--- layout: default --- {% include nav.html %}
Founded in 1986 by Howard Serwer, Paul Traver, and J. Merrill Knapp, The American Handel Society seeks to foster study of the life, works, and times of George Frideric Handel, and to encourage and support the performance of his music. To that end, The American Handel Society:
President |
Graydon Beeks |
Vice President |
Nathan Link |
Secretary/Treasurer |
Marjorie Pomeroy |
Graydon Beeks; Ireri Chávez-Bárcenas; Norbert Dubowy; Fredric Fehleisen; Roger Freitas; William Gudger*; Ellen Harris; Wendy Heller; David Hurley; Robert Ketterer; Richard King*; Nathan Link; Nicholas McGegan*; Ken Nott; Marjorie Pomeroy; John Roberts; Marty Ronish*; Ellen Rosand; Ayana Smith; Minji Kim (Newsletter editor); Regina Compton (Web designer)
* Honorary Director


This is my first review that I post to this newsgroup, and I kind of feel like I have to say something negative about this film. No one else seems to care that it takes certain liberties that should not be taken with a historical story. However, even if one thinks of it as fiction, "The Prince of Egypt" remains shallow. But I'll begin from the beginning. The biggest difference between the original, Biblical story and this version is that Moses has some semblance of divinity in the Bible, whereas the animated version gives the impression of a reluctant hero. Maybe it's just me, but if I knew I had God on my side, I'd have a little bit more confidence. There are other differences as well, such as a lack of important female characters and the passing over of the original Pharoah's death like it's nothing but a thing. Most of all, though, the story's focus has shifted. Rather than being a story about father-son, man-ethnicity, man-God, "The Prince of Egypt" is almost solely about the brother-brother relationship between Rameses and Moses. I was originally excited about this story element until I saw the movie, where it came off as maudlin. Rameses and Moses squabble like children and then, in the interests of peace, Moses saves Rameses' butt, even though Moses was the one who started it... *yawn* I didn't care anymore by the time Moses ran off for murdering an overseer (which, of course, never happened in the original story - Moses was exiled). But enough about the differences. Let's talk about the movie itself. It features a Moses who, in the interests of making the character more human, lacks any divinity whatsoever, which isn't convincing at all to anyone who knows who God is. It features a cookie-cutter "I-wanna-please-dada" Rameses, who at least is given dignity by the voicework of Ralph Fiennes (from Schindler's List, among other things). The film also features an extremely annoying character by way of the "new-improved" Miriam, voiced by the ever-antsy Sandra Bullock, even though the most irritating thing about her is her tendency to burst into song for no apparent reason (here Bullock is replaced by a singing voice). Speaking of which, it contains *very* annoying music. It contains shallow writing. It features extraordinary animation, which is one good thing I can say about this film. Most of all, it contains an attempt to commercialize, homogenize, and mass-market a story about a Manifestation of God. I am not Christian, but I got the impression of blasphemy. The writers and producers took a butcher knife and chop-chopped into the story.
Dasrik's Rating is 3 Stars out of 10.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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{% load staticfiles %}
Why only adults have access to banks?
Introducting EduPiggybank - your child's personalise bank account wherein he will learn basic banking in a funway with REAL money.


Image Tag

alt is what gets displayed if image fails to load (also to help blind people).
Images do NOT have a closing tag
Break Tag

Paragraph Tag

Span / Div Elements
You will be using HTML in the next few lessons, so it will be helpful if you're comfortable with the jargon.
• Tag: An HTML tag is always contained within angled brackets. Most tags have an opening tag (
 for example) and a closing tag, (
). Some tags (called "void" tags) do not require a closing tag (like the
 tag).
• Element: An HTML element refers to everything within a set of opening and closing tags.
• Attribute: This is a property of an HTML element. For example, to set the href attribute of an anchor tag to the Udacity URL, you would write


First load the package
library(magrittr); library(dplyr);
library(data.table)
library(FixedEffectjlr)
JULIA_HOME <- "/Applications/Julia-0.6.app/Contents/Resources/julia/bin/"
FixedEffect_setup(JULIA_HOME)
We are going to use the standard dataset:
IFE is similar to a principal component analysis whenever there is no right hand side parameter. The standard specification is:
To compare with the principal component procedure, we look at loadings and factors first and then the residuals.
Before we can use prcomp
we need to transform the data from long to wide:
Now we are able to directly run the PC analysis. Note that IFE does not rescale the variable, so we are going to run it with both centering and rescaling options turned off:
pca_res <- prcomp(dt_wide[, -"year"], center=FALSE, scale=FALSE)
# keep loadings and factors separately
pca_loadings <- cbind(unique(dt[, .(state)]),
data.table(pca_res$rotation)[, .(pc_loadings1=PC1, pc_loadings2=PC2) ] )
pca_factors <- cbind(unique(dt[, .(year)]),
data.table(pca_res$x)[, .(pc_factors1 = PC1, pc_factors2 = PC2) ] )
# remerge to estimate residuals
dt_pca <- dt[, .(state, year, sales) ] %>%
merge(., pca_loadings, by = c("state") , all.x = T) %>%
merge(., pca_factors, by = c("year"), all.x = T)
Finally we compare loadings, factors and \(R^2\):
# loadings
dt_loadings <- merge(pca_loadings, ife_loadings, by = c("state"))
# are they proportional to each other (difference in scale)
dt_loadings[, .(state, prop_loadings1 = ife_loadings1/pc_loadings1)] %>% as_tibble
## # A tibble: 46 x 2
## state prop_loadings1
## <int> <dbl>
## 1 1 -4721
## 2 3 -4721
## 3 4 -4721
## 4 5 -4721
## 5 7 -4721
## 6 8 -4721
## 7 9 -4721
## 8 10 -4721
## 9 11 -4721
## 10 13 -4721
## # ... with 36 more rows
We do the same thing for factors
# factors
dt_factors <- merge(pca_factors, ife_factors, by = c("year"))
# are they proportional to each other (difference in scale)
dt_factors[, .(year, prop_factors1 = ife_factors1/pc_factors1)] %>% as_tibble
## # A tibble: 30 x 2
## year prop_factors1
## <int> <dbl>
## 1 63 -0.000212
## 2 64 -0.000212
## 3 65 -0.000212
## 4 66 -0.000212
## 5 67 -0.000212
## 6 68 -0.000212
## 7 69 -0.000212
## 8 70 -0.000212
## 9 71 -0.000212
## 10 72 -0.000212
## # ... with 20 more rows
Last note that the proportionality factors are actually the inverse of each other:
dt_loadings[, .(mean(ife_loadings1/pc_loadings1)) ] *
dt_factors[, .(mean(ife_factors1/pc_factors1))]
## V1
## 1: 1
And finally we look to see if residuals are the same:
# residuals for only one factor
dt_residuals <-
merge(ife$dt_augment[, .(state, year, ife_residuals=residuals)],
dt_pca[, .(state, year, pc_residuals=sales-pc_loadings1*pc_factors1)],
by = c("year", "state"))
dt_residuals[, .(ife_residuals = sum(ife_residuals^2),
pc_residuals = sum(pc_residuals^2)) ]
## ife_residuals pc_residuals
## 1: 242566.2 242566.2
We start with the PC analysis. Except that this time we allow for a centering:
pca_res <- prcomp(dt_wide[, -"year"], center=TRUE, scale=FALSE)
# keep loadings and factors separately
pca_loadings <- cbind(unique(dt[, .(state)]),
data.table(pca_res$rotation)[, .(pc_loadings1=PC1, pc_loadings2=PC2) ] )
pca_factors <- cbind(unique(dt[, .(year)]),
data.table(pca_res$x)[, .(pc_factors1 = PC1, pc_factors2 = PC2) ] )
# remerge to estimate residuals
dt_pca <- dt[, .(state, year, sales) ] %>%
merge(., pca_loadings, by = c("state") , all.x = T) %>%
merge(., pca_factors, by = c("year"), all.x = T)
This is similar to removing the within group average, which corresponds to group fixed effects. We obtain the centering in IFE through:
ife <-
FixedEffectInteract(dt, lhs = "sales", rhs = "0",
ife = "state+year", rank=1,
fe = "state")
ife_loadings <- unique(ife$dt_augment[, .(state, ife_loadings1=loadings1)])
ife_factors <- unique(ife$dt_augment[, .(year, ife_factors1=factors1)])
As in the previous example, we merge loadings, factors and compare \(R^2\):
# loadings
dt_loadings <- merge(pca_loadings, ife_loadings, by = c("state"))
# are they proportional to each other (difference in scale)
dt_loadings[, .(state, prop_loadings1 = ife_loadings1/pc_loadings1)] %>% as_tibble
## # A tibble: 46 x 2
## state prop_loadings1
## <int> <dbl>
## 1 1 488
## 2 3 488
## 3 4 488
## 4 5 488
## 5 7 488
## 6 8 488
## 7 9 488
## 8 10 488
## 9 11 488
## 10 13 488
## # ... with 36 more rows
# factors
dt_factors <- merge(pca_factors, ife_factors, by = c("year"))
# are they proportional to each other (difference in scale)
dt_factors[, .(year, prop_factors1 = ife_factors1/pc_factors1)] %>% as_tibble
## # A tibble: 30 x 2
## year prop_factors1
## <int> <dbl>
## 1 63 0.00205
## 2 64 0.00205
## 3 65 0.00205
## 4 66 0.00205
## 5 67 0.00205
## 6 68 0.00205
## 7 69 0.00205
## 8 70 0.00205
## 9 71 0.00205
## 10 72 0.00205
## # ... with 20 more rows
We confirm that the proportionality factors are actually the inverse of each other:
dt_loadings[, .(mean(ife_loadings1/pc_loadings1)) ] *
dt_factors[, .(mean(ife_factors1/pc_factors1))]
## V1
## 1: 1.000045
And finally we look to see if residuals are the same:
# residuals for only one factor
dt_residuals <-
merge(ife$dt_augment[, .(state, year, ife_residuals=residuals)],
dt_pca[, .(year, pc_residuals=sales-mean(sales)-pc_loadings1*pc_factors1),
by = .(state) ],
by = c("year", "state"))
dt_residuals[, .(ife_residuals = sum(ife_residuals^2),
pc_residuals = sum(pc_residuals^2)) ]
## ife_residuals pc_residuals
## 1: 174608.2 174608.2


