = nil @checksums = {} @store = {} @proxy_types = {} @object_types = {:root => [:root]} @notfound = {} @loaded_objects = 0 @available_objects = 0 @locales = {} @store[:root] = CodeObjects::RootObject.allocate @store[:root].send(:initialize, nil, :root) end
Public Instance Methods
Deletes an object at a given path @param [#to_sym] key the key to delete @return [void]
# File lib/yard/registry_store.rb, line 74 def delete(key) @store.delete(key.to_sym) end
Deletes the .yardoc database on disk
@param [Boolean] force if force is not set to true, the file/directory
will only be removed if it ends with .yardoc. This helps with cases where the directory might have been named incorrectly.
@return [Boolean] true if the .yardoc database was deleted, false
otherwise.
# File lib/yard/registry_store.rb, line 212 def destroy(force = false) if (!force && file =~ /\.yardoc$/) || force if File.file?(@file) # Handle silent upgrade of old .yardoc format File.unlink(@file) elsif File.directory?(@file) FileUtils.rm_rf(@file) end true else false end end
Gets a {CodeObjects::Base} from the store
@param [String, Symbol] key the path name of the object to look for.
If it is empty or :root, returns the {#root} object.
@return [CodeObjects::Base, nil] a code object or nil if none is found
# File lib/yard/registry_store.rb, line 32 def get(key) key = :root if key == '' key = key.to_sym return @store[key] if @store[key] return if @loaded_objects >= @available_objects # check disk return if @notfound[key] obj = @serializer.deserialize(key) if obj @loaded_objects += 1 put(key, obj) else @notfound[key] = true nil end end
Gets all path names from the store. Loads the entire database if
reload
is true
@param [Boolean] reload if false, does not load the entire database
before a lookup.
@return [Array<Symbol>] the path names of all the code objects
# File lib/yard/registry_store.rb, line 82 def keys(reload = false) load_all if reload; @store.keys end
@param [String, nil] file the name of the yardoc db to load @return [Boolean] whether the database was loaded
# File lib/yard/registry_store.rb, line 122 def load(file = nil) initialize @file = file @serializer = Serializers::YardocSerializer.new(@file) load_yardoc end
Loads the .yardoc file and loads all cached objects into memory automatically.
@param [String, nil] file the name of the yardoc db to load @return [Boolean] whether the database was loaded @see load_all @since 0.5.1
# File lib/yard/registry_store.rb, line 136 def load!(file = nil) if load(file) load_all true else false end end
Loads all cached objects into memory @return [void]
# File lib/yard/registry_store.rb, line 147 def load_all return unless @file return if @loaded_objects >= @available_objects log.debug "Loading entire database: #{@file} ..." objects = [] all_disk_objects.sort_by(&:size).each do |path| obj = @serializer.deserialize(path, true) objects << obj if obj end objects.each do |obj| put(obj.path, obj) end @loaded_objects += objects.size log.debug "Loaded database (file='#{@file}' count=#{objects.size} total=#{@available_objects})" end
@param [String] name the locale name. @return [I18n::Locale] the locale
object for name
. @since 0.8.3
# File lib/yard/registry_store.rb, line 116 def locale(name) @locales[name] ||= load_locale(name) end
(see YARD::Serializers::YardocSerializer#lock_for_writing) @param file [String] if supplied, the path to the database
# File lib/yard/registry_store.rb, line 195 def lock_for_writing(file = nil, &block) Serializers::YardocSerializer.new(file || @file).lock_for_writing(&block) end
(see YARD::Serializers::YardocSerializer#locked_for_writing?) @param file [String] if supplied, the path to the database
# File lib/yard/registry_store.rb, line 201 def locked_for_writing?(file = nil) Serializers::YardocSerializer.new(file || @file).locked_for_writing? end
@param [Symbol] type the type to look for @return [Array<String>] a list of object paths with a given
{CodeObjects::Base#type}
@since 0.8.0
# File lib/yard/registry_store.rb, line 96 def paths_for_type(type, reload = false) load_all if reload @object_types[type] || [] end
Associates an object with a path @param [String, Symbol] key the path name
(:root or '' for root object) @param [CodeObjects::Base] value the
object to store @return [CodeObjects::Base] returns value
# File lib/yard/registry_store.rb, line 54 def put(key, value) if key == '' @object_types[:root] = [:root] @store[:root] = value else @notfound.delete(key.to_sym) (@object_types[value.type] ||= []) << key.to_s if @store[key.to_sym] @object_types[@store[key.to_sym].type].delete(key.to_s) end @store[key.to_sym] = value end end
@return [CodeObjects::RootObject] the root object
# File lib/yard/registry_store.rb, line 111 def root; @store[:root] end
Saves the database to disk @param [Boolean] merge if true, merges the data in memory with the
data on disk, otherwise the data on disk is deleted.
