UAE, Bahrain Discuss Regional Developments
Prospects for further strengthening the long-standing relations between the UAE and the Kingdom of Bahrain were reviewed on Saturday by Sheikh Mohammed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces, and Bahraini Foreign Minister Sheikh Khalid bin Ahmed bin Mohamed Al Khalifa.
Sheikh Mohammed received the Bahraini top diplomat in Abu Dhabi, with the two sides echoing their two countries' determination to grow bilateral relations for the common good of their peoples.
During the meeting their reviewed regional developments and exchanged viewpoints on a number of issues of mutual concern.


Tourism in Kenya has finally picked up when the country reopened it's borders to international travellers on the green list


If you haven’t already done so, find and install a text editor. Here are two we recommend:
Go ahead and create a folder to hold everything in this workshop. Call it something like HTMLWorkshop
. Navigate into that directory and create a file called index.html
using your new code editor.
Now go ahead and write this in your index.html
Hello World!
Then navigate to the file in your browser to view it. On Macs, you can also drag the file from Finder into Chrome to open it. You should see something like this:
Now go ahead and paste this code into your index.html:
<!DOCTYPE html>

<title>Welcome to HTML and CSS!</title>

My first HTML page

This is my first HTML page

This is a link

Now refresh the browser to view this new content.
The elements inside the angled brackets are called tags
. Most of these tags require a corresponding closing tag, ie

and

. Here’s what each of these do:
<!DOCTYPE html>
Tells the browser this is an HTML5 file.
Root element of HTML page
Contains undisplayed information about this HTML document<title>
Denotes title (displayed on tab or window bar) of page
Contains all content of an HTML document

Denotes a division (like a section) of an HTML document

Largest header tag - used for key headers

A paragraph tag - used for most text content
An anchor tag (used to link; the href
specifies where the content links to)There are many more types of tags you’ll encounter as you continue learning frontend, but these should be enough for now.
There are two ways HTML can be displayed:
block
: these elements take up the full width available
div
, p
, h1
inline
: these elements take as much width as they need
span
, a
To see this, try placing two
tags next to each other and two p
tags next to each other and see what happens:

Paragraph 1

Paragraph 2

Anchor 1
Anchor 2
Note: you can also change the display
type of an element with CSS, which we’ll get into now.
Given your newfound knowledge of basic HTML, it’s time to dive into basic CSS.
First let’s try something. Change your

tag in your HTML file to

. Refresh your browser and see what happens.
Imagine if you had to add inline styles (like we just did) to style every element on the page. There must be some more reusable and less cluttery way to denote styling.
This is where classes and ids come in. CSS can specify style properties that define the behavior of certain classes, ids, and HTML tags. This gives more power and separated, reusable code. (Note that ids are unique so you can only have one of a certain id on a page).
However, we’ll first need to link our stylesheet. Create a file called stylesheet.css
and add this line to in your HTML head
.
<link rel="stylesheet" href="stylesheet.css">
link
: tags define a link to an external resource (usually stylesheets)rel
: specifies external resource’s relationship to this document (in this case, it’s a stylesheet)href
: specifies location of documentNow, you can add classes and ids like so:

Content here

Let’s try to add a class and add properties with our stylesheet. Add a class to your

and add this in your CSS, replacing the class name.

My first HTML page

/* This is a css comment */
.your-class-name {
color: blue;
}
Now refresh your page and see what happens. Cool, right? Note that the period before the class name in the css is necessary (but in the HTML don’t include the period). Similar for hashtags, which are used for ids. Tags (like

or

) require no preceding characters. Here’s the syntax:
#your-id-name {
/* Your properties */
}
div {
/* Your properties */
}
An important note about css: Generally (but not always), later properties will override new ones. Try adding color: blue
and color: red
to the same class and see what happens.
Now that you’ve got a hang of basic HTML and CSS, we can move on to more advanced content. Copy paste this HTML into a new file and create an empty file called stylesheet.css
in the same directory. Then, open the HTML file in your browser.
<!DOCTYPE html>

<title>The Gnat and the Bull</title>
<link rel="stylesheet" href="stylesheet.css" />

The Gnat and the Bull

A Gnat alighted on one of the horns of a Bull, and remained sitting there for a considerable time. When it had rested sufficiently and was about to fly away, it said to the Bull, "Do you mind if I go now?" The Bull merely raised his eyes and remarked, without interest, "It’s all one to me; I didn’t notice when you came, and I shan’t know when you go away."

We may often be of more consequence in our own eyes than in the eyes of our neighbours.

Read more Aesop's Fables here

There’s definitely more we can do though. How about using a nicer font? Let’s start with a web safe font. We’ll pick a nice, popular sans-serif, Comic Sans Helvetica.
/* See how we can deal with multiple tags at once
* Also note that we're using 'sans-serif' as a fallback
* font for Helvetica.
*/
body {
font-family: "Helvetica", sans-serif;
}
Side note: try playing around with other web-safe fonts.
Now let’s add a hero for the title. Add a

to wrap your title

tag. Make sure to include the closing

.
.hero {
padding-top: 20vh;
padding-bottom: 20vh;
text-align: center;
background-color: gray; /* Or your choice color */
}
The white border on the edge of the page can be gotten rid of with:
html, body {
padding: 0;
margin: 0;
}
What if we added an image to be the background of our page? You can look in Unsplash for good stock photos or use the example provided
.hero {
/* background-image: url("path/to/your/image"); */
background-image: url("http://designatucsd.org/examples/htmlcss1/bull_background.jpg");
background-size: cover;
}
.hero h1 {
color: white; /* Or your choice color */
}
.hero h1
: This property applies to all h1 descendants of an element with class ‘hero’background-image
: self-explanatorybackground-size: cover
: Tells the browser to stretch the image to fit windowRight now, our text appears to span the entire width of the page, making it difficult to read, so let’s update that. Add a class for your

tag with a relevant name (like ‘container’) and add some css.
.your-div-class-name {
margin: auto;
max-width: 700px;
padding: 10px;
color: #555;
}
margin
: denotes space around (outside) elements; auto denotes to base it off width of element and width of containerpadding
: denotes space around (on the inside) elements. 10px
means there will be a space between content and the edge of the container 10px wide (on all edges).max-width
: denotes the largest width of an elementpadding-left
and margin-left
and for each other direction.Now viewing this in desktop, we can see that the content centers nicely on the page. Let’s try a custom font to add more personality. Here at DesignatUCSD, we’re big fans of Lato. You can always head to fonts.google.com to pick a different font to use. Just make sure to also use font-family
in the tags/classes/ids where you’d like the font to be used.
@import url('https://fonts.googleapis.com/css?family=Lato');
/* You can edit your existing body tag */
body {
font-family: "Lato", "Helvetica", sans-serif;
line-height: 1.4;
}
line-height
: denotes the height of the line relative to the size of text.We can also update font sizes to improve readability. Try changing the font-size
property for your .hero h1
to 48px and p
elements to 20px using CSS.
.hero h1 {
font-size: 48px;
}
p {
/* Try figuring this out */
}
Cards are a major feature that are often show up on many sites and help separate different sections of content. In this case, let’s try separating the majority of our content, Let’s try making our different paragraphs basic cards. Try to add these properties into your existing p
selector in CSS.
p {
/* Previous styles */
padding: 10px;
background-color: #f1f1f1; /* Or your choice color */
box-shadow: 0 4px 8px 0 #ddd; /* This adds some depth */
margin-top: 10px;
}
Try refreshing and observe how the cards help give the page some organization.
Can you think of more touches you’d like to add to your site? Here’s some things you can play around with:
How about adding a class and making Aesop’s punchline “We may often be…” more unique? Here’s an example of what your site might look like at the end of this tutorial. You can view its code here.
Here at Design at UCSD, we’re passionate about putting forward educational, hands-on and fun workshops, and are always looking to improve and iterate, so if you’ve completed this tutorial (remotely or on-site), please fill out this form to let us know how we did.
Congratulations! If you’ve done everything up to this point this means you’ve built a basic HTML and CSS site. If you’re wondering where to go from here, there are many, many available resources and clubs at UCSD; here’s some you should check out:
Our advice? Either get involved with a project or start building your own website. There are many projects occurring on campus that are in need of passionate designers and developers.
If you’re ever interested in getting more involved with DesignatUCSD, have general feedback, or just want to love us, email us at<EMAIL_ADDRESS>Now that we’ve covered the basics of HTML and CSS, what’s next? To become a frontend developer, you’d need to learn and do a couple more things:
Here are some resources for you to continue learning frontend:


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The aim of this is to skimly assess the reduction of risk with investing in these indices.
Investing into companies can be very daunting. Yet, even if we are quite knowledgable about a certain company, they are still affected by their local macro-environment.The ideal scenario is to invest in companies with close to 0 correlation comparatively than closer to the -1 > x < 1 ends of the spectrum. Unforunately, very rarely do we find companies in the same locality to have a value close to zero, though they do exist. So rather than, search within the same fish bowl, let us look across the Pacific Ocean with the two markets from the US and Hong Kong. We will also look at the metal stock to see can that help us allivate risk.
import pickle
import pandas as pd
%matplotlib inline
import random
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import datetime as dt
import pandas_datareader.data as web
import requests
import os
import bs4 as bs
Pull data from Yahoo Finance through the 'pandas_datareader' module
start = dt.datetime(1970, 1, 1)
end = dt.datetime.now()
SP_500_df = web.DataReader('^GSPC', 'yahoo', start, end)
Hang_seng_df = web.DataReader('^HSI', 'yahoo', start, end)
Using beautiful soup to read and copy ticker names from a certain column of a table.
resp = requests.get('https://topforeignstocks.com/stock-lists/the-complete-list-of-precious-metals-stocks-trading-on-the-nyse/')
soup = bs.BeautifulSoup(resp.text, 'lxml')
table = soup.find('table', {'class': 'tablepress'})
tickers_metal = []
for row in table.findAll('tr')[1:]:
ticker = row.findAll('td')[2].text
totickers_metal.append(ticker)
#Clean the tickers
tickers_metal = [s.replace ('\n', '') for s in tickers_metal]
# If one wishes to have the names of the companies
metal_companies = []
for row in table.findAll('tr')[1:]:
ticker = row.findAll('td')[1].text
metal_companies.append(ticker)
metal_companies =[s.replace ('\n', '') for s in metal_companies]
Use the tickers names from a list to pull data into a DataFrame.
# metal companies' ticker
for ticker in tickers_metal:
if not os.path.exists('metal_ticks/{}.pkl'.format(ticker)):
try:
df = web.DataReader(ticker, 'yahoo', start, end)
except:
pass
df.reset_index(inplace=True)
df.set_index("Date", inplace=True)
#df = df.drop("Symbol", axis=1)
df.to_pickle('metal_ticks/{}.pkl'.format(ticker))
else:
print('Already have {}'.format(ticker))
Append all the metal companies to a single dataframe
metal_df = pd.DataFrame()
for count, ticker in enumerate(tickers_metal):
df = pd.read_pickle('metal_ticks/{}.pkl'.format(ticker))
df.rename(columns={'Adj Close': ticker}, inplace=True)
df.drop(['Open', 'High', 'Low', 'Close', 'Volume'], 1, inplace=True)
if metal_df.empty:
metal_df = df
else:
metal_df = metal_df.join(df, how='outer')
metal_df.head()
Save the information into a pickle file to load later
with open("metal_df.pkl", "wb") as f:
pickle.dump(metal_df, f)
with open('metal_df.pkl','rb') as f:
metal_df = pickle.load(f)
Average the values into monthly values
metal_df.index = pd.to_datetime(metal_df.index)
metal_monthly = metal_df.resample("M").mean()
metal_monthly['mean'] = metal_monthly.mean(axis=1)
metal_mean = metal_monthly['mean']
SP_500_df = SP_500_df['Adj Close']
SP_500_df_monthly = SP_500_df.resample("M").mean()
Hang_seng_df = Hang_seng_df['Adj Close']
Hang_seng_monthly = Hang_seng_df.resample("M").mean()
SP_500_df_monthly.head()
Put the separate DataFrames objects into a single DataFrame
tri_df = pd.DataFrame()
tri_df['SP500'] = SP_500_df_monthly
tri_df['Hang Seng'] = Hang_seng_monthly
tri_df['metal'] = metal_mean
tri_df.plot()
# Set the starting date to 1987,as Hang Seng Index data set starts at this date.
tri_df = tri_df[(tri_df.index > '1987-01-31')]
tri_df.plot()
tri_df_corr = tri_df.corr()
tri_df_corr.plot()
multiple the columns of metal and Hang Seng by 10 to allow the viewing to be done on a single graph. This will not interfer with the percentage change, and therefore will not affect the correlation.
tri_df_adj = tri_df
tri_df_adj['Hang Seng'] = tri_df['Hang Seng'] / 10
tri_df_adj['metal'] = tri_df['metal'] * 10
tri_df_adj.plot()
View the percentage change of the graph
pct_change_hk = Hang_seng_monthly.pct_change(periods =1)
pct_change_sp = SP_500_df_monthly.pct_change(periods =1)
pct_change_metal = metal_mean.pct_change(periods =1)
tri_df_pct = pd.DataFrame()
tri_df_pct['HK'] = pct_change_hk
tri_df_pct['sp500'] = pct_change_sp
tri_df_pct['metal'] = pct_change_metal
tri_df_pct.plot()
The corr_graph function allows the correlations to be viewed in a coloured grid
def corr_graph(data):
data1 = data.values
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
heatmap1 = ax1.pcolor(data1, cmap=plt.cm.RdYlGn)
fig1.colorbar(heatmap1)
ax1.set_xticks(np.arange(data1.shape[1]) + 0.5, minor=False)
ax1.set_yticks(np.arange(data1.shape[0]) + 0.5, minor=False)
ax1.invert_yaxis()
ax1.xaxis.tick_top()
column_labels = data.columns
row_labels = data.index
ax1.set_xticklabels(column_labels)
ax1.set_yticklabels(row_labels)
plt.xticks(rotation=90)
heatmap1.set_clim(-1, 1)
plt.tight_layout()
plt.show()
tri_df_pct_corr
corr_graph(tri_df_pct_corr)
tri_df_adj_corr =tri_df_adj.corr()
(tri_df_adj_corr)
corr_graph(tri_df_adj_corr)
It appears that in the long run there is a negative correlation with the metal companies as decrease over time in value.
Yet, there is a positive correlation with the monthly percentage change.
SP_500_df.plot()
tri_df_crash = tri_df_adj[(tri_df_adj.index > '1997-1-1') & (tri_df_adj.index <= '1999-01-01')]
tri_df_2007_boom = tri_df_adj[(tri_df_adj.index > '2005-1-1') & (tri_df_adj.index <= '2008-01-01')]
tri_df_present_boom = tri_df_adj[(tri_df_adj.index > '2015-1-1') & (tri_df_adj.index <= '2019-03-31')]
tri_df_2008_crash = tri_df_adj[(tri_df_adj.index > '2008-1-1') & (tri_df_adj.index <= '2011-01-01')]
fig=plt.figure(figsize=(20, 12), dpi= 80, facecolor='w', edgecolor='k')
from pylab import *
from matplotlib.gridspec import GridSpec
gs=GridSpec(2,4)
subplot(gs[0,0])
title('Hong Kong Bust, 1996')
plot(tri_df_crash)
plt.tick_params(
axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
subplot(gs[0,1])
title('2005 - 2008 boom')
plot(tri_df_2007_boom)
plt.tick_params(
axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
subplot(gs[0,2])
title('international bust of 2008')
plot(tri_df_2008_crash)
plt.tick_params(
axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
subplot(gs[0,3])
title('Current rising tide from 2011 till present')
plot(tri_df_present_boom)
plt.tick_params(
axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
subplot(gs[1,:])
title('From 1987 to 2019')
plot(tri_df_adj)
plt.legend( # The line objects
labels=['SP500', 'Hang Seng', 'Metal'],
loc="upper left",
borderaxespad=0.2,
title="Legend",
prop={'size': 13})
show()
tri_df_crash_corr = tri_df_crash.corr()
tri_df_2007_boom_corr = tri_df_2007_boom.corr()
tri_df_present_boom_corr = tri_df_present_boom.corr()
tri_df_2008_crash_corr = tri_df_2008_crash.corr()
gs=GridSpec(1,5)
print('Correlation between 1987 to 2019')
print(tri_df_adj_corr)
corr_graph(tri_df_adj_corr)
print('Correlation between 1997 to 1999')
print(tri_df_crash_corr)
corr_graph(tri_df_crash_corr)
print('Correlation between 2005 to 2008')
print(tri_df_2007_boom_corr)
corr_graph(tri_df_2007_boom_corr)
print('Correlation between 2008 to 2011')
print(tri_df_2008_crash_corr)
corr_graph(tri_df_2008_crash_corr)
print('Correlation between 2015 to 2019')
print(tri_df_present_boom_corr)
corr_graph(tri_df_present_boom_corr)
It seems that the value of metal as an investment is a bad investment with this ~ 30 year span data.
As for the correlation of SP500 and the Hang Seng are both highly correlated bar the localised Hong Kong crash of 1997.
As we can see that for the most part the Hong Kong and the US stocks are correlated. This should come to no surprised as the Hong Kong dollar is based off the US dollar, so even if businesses are conducting work in varying fields, the finanical bread and butter is based from Uncle Sam's churning methods. Yet, with the opening of the Chinese mainland stock onto the Hong Kong stock exchange we can expect that there could be a divergence in the correlation. Though, as we have seen from the recent trade wars, the US economy and the Sino Economy are still heavily dependent on one another.