{% extends "layout.html" %} {% block content %} Azure Services. Upload File for Analysis Send Test Call Under Construction Under Construction {% endblock %}


RT @arunagw: If you are using ruby 2.1 and having some issues with gem installation. Upgrade to bundler 1.5.1 seems solve those. https://t.…
12/29/2013, 8:51:39 AM
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RT @arunagw: If you are using ruby 2.1 and having some issues with gem installation. Upgrade to bundler 1.5.1 seems solve those. https://t.…
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net.sourceforge.cilib.Domain
Class Compound
java.lang.Object
net.sourceforge.cilib.Domain.DomainComponent
net.sourceforge.cilib.Domain.Vector
net.sourceforge.cilib.Domain.Compound
- public class Compound
- extends Vector
This class can be used to represent domains of the form C^N where C is another domain component
and N is an integer representing the dimension of this component. A special case is where N is the
character 'N' which represents an unkown or unbounded dimension.
- Author:
- espeer
| Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
UNBOUNDED_DIMENSION
public static final int UNBOUNDED_DIMENSION
- See Also:
- Constant Field Values
Compound
public Compound(int dimension,
DomainComponent component)
Compound
public Compound(java.lang.String representation)
getDimension
public int getDimension()
- Description copied from class:
DomainComponent
- Returns the dimension of this domain component.
- Overrides:
getDimension
in class DomainComponent
- Returns:
- the dimension of this domain component.
getRepresentation
public java.lang.String getRepresentation()
- Description copied from class:
DomainComponent
- Returns the string representation for this domain component.
- Specified by:
getRepresentation
in class DomainComponent
- Returns:
- the string representation for this domain component
getComponent
public DomainComponent getComponent(int i)
- Description copied from class:
DomainComponent
- Returns the i'th subcomponent of this domain component.
- Overrides:
getComponent
in class DomainComponent
- Parameters:
i
- the index of the subcomponent
- Returns:
- the i'th subcomponent
Copyright © 2004 CIRG, University of Pretoria. All Rights Reserved.


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(implements org.fishwife.jrugged.
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)
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(implements org.springframework.beans.factory.InitializingBean)
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(implements javax.servlet.Filter)
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(implements org.springframework.beans.factory.InitializingBean)
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RequestCounter
(implements org.fishwife.jrugged.
ServiceWrapper
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(implements org.fishwife.jrugged.spring.
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ServiceWrapperInterceptor
(implements org.aopalliance.intercept.
MethodInterceptor
)
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Swedbank accused of violating US sanctions against Russia
by The LocalUS authorities are investigating Swedish banking giant Swedbank over transactions that may have violated American sanctions against Russia, according to new reports.
SVT's investigative news programme Uppdrag Granskning reported that the investigation related to documents showing payments which violated the sanctions the US imposed against Russia after the annexation of Crimea in 2014.
The transactions involved money transfers made from shareholders in Russian weapons manufacturer Kalashnikov to its American counterpart.
The total sum of the four payments is around ten million kronor, with the largest transaction taking place in 2017.
Kalashnikov USA was allowed to continue to operate after the introduction of sanctions on the condition that there were no financial links between it and the Russian company, but on Wednesday Uppdrag Granskning reported that money was transferred between the two, via Swedbank. EU sanctions also mean that the two companies should not do business with each other.
According to law firm Clifford Chance, which is heading an internal investigation at the bank in the wake of money laundering allegations, the document in question was already known to the internal investigation.
The bank's new CEO Jens Henriksson said in a statement that it was "a good thing" the bank was being investigated.
Asked in the programme what would happen if Swedbank had been involved in sanctions violations, Henriksson said: "It would probably mean we would be fined and reprimanded."