@param [String, nil] file if supplied, the name of the file to save to @return [Boolean] whether the database was saved
# File lib/yard/registry_store.rb, line 171 def save(merge = true, file = nil) if file && file != @file @file = file @serializer = Serializers::YardocSerializer.new(@file) end destroy unless merge sdb = Registry.single_object_db if sdb == true || sdb.nil? @serializer.serialize(@store) else values(false).each do |object| @serializer.serialize(object) end end write_proxy_types write_object_types write_checksums write_complete_lock true end
Gets all code objects from the store. Loads the entire database if
reload
is true
@param [Boolean] reload if false, does not load the entire database
before a lookup.
@return [Array<CodeObjects::Base>] all the code objects
# File lib/yard/registry_store.rb, line 90 def values(reload = false) load_all if reload; @store.values end
@param [Symbol] type the type to look for @return [Array<CodeObjects::Base>] a list of objects with a given
{CodeObjects::Base#type}
@since 0.8.0
# File lib/yard/registry_store.rb, line 105 def values_for_type(type, reload = false) load_all if reload paths_for_type(type).map {|t| @store[t.to_sym] } end
Protected Instance Methods
# File lib/yard/registry_store.rb, line 237 def checksums_path @serializer.checksums_path end
# File lib/yard/registry_store.rb, line 245 def load_yardoc return false unless @file if File.directory?(@file) # new format @loaded_objects = 0 @available_objects = all_disk_objects.size load_proxy_types load_checksums load_root load_object_types true elsif File.file?(@file) # old format load_yardoc_old true else false end end
# File lib/yard/registry_store.rb, line 241 def object_types_path @serializer.object_types_path end
# File lib/yard/registry_store.rb, line 228 def objects_path @serializer.objects_path end
@deprecated The registry no longer tracks proxy types
# File lib/yard/registry_store.rb, line 233 def proxy_types_path @serializer.proxy_types_path end
Private Instance Methods
# File lib/yard/registry_store.rb, line 313 def all_disk_objects Dir.glob(File.join(objects_path, '**/*')).select {|f| File.file?(f) } end
# File lib/yard/registry_store.rb, line 285 def load_checksums return unless File.file?(checksums_path) lines = File.readlines(checksums_path).map do |line| line.strip.split(/\s+/) end @checksums = Hash[lines] end
# File lib/yard/registry_store.rb, line 307 def load_locale(name) locale = I18n::Locale.new(name) locale.load(Registry.po_dir) locale end
# File lib/yard/registry_store.rb, line 275 def load_object_types if File.file?(object_types_path) @object_types = Marshal.load(File.read_binary(object_types_path)) else # migrate db without object_types values.each do |object| (@object_types[object.type] ||= []) << object.path end end end
@deprecated The registry no longer tracks proxy types