{# # Copyright 2015 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #} {% extends "template.html" %} {% block content %} {% for label, img in labels|dictsort %}
{% endfor %} {% if not labels %}


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Cluster Sparks¶
After the clustering was performed, all voltage breakdown points are still unclustered. We chose to assign all these points to the cluster that happened previously if they contained at least one spark. Suppose for example that from minute 10-20 (of an arbitrary day/hour) the source was running stable in cluster 1, from minute 20-30 unstable in cluster 3 and minute 30-31 was marked as a voltage breakdown with two sparks happening. Then we would assign this minute to cluster 3 with an unstable source. If during minutes 20-30 the source would have been stable, we would assign minute 30-31 to cluster 3 of a stable source.
This is purely a matter of definition which we chose because we wanted to see how various settings of the source lead to a different number of breakdowns.
How to use it¶
The program needs two command line arguments: The input file and the output file.


--- layout: default name: Groep 33, DAV theme: jekyll-theme-minimal ---
There are numerous factors that influence global food prices. To prevent food crises it is important to analyze the food prices and research how they influence each other, so that preventive measures can be taken in advance.
Our goals for this project were to answer the following questions:
Are there any food prices that are show negative/positive correlation, and is this correlation present throughout the years, or perhaps only in certain period? Can you perhaps detect possible ingredients of a certain other food product?
Do countries in similar regions, also show similar price differences? And if differences occur, can you find a potential explanation?
Can we see any correlations between other types of data, like: currency fluctuations, weather patterns, and/or refugee movements. Can we perhaps visualize refugee movements from the food price data? Do certain weather conditions influence market prices?


G. Pape
runit
svlogd - runit’s service logging daemon
svlogd [-tttv] [-r c] [-R
xyz] [-l len] [-b buflen] logs
logs consists of one or more arguments,
each specifying a directory.
svlogd continuously reads log data from its
standard input, optionally filters log messages, and writes the data to
one or more automatically rotated logs.
Recent log files can automatically
be processed by an arbitrary processor program when they are rotated, and
svlogd can be told to alert selected log messages to standard error, and
through udp.
svlogd runs until it sees end-of-file on standard input or is
sent a TERM signal, see below.
A log directory log contains
some number of old log files, and the current log file current. Old log
files have a file name starting with @ followed by a precise timestamp
(see the daemontools’ tai64n program), indicating when current was rotated
and renamed to this file.
A log directory additionally contains the lock
file lock, maybe state and newstate, and optionally the file config. svlogd
creates necessary files if they don’t exist.
If svlogd has trouble opening
a log directory, it prints a warning, and ignores this log directory. If
svlogd is unable to open all log directories given at the command line,
it exits with an error. This can happen on start-up or after receiving a
HUP signal.
svlogd appends selected log messages to the
current log file. If current has size bytes or more (or there is a new-line
within the last len of size bytes), or is older than a specified amount
of time, current is rotated:
svlogd closes current, changes permission
of current to 0755, renames current to @timestamp.s, and starts with a new
empty current. If svlogd sees num or more old log files in the log directory,
it removes the oldest one. Note that this doesn’t decrease the number of
log files if there are already more than num log files, this must be done
manually, e.g. for keeping 10 log files:
ls -1 \@* |sort |sed -ne ’10,$p’ |xargs
rm
If svlogd is told to process recent log files, it saves current
to @timestamp.u, feeds @timestamp.u through ‘‘sh -c "processor"’’ and writes the
output to @timestamp.t. If the processor finishes successfully, @timestamp.t
is renamed to @timestamp.s, and @timestamp.u is deleted; otherwise @timestamp.t
is deleted and the processor is started again. svlogd also saves any output
that the processor writes to file descriptor 5, and makes that output available
on file descriptor 4 when running processor on the next log file rotation.
A processor is run in the background. If svlogd sees a previously started
processor still running when trying to start a new one for the same log,
it blocks until the currently running processor has finished successfully.
Only the HUP signal works in that situation. Note that this may block any
program feeding its log data to svlogd.
On startup, and after receiving
a HUP signal, svlogd checks for each log directory logdir if the configuration
file logdir/config exists, and if so, reads the file line by line and adjusts
configuration for log as follows:
If the line is empty, or starts with
a ‘‘#’’, it is ignored. A line of the form
- ssize
- sets the maximum file size
of current when svlogd should rotate the current log file to size bytes.
Default is 1000000. If size is zero, svlogd doesn’t rotate log files. You
should set size to at least (2 * len).
- nnum
- sets the number of old log files
svlogd should maintain to num. If svlogd sees more that num old log files
in log after log file rotation, it deletes the oldest one. Default is 10.
If num is zero, svlogd doesn’t remove old log files.
- Nmin
- sets the minimum
number of old log files svlogd should maintain to min. min must be less
than num. If min is set, and svlogd cannot write to current because the
filesystem is full, and it sees more than min old log files, it deletes
the oldest one.
- ttimeout
- sets the maximum age of the current log file when
svlogd should rotate the current log file to timeout seconds. If current
is timeout seconds old, and is not empty, svlogd forces log file rotation.
- !processor
- tells svlogd to feed each recent log file through processor
(see above) on log file rotation. By default log files are not processed.
- ua.b.c.d[:port]
- tells svlogd to transmit the first len characters of selected
log messages to the IP address a.b.c.d, port number port. If port isn’t set,
the default port for syslog is used (514). len can be set through the -l
option, see below. If svlogd has trouble sending udp packets, it writes
error messages to the log directory. Attention: logging through udp is unreliable,
and should be used in private networks only.
- Ua.b.c.d[:port]
- is the same as
the u line above, but the log messages are no longer written to the log
directory, but transmitted through udp only. Error messages from svlogd
concerning sending udp packages still go to the log directory.
- pprefix
- tells
svlogd to prefix each line to be written to the log directory, to standard
error, or through UDP, with prefix.
If a line starts with a -, +, e, or E,
svlogd matches the first len characters of each log message against pattern
and acts accordingly:
- -pattern
- the log message is deselected.
- +pattern
- the
log message is selected.
- epattern
- the log message is selected to be printed
to standard error.
- Epattern
- the log message is deselected to be printed
to standard error.
Initially each line is selected to be written to log/current.
Deselected log messages are discarded from log. Initially each line is deselected
to be written to standard err. Log messages selected for standard error
are written to standard error.
svlogd matches a log message
against the string pattern as follows:
pattern is applied to the log message
one character by one, starting with the first. A character not a star (‘‘*’’)
and not a plus (‘‘+’’) matches itself. A plus matches the next character in
pattern in the log message one or more times. A star before the end of pattern
matches any string in the log message that does not include the next character
in pattern. A star at the end of pattern matches any string.
Timestamps optionally
added by svlogd are not considered part of the log message.
An svlogd pattern
is not a regular expression. For example consider a log message like this
2005-12-18_09:13:50.97618 tcpsvd: info: pid 1977 from <IP_ADDRESS>
The following pattern doesn’t match
-*pid*
because the first star matches up to the first p in tcpsvd, and then the
match fails because i is not s. To match this log message, you can use a
pattern like this instead
-*: *: pid *
- -t
- timestamp. Prefix each selected line with a precise timestamp
(see the daemontools’ tai64n program) when writing to log or to standard
error.
- -tt
- timestamp. Prefix each selected line with a human readable, sortable
UTC timestamp of the form YYYY-MM-DD_HH:MM:SS.xxxxx when writing to log or
to standard error.
- -ttt
- timestamp. Prefix each selected line with a human
readable, sortable UTC timestamp of the form YYYY-MM-DDTHH:MM:SS.xxxxx when
writing to log or to standard error.
- -r c
- replace. c must be a single character.
Replace non-printable characters in log messages with c. Characters are replaced
before pattern matching is applied.
- -R xyz
- replace charset. Additionally to
non-printable characters, replace all characters found in xyz with c (default
‘‘_’’).
- -l len
- line length. Pattern matching applies to the first len characters
of a log message only. Default is 1000.
- -b buflen
- buffer size. Set the size
of the buffer svlogd uses when reading from standard input and writing
to logs to buflen. Default is 1024. buflen must be greater than len. For svlogd
instances that process a lot of data in short time, the buffer size should
be increased to improve performance.
- -v
- verbose. Print verbose messages to
standard error.
If svlogd is sent a HUP signal, it closes and reopens
all logs, and updates their configuration according to logdir/config. If svlogd
has trouble opening a log directory, it prints a warning, and discards
this log directory. If svlogd is unable to open all log directories given
at the command line, it exits with an error.
If svlogd is sent a TERM signal,
or if it sees end-of-file on standard input, it stops reading standard input,
processes the data in the buffer, waits for all processor subprocesses
to finish if any, and exits 0 as soon as possible.
If svlogd is sent an
ALRM signal, it forces log file rotation for all logs with a non empty
current log file.
sv(8), runsv(8), chpst(8), runit(8), runit-init(8),
runsvdir(8), runsvchdir(8)
http://smarden.org/runit/
Gerrit Pape<EMAIL_ADDRESS>Table of Contents


