Hello there! Would you want to know the exact day of the week you were born? Well, then this site will help you achieve that. Most importantly it also outputs your AKAN name.
That said, you could be probably wondering what AKAN names are. Frequently in Ghana, children are given their first name as a 'day name' which corresponds
to the day in the week they were born,and that is there AKAN name. All you need to do here is to enter the date,month and year that you were born.
Just as as simple as that then click the submit button.


Simon Zebo welcomes third child with hospital snap on Twitter
by By Digital Desk staffSimon Zebo and his partner Elvira Fernández have welcomed their third child into the world, with the rugby star tweeting a picture from hospital.
Noah Anthony Fernández Zebo is the couple's third child, with elder son Jacob born in 2015 and daughter Sofia arriving a year after her older brother.
The former Munster star has settled into Paris life well since joining Racing 92 last season, but was on the bench for last weekend's Champions Cup game against Saracens when the French side ran out 30-10 winners.
READ MORE
Where does your club rank? Cork GAA confirms grading for 2020 Championships


We fetch sequence data and metadata. We query
the metadata in multiple ways using SPARQL and onthologies
We explore the Arvados command line and API
We submit a sequence to the database
We modify a workflow to get new output
We modify metadata for all to use!


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{% extends "_base.html" %} {% block page_name %}library search{% endblock %} {% block css %} {% if request.GET.before or request.GET.after %} {% endif %} {% endblock %} {% block content %} {% spaceless %} {% if request.GET.search and not tag %} {{ request.GET.search }} {% else %} Search through library {% endif %} {% for each in tags %} {{ each }} {% endfor %} Search Only show work on {{ SYSTEM_NAME_PLURAL }} Show advanced options {% endspaceless %} Published before Published after Find by place View our map Restrict by type Academic paper Report Newspaper article {% if show_results %}
{{ each.introduction }}


This pattern will never match the case statement's word. Double check them.
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Often, this is due to mistakes in the case statement word that results in unintended literal characters. In the problematic code, there's a trailing space that will prevent the match from ever succeeding.
For more examples of when this could happen, see SC2193 for the equivalent warning for [[ .. ]]
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*.png | *.jpg) echo "It's an image"
esac
None. If you encounter a bug and wish to [[ignore]] this warning, make sure the directive goes in front of the case
and not the individual branch.


Created using the PaleoDB API, these applications showcase how the database can be leveraged for data exploration, research, and visualization. To submit your application, head over to the app submission page (Coming soon).
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Space, time, & taxa
Explore geology and taxa from your iOS device


Digital Women is an Iowa State University student organization dedicated to encouraging, supporting and retaining women in Computer Science, Software Engineering, Electrical Engineering, Computer Engineering and MIS.
We want to help you succeed by providing opportunities for networking, career development, and much-needed fun with other like-minded women in technical majors.
Digital Women meets every Thursday at 6:30 p.m. in Coover 1016.
Joining Digital Women is easy! All you need to do is sign up on our stuorg website and begin showing up to meetings.
You can find our schedule of meetings here.
You can also find Digital Women on Facebook, Twitter, GitHub and our blog!
| Date | Meeting | Time | Location |
|---|---|---|---|
| 9/3/15 | Ice Breaker & Developers/Cards Against Humanity | 6:30 p.m. | Coover 1016 |
| 9/10/15 | Company & Minty Boost Chargers | 6:30 p.m. | Coover 1016 |
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You can easily change default icons used in the application to your owns. All you need is to create or find somewhere a suitable icon set and point your application to use these icons via user-defined styles. It is possible to use separate images as well as image sprites.
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Version notes
3.0.0 Beta 11
- Fix a bug on preserving article currently being read on refreshes
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- New icons (thanks to Nick Dazé and romiq !)
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CrimPapers
Articles published on 31 July 2019
On 31 July 2019, 6 new articles were published in 6 journals.
Robyn Holder, Just Interests: Victims, Citizens and the Potential For Justice by Mary Iliadis
Researching Cross-National Policy Mobilities in Crime Control by Trevor Jones, Jarrett Blaustein, Tim Newburn
Copycat Crime Among Non-Incarcerated Adults by Ray Surette, Derek Chadee
Implementation of A Problem-Solving Training Initiative to Reduce Self-Harm in Prisons: A Qualitative Perspective of Prison Staff, Field Researchers and Prisoners At Risk of Self-Harm by Amanda E. Perry, Mitch G. Waterman, Allan O. House and Joanne Greenhalgh
What’s in A Name? A Synthesis of “Allyship” Elements From Academic and Activist Literature
Combating Street Harassment: A Challenge For Pakistan by Bilal Ahmed, Farhan Navid Yousaf, Umm-E-Rubab Asif