# File lib/yard/registry_store.rb, line 270 def load_proxy_types return unless File.file?(proxy_types_path) @proxy_types = Marshal.load(File.read_binary(proxy_types_path)) end
# File lib/yard/registry_store.rb, line 293 def load_root root = @serializer.deserialize('root') return if root.nil? @loaded_objects += 1 if root.is_a?(Hash) # single object db log.debug "Loading single object DB from .yardoc" @loaded_objects += (root.keys.size - 1) @store = root else # just the root object @store[:root] = root end end
# File lib/yard/registry_store.rb, line 265 def load_yardoc_old @store, @proxy_types = *Marshal.load(File.read_binary(@file)) end
# File lib/yard/registry_store.rb, line 326 def write_checksums File.open!(checksums_path, 'w') do |f| @checksums.each {|k, v| f.puts("#{k} #{v}") } end end
# File lib/yard/registry_store.rb, line 332 def write_complete_lock<EMAIL_ADDRESS>'w') {} end
# File lib/yard/registry_store.rb, line 322 def write_object_types File.open!(object_types_path, 'wb') {|f| f.write(Marshal.dump(@object_types)) } end
@deprecated The registry no longer tracks proxy types
# File lib/yard/registry_store.rb, line 318 def write_proxy_types File.open!(proxy_types_path, 'wb') {|f| f.write(Marshal.dump(@proxy_types)) } end


Welcome to Hapless Path! Choose an XML file, then get started learning XPath!
Choose an XML file:
Trees
Organization
Species
Courses
Input an XPath Expression
Tree goes here...


Schedule
Week 1
Lecture 1: Introduction
Monday, Aug. 22What is data science? Why is it important? Who are we? Course overview.
Recommended reading
- Cathy O’Neil and Rachel Schutt, Doing Data Science. (2014) Chapter 1.
- David Donoho, 50 years of Data Science. (2015).
Lab 1: Introduction to Programming in Python
Wednesday, Aug. 24Running a Python program, IPython, Jupyter notebook, variables and data types, operations, functions, scope.
Lab 2: Introduction to Programming in Python II
Friday, Aug. 26Data types and operators, conditions, lists, loops.
Week 2
Lecture 2: Introduction to Descriptive Statistics
Monday, August 29Variable types, basic summary statistics and plotting, covariance and correlation, and confounders.
Mandatory reading
- Grus, Ch.5
Lab 3: Advanced Data Structures
Wednesday, August 31Sets, dictionaries, pandas series, working with modules.
Lab 4: Pandas DataFrames
Friday, September 2Reading and writing data, pandas data frames, basic plotting.
Recommended reading
- Learning the Pandas Library: Python Tools for Data Munging, Analysis, and Visualization. Matt Harrison
Week 3
Monday: Labor DayLecture 3: Hypothesis Testing and Statistical Inference
Wednesday, September 7Bernoulli, Binomial, and Normal distributions, Central Limit Theorem, and Introduction to Hypothesis Testing.
Mandatory reading
- Grus, Ch.7
Lab 5: Practical Visualization
Friday, September 9Visualization in Python using ggplot
Guest lecturer: Shirley ZhaoRecommended reading
Week 4
Lab 6: Temporal Data Analysis and Applications to Stock Analysis
Monday, September 12Downloading, cleaning, analyzing, and visualizing stock data
Guest lecturer: Curtis MillerLecture 4: Hypothesis Testing and Statistical Inference, part 2
Wednesday, September 14Hypothesis Testing with applications to A/B Testing
Mandatory reading
Lecture 5: Linear Regression 1
Friday, September 16Introduction to ordinary linear regression
Recommended reading
- ISLR, Ch. 3
Week 5
Lecture 6: Linear Regression 2
Monday, September 19multilinear regression, statistical inference
Recommended reading
- ISLR, Ch. 3
Lab 8: Web Scraping
Wednesday, September 21Scrape HTML websites with Beautiful Soup. Data Cleanup with Pandas.