Drive Value Through Engagement
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From creative posts and crisp videos to engaging polls and interactive digital experiences, we don't just help you connect with your patients - we help you build a lasting community.
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public class NodeJoinController extends AbstractComponent
ZenDiscovery
). Incoming nodes
are directly added to the cluster state or are accumulated during master election.| Modifier and Type | Class and Description |
|---|---|
static interface |
NodeJoinController.ElectionCallback |
| Modifier and Type | Field and Description |
|---|---|
protected java.util.Map<DiscoveryNode,java.util.List<MembershipAction.JoinCallback>> |
pendingJoinRequests |
deprecationLogger, logger, settings
| Constructor and Description |
|---|
NodeJoinController(ClusterService clusterService,
RoutingService routingService,
DiscoverySettings discoverySettings,
Settings settings) |
| Modifier and Type | Method and Description |
|---|---|
void |
handleJoinRequest(DiscoveryNode node,
MembershipAction.JoinCallback callback)
processes or queues an incoming join request.
|
void |
startAccumulatingJoins()
Accumulates any future incoming join request.
|
void |
stopAccumulatingJoins(java.lang.String reason)
Stopped accumulating joins.
|
void |
waitToBeElectedAsMaster(int requiredMasterJoins,
TimeValue timeValue,
NodeJoinController.ElectionCallback callback)
waits for enough incoming joins from master eligible nodes to complete the master election
|
logDeprecatedSetting, logRemovedSetting, nodeName
protected final java.util.Map<DiscoveryNode,java.util.List<MembershipAction.JoinCallback>> pendingJoinRequests
public NodeJoinController(ClusterService clusterService, RoutingService routingService, DiscoverySettings discoverySettings, Settings settings)
public void waitToBeElectedAsMaster(int requiredMasterJoins, TimeValue timeValue, NodeJoinController.ElectionCallback callback)
You must start accumulating joins before calling this method. See startAccumulatingJoins()
The method will return once the local node has been elected as master or some failure/timeout has happened. The exact outcome is communicated via the callback parameter, which is guaranteed to be called.
requiredMasterJoins
- the number of joins from master eligible needed to complete the electiontimeValue
- how long to wait before failing. a timeout is communicated via the callback's onFailure method.callback
- the result of the election (success or failure) will be communicated by calling methods on this
objectpublic void startAccumulatingJoins()
stopAccumulatingJoins(String)
is called.public void stopAccumulatingJoins(java.lang.String reason)
public void handleJoinRequest(DiscoveryNode node, MembershipAction.JoinCallback callback)
Note: doesn't do any validation. This should have been done before.