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Pepperell makes watery exit after Tin Cup moment
(Reuters) - Briton Eddie Pepperell peppered every ball he had into a pond at the Turkish Airlines Open on Saturday, then walked off the course.
The world number 47 was later disqualified for “failure to complete a hole”, officials said after the 28-year-old emptied his bag at the par-five fourth.
“Eddie hit his shots to the green then came over to tell us he had run out of balls,” former U.S. Open champion Martin Kaymer told reporters. “Then he walked off.”
Kaymer wasn’t sure if Pepperell had hit four or five balls.
“He was quick, so it was hard to keep track. He did not ask if he could borrow one. It did not look like he wanted to play.”
Only in the movie “Tin Cup” had he ever seen anything like it, Kaymer said.
Contacted by text in his hotel, Pepperell would only say there was “nothing to add really” although he admitted he too was unsure how many balls he had launched into the water, Golfworld reported.


2.4 Applying statistical tests on Dataframes
2.4.1 Intro
The t-test (or Student’s t-test) is one of the most used statistical tests. It can help determine whether the means of two samples are differing significantly. This form is the two-sample t-test. In its alternative form, you can compare a single sample to an expected (population) mean (one-sample t-test). An important assumption of the test is that the data follow a normal (Gaussian) distribution (that is why the t-test is one of the parametric tests). If this is not the case (or if you simply don’t want to test for or assume normality), you can use the non-parametric t-test counterpart, the Mann-Whitney U Test. If more that two groups are involved, ANOVA is your go-to guy.
To investigate the application of these statistical tests on dataframes, we’ll use the built-in dataset called PlantGrowth
. This dataset shows the yield of a certain plant species under three different conditions (control and two treatments).
My initial scientific question is “Is there a difference between the yields under these different conditions?”. My null hypothesis H0 would therefore be “There is no difference in yield”.
2.4.2 Data exploration
Of course, you always visually inspect your data before embarking on a statistical-testing quest:
This plot indicates that the distributions of ctrl and trt1 are quite close, and that trt2 differs from the others.
The t-test and ANOVA assume normality. Normality can be roughly assessed from boxplots (as in the figure above - do you know how?), but histograms and density curves are better suited for this purpose. Let’s create a histogram.
That looks pretty “normal” doesn’t it? Adding a real normal curve usually helps a lot:
x <- seq(0,20,0.005)
m <- mean(PlantGrowth$weight)
weight.sd <- sd(PlantGrowth$weight)
hist(PlantGrowth$weight,
prob=TRUE,
main = NA,
xlab="Yield (g dry weight)")
lines(x, dnorm(x, mean=m, sd=weight.sd), col="darkred", lwd=2)
Alternatively, you can plot the density curve of the data and add the normal distribution.
plot(density(PlantGrowth$weight), main = NA, ylim = c(0, 0.6))
lines(x, dnorm(x, mean=m, sd=weight.sd), col="darkred", lwd=2)
OK, conclusion: it looks like a normal distribution, albeit not a perfect one. Let’s proceed for now.
2.4.3 The t-test
Since t-tests are designed for comparing at most 2 groups, we’ll compare the two groups that are most alike in yield distribution: ctrl and trt1.
test.result <- t.test(x = PlantGrowth$weight[PlantGrowth$group == "ctrl"],
y = PlantGrowth$weight[PlantGrowth$group == "trt1"])
test.result
##
## Welch Two Sample t-test
##
## data: PlantGrowth$weight[PlantGrowth$group == "ctrl"] and PlantGrowth$weight[PlantGrowth$group == "trt1"]
## t = 1.1913, df = 16.524, p-value = 0.2504
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2875162 1.0295162
## sample estimates:
## mean of x mean of y
## 5.032 4.661
It’s as simple as that! To extract the p-value (or any other attribute), use the dollar operator:
## [1] 0.2503825
## [1] -0.2875162 1.0295162
## attr(,"conf.level")
## [1] 0.95
As you can see, the p-value is 0.250, well above the usual cutoff of 5% (0.05). Therefore, we can conclude that the yields do not differ significantly between the control and treatment 1 plants (and accept the null hypothesis).
Comparing control versus treatment 2 gives
test.result2 <- t.test(x = PlantGrowth$weight[PlantGrowth$group == "ctrl"],
y = PlantGrowth$weight[PlantGrowth$group == "trt2"])
test.result2
##
## Welch Two Sample t-test
##
## data: PlantGrowth$weight[PlantGrowth$group == "ctrl"] and PlantGrowth$weight[PlantGrowth$group == "trt2"]
## t = -2.134, df = 16.786, p-value = 0.0479
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.98287213 -0.00512787
## sample estimates:
## mean of x mean of y
## 5.032 5.526
And this p-value indicates there may be a statistically relevant effect of treatment 2 (p-value = 0.0478993, just below the 5% cutoff).
Now, let’s extend this to the situation where you want to apply a t-test over the rows of a dataframe, for example when you have a dataframe holding expression values of genes (rows) in two groups of samples (columns).
Here is a very simple dataframe to explore this scenario. Columns a through d represent measurements for condition A (e.g. the control group) and e through h condition B (e.g. treatment with a novel drug).
gene.expr <- data.frame(a = c(20, 34, 24, 51),
b = c(21, 32, 27, 45),
c = c(17, 26, 21, 49),
d = c(18, 28, 28, 53),
e = c(22, 33, 29, 44),
f = c(16, 34, 26, 42),
g = c(19, 31, 31, 41),
h = c(20, 28, 30, 43))
rownames(gene.expr) <- c("SRSSHT", "GeNPL-1", "DUNNO", "WHTVR")
controls <- c("a", "b", "c", "d")
treatment <- c("e", "f", "g", "h")
##have a look
gene.expr
## a b c d e f g h
## SRSSHT 20 21 17 18 22 16 19 20
## GeNPL-1 34 32 26 28 33 34 31 28
## DUNNO 24 27 21 28 29 26 31 30
## WHTVR 51 45 49 53 44 42 41 43
I am only interested in the p-value to be able to select the most interesting genes. Here is a custom function to extract this from a row of my dataframe.
and now the t-test applied over the rows
## SRSSHT GeNPL-1 DUNNO WHTVR
## 0.87746477 0.53288953 0.08786967 0.02001060
As you can see, only one gene in this example meets the standard 95% p value cutoff: WHTVR.
(NB: of course, no multiple testing corrections has been applied here - that is out of scope for this use case).
2.4.4 ANOVA
The technique (one-way) Analysis of Variance (ANOVA) is an extension of the two-sample t test for independent groups covering situations where there are more than two groups being compared. In one-way ANOVA the data is sub-divided into groups based on a single classification factor.
Before an ANOVA can be carried out, it has to be determined that the variances for all factors are equal. You cann do this using the bartlett.test
function:
##
## Bartlett test of homogeneity of variances
##
## data: weight by group
## Bartlett's K-squared = 2.8786, df = 2, p-value = 0.2371
From the output we can see that the p-value of 0.2371 is not less than the significance level of 0.05. This means we cannot reject the null hypothesis that the variance is the same for all treatment groups. This means that there is no evidence to suggest that the variance in plant growth is different for the three treatment groups.
In this example, we will analyse all three groups of the PlantGrowth dataset together. To investigate the differences between these groups, we fit the one-way ANOVA model using the lm
function:
Then, using the summary
function, have a look at the parameter estimates and standard errors for the treatment effects:
##
## Call:
## lm(formula = weight ~ group, data = PlantGrowth)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0710 -0.4180 -0.0060 0.2627 1.3690
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0320 0.1971 25.527 <2e-16 ***
## grouptrt1 -0.3710 0.2788 -1.331 0.1944
## grouptrt2 0.4940 0.2788 1.772 0.0877 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6234 on 27 degrees of freedom
## Multiple R-squared: 0.2641, Adjusted R-squared: 0.2096
## F-statistic: 4.846 on 2 and 27 DF, p-value: 0.01591
The model output indicates some evidence of a difference in the average growth for the 2nd treatment compared to the control group. An analysis of variance (ANOVA) table for this model can be produced via the anova command (passing the linear model object):
## Analysis of Variance Table
##
## Response: weight
## Df Sum Sq Mean Sq F value Pr(>F)
## group 2 3.7663 1.8832 4.8461 0.01591 *
## Residuals 27 10.4921 0.3886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
This table confirms that there are differences between the groups which were highlighted in the model summary. The function confint is used to calculate confidence intervals on the treatment parameters, by default 95% confidence intervals:
## 2.5 % 97.5 %
## (Intercept) 4.62752600 5.4364740
## grouptrt1 -0.94301261 0.2010126
## grouptrt2 -0.07801261 1.0660126
The model residuals can be plotted against the fitted values to investigate the model assumptions. First we create a data frame with the fitted values, residuals and treatment identifiers:
plant.mod = data.frame(Fitted = fitted(yield.lm),
Residuals = resid(yield.lm), Treatment = PlantGrowth$group)
and then produce the plot:
We can see that there is no major problem with the diagnostic plot but some evidence of different variabilities in the spread of the residuals for the three treatment groups.
ALTERNATIVELY, WE COULD HAVE DONE THIS: 1: fit a model
## Call:
## aov(formula = weight ~ group, data = PlantGrowth)
##
## Terms:
## group Residuals
## Sum of Squares 3.76634 10.49209
## Deg. of Freedom 2 27
##
## Residual standard error: 0.6233746
## Estimated effects may be unbalanced
2: diagnostic plots Diagnostic plots provide checks for heteroscedasticity, normality, and influential observerations.
3: evaluate model effects
## Df Sum Sq Mean Sq F value Pr(>F)
## group 2 3.766 1.8832 4.846 0.0159 *
## Residuals 27 10.492 0.3886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Single term deletions
##
## Model:
## weight ~ group
## Df Sum of Sq RSS AIC F value Pr(>F)
## <none> 10.492 -25.517
## group 2 3.7663 14.258 -20.316 4.8461 0.01591 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Now suppose we want to apply this to a dataframe of gene expression values, as shown before in the example of the t-test, but now with three instead of two groups:
gene.expr2 <- data.frame(a = c(20, 34, 24, 51),
b = c(21, 32, 27, 45),
c = c(17, 26, 21, 49),
d = c(18, 28, 28, 53),
e = c(22, 33, 29, 44),
f = c(16, 34, 26, 42),
g = c(19, 31, 31, 41),
h = c(20, 28, 30, 43),
i = c(20, 32, 22, 50),
j = c(18, 33, 21, 52),
k = c(21, 29, 19, 51),
l = c(22, 27, 23, 54))
rownames(gene.expr2) <- c("SRSSHT", "GeNPL-1", "DUNNO", "WHTVR")
controls <- c("a", "b", "c", "d")
treatment1 <- c("e", "f", "g", "h")
treatment2 <- c("i", "j", "k", "l")
##have a look
gene.expr2
## a b c d e f g h i j k l
## SRSSHT 20 21 17 18 22 16 19 20 20 18 21 22
## GeNPL-1 34 32 26 28 33 34 31 28 32 33 29 27
## DUNNO 24 27 21 28 29 26 31 30 22 21 19 23
## WHTVR 51 45 49 53 44 42 41 43 50 52 51 54
We need to apply anova to each row and extract the p-value. First, we need the custom function to apply with:
my.anova <- function(x) {
#define factor
gr.factor <- factor(rep(x = c("control", "treatment1", "treatment2"), each = 4))
#perform anova
my.oav <- aov(formula = expr ~ group,
data = data.frame(expr = x, group = gr.factor))
#return p-value
summary(my.oav)[[1]]$'Pr(>F)'[1]
}
Now, it’s a simple apply call again:
## SRSSHT GeNPL-1 DUNNO WHTVR
## 0.6692884795 0.7638487821 0.0047833839 0.0008448382
These results indicate a significant effect in genes DUNNO and WHTVR (again, without having applied multiple testing correction!). The next step would be to investigate the effect size (if anything significant has been found). This can be carried out using Tukeys HSD.