Recommended reading
Lab 9: Regression in Practice
Friday, September 23Regression on the example of a credit card dataset
Week 6
Lecture 7: Classification I, Logistic Regression
Monday, September 26Introduction to classification and the logistic regression method
Recommended reading
- Grus Ch. 16
- ISLR Ch. 4
Lecture 8: Classification II, K-Nearest Neighbors
Monday, September 26Overfitting, bias-variance
Recommended reading
Lab 9: Collecting Data from Web APIs
Friday, September 30Connect to APIs such as Twitter, Reddit. JSON, REST.
Week 7
Lecture 9: Decision Trees
Monday, October 3Recommended reading
- Visual Intro to Machine Learning
- Grus, Ch.17
Lecture 10: Support Vector Machines
Wednesday, October 5Lab 10: Classification Methods in Practice
Friday, October 7Week 8
Fall BreakWeek 9
Lab 11: Version Control; Project Introduction
Monday, October 17What is version control, how to make use of it. Introduction to git and GitHub. Introduction to the final project.
Lecture 11: Clustering
Wednesday, October 19Clustering Basics. Partitional and Hierarchical Clustering Approaches. Two part lecture.
Mandatory reading
- Grus, Ch. 19
Lecture 12: Clustering
Friday, October 21Clustering Basics. Partitional and Hierarchical Clustering Approaches. Two part lecture.
Mandatory reading
- Grus, Ch. 19
Week 10
Lecture 13: Dimensionality Reduction
Monday, October 24Principal Component Analysis. Multidimensional Scaling
Lab 12: Clustering Example
Wednesday, October 26Clustering based on the MNIST dataset
Recommended reading
Week 11
Lecture 14: Introduction to Networks and Network Visualization
Monday, October 31Basics about Networks. Visualization methods for general graphs and trees.
Lab 13: Network Analysis
Wednesday, November 2Graph algorithms - path search, centrality, pagerank.
Mandatory reading
- Grus Ch. 21
Lecture 15: Elections
Friday, November 4History; election methods; Arrow’s impossibility theorem; Elections vs. rankings.
Week 12
Lecture 16: Rankings
Monday, November 7Lab 14: Project Peer Feedback
Wednesday, November 9Pitch your project to another group and receive feedback, give feedback to another group.
Lab 15: Ranking in Practice
Friday, November 11Week 13
Lab 16: Regular Expressions
Monday, November 14Lab 17: Practical NLP
Friday, November 18Week 14
Lab 18: Databases
Wednesday, November 23Working with relational databases in Python. Introduction to the Structured Query Language.
Week 15
Lecture 19: Weapons of Math Destruction: Discrimination by Algorithms
Monday, November 28Lab 19: Large Data Analysis
Wednesday, November 30Parallel programming. MapReduce.
Lecture 20: Recap, Wrap-up, Outlook
Friday, December 2What did we learn, what else is out there, what can you learn next?
Week 16
Lecture 21: Project Presentations
Monday, December 5Students present their final project.
Lecture 22: Project Presentations
Wednesday, December 7Students present their final project.


[1]
miles better then the sister hotel in guangzhou but still not what one expects from a crowne plaza


--- layout: post status: publish published: true title: Music Review authors: ["thom"] author_login: Thom Wiggers author_url: http://thomwiggers.nl date: '2013-09-29 21:00:42 +0200' date_gmt: '2013-09-29 20:00:42 +0200' categories: - Music tags: - kill paris - london grammar - Arcade Fire - If You Wait - The Beginning - The Suburbs - review comments: [] ---
It's been a while since I posted. Today I decided it was time to behave myself a bit like the pretentious hipster I secretly am and post a bit about the music that I discovered lately.
London Grammar are an UK based indie pop band. They released their debut album If You Wait a couple of weeks ago. Pitchfork already wrote a great review about them, so I'm not going to repeat that, but I'd like to highlight their great cover of Kavinsky's Nightcall, capturing the feeling of the original track so nicely.