Presented by Mike Smith
RA at the University of Sheffield
<EMAIL_ADDRESS>You have some training data. Each item consists of some values, and is of a known class (i.e. is labelled).
You want to classify new data that you don't know the class (label) of.
Because the data is labelled, this is an example of supervised learning.
Need to assess who needs treatment.
In-patient care provided to those with Severe Acute Malnutrition (SAM).
How to decide who has SAM?
Most common methods:
Which is best?
Researchers looked at outcomes of children presenting with different MUACs and z-scores.
Aim: Classify, based on MUAC, whether a child will recover without intervention.
e.g. Laillou, Arnaud, et al. "Optimal screening of children with acute malnutrition requires a change in current WHO guidelines as MUAC and WHZ identify different patient groups." PloS one 9.7 (2014): e101159.
SIMULATED data from 29 children who recovered without treatment and 19 who didn't
Where should we put the threshold to decide who to treat?
Is this a better threshold?
What about in the middle?
We can keep plotting points on this graph, to generate a curve. This is called the "Receiver operating characteristic" (ROC) curve.
We sometimes use this to compare classifiers - area under the curve.
Cost Matrix: What is the cost of a False Positive compared to the cost of a False Negative?
We might be able to do better with our screening if we use the z-score data?
We might be able to do better with our screening if we combine the MUAC data with the z-score data
Which class do people think the ? child belongs in?
We could assign the class of the child's "nearest neighbour".
We could look at the class of the three nearest neighbours and pick the most common class.
We could draw a straight line to try to classify the children?
Many other types of classifier... (some are good for when there's more dimensions, or when inputs are correlated, or if there's more or less noise...)
If we imagine our data is more random (like this). Could we still classify the children?
We could draw a complicated decision boundary (like this).
All training points are classified correctly, but...
...what about a new child?
To test our classifier we need to divide the data up into TRAINING data and TEST data.
We normally do this by leaving out one item at a time.
Here we've left out one of the children from the training data. We can now TEST to see if the classifier got it correct. In this case it didn't.
Leave-one-out cross-validation allows us to say how well our classifier will generalise to unknown data.
Warning note: When leaving 'one' out, you need to be careful to think about other correlated data. For example if each child had a twin, you might want to leave both twins out for the cross-validation.
This decision boundary is an example of over-fitting.
Our 'model' is overly complicated and describes the noise, rather than the underlying structure.
There might be many parameters we can adjust in our model (something you'll see shortly). For example the number of neighbours, or the curviness of our decision boundary.
We could keep fiddling with these parameters (this could even be done automatically) until we get a good result on our training set...
...does that seem 'honest'?
Once you've finished adjusting the model (using the training and test sets), use a validation dataset, that you'd not used yet for anything else.
Typically, as a ML-expert, you'll be given a set of data and will need to decide which parts of the data will be useful to answer a problem...
Usually requires, looking at the data and discussion with the domain-experts.
Often depends on the quality and biases in the collected data
Example problem: Count the number of sickle cells
What features of the cells would be useful?
i.e. which features are "invariant".
| Circumference (pixels) | Average pixel value | Area (pixels${}^2$) | Sickle? (manually labelled) | |
|---|---|---|---|---|
| 45 | 72 | 201 | Yes | |
| 62 | 51 | 340 | No | |
| 30 | 36 | 85 | Yes | |
| 55 | 65 | 126 | Yes | |
| 50 | 62 | 125 | No |
These could be fed into one of the classifiers discussed. Also see Scale Invariant Feature Transforms (SIFTs).
The example was of classifying images, something the CNN community have worked on considerably. Does the CNN learn the features itself? [is this data efficient?]
Many (most?) problems benefit still from feature engineering. Maybe one could localise the cells first and then run the DNN on each one, rather than the whole slide?
Can include expert knowledge (e.g. we might want to hide features that might differ in future tests - data shift)
"I have a binary classification problem and one class is present with 60:1 ratio in my training set. I used the logistic regression and the result seems to just ignores one class."
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
Don't just report 'accuracy'! Also Area under the curve ROC isn't ideal if imbalanced. Use F-score? Or even better a confusion matrix.
Some classifiers need balanced training data. Could resample to even up class sizes (or use additional synthetic data). Some classifiers let you penalise one type of mistake more.
The F-score gives a description of a classifier's accuracy combining both the precision and recall.
Example: 100 cells were tested.
25 were really sickle. The classifier found 20 of these.
It also identified 30 of the non-sickle as sickle.
What is the Precision (detected true positives/all detected positives)?
What is the Recall (detected true positives/all real positives)?
Image from wikipedia
What is the Precision (detected true positives/all detected positives)?
20/50 = 0.4
What is the Recall (detected true positives/all real positives)?
20/25 = 0.8
$\text{F-score} = (\frac{\text{recall}^{-1} + \text{precision}^{-1}}{2})^{-1}$
$[(2.5 + 1.25)/2]^{-1} = 0.53$
Issue: weights precision and recall equally.
We take an image and add some carefully crafted noise.
Dog becomes ostrich
Note: Noise image scaled by 10x
Szegedy, et al. 2014 (original paper describing AEs)
Note: These are examples of 'test-time' attacks (we're altering the test image not the training data)
Fischer, 2017
based on http://karpathy.github.io/2015/03/30/breaking-convnets/
based on http://karpathy.github.io/2015/03/30/breaking-convnets/
The noise must be crafted, e.g. Fawzi, 2015 show the difference between random noise and an adversarial example.
Maximally perturb the feature (pixel) with greatest gradient (wrt class) [Papernot, 2016].
They find they modify about 4% of pixels in MNIST to produce AEs.
Attack specific L-Norm. Carlini, 2017: Good overview - basically the last two are just different norms.
Fast Gradient Sign Method. Changes all pixels simultaneously, but by either $\pm\epsilon$.
Black Box. Papernot 2017, train a DNN on the classifications of a black-box classifier. AEs produced on their new emulation worked against the target (e.g. Amazon and Google's image recognition systems).
Blackbox+Genetic Algorithm. Su et al., 2017: single pixels. I'm not clear why we can't just check each pixel?
Adversarial Sample created on row ML-technique and then they were tested on the column technique.
Papernot 2016
Moosavi-Dezfooli, 2016
Alexey, 2016
Evtimov, 2017 (published?)
Originally people suggested 'overfitting', but it may be more to do with the linear nature of the classifier.
Goodfellow, 2014
Various ideas:


This week was all about picking up Javascript. I don't think I would be doing anyone a favor if I failed to mention how limiting and redundant Javascript felt compared to Ruby. But there was one area in particular, while learning the fundamentals of Javascript, that sort of surprised me in how wonderful it was to use; that would be Javascript Objects versus Ruby Hashes.
These both look syntactically similar. Hashes and Objects use the colon in literal definitions and calls (see below) and they function pretty much the same in terms of being structured as key and value pairs. They can also both hold arrays, integers, strings, other objects, or a combination of any or all of these.
How do they differ? A JavaScript Object is a collection of properties that references other objects, whereas a Ruby Hash contains instance variables which refer to other objects. In other words, Ruby Hashes hold variables which point to other objects elsewhere in memory, while JavaScript Objects hold properties which are tied specifically to that object.
While I’m not yet familiar with the ins and outs of that, I will say that it seems much more easy to read and write to JavaScript Objects than Ruby Hashes. For example, accessing values inside an object is as simple as:
In addition to the way that you might call an object or hash by passing into item[] either the key value or an expression that evaluates to the key value, Javascript gives you a special way to call upon the values of properties, which is:
To add a property to the cat object that does not yet exsist we can do the following:
Don’t like a property and want to get rid of it?
It’s that simple. Note: In JavaScript, all attributes can be read, but only the value attribute can be changed (and only if the property is writable).
I hope you enjoyed this brief comparison of Ruby hashes and JavaScript Objects, please feel free to contact me with any questions about this post.
bendiciónes! mmc


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|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object java.text.AttributedString
public class AttributedString
An AttributedString holds text and related attribute information. It may be used as the actual data storage in some cases where a text reader wants to access attributed text through the AttributedCharacterIterator interface.
AttributedCharacterIterator
,
Annotation
| Constructor Summary | |
|---|---|
AttributedString(AttributedCharacterIterator text)
Constructs an AttributedString instance with the given attributed text represented by AttributedCharacterIterator. |
|
AttributedString(AttributedCharacterIterator text,
int beginIndex,
int endIndex)
Constructs an AttributedString instance with the subrange of the given attributed text represented by AttributedCharacterIterator. |
|
AttributedString(AttributedCharacterIterator text,
int beginIndex,
int endIndex,
AttributedCharacterIterator.Attribute[] attributes)
Constructs an AttributedString instance with the subrange of the given attributed text represented by AttributedCharacterIterator. |
|
AttributedString(String text)
Constructs an AttributedString instance with the given text. |
|
AttributedString(String text,
Map<? extends AttributedCharacterIterator.Attribute,?> attributes)
Constructs an AttributedString instance with the given text and attributes. |
| Method Summary | |
|---|---|
void |
addAttribute(AttributedCharacterIterator.Attribute attribute,
Object value)
Adds an attribute to the entire string. |
void |
addAttribute(AttributedCharacterIterator.Attribute attribute,
Object value,
int beginIndex,
int endIndex)
Adds an attribute to a subrange of the string. |
void |
addAttributes(Map<? extends AttributedCharacterIterator.Attribute,?> attributes,
int beginIndex,
int endIndex)
Adds a set of attributes to a subrange of the string. |
AttributedCharacterIterator |
getIterator()
Creates an AttributedCharacterIterator instance that provides access to the entire contents of this string. |
AttributedCharacterIterator |
getIterator(AttributedCharacterIterator.Attribute[] attributes)
Creates an AttributedCharacterIterator instance that provides access to selected contents of this string. |
AttributedCharacterIterator |
getIterator(AttributedCharacterIterator.Attribute[] attributes,
int beginIndex,
int endIndex)
Creates an AttributedCharacterIterator instance that provides access to selected contents of this string. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public AttributedString(String text)
text
- The text for this attributed string.public AttributedString(String text, Map<? extends AttributedCharacterIterator.Attribute,?> attributes)
text
- The text for this attributed string.attributes
- The attributes that apply to the entire string.
IllegalArgumentException
- if the text has length 0
and the attributes parameter is not an empty Map (attributes
cannot be applied to a 0-length range).public AttributedString(AttributedCharacterIterator text)
text
- The text for this attributed string.public AttributedString(AttributedCharacterIterator text, int beginIndex, int endIndex)
text
- The text for this attributed string.beginIndex
- Index of the first character of the range.endIndex
- Index of the character following the last character
of the range.
IllegalArgumentException
- if the subrange given by
beginIndex and endIndex is out of the text range.Annotation
public AttributedString(AttributedCharacterIterator text, int beginIndex, int endIndex, AttributedCharacterIterator.Attribute[] attributes)
text
- The text for this attributed string.beginIndex
- Index of the first character of the range.endIndex
- Index of the character following the last character
of the range.attributes
- Specifies attributes to be extracted
from the text. If null is specified, all available attributes will
be used.
IllegalArgumentException
- if the subrange given by
beginIndex and endIndex is out of the text range.Annotation
| Method Detail |
|---|
public void addAttribute(AttributedCharacterIterator.Attribute attribute, Object value)
attribute
- the attribute keyvalue
- the value of the attribute; may be null
IllegalArgumentException
- if the AttributedString has length 0
(attributes cannot be applied to a 0-length range).public void addAttribute(AttributedCharacterIterator.Attribute attribute, Object value, int beginIndex, int endIndex)
attribute
- the attribute keyvalue
- The value of the attribute. May be null.beginIndex
- Index of the first character of the range.endIndex
- Index of the character following the last character of the range.
IllegalArgumentException
- if beginIndex is less then 0, endIndex is
greater than the length of the string, or beginIndex and endIndex together don't
define a non-empty subrange of the string.public void addAttributes(Map<? extends AttributedCharacterIterator.Attribute,?> attributes, int beginIndex, int endIndex)
attributes
- The attributes to be added to the string.beginIndex
- Index of the first character of the range.endIndex
- Index of the character following the last
character of the range.
IllegalArgumentException
- if beginIndex is less then
0, endIndex is greater than the length of the string, or
beginIndex and endIndex together don't define a non-empty
subrange of the string and the attributes parameter is not an
empty Map.public AttributedCharacterIterator getIterator()
public AttributedCharacterIterator getIterator(AttributedCharacterIterator.Attribute[] attributes)
attributes
- a list of attributes that the client is interested in
public AttributedCharacterIterator getIterator(AttributedCharacterIterator.Attribute[] attributes, int beginIndex, int endIndex)
attributes
- a list of attributes that the client is interested inbeginIndex
- the index of the first characterendIndex
- the index of the character following the last character
IllegalArgumentException
- if beginIndex is less then 0,
endIndex is greater than the length of the string, or beginIndex is
greater than endIndex.
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