About Me
[Currently Located in Toronto, Canada]
Niki is student of full stack web development, with a love for learning, good coffee, and code! Niki began her coding journey very early on (MySpace days) in the mid-2000s. She was also very interested in photography, film, and graphic design - and decided to pursue her Bachelor's Degree at Ryerson University in Toronto, studying Graphic Communications Management. In this program, she had the opportunity to learn about the graphic design, commercial printing, and packaging industries. She took a couple of courses during this time to brush up on her coding skills (HTML/CSS/JS).
Her first job in the industry was as a Project Coordinator Intern at Jar Creative in Toronto, at that time it was primarly a web development agency, where she was exposed to small agency structure (many hats, yay!) as well as the project management side. Following this, she spent 3 years working for Schawk (a division of SGK) as a Client Coordinator for graphic design and prepress for packaging and advertising. This opportunity allowed her to further pursue her interests in project management and client relations by acting as a gate keeper between the client and production.
In 2017, Niki joined the Loblaw Brands team as Assistant Packaging Manager, where she was responsible for data integrity around the company's product and packaging line. She also had an opportunity to work with printers managing the Loblaw Brand Printer program, collaborating with the sourcing and procurement teams. During her last year at Loblaw, she joined the PLM (Product Lifecycle Management Team) where she was involved in the UX and development of a new system to manage the product lifecycle from conception to production launch.
Resume GitHub LinkedIn Email Call


Time:
{{session.timeStart}} - {{session.timeEnd}}
Location:
{{session.location}}
{{session.description}}


Title
Subtitle
And here is that superficial but very important section in which we place all of our important information. We might even possibly truncate all of this information.
Title
Subtitle
And here is that superficial but very important section in which we place all of our important information. We might even possibly truncate all of this information.


The main feature of the floating callout is that text can wrap
under and over it because it is a float.
Wraps under and over
You can float a callout to the right using float:right
.
You can use width
to set the callout's width.
You can use margin
to put distance between the callout and
text outside the callout. You can use padding
to put space
between the callout's internal text and its borders. You can use
position:relative
and top
to adjust the vertical
position of the callout.