 Electronic masterpieces, having influences of dubstep, indie electro and UK Garage. The tracks remind you of several popular and less popular pieces of music, but done in such a way that it's brand new and just makes you want to dance.
This album is available from Bandcamp.
An album which I completely missed back when it was released. Grammy winner (which led to hilarious reactions) and overal awesome release. I particularly like the album's second track, Ready To Start.
Arcade Fire is releasing a new album soon called Reflektor. It's title track Reflektor has already been released and is also a great way to start your day.
Great, happy indie pop. The band picked this name because you wouldn't be able to find them in Google's search engine if they'd be named "Churches". This is their first album. Great stuff to listen to if you're in a happy, uncomplicated mood.


| Package | Description |
|---|---|
| com.opengamma.strata.pricer.bond |
Calculators for bonds.
|
| com.opengamma.strata.product.bond |
Entity objects describing bonds.
|
| Class and Description |
|---|
| CapitalIndexedBondPaymentPeriod
A coupon or nominal payment of capital indexed bonds.
|
| FixedCouponBondPaymentPeriod
A period over which a fixed coupon is paid.
|
| ResolvedBondFuture
A futures contract based on a basket of fixed coupon bonds, resolved for pricing.
|
| ResolvedBondFutureOption
A futures option contract based on a basket of fixed coupon bonds, resolved for pricing.
|
| ResolvedBondFutureOptionTrade
A trade in in an option on a futures contract based on a basket of fixed coupon bonds, resolved for pricing.
|
| ResolvedBondFutureTrade
A trade in a futures contract based on a basket of fixed coupon bonds, resolved for pricing.
|
| ResolvedCapitalIndexedBond
A capital indexed bond.
|
| ResolvedCapitalIndexedBondTrade
A trade in a capital indexed bond, resolved for pricing.
|
| ResolvedFixedCouponBond
A fixed coupon bond, resolved for pricing.
|
| ResolvedFixedCouponBondTrade
A trade in a fixed coupon bond, resolved for pricing.
|
| Class and Description |
|---|
| BondFuture
A futures contract, based on a basket of fixed coupon bonds.
|
| BondFuture.Builder
The bean-builder for
BondFuture . |
| BondFuture.Meta
The meta-bean for
BondFuture . |
| BondFutureOption
A futures option contract, based on bonds.
|
| BondFutureOption.Builder
The bean-builder for
BondFutureOption . |
| BondFutureOption.Meta
The meta-bean for
BondFutureOption . |
| BondFutureOptionSecurity
A security representing a futures contract, based on a basket of fixed coupon bonds.
|
| BondFutureOptionSecurity.Builder
The bean-builder for
BondFutureOptionSecurity . |
| BondFutureOptionSecurity.Meta
The meta-bean for
BondFutureOptionSecurity . |
| BondFutureOptionTrade
A trade representing an option on a futures contract based on bonds.
|
| BondFutureOptionTrade.Builder
The bean-builder for
BondFutureOptionTrade . |
| BondFutureOptionTrade.Meta
The meta-bean for
BondFutureOptionTrade . |
| BondFutureSecurity
A security representing a futures contract, based on a basket of fixed coupon bonds.
|
| BondFutureSecurity.Builder
The bean-builder for
BondFutureSecurity . |
| BondFutureSecurity.Meta
The meta-bean for
BondFutureSecurity . |
| BondFutureTrade
A trade representing a futures contract based on a fixed coupon bond.
|
| BondFutureTrade.Builder
The bean-builder for
BondFutureTrade . |
| BondFutureTrade.Meta
The meta-bean for
BondFutureTrade . |
| CapitalIndexedBond
A capital indexed bond.
|
| CapitalIndexedBond.Builder
The bean-builder for
CapitalIndexedBond . |
| CapitalIndexedBond.Meta
The meta-bean for
CapitalIndexedBond . |
| CapitalIndexedBondPaymentPeriod
A coupon or nominal payment of capital indexed bonds.