Burndown
What is it?
A burndown chart is a visualisation of work left to do within a specified time period. The remaining work (or backlog) is often on the vertical axis, with time along the horizontal.
Teams often use burndown charts to track progress within fixed time periods (for example, Sprint Burndown charts). They can also be used to visualise progress for longer time periods and milestones (for example, Release Burndown charts).
Why use a Burndown chart?
Burndown charts provide real data about a team’s velocity and provides a mechanism as to whether the team’s current velocity and trajectory will result in it meeting its committed goal(s).
Related Practices
Retrospectives provide excellent opportunities to review recent burndown charts and to facilitate conversations on what can be learned from the data presented in them.
Who do you need?
- A facilitator (such as a Scrum Master).
- The full team who are responsible for delivering the work visualised on the chart.
Suggested Time
- 5-10 minutes to produce the base chart and introduce the practice.
- Ad hoc updates performed on an ongoing basis (with facilitator driving the continuous adoption, use and value of the practice).
Difficulty
- Facilitator - Medium
- Participants - Easy
Facilitation Materials Needed
- A large piece of flip-chart paper - ideally with gridded lines.
- Flip chart pens
- Long ruler
Process Phase
- Draw a horizonatal and vertical lines on the flip-chart
- Mark the horizontal axis with the time measurement (for a Sprint Burndown, this is likely to be each day in the Sprint)
- Mark the vertical axis with the work being measured (this may be number of tasks, number of stories/features, number of story points or estimated time)
- Draw a diagonal line connecting the maximum work units to the last time measurement - this line represents the target/aspirational trend line
- As work is complete, draw a line (in a differnt color) representing the time point the remaining work has ‘burned down’ and how much work is remaining
- Re-visit and discuss what information the chart is radiating with the team and interested stakeholders.


Deputy Minister Eddin Syazlee collapses in Dewan Rakyat, believed from heart attack
by BY AZRIL ANNUARKUALA LUMPUR, Nov 11 — Communications and Multimedia Deputy Minister Eddin Syazlee Shith collapsed and required immediate medical attention in the Lower House this morning.
Deputy Speaker Datuk Rashid Hasnon called for a 10-minute recess in the Dewan Rakyat proceedings during Question Time when the incident occurred.
Kapar MP Datuk Abdullah Sani Abdul Hamid told the Malay Mail that he believed his PBBM colleague suffered from a heart attack.
On Oct 21, Nibong Tebal MP Datuk Mansor Othman also collapsed while he was delivering his Budget 2020 speech.
Mansor was halfway through his speech when he had started slurring his words. He then collapsed.
MORE TO COME


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Hello!
My name is Eunice Choi, and I am a San Francisco Bobolink at Dev Bootcamp. I accidentally fell in love with programming when I took an Intro Python course during my senior year at UCLA. Ever since, I have been working my way to become a full stack web developer.
My interests include painting, biology, and other life style shenanigans, such as traveling, cooking, and beauty/fashion. My favorite combination is coding while drinking hot coffee and listening to my electroacoustic playlist. I also love spending hours at any crafts store.
I look forward to improving and adding content to this site as I learn new languages and frameworks. Thanks for visiting, and feel free to look around!


The NuGet packages being referenced by your .csproj file are not supported by .NET Core. If your API is incompatible with .NET Core the next step is to go to NuGet.org and find compatible APIs that can replace the incompatible API.


|
Mercado Pago SDK .Net
1.7.0
Mercado Pago SDK .Net
|
Public Member Functions | |
| MPRESTClient () | |
| Class constructor. More... | |
| MPRESTClient (string proxyHostName, int proxyPort) | |
| Set class variables. More... | |
| JToken | ExecuteGenericRequest (HttpMethod httpMethod, string path, PayloadType payloadType, JObject payload) |
| MPAPIResponse | ExecuteRequest (HttpMethod httpMethod, string path, PayloadType payloadType, JObject payload, WebHeaderCollection colHeaders, int requestTimeout, int retries) |
| Execute a request to an endpoint. More... | |
| MPAPIResponse | ExecuteRequestCore (HttpMethod httpMethod, string path, PayloadType payloadType, JObject payload, WebHeaderCollection colHeaders, int connectionTimeout, int retries) |
| Core module implementation. Execute a request to an endpoint. This method is deprecated and will be removed in a future version, please use the ExecuteRequest(HttpMethod, string, PayloadType, JObject, WebHeaderCollection, int, int) method instead. More... | |
| MPAPIResponse | ExecuteRequest (HttpMethod httpMethod, string path, PayloadType payloadType, JObject payload) |
| Execute a request to an api endpoint. More... | |
| MPAPIResponse | ExecuteRequest (HttpMethod httpMethod, string path, PayloadType payloadType, JObject payload, MPRequestOptions requestOptions) |
| Execute a request to an api endpoint. More... | |
| MPRequest | CreateRequest (HttpMethod httpMethod, string path, PayloadType payloadType, JObject payload, WebHeaderCollection colHeaders, int connectionTimeout, int retries) |
| Create a request to use in the call to a certain endpoint. More... | |
| MPRequest | CreateRequest (HttpMethod httpMethod, string path, PayloadType payloadType, JObject payload, MPRequestOptions requestOptions) |
| Create a request to use in the call to a certain endpoint. More... | |
Properties | |
| string | ProxyHostName [get, set] |
| int | ProxyPort [get, set] |
| MercadoPago.MPRESTClient.MPRESTClient | ( | ) |
Class constructor.
| MercadoPago.MPRESTClient.MPRESTClient | ( | string | proxyHostName, |
| int | proxyPort | ||
| ) |
Set class variables.
| proxyHostName | Proxy host to use. |
| proxyPort | Proxy port to use in the proxy host. |
| MPRequest MercadoPago.MPRESTClient.CreateRequest | ( | HttpMethod | httpMethod, |
| string | path, | ||
| PayloadType | payloadType, | ||
| JObject | payload, | ||
| MPRequestOptions | requestOptions | ||
| ) |
Create a request to use in the call to a certain endpoint.
| MPRequest MercadoPago.MPRESTClient.CreateRequest | ( | HttpMethod | httpMethod, |
| string | path, | ||
| PayloadType | payloadType, | ||
| JObject | payload, | ||
| WebHeaderCollection | colHeaders, | ||
| int | connectionTimeout, | ||
| int | retries | ||
| ) |
Create a request to use in the call to a certain endpoint.
| JToken MercadoPago.MPRESTClient.ExecuteGenericRequest | ( | HttpMethod | httpMethod, |
| string | path, | ||
| PayloadType | payloadType, | ||
| JObject | payload | ||
| ) |
| MPAPIResponse MercadoPago.MPRESTClient.ExecuteRequest | ( | HttpMethod | httpMethod, |
| string | path, | ||
| PayloadType | payloadType, | ||
| JObject | payload | ||
| ) |
Execute a request to an api endpoint.
| MPAPIResponse MercadoPago.MPRESTClient.ExecuteRequest | ( | HttpMethod | httpMethod, |
| string | path, | ||
| PayloadType | payloadType, | ||
| JObject | payload, | ||
| MPRequestOptions | requestOptions | ||
| ) |
Execute a request to an api endpoint.
| MPAPIResponse MercadoPago.MPRESTClient.ExecuteRequest | ( | HttpMethod | httpMethod, |
| string | path, | ||
| PayloadType | payloadType, | ||
| JObject | payload, | ||
| WebHeaderCollection | colHeaders, | ||
| int | requestTimeout, | ||
| int | retries | ||
| ) |
Execute a request to an endpoint.
| httpMethod | Method to use in the request. |
| path | Endpoint we are pointing. |
| payloadType | Type of payload we are sending along with the request. |
| payload | The data we are sending. |
| colHeaders | Extra headers to send with the request. |
| MPAPIResponse MercadoPago.MPRESTClient.ExecuteRequestCore | ( | HttpMethod | httpMethod, |
| string | path, | ||
| PayloadType | payloadType, | ||
| JObject | payload, | ||
| WebHeaderCollection | colHeaders, | ||
| int | connectionTimeout, | ||
| int | retries | ||
| ) |
Core module implementation. Execute a request to an endpoint. This method is deprecated and will be removed in a future version, please use the ExecuteRequest(HttpMethod, string, PayloadType, JObject, WebHeaderCollection, int, int) method instead.
|
getset |
|
getset |