Redskins come back to beat Lions for 2nd win of season
by STEPHEN WHYNOLANDOVER, Md. (AP) - Quinton Dunbar intercepted Jeff Driskel in the final minute, Dustin Hopkins hit the go-ahead field goal from 39 yards out with 16 seconds left and the Washington Redskins beat the Detroit Lions 19-16 Sunday to snap a four-game losing streak.
Rookie quarterback Dwayne Haskins picked up his first NFL win after leading Washington (2-9) into field goal range following Dunbar’s interception. Haskins finished 13 of 29 for 156 yards and an interception but made a couple fewer mistakes than Driskel, who was picked off three times as the Lions (3-7-1) lost their fourth in a row and third since starter Matthew Stafford went out with injury.
For long stretches, the game was a comedy of errors with neither team looking like it wanted to win. Haskins fumbled on third down on Washington’s first possession, Detroit turned the ball over on consecutive offensive plays, Lions kicker Matt Prater missed a 39-yard field goal and his teammates were baffled on Steven Sims’ 91-yard kickoff return for a Redskins touchdown - after muffing the catch.
Driskel finished 20 of 32 for 207 yards, with a touchdown pass and the three interceptions.
NORMAN BENCHED
Redskins cornerback Josh Norman was in uniform but only saw the field on special teams. After multiple reports Sunday morning that he’d be inactive, it was a bit of surprise Norman played at all.
“Where you are today isn’t the determining factor of where you’ll be tomorrow,” Norman tweeted before the game. “But Everyday is its own starting point to create a dynasty that’ll last when you’re gone.”
Norman has drawn his share of criticism during a rough season, his fourth since signing a $75 million, five-year deal with Washington. Defensive coordinator Greg Manusky said he “couldn’t tell you why” Norman has struggled.
“I think from our situation, we’re putting him in situations to make some plays, and we’re just not making the plays,” Manusky said this week.
SPECIAL DAY
In a game of inept offenses, special teams shined for the Redskins and Lions. In addition to Sims’ kickoff return TD that was Washington’s first since 2015, Hopkins made field goals of 28, 37 and 42 yards before the go-ahead score and Prater connected from 24, 49 and 21 yards.
TD THOMAS
Quarterback-turned-tight end Logan Thomas made his first touchdown catch with the Lions, a 12-yard reception from Driskel, with family and friends in attendance. Thomas is a native of Lynchburg, Virginia, about a three-hour drive away, and played QB at Virginia Tech.
INJURIES
Lions: DT Damon Harrison was injured late in the fourth quarter. … WR Marvin Hall left early in the fourth with a foot injury. … Stafford was one of six starters out with injury.
Redskins: LB Ryan Kerrigan left with a concussion in the fourth quarter. … WR Paul Richardson was ruled out at halftime with a hamstring strain. Richardson missed the Jets game last week with a hamstring injury. … RB Chris Thompson missed his fifth consecutive game with a toe injury. … DL Daron Payne was out with an ankle injury.
UP NEXT
Lions: Host the Chicago Bears on Thanksgiving.
Redskins: Visit the Carolina Panthers next Sunday.
Follow AP Sports Writer Stephen Whyno on Twitter at https://twitter.com/SWhyno
More AP NFL: https://apnews.com/NFL and https://twitter.com/AP_NFL
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


This example is the same as the DragAndDrop example, except it uses hitArea to make only the center part of the daisy respond to mouse interactions.
hitArea


The popup title below is shared between all MyDaemon program nodes through the driver.Cannot be empty.


My Thinking Style
Sunday March 15, 2015
My Gregorc thinking style is likely a mix of Abstract Sequential and Abstract Random, since I scored about the same for each learning style. Reading the descriptions of each learning style, elements of both resonate with me. Overall, I think being balanced between the two works well for my learning style but I have already noted a few concerns.
Some of the challenges I face from more of my Abstract Random side is my dislike for routine procedures and orderliness and working within time limits. While I don't think there's anything wrong with disliking routine, it does sometimes leave me feeling overwhelmed as I'm not the best at being organized or managing my time. I think in order to learn more effectively during DBC, it will be important for me to stay organized and stay on a relatively strict timeline for completing challenges. I plan to use timeboxing to help manage my time and thankfully so far, DBC's curriculum on GitHub has been very useful for keeping myself organized!
One of the challenges I face from the Abstract Sequential side is my fear of appearing foolish/uninformed, which sometimes prevents me from being comfortable working with others. Unless I feel like I have a very thorough grasp of a concept or skill, I don't typically like to talk through or work through problems with others. I'm realizing now at DBC that as I learn to code, there will likely always be an abundance of things I don't know so I need to start getting comfortable with gaps in my knowledge. I'm already working on this and having talked to several other engineers about how there's still so much for them to learn, I'm getting more comfortable with my own ignorance. I find the easiest way to handle a situation with someone when I don't know something is to just be honest about it and google it!


---
layout: post
status: publish
published: true
title: 'Leopard’s Illuminating Graphical User Interface: Illuminous'
author: Leask
author_login: leask
author_email<EMAIL_ADDRESS>author_url: https://leaskh.com
wordpress_id: 387
wordpress_url: http://leaskh.wordpress.com/2006/12/20/leopard%e2%80%99s-illuminating-graphical-user-interface-illuminous
date: '2006-12-20 13:53:24 +0800'
date_gmt: '2006-12-20 13:53:24 +0800'
categories:
- Computers and Internet
tags: []
comments: []
---
Mac OS X’s Aqua has matured very slowly over the last few years. It seems like many OS X users are tired of Aqua and ready for a new revolutionary GUI to compliment and enhance the experience of using a Mac. An increasingly popular theme for Apple’s latest applications have been smooth unified metal and dark glass. With each major update to one of Apple’s applications, its seems like the days with blue gel scroll-bars and candy bar progress bars are ending.
Its no question that Leopard will feature a new GUI, but no one really knows what it will look like. Its no doubt that Leopard will feature resolution-independent graphics support that will allow for greater resolution displays with the same and windows sizes. However, the details of the interface have not yet been revealed, but many are hoping that we will see the final announcement of Leopard features at Macworld 2007, where chances of a GUI announcement are high.
Apple Gazette has featured a new rumor from a supposed insider that has claimed that the new GUI that will replace Aqua will be nicknamed ‘Illuminous’. The report has no solid information on what the Illuminous theme will look like, but I believe I know exactly where Apple might be headed.
As I said, Apple’s latest trend in its software GUI is a darker, flatter, translucent, reflective, “illuminating” glass appearance. The GUI is also making headway beyond the sheer appearance, to window sizes, visual effects, icons, etc. Fullscreen applications will definitely be a hit in Leopard, to clear your desktop from clutter and manage your workspace more efficiently. Just like Aperture and iPhoto, imagine fullscreen browsing, video-editing, Garageband, iWork, etc. Sleek graphic effects when opening windows, files, applications, are also likely to be improved making the OS seem more alive, similar to the widget ripple effect. And with the new GUI change, I am also expecting Apple to replace their outdated icons. Ultimately, Apple is heading towards a sleeker, darker, simpler, livelier GUI.
Apple has been the leader in computer and GUI design for years, and Vista’s new Aero interface has probably provoked Apple’s design team to topping them off with a mind-blowing interface (that requires half of Aero’s hardware requirements). It would be a true disappointment if there was no sort of GUI announcement when Steve finished giving the major features in Leopard.
The dark translucent appearance Apple has developed over the last three years has seemed to have expanded to many of the operating system’s core applications. Here are a few of the many traces of the new dark glass appearance rumored to be featured in Leopard:
Aperture 1.5’s navigation window:
Photo’s Adjust Window:
Leopard’s new QuickLook feature:
Front Row’s sliding dark glass:
iTunes 7’s new darker appearance:


The TMCreateFile API function provides the information necessary (file name and extension) to create a new file and file name in the current directory on the current device on the 1-Wire network. It returns a file_handle, which can then be used later to write a file and file name to the part with a call to TMWriteFile. The new file name is provided in the FileEntry structure 'fentry'. Only the 'name' and 'extension' portions of FileEntry need to be set before calling TMCreateFile. Repeat file names are not permitted. This API returns a file handle >= 0. Please be aware that there is a limited number of file handles (4) that can be used at one time. When finished writing a file, please close the file (and free up the file handle) withTMCloseFile.
The API call also returns the estimated maximum number of bytes, ‘maxwrite,’ available for this new file. The ‘maxwrite’ variable has no meaning until returned by the funtion call (it is an output variable). The file name must be left justified padded with spaces. The valid file name characters are (ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!#$%&'-@^_`{}~). The valid value of the extension is 0-99 decimal for normal files, 100 for an ‘AddFile’ and 101 for a ‘Monetary File’. The ‘AddFile’ is described in the API function description of TMWriteAddFile. The ‘Monetary File’ is a file that is only created on a DS1962, DS1963, DS2422 and DS2423. The data page of this file can only be located on a page that has a corresponding counter. If a page with a counter is not available on the device, then the ‘Monetary File’ will not be created. In case of the ‘Monetary File’, some of its other values are set upon returning from this function. In particular, the page number where the file is copied to is set in the ‘start page’ value of the FileEntry structure. Also, the Counter and Tamper bytes are written into the first eight bytes of the ‘bitmap’ section of the FileEntry structure. Note that the Counter value reported is the current Counter value. If the counter is a page write cycle counter as in the DS1963, then 1 must be added to the counter value to reflect the upcoming copy.
For a description of the above types, see the TMEX Type Descriptions.
Parameters
session_handle
Specifies the session handle returned from the API function TMExtendedStartSession that specifies the desired 1-Wire network port. This parameter is required by most TMEX API functions.
state_buffer
Specifies a pointer to a memory location that TMEX keeps all of the state information for the 1-Wire networks. This parameter is required by most TMEX API functions.
maxwrite
Specifies a pointer to a memory location that will contain the estimated maximum file size to write (how much room is left on the part) after returning from the API. The ‘maxwrite’ variable has no meaning until returned by the funtion call (it is an output variable). The maxwrite size may be off by +-28 bytes.
fentry
Specifies a pointer to a memory location that contains a FileEntry structure. This structure must have the name and extension portions set before calling this API.
Return Value
>= 0 => file created, and this is the file handle
<0 => TMEX File Operations Error Return Code
See Also
TMWriteFile, TMFirstFile, TMNextFile, TMOpenFile


Contents
Index
Introduction
Introduction
Basic elements of the SEXTANTE GUI
The SEXTANTE toolbox
Introduction
The algorithm dialog
The parameters tab
The raster output tab
Data objects generated by SEXTANTE algorithms
Context help
Configuring SEXTANTE
General
Folders
WPS and GRASS
The SEXTANTE graphical modeler
Introduction
Definition of inputs
Definition of the workflow
Editing the model
Saving and loading models
The SEXTANTE batch processing interface
Introducción
The parameters table
Filling the parameters table
Setting raster output characteristics
Executing the batch process
The SEXTANTE command-line interface
Introduction
The interface
Getting information about data
Getting information about algorithms
Running an algorithm
Adjusting output raster characteristics
Running a model
Managing layers from the command-line interface
The SEXTANTE history manager
Introduction
Victor Olaya 2010-02-18


{% extends 'base_email.html' %} {% block preheader %}Travel reimbursement update for {{ h_name }}{% endblock %} {% block content %}
Dear {{ app.user.get_full_name }},
Unfortunately, we aren’t able to offer you travel reimbursement for this edition of {{ h_name }}. We still hope you can join us at {{ h_name }}!
Please remember to confirm your spot if you will be joining us. If your plans have changed, please let us know by cancelling your invite here.
{% if app.visas %} {% include 'mails/include/visas.html' %} {% endif %} {% include 'mails/include/closing.html' %} {% endblock %}


Decsendent Selector (space)
p1 in the div.
p2 in the div.
p3 in the div.
p4 in the div.
p5 in the div.


Digging deep ~ A time to study and dig deep into the scriptures. All questions will be answered (5:30pm - 7:00pm)
Faith Clinic ~ A time when we pour our hearts to God (5:30pm - 7:00pm)
Holy Ghost Service @ The Redemtion camp Lagos-Ibadan Express way.


{% extends 'bom/bom-base.html' %} {% load staticfiles %} {% block head-title %}{{ title }}{% endblock %} {% block main %} {% endblock %} {% block bom-menu %}
To batch create parts, upload a csv that contains columns with the headers 'part_class' or 'part_number', 'description' or 'value' along with 'value_units', and 'revision'. If you specify a part number then it must be one that is not already being used. If you don't specify a part number then IndaBOM will create one for you.
You may optionally specify a manufacturer and manufacturer's part number using columns with the headers 'manufacturer' and a 'manufacturer_part_number'. For the names of other optional columns click here. {% else %} {% include 'bom/nothing-to-see.html' with required_privilege='Admin' %} {% endif %}