|
| CapitalIndexedBondPaymentPeriod.Builder
The bean-builder for
CapitalIndexedBondPaymentPeriod . |
| CapitalIndexedBondPaymentPeriod.Meta
The meta-bean for
CapitalIndexedBondPaymentPeriod . |
| CapitalIndexedBondSecurity
A security representing a capital indexed bond.
|
| CapitalIndexedBondSecurity.Builder
The bean-builder for
CapitalIndexedBondSecurity . |
| CapitalIndexedBondSecurity.Meta
The meta-bean for
CapitalIndexedBondSecurity . |
| CapitalIndexedBondTrade
A trade representing a capital indexed bond.
|
| CapitalIndexedBondTrade.Builder
The bean-builder for
CapitalIndexedBondTrade . |
| CapitalIndexedBondTrade.Meta
The meta-bean for
CapitalIndexedBondTrade . |
| CapitalIndexedBondYieldConvention
A convention defining accrued interest calculation type for inflation bond securities.
|
| FixedCouponBond
A fixed coupon bond.
|
| FixedCouponBond.Builder
The bean-builder for
FixedCouponBond . |
| FixedCouponBond.Meta
The meta-bean for
FixedCouponBond . |
| FixedCouponBondPaymentPeriod
A period over which a fixed coupon is paid.
|
| FixedCouponBondPaymentPeriod.Builder
The bean-builder for
FixedCouponBondPaymentPeriod . |
| FixedCouponBondPaymentPeriod.Meta
The meta-bean for
FixedCouponBondPaymentPeriod . |
| FixedCouponBondSecurity
A security representing a fixed coupon bond.
|
| FixedCouponBondSecurity.Builder
The bean-builder for
FixedCouponBondSecurity . |
| FixedCouponBondSecurity.Meta
The meta-bean for
FixedCouponBondSecurity . |
| FixedCouponBondTrade
A trade representing a fixed coupon bond.
|
| FixedCouponBondTrade.Builder
The bean-builder for
FixedCouponBondTrade . |
| FixedCouponBondTrade.Meta
The meta-bean for
FixedCouponBondTrade . |
| FixedCouponBondYieldConvention
A convention defining accrued interest calculation type for a bond security.
|
| ResolvedBondFuture
A futures contract based on a basket of fixed coupon bonds, resolved for pricing.
|
| ResolvedBondFuture.Builder
The bean-builder for
ResolvedBondFuture . |
| ResolvedBondFuture.Meta
The meta-bean for
ResolvedBondFuture . |
| ResolvedBondFutureOption
A futures option contract based on a basket of fixed coupon bonds, resolved for pricing.
|
| ResolvedBondFutureOption.Builder
The bean-builder for
ResolvedBondFutureOption . |
| ResolvedBondFutureOption.Meta
The meta-bean for
ResolvedBondFutureOption . |
| ResolvedBondFutureOptionTrade
A trade in in an option on a futures contract based on a basket of fixed coupon bonds, resolved for pricing.
|
| ResolvedBondFutureOptionTrade.Builder
The bean-builder for
ResolvedBondFutureOptionTrade . |
| ResolvedBondFutureOptionTrade.Meta
The meta-bean for
ResolvedBondFutureOptionTrade . |
| ResolvedBondFutureTrade
A trade in a futures contract based on a basket of fixed coupon bonds, resolved for pricing.
|
| ResolvedBondFutureTrade.Builder
The bean-builder for
ResolvedBondFutureTrade . |
| ResolvedBondFutureTrade.Meta
The meta-bean for
ResolvedBondFutureTrade . |
| ResolvedCapitalIndexedBond
A capital indexed bond.
|
| ResolvedCapitalIndexedBond.Builder
The bean-builder for
ResolvedCapitalIndexedBond . |
| ResolvedCapitalIndexedBond.Meta
The meta-bean for
ResolvedCapitalIndexedBond . |
| ResolvedCapitalIndexedBondTrade
A trade in a capital indexed bond, resolved for pricing.