See: Description
| Class | Description |
|---|---|
| AgclProcessingSwitch<T> |
An AGCL-specific visitor (processing switch), very similar to
AadlProcessingSwitch but works on AGCL's
constructs. |
| CommonAGCLAnalysisSwitch |
Base class for AGCL Analysis visitors
|
| PSLASTPrinter |
This class is intended to return a serialized version of the AST of a PSL formula.
|
| PSLDeepCopyerExplicitSwitch |
A class to perform explicit copy of PSL terms, because there doesn't seem to be a simple way in Ecore.
|
| PSLHomomorphismExplicitSwitch |
This class implements the core of any homomorphic map over PSL expressions.
|
| PSLSerializerExplicit |
An explicit serializer for PSL formulas.
|
| ViewpointContext |
A ViewpointContext encapsulates the collection of viewpoints declared and the
(declared) viewpoints to verify in a given analysis run.
|
| ViewpointContextSwitch |
This class initializes the viewpoint context used by the different analysis switches
by collecting declared viewpoints and viewpoints to verify.
|
It provides the link between actions in the org.osate.xtext.aadl2.agcl.analysis.actions
package and
the corresponding algorithms in the org.osate.xtext.aadl2.agcl.analysis.actions
.
When an action is invoked, the visitors in this package, traverse the AADL model, visiting the relevant AGCL clauses from which the visitor extracts the relevant model and clause information, and invokes the corresponding analysis algorithm.
This core functionality is defined in a common base class CommonAGCLAnalysisSwitch
which finds general
AnnexLibrary
and AnnexSubclause
nodes and delegates processing to the concrete visitors to process
AGCLAnnexLibrary
and AGCLAnnexSubclause
nodes.
It also provides other useful visitors for processing AGCL clauses and PSL specifications.


@import uk.gov.hmrc.govukfrontend.views.html.components._ @this() @()
https://www.tax.service.gov.uk/digital-forms/form/claim-tax-back-when-you-have-stopped-working/draft/guide


Application Parallelism Enumerations.
| Module | Services | Data types | Constants |
|---|---|---|---|
| parallelism_model | ApplicationParallelismType |
Enumeration of application parallelism supported by Airavata SERIAL: Single processor applications without any parallelization. MPI: Messaging Passing Interface. OPENMP: Shared Memory Implementtaion. OPENMP_MPI: Hybrid Applications.
SERIAL | 0 | |
MPI | 1 | |
OPENMP | 2 | |
OPENMP_MPI | 3 | |
CCM | 4 | |
CRAY_MPI | 5 |


I love overhearing people’s conversations that take place in public. The following overheard conversation is about as close to verbatim as can be. I just typed this out as two older women are sitting at the table next to me at my local coffee shop:
Older Woman #1: “This last weekend my granddaughter and I watched a movie about a guy named Napoleon.”
Older Woman #2: “Oh, I do like documentaries. Sometimes I watch them on the public television station. I haven’t seen any movies about Napoleon Bonaparte. Was this one any good?”
Older Woman #1: “I don’t think it was a very realistic documentary. This Napoleon Bonaparte lived in North Dakota.”
Older Woman #2: “I’m pretty sure Napoleon Bonaparte lived in France. I’m interested if the reason he invaded other countries was because he was overcompensating for being really short.”
Older Woman #1: “I think this Napoleon Bonaparte was really tall and had lots of red hair.”
Older Woman #2: “That doesn’t sound like Napoleon Bonaparte to me, but what do I know. The movie must have at least talked about the relationship of Napoleon and Josephine?”
Older Woman #1: “I don’t really remember anyone named Josephine. I just think the movie was about the relationship of Napoleon Bonaparte and some guy named Pedro.”
Older Woman #2: “Napoleon and a guy named Pedro? That’s funny, I don’t think Napoleon Bonaparte was a homosexual. I thought he was married to a woman named Josephine.  Did she stop going to church?”

Older Woman #2: “Explosives? You need to make sure your granddaughter goes to church!”