The repository at https://github.com/mustache/spec is the formal standard for
Mustache. It defines both normal usage and edge-case behavior for libraries
parsing the Mustache templating language (or a superset thereof).
The specification is developed as a series of YAML files, under the specs
directory.
Versioning
This specification is being semantically versioned.
Roughly described, major version changes will always represent backwards
incompatible changes, minor version changes will always represent new language
features and will be backwards compatible, and patch ('tiny') version changes
will always be bug fixes. For the purposes of semantic versioning, the public
API is the contents of the specs directory and the algorithm for testing
against it.
Mustache implementations SHOULD report the most recent version of the spec
(major and minor version numbers). If an implementation has support for any
optional modules, they SHOULD indicate so with a remark attached to the
version number (e.g. "vX.Y, including lambdas" or "v.X.Y+λ"). It is
RECOMMENDED that implementations not supporting at least v1.0.0 of this spec
refer to themselves as "Mustache-like", or "Mustache-inspired".
Alternate Formats
Since YAML is a reasonably complex format that not every language has good
tools for working with, we also provide JSON versions of the specs on a
best-effort basis.
These should be identical to the YAML specifications, but if you find the need
to regenerate them, they can be trivially rebuilt by invoking rake build.
It is also worth noting that some specifications (notably, the lambda module)
rely on YAML "tags" to denote special types of data (e.g. source code). Since
JSON offers no way to denote this, a special key ("__tag__") is injected
with the name of the tag as its value. See TESTING.md for more information
about handling tagged data.
Optional Modules
Specification files beginning with a tilde (~) describe optional modules.
At present, the only module being described as optional is regarding support
for lambdas. As a guideline, a module may be a candidate for optionality
when:
It does not affect the core syntax of the language.
It does not significantly affect the output of rendered templates.
It concerns implementation language features or data types that are not
common to or core in every targeted language.
The lack of support by an implementation does not diminish the usage of
Mustache in the target language.
As an example, the lambda module is primarily concerned with the handling of a
particular data type (code). This is a type of data that may be difficult to
support in some languages, and users of those languages will not see the lack
as an 'inconsistency' between implementations.
Support for specific pragmas or syntax extensions, however, are best managed
outside this core specification, as adjunct specifications.
Implementors are strongly encouraged to support any and all modules they are
reasonably capable of supporting.
The repository at https://github.com/mustache/spec is the formal standard for Mustache. It defines both normal usage and edge-case behavior for libraries parsing the Mustache templating language (or a superset thereof).
The specification is developed as a series of YAML files, under the
specs
directory.Versioning
This specification is being semantically versioned. Roughly described, major version changes will always represent backwards incompatible changes, minor version changes will always represent new language features and will be backwards compatible, and patch ('tiny') version changes will always be bug fixes. For the purposes of semantic versioning, the public API is the contents of the
specs
directory and the algorithm for testing against it.Mustache implementations SHOULD report the most recent version of the spec (major and minor version numbers). If an implementation has support for any optional modules, they SHOULD indicate so with a remark attached to the version number (e.g. "vX.Y, including lambdas" or "v.X.Y+λ"). It is RECOMMENDED that implementations not supporting at least v1.0.0 of this spec refer to themselves as "Mustache-like", or "Mustache-inspired".
Alternate Formats
Since YAML is a reasonably complex format that not every language has good tools for working with, we also provide JSON versions of the specs on a best-effort basis.
These should be identical to the YAML specifications, but if you find the need to regenerate them, they can be trivially rebuilt by invoking
rake build
.It is also worth noting that some specifications (notably, the lambda module) rely on YAML "tags" to denote special types of data (e.g. source code). Since JSON offers no way to denote this, a special key ("
__tag__
") is injected with the name of the tag as its value. SeeTESTING.md
for more information about handling tagged data.Optional Modules
Specification files beginning with a tilde (
~
) describe optional modules. At present, the only module being described as optional is regarding support for lambdas. As a guideline, a module may be a candidate for optionality when:As an example, the lambda module is primarily concerned with the handling of a particular data type (code). This is a type of data that may be difficult to support in some languages, and users of those languages will not see the lack as an 'inconsistency' between implementations.
Support for specific pragmas or syntax extensions, however, are best managed outside this core specification, as adjunct specifications.
Implementors are strongly encouraged to support any and all modules they are reasonably capable of supporting.


Third-party insurance also referred to as liability insurance essentially provides financial coverage to an insured person against any liability incurred in case of any loss/damage caused to third-party property or the person.
Under motor laws of India, third party insurance is compulsory. The policy is termed as a 'third-party' cover as the beneficiary of the car policy is a third party, who is not a part of the insurance contract. Hence, the policy doesn’t extend policy coverage to the insured. As per the contract between the two parties, the insurance company pays for the liability arises. It covers any legal liability of policyholder for causing accidental death/ bodily injury or property damage to a third party, As mentioned above, third party car insurance online does not cover any loss or damages to the insured car due to theft, accident, etc.
Third party insurance is a type of insurance policy that covers only third party liabilities of the insured. It provides financial protection against damages caused to a third party property, vehicle and physical injuries to third parties. Also known as the act-only insurance’, the beneficiary under this insurance is a third party and not the policyholder (first party) or the insurance company (second party).
TBesides the legal clause, Third Party Insurance comes in handy when your vehicle hits another vehicle. You can’t measure the level of damage caused by an accident - it might lead to death as well. In such instances, the victim is allowed to register a case claiming for compensation. Here third party insurance comes into the picture. It covers the insured vehicle in case any liability claim arises out of bodily injury, property damage, or death of the third party. As per the guidelines of IRDA, while there is no limit in case of death, the property damage cover is limited up to Rs. 7.5 lakh in case of
As stated earlier, Third Party Insurance is liability insurance that works towards easing out the legal liabilities of the first party for causing damages to the third party. The first party refers to the insured who is responsible for the losses/ damages caused to the third party, who is the person filing liability claims against the insured. The second party or the insurance company aids the financial burden of the insured by paying the legal liabilities towards the third party.
Insurance companies cover two kinds of motor Third Party Insurance claims – bodily injury liability and property damage liability.
Third party bodily injury liability claim arises out of the insured causing physical injuries to another person with his vehicle. Such claims provide coverage for hospitalization expenses, pain & suffering, loss of income as well as death or permanent disability as a result of the accident.
Third party property damage liability claim covers the property damage or complete loss of property caused by the insured vehicle. It includes claims related to damaged landscapes, such as a ruined fence, front lawn, etc. damaged property such as mailboxes, the fence gates, etc. as well as replacement of loss of structures, such as shops.
The following is taken care of by the standard third party car insurance in case of the accident caused by your vehicle results in:


171_xiaochong1117.py
class Solution:
def findLexSmallestString(self, s: str, a: int, b: int) -> str:
smallest = s
visited = set()
visited.add(s)
queue = collections.deque()
queue.append(s)
while len(queue) > 0:
string = queue.popleft()
smallest = min(smallest, string)
# try add a
neighbor = list(string)
for i in range(1, len(neighbor), 2):
neighbor[i] = str((int(neighbor[i]) + a) % 10)
neighbor = ''.join(neighbor)
if neighbor not in visited:
visited.add(neighbor)
queue.append(neighbor)
# try rotate b
neighbor = string[-b:] + string[:len(string)-b]
if neighbor not in visited:
visited.add(neighbor)
queue.append(neighbor)
return smallest


Select Category to View and Edit Details |
|---|
| Category Name | Applicable Fee |
|---|
| Category Name | Applicable Fee | Initial Limit | Amount Per Additional Copy |
|---|
Add New Category |
|---|
Issued Recommendations | ||||
|---|---|---|---|---|
| Recommender | Requester Reference | Date | Number of Copies | Amount Paid |
|
Issued Recommendations
|
||||
|---|---|---|---|---|
| All: | Custom: |
|
|
||
| Recommender | Requester Reference | Date | Copies | Amount Paid |
Manage Recommenders | ||||
|---|---|---|---|---|
| Recommender Name | Date Added | No. Signed | Status |


BREAKING: At least six injured in U.S. school shooting
by Nike AdebowaleAt least six people were injured during a Thursday shooting at a Southern California high school in the U.S., CNN reports.
The report indicates that three of the victims are in critical condition.
According to the report, the L.A County Sheriff’s Department is responding to a shooting at Saugus High School, about 30 miles of north of Los Angeles.
The gunman who carried out the shooting has also been reported dead.
The authorities focused on evacuating the students from the school premises after the shooting.
Thursday’s incident joins a growing list of gun violence in the U.S.
Details later…


--- title: ECLiPSe interface --- The ECLiPSe 6.1 constraint programming system features an interface to Gecode, provided as the lib(gfd) library.


Welcome to Mr. ROBOger's Neighborhood!
Enter a number!
Your Name:
Neighborize!
Neighborize Backwards!
Try Again!