|
| ResolvedCapitalIndexedBondTrade.Builder
The bean-builder for
ResolvedCapitalIndexedBondTrade . |
| ResolvedCapitalIndexedBondTrade.Meta
The meta-bean for
ResolvedCapitalIndexedBondTrade . |
| ResolvedFixedCouponBond
A fixed coupon bond, resolved for pricing.
|
| ResolvedFixedCouponBond.Builder
The bean-builder for
ResolvedFixedCouponBond . |
| ResolvedFixedCouponBond.Meta
The meta-bean for
ResolvedFixedCouponBond . |
| ResolvedFixedCouponBondTrade
A trade in a fixed coupon bond, resolved for pricing.
|
| ResolvedFixedCouponBondTrade.Builder
The bean-builder for
ResolvedFixedCouponBondTrade . |
| ResolvedFixedCouponBondTrade.Meta
The meta-bean for
ResolvedFixedCouponBondTrade . |
Copyright 2009-Present by OpenGamma Inc. and individual contributors
Apache v2 licensed
Additional documentation can be found at opengamma.github.io/StrataDocs.


Settings
Allowlist
Host
Endpoint
Additional Parameters
Bypass allowlist check
Warning:
Bypassing allowlist will send request on each navigation.
Experiment only.
Query
Host
Endpoint
Additional URL Parameters
Lookup
Host
Endpoint (Text)
Endpoint (Image)
Endpoint (Link)
Additional URL Parameters
Additional POST Parameters. This will override any previous post values.
input as & seperated key value pairs. (key1=value1&key2=value2)


EBD - Easton Bible DIctionary
Baal-shalisha
BAAL-SHALISHA
"BAAL-SHALISHA" Lord of Shalisha, a place from which a man came with provisions for Elisha, apparently not far from Gilgal (
2 Kings 4:42
).
It has been identified with Sirisia, 13 miles north of Lydda.
"BAAL-SHALISHA" Lord of Shalisha, a place from which a man came with provisions for Elisha, apparently not far from Gilgal (2 Kings 4:42).
It has been identified with Sirisia, 13 miles north of Lydda.


This is the first time we're having a pre-conference workshop at iOS Conf SG. We have selected two topics that are going to be extremely beneficial in building high-class iOS apps.
Design architecture of complex iOS codebase from scratch. What app architecture pattern you should use for your unique needs and why.
Lunch will be served at 12:30 PM Afternoon Session: 1:30PM - 5PMSee significant improvements to your product development by developing your existing knowledge of functional programming in Swift and adding a raft of new skills and concepts to your toolbelt.


--- layout: rustwasm title: web-sys description: web-sys - Raw API bindings for Web APIs keyword: web-sys, web apis ---
web-sys is a crate, a library in Rust, that contains Raw API bindings for Web APIs. For example, if you need to use the "Window.console.log" function (browser's debugging console) in rustwasm, one way to do this is, you can define define the bindings for this WEB API using "wasm-bindgen". Alternatively, you can save some work on the definition by simply making use of web_sys. To use an API in web_sys, you will just need to enable the features for the types used in the API.
The Cargo.toml below enables the use of the "Window" object and also other useful Web APIs objects (or Structs in Rust) such as "Document" or "HtmlElement".
Cargo.toml
[package]
name = "dom"
version = "0.1.0"
authors = ["www.webassemblyman.com"]
edition = "2019"
[lib]
crate-type = ["cdylib"]
[dependencies]
wasm-bindgen = "0.2.50"
[dependencies.web-sys]
version = "0.3.4"
features = [
'Document',
'Element',
'HtmlElement',
'Node',
'Window',
]
After enabling the features of the Web APIs in web_sys, you can easily use them in your rustwasm programs as shown below. The exanple below illustrates how to add a <p> element to the Document Object Model (DOM).