---
title: People - Clifford Lab
permalink: "/people/alum.html"
layout: default
---
Alumni
DPhil / PhD
- Osipov, M. (D.Phil.) Towards automated symptoms assessment in mental health, University of Oxford (2011-2016) [*] [Now working in a startup]
- Zhu, T. (D.Phil.) [*] Bayesian Fusion of Continuous-Valued Labels in Biomedical Applications, University of Oxford (2011-2016) [Now a postdoc at University of Oxford]
- Raghu, A. (D.Phil.) CVD Risk Management in India, University of Oxford (2011-2015) [#] [Now a data scientist for Cambridge Analytica]
- Stroux, L., (D.Phil.) Low-resource Perinatal Care, University of Oxford (2011-2015) [*] [On maternity leave]
- Springer, D. (D.Phil.) Resource Constrained Heart sound Analysis, University of Oxford (2011-2015) [#] [Now a data scientist at Echobox, a London-based startup]
- Krug, J., (Ph.D.) ECG Analysis During MRI, University of Madburg (2011-2015) [&]
- Johnson, A. (D.Phil.) Mortality Prediction in the ICU, University of Oxford (2009-2015) [*] [Now a postdoc at MIT for 2015]
- Behar, J. (D.Phil.) Extraction of Clinical Information from the Non-Invasive Fetal Electrocardiogram, University of Oxford (2011-2015) [*] [Secured postdoc at the Technion, Haifa, Israel for 2015]
- Heinrich, A. (Ph.D.) Advances in video motion analysis research for mature and emerging application areas, Eindhoven University (2015) [&] [Now working in Philips Research, Eindhoven]
- Roebuck, A. (D.Phil.) Sleep Apneoa Detection Using Audio, University of Oxford (2009-2014) [*] [Now working in startup ‘SmartCare’ based on PhD]
- Mayaud, L. (D.Phil.) Prediction of Septic Shock, University of Oxford (2009-2014) [#] [Now Research Director at Mensia Technologies]
- Vilakazi, B. (D.Phil.) ST Analysis, University of Oxford (2009-2012) [&] [Now postdoc at Council for Scientific and Industrial Research (CSIR), South Africa]
- Sayadi, O. (Ph.D.) ECG Modelling, Shiraz University (2009-2011) [Now postdoc at Harvard/MGH] [&]
- Nemati, S. (Ph.D.) Robust Data Fusion, MIT (2008-2012) [Subsequently postdoc at Harvard University, USA, then Assistant Professor at Emory University] [*/#]
- Hug, C. (Ph.D.) Intelligent Hypotension Detection (undertaken during PhD), MIT (2007-2009) [Now Researcher at Microsoft, WA, USA] [&]
- Li, Q. (Ph.D.) ECG and BP Signal Quality, MIT & Shandong University, (2006-2008) [Now Research Assistant Professor of Biomedical Engineering, BMI, Emory University, USA] [*]
- Sameni, R. (Ph.D.) Fetal ECG analysis, University of Grenoble, (2005-2008) [Now Assistant Professor, University of Shiraz, Iran] [&]
Masters & UROP students
- Briggs, S. (M.Eng.) ECG-based sleep staging, University of Oxford (2013-2014) [*i]
- Bent, O. (M.Eng.) Low-cost pulse oximetry for neonatal apnea, University of Oxford (2012-2013) [*]
- Morton, A. (M.Eng.) Resource-constrained spirometry, University of Oxford (2012-2013) [*]
- Colloca, R. (M.Sc.) Robust AF detection, Politecnico di Milano (2012-2013) [Now PhD at Politecnico di Milano] [#]
- Carpseken, W. (M.Sc.) Telespiro: low-cost spirometry for resource poor settings, University of Oxford (2011-2012) [Now: MD at Harvard] [*]
- Lopez, D. (M.Eng./UROP) ECG Signal Quality, Imperial College (2011) [Returned to Imperial College to Complete Studies] [*]
- Williams, J. (M.Eng.) The Application of Phase-Rectified Signal Averaging to Intrapartum Cardiotocography Signals to Predict Fetal Acidemia, MEng Thesis, University of Oxford, (2011-2012) [#]
- Daly, J. (M.Eng.) The Design of a Neonatal Apnoea Monitor for Resource-constrained Environments, MEng Thesis, University of Oxford, (2011-2012) [Now: PhD candidate at University of Oxford] [*]
- Higgins, H. (M.Eng.), The Detection of Obstructive Sleep Apneoa Using a Mobile Phone, University of Oxford, (2011-2012) [*]
- Dunkley, N. (M.Sc.) Mortality prediction in the ICU, University of Oxford (2010-2011) [Now: PhD candidate at University of Oxford] [*]
- Burgess, F. (M.Eng.) Detection of Sleep Apneoa, University of Oxford (2010-2011) [*]
- Fedder, G. (M.Eng.) Fetal ECG signal Quality, University of Oxford (2010-2011) [*]
- McDonnell, C. (M.Sc.) Fetal ECG Analysis, University of Oxford (2010-2011) [*]
- Ghassemi, M. (M.Phil.) Prediction of Hypotension, University of Oxford (2009-2010) [Now: PhD candidate at MIT][&]
- Kuan, K. (M.Eng.) Lung Sound Analysis on a Cellphone, MIT (2009-2010) [Now at Google, CA] [*]
- Chen, T. (M.Eng.) Etiology of Hypotensive Events, MIT (2008-2009) [Now at Johnson & Johnson, Shanghai] [&]
- Deshmane, A. (M.Eng.) False Alarm Suppression in the ICU, MIT (2008-2009) [Now PhD candidate at Case Western] [#]
- Celi, L. (MSc) Mobile Diagnostics in Resource Poor Environments, MIT (Spring 2008) [Now: MD, Harvard] [&]
- Amjad Khan, I. (UROP) Cardiovascular Signals Forensics, MIT (Spring 2008) [*]
- Aboukhalil, A. (M.Eng.) False Alarm Suppression in the ICU, MIT (2006-2007) [Now: PhD candidate at Harvard][#]
- Neamatulla, I. (M.Eng) Automated De-Identification of Free-Text Medical Records, MIT (2005-2006) [#]
- Shu, J. (M.Eng) Free Text Phrase Encoding and Information Extraction from Medical Notes, MIT (2004-2006) [#]
- Kyaw, T. H. (M.Eng) Formatting and Searching a Massive Multi-parameter Clinical Information Database, MIT (2003-2005) [Now at Yahoo, CA][&]
- Douglass, M. (M.Eng) Computer-assisted de-identification of free-text nursing notes, MIT (2003-2005)[#]
- Zapanta, L. (M.Eng) Heart rate variability in mice with coronary heart disease, MIT (2003-2005) [] [*]
- Abdala, O. (M.Eng) The Annotation Station: an open source technology for data visualization and annotation large biomedical databases, MIT (2003-2004) [Now PhD, Harvard][#]
- Nnewihe, A. (UROP) ECG on a Soundcard, MIT (2004-2005) [*]
- Willsey, M. (UROP) False VEB Suppression, MIT (2003-2004) [*]
- Hung, A. (Intern) Web Interface for Database Search Engine, MIT (Summer 2005) [*]
Postdocs & Research Engineers
- Tsanas, A. (Postdoctoral Researcher), Sleep and Circadian Neuroscience Institute, University of Oxford (2012-2015) [Currently postdoc at Oxford] [*]
- Hopkins, M. (Postdoctoral Database Engineer), Emory University (2014-2015) [Currently Research Scientist at Intel, Oregon, USA for 2015] [*]
- Oster, J. (Postdoc) MHD suppression in the ECG, University of Oxford (2011-2014) [Currently postdoc at Oxford] [*]
- Li, Q (Postdoc) False alarm suppression in the ICU, University of Oxford (2011-2014) [Currently Assistant Professor at Emory University] [*]
- Stephens, K (Software Engineering Intern) Department of Biomedical Informatics Emory University (2016-2017)
- Sutherland, T (Software Engineering Intern) Department of Biomedical Informatics Emory University (2016-2017)
- Houghton, P. (RA) Drug-drug Interactions, Universities of Oxford & Liverpool (2011-2013) [Currently freelance programmer] [#]
- Monasterio, V. (Postdoc) Neonatal apnea detection, University of Oxford (2011-2012) [Currently Assistant Professor (Lecturer) at University of Zaragoza, Spain] [*]
- Llinares, R. (Postdoc) ECG models for ischemia and MRI, University of Oxford (2010-2011) [Currently Assistant Professor (Lecturer) at University of Alicante, Spain] [*]
- Scott, D. (Postdoc) Database & Network Admin., MIT (2008-2009) [Currently Systems Engineer at an insurance startup, QMetric Group Ltd., London, UK] [*]
- Villarroel, M. (RA) Database & Network Admin., MIT (2005-2009) [Subsequently Research Assistant at University of Oxford, UK and now Research Scientist at MIT, Boston, USA] [*]
- Singer, J. (RA) Java Developer, MIT (2008) [*]
- Lehman, L. (Postdoc) Java Developer & Database Admin., MIT (2006-2008) [Currently postdoc at MIT] [*]
Visiting Researchers
- Johannes Krug (Universität Magdeburg, Germany) 2013-2015
- Fernando Andreotti (Universität Dresden, Germany) 2014-2015
- Jan Gierałtowski (Center for Advanced Studies Warsaw University of Technology, Poland) Spring 2015
- Qichen Li (University of Oxford, UK) Summer 2015
- Faezeh Marzbanrad (University of Melbourne, Australia) August-October 2015
* indicates sole supervision
# indicates co-supervision as primary supervisor
& indicates significant supervision or co-supervision as secondary supervisor


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