---
title: Fiorella Meyer, Admin.
position: Office Manager
image: fiorella.jpg
email<EMAIL_ADDRESS>office: CAB H86.3
website:
phone: +41 44 632 82 09
cv:
---
professional skills
- sound knowledge of finance and personnel administration
- broad experience in secretarial management
- versatile language skills, experienced computer systems user
- previous work experience in various sectors (University, bank, hospital, hotel, childrens home, farming) in Switzerland as well as abroad
- events management, inter-cultural communication
- working at ETH since 2006 (Institute of Energy + Process Engineering, SCCER "Efficient Technologies + Systems for Mobility", Institute of Programming Languages and Systems)
education
- accounting officer edupool.ch at KV Business School Zurich
- personnel officer edupool.ch at KV Business School Zurich | HR assistant HRSE
- higher vocational diploma in business administration
- programming IT-systems finance sector
personal interests
- energy + mobility topics, nature, reading, gardening, dog training, sewing, travelling, languages


Only a couple of external tools are allowed to be used or discussed. Check this tool page for an official list of them all. Any other tools will get you banned.
D2STATS AND LOOT FILTER
D2stats is a stat reader AND a loot filter. Here is the thread about it
The loot filter is customizable and will allow you to see only the item you choose to see, and also be notified when some elite item drops ( colored text in the upper left corner)
The loot filter is included ( but can be modified)
Allowed online
SPEED CALCULATOR
This tool enables you to calculate all your character's speed breakpoints. Useful for min-maxing a char
OFFLINE TOOLS
Median XL Offline Tools adds a nice way to manipulate character save files. It enables users to respec, rename, convert and downlevel characters. Also allows viewing, searching and mass disenchanting items, as well as sorting stash. Support thread
Singleplayer only
MAP REVEAL
Map reveal in implemented into the game. No need to use any external software (they'll get you banned).
You can reveal map by pressing the 'R' key. Or you can change this key binding in the in-game settings.
LANGUAGES
The official language of the game is English. That said there is a translator community who made the game available in russian, french, chinese, spanish, and other languges. Our official thread list every up to date languages.
ACCESSIBILITY
If you suffer from Color blindness, vision impairment or shitty screen colors syndrome, there is an in-game setting that allow to mark rare/unique/sets with an added text marker.
Additionally, if like me, the in-game font isn't very readable you can swap for another ready made font. The installation process is less straight forward but not very complex. Here is my guide and personal page to get all the different font we've made
Feel free to check the other tools


Indian pacers with pink ball spell blues for Bangladesh in Kolkata Test
by bdnews24.comThe Day 1 of the historic first day-night Test between India and Bangladesh in Kolkata will be a day the tourists will want to forget as Indian quicks with the pink ball have helped the hosts to take control with a 68-run lead. Related Stories
Indian openers Mayank Agarwal (14) and Rohit Sharma (21) appeared at ease against Bangladesh’s pink ball attack but neither could convert their starts at the Eden Gardens on Friday.
Cheteshwar Pujara and Virat Kohli then smashed fifties in a 95-run stand to put India well on course for a victory in their opening day-night Test and 12th consecutive home series victory. They have a 1-0 lead after winning the first Test by an innings and 130 runs inside three days.
Kohli on 59 and his partner Ajinkya Rahane on 23 at stumps will resume play on Saturday hoping to bat Bangladesh out of the second and final Test and sweep the series. Only a miracle can now see the Tigers level the series.
Bangladesh were dismissed for just 106 as Indian pacers led by Ishant Sharma wreaked havoc with the pink cherry in their hand.
Mohammed Shami wrapped it up as Abu Jayed was the last man to go in the 31st over before halfway through the second session of Day 1.
It is Bangladesh’s second lowest innings score against India, the No. 1 Test playing nation in ICC rankings now, after 91 in the second innings of their inaugural Test against the South Asian neighbours.
After Liton Das (24) and Nayeem Hasan were struck on the helmet by Shami’s bouncers, Bangladesh were forced to make two concussion replacements, the first such incidence since the ICC allowed concussion replacements in all formats in August.
Mehidy Hasan Miraz and Taijul Islam are playing the remainder of the Test in place of Liton and Nayeem.
Opener Shadman Islam topscored for Bangladesh with 29.
Ishant’s five-wicket haul in exchange for 22 runs is his best at home.
He took the wickets of captain Mominul Haque and Mohammad Mithun before they could score in the 11th over before Shami sent back Mushfiqur Rahim for another duck in the next over.
Umesh Yadav did the early damage in the first session by picking up three wickets while Shami accounted for two after Mominul won the toss and chose to bat first.
Among the crowd in Kolkata was Prime Minister Sheikh Hasina who rang the bell of the match.


Select one of the above list so others will not need to bring the
same gifts. However it is not mandatory for you to select
one from the list above.
Your presence is much more important and needed
for my birthday celebration!


Dr. Sabareesh Geetha Rajasekharan
Assistant Professor
Email Id<EMAIL_ADDRESS>Dr. Aruna Malapati
Assistant Professor
Email Id<EMAIL_ADDRESS>Kuldeep Singh Sidhu
Student
Email Id<EMAIL_ADDRESS>Siddharth Dharm
Student
Email Id: siddharthdharm@gmail.com


Covering science: The history of Nature’s front page
A look back through 150 years of Nature front page designs.
by Dan FoxNature’s first edition was published in 1869. Over the last 150 years the science contained inside the journal has changed a lot - but so has the front cover.
Kelly Krause, Creative Director of Nature dives into the archives to discover some front covers from Nature’s long history and see how the design of the magazine reflected the era in which it was made.
doi: 10.1038/d41586-019-03442-2


Bigg Boss 13: Is Rashami Desai in Love With Siddharth Shukla? Latter Pops up With 'Pyaar' Question
Bigg Boss 13: Rashami Desai left shocked as Siddharth Shukla asks if she is in love with him, watch viral video inside
The Weekend Ka Vaar episode of Salman Khan hosted Bigg Boss 13 never lacks masala and as another episode awaits to be aired tonight, fans can’t help but wait on the edge with excitement and especially because of Rashami Desai and Siddharth Shukla’s sizzling chemistry. In tonight’s episode, while Hindustani Bhau and Vishal Singh Aditya get their legs waxed, Siddharth will be seen doubling down with laughter as Salman watches on.
A promo clip currently going viral shows Siddharth itching to ask before Salman, “Kya aap Siddharth Shukla se pyaar karti hain?” The question leaves Rashami evidently shocked and fans can’t wait for the episode to go on air already.
Bigg Boss 13 will witness one more eviction tonight at the Weekend ka Vaar episode. As per the latest sources, Rashami Desai’s alleged boyfriend Arhaan Khan will be eliminated leaving housemates shocked. During his stay, Arhaan has got into fights with Siddharth Shukla and Asim Riaz. Not only this, he also called Asim, Siddharth’s shadow.
In the last few episodes, Rashami was also caught on camera confessing that she likes him but Arhaan kept his distance and maintained to be a good friend.However, there was news that Arhaan and Reshami might get married inside the house but they rubbished the news.
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on Entertainment Latest News on India.com.
Story Timeline
- 17 Nov 2019 Bigg Boss 13: Former Bigg Boss Contestant Kamya Punjabi Gets Trolled For Supporting Siddharth Shukla
- 17 Nov 2019 Bigg Boss 13: Rashami Desai's Rumoured Boyfriend Arhaan Khan to Get Eliminated Tonight?
- 17 Nov 2019 Bigg Boss 13: Krushna Abhishek Praises Arti Singh as She Stops Giving 'Bhav' to Siddharth Shukla
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