src/lib.rs
use wasm_bindgen::prelude::*;
#[wasm_bindgen(start)]
pub fn run() -> Result<(), JsValue> {
let window = web_sys::window().expect("no window exists");
let document = window.document().expect("window should have a document");
let body = document.body().expect("document should have a body");
let val = document.create_element("p")?;
val.set_inner_html("Hello World from webassemblyMan!");
body.append_child(&val)?;
Ok(())
}


Home
Technical Blogs
Sprint 01 Technical
Sprint 02 Technical
Sprint 03 Technical
Sprint 04 Technical
Sprint 05 Technical
Bootcamp Work
Cultural Blogs
Sprint 01 Cultural
Sprint 02 Cultural
Sprint 03 Cultural
Sprint 04 Cultural
Sprint 05 Cultural
Contact
EDA Bootcamp work from Shelly Mutu-Grigg
Ka mua, ka muri: ....walking backwards into the future
db.io Wombles of Wimbledon ERD Diagram
ERD design for Wombles of Wimbledon challenge
Have a look!
Top of page


David Lawrence Herbaly
Republican
Manager, Herbaly Exploration LLC, a small family owned oil and gas producer, since 1978
B.S. Business, Indiana University M.B.A., University of California, Berkeley
80120
Donated to Mike Coffman for Congress, 2016 and 2018. Donated to 12 Republican US Senate candidates 2020. (That was a first.)
Professional organizations – Active member of Denver Association of Petroleum Landmen, American Association of Professional Landmen. Member of Western Energy Alliance, Colorado Oil & Gas Association, Petroleum Association of Wyoming. Financial donor to numerous civic groups including Salvation Army, High Line Canal Conservancy, Colorado Fourteener Initiative, Colorado Public Radio, Food Bank of the Rockies, churches and religious organizations. Prior to 2015, board member and president of homeowners’ association, board member and president of Denver Association of Petroleum Landmen, church board member, 20 years as youth mentor and volunteer, 5 years as volunteer tutor.
My experience has been through the organizations listed elsewhere in this application.
Redistricting has become a partisan political process in most states, with the party controlling the legislature drawing maps which favor their party. Many of these maps are then litigated in courts. My hope is that the new commissions will draw truly fair, nonpartisan districts which represent the communities of Colorado and that as many districts as possible will be truly competitive. Control of the governments shifts between the parties, and the districts should be fair to both sides. I have not served on a governmental citizens committee, and I would like to join others in taking my turn to serve.
Being president of the homeowners association gave me experience in working with a community, dealing with conflicts, and developing policies and solutions on which most of the community could agree. I learned that you can’t please all of the people any of the time. If it could be done in compliance with Colorado’s open meeting laws, I would like to host a dinner for the commission members in my home so that we could get to know each other on a personal level. It’s easier to work together and find common ground that way. Both on boards and off, I think I can be fair and impartial because I recognize others have valid views and I try to understand them. In my business, I have spent years making deals that have to work for all of the parties.
I have forty years of business experience. Every day I analyze business opportunities, which requires analyzing the numbers, and negotiating deals that are fair and equitable to all of the parties. In many cases, I have been able to complete transactions that were rejected at first by explaining my reasoning and the numbers behind my proposals. I think redistricting would involve similar skills.
Male


Research Activity
My research interests are working on different NLP problems and come up with ingenious solutions for them.
Research Experience
"A System for Providing Transparency in Government Transaction using Private Blockchain"
We used blockchain to store important government documents for safekeeping. In this paper we addressed the problem of corruption in Bangladeh and how it has a deep effect in public sector of government. We proposed a system using blockchain that will store the transaction of government project and provide transparency and security to the data. Any discrepency found would be submitted to proper authority. We used private blockchain for our system. Users of our system enter transaction information into our system and later verified from relevent information. Any inconsistency is reported to moderator of the system. We used Hyperledger Fabric and Hyperledger Sawtooth to build our system.