A file contains a document that can be opened in CrypTool. Information on the creation and editing of documents will be found in the help index.
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Module: ActionDispatch::Routing::Mapper::CustomUrls
| Relationships & Source Files | |
| Extension / Inclusion / Inheritance Descendants | |
|
Included In:
| |
| Defined in: | actionpack/lib/action_dispatch/routing/mapper.rb |
Instance Method Summary
-
#direct(name, options = {}, &block)
Define custom URL helpers that will be added to the application’s routes.
-
#resolve(*args, &block)
Define custom polymorphic mappings of models to URLs.
Instance Method Details
#direct(name, options = {}, &block)
Define custom URL helpers that will be added to the application’s routes. This allows you to override and/or replace the default behavior of routing helpers, e.g:
direct :homepage do
"https://rubyonrails.org"
end
direct :commentable do |model|
[ model, anchor: model.dom_id ]
end
direct :main do
{ controller: "pages", action: "index", subdomain: "www" }
end
The return value from the block passed to direct
must be a valid set of arguments for url_for
which will actually build the URL string. This can be one of the following:
-
A string, which is treated as a generated URL
-
A hash, e.g.
{ controller: "pages", action: "index" }
-
An array, which is passed to
polymorphic_url
-
An Active Model instance
-
An Active Model class
NOTE: Other URL helpers can be called in the block but be careful not to invoke your custom URL helper again otherwise it will result in a stack overflow error.
You can also specify default options that will be passed through to your URL helper definition, e.g:
direct :browse, page: 1, size: 10 do ||
[ :products, .merge(params.permit(:page, :size).to_h.symbolize_keys) ]
end
In this instance the params
object comes from the context in which the block is executed, e.g. generating a URL inside a controller action or a view. If the block is executed where there isn’t a params
object such as this:
Rails.application.routes.url_helpers.browse_path
then it will raise a ::NameError
. Because of this you need to be aware of the context in which you will use your custom URL helper when defining it.
NOTE: The direct
method can’t be used inside of a scope block such as namespace
or scope
and will raise an error if it detects that it is.
# File 'actionpack/lib/action_dispatch/routing/mapper.rb', line 2159
def direct(name, = {}, &block) unless @scope.root? raise RuntimeError, "The direct method can't be used inside a routes scope block" end @set.add_url_helper(name, , &block) end
#resolve(*args, &block)
Define custom polymorphic mappings of models to URLs. This alters the behavior of polymorphic_url
and consequently the behavior of link_to
and form_for
when passed a model instance, e.g:
resource :basket
resolve "Basket" do
[:basket]
end
This will now generate “/basket” when a Basket
instance is passed to link_to
or form_for
instead of the standard “/baskets/:id”.
NOTE: This custom behavior only applies to simple polymorphic URLs where a single model instance is passed and not more complicated forms, e.g:
# config/routes.rb
resource :profile
namespace :admin do
resources :users
end
resolve("User") { [:profile] }
# app/views/application/_menu.html.erb
link_to "Profile", @current_user
link_to "Profile", [:admin, @current_user]
The first link_to
will generate “/profile” but the second will generate the standard polymorphic URL of “/admin/users/1”.
You can pass options to a polymorphic mapping - the arity for the block needs to be two as the instance is passed as the first argument, e.g:
resolve "Basket", anchor: "items" do |basket, |
[:basket, ]
end
This generates the URL “/basket#items” because when the last item in an array passed to polymorphic_url
is a hash then it’s treated as options to the URL helper that gets called.
NOTE: The resolve
method can’t be used inside of a scope block such as namespace
or scope
and will raise an error if it detects that it is.
# File 'actionpack/lib/action_dispatch/routing/mapper.rb', line 2211
def resolve(*args, &block) unless @scope.root? raise RuntimeError, "The resolve method can't be used inside a routes scope block" end = args. args = args.flatten(1) args.each do |klass| @set.add_polymorphic_mapping(klass, , &block) end end


Tutorial 05: public data training (classification)
Posted on Wed 06 December 2017 in misc by Kazuhiro Terao
In this notebook we train a simple, 10-layers deep convolutional neural network for classifying 5 particle images in a simulated LArTPC detecotr available from the public dataset. We use tensorflow to train the network and larcv_threadio
to fetch data from larcv files. If you are completely unfamiliar with larcv_threadio
, go look at this quick start. First let's prepare data samples. For the setup of this example, I need to prepare practice_train_10k.root
and practice_test_10k.root
in the current directory. Let us make symbolic links.
%%bash
# Preparation: make symbolic links for practice_train_10k.root and practice_test_10k.root
PRACTICE_FILE_DIR=../..
ln -sf $PRACTICE_FILE_DIR/practice_train_5k.root ./train.root
ln -sf $PRACTICE_FILE_DIR/practice_test_5k.root ./test.root
Imports¶
from larcv import larcv
from larcv.dataloader2 import larcv_threadio
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import os,sys,time
# tensorflow/gpu start-up configuration
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=2
import tensorflow as tf
We set os.environ['TF_CPP_MIN_LOG_LEVEL']
to suppress lots of non-error (standard) output from tensorflow because it can overwhelm ipython's capability to fetch stdout
stream.
Configurations¶
Next, let's define configuration variables.
TUTORIAL_DIR = '..'
TRAIN_IO_CONFIG = os.path.join(TUTORIAL_DIR, 'tf/io_train.cfg')
TEST_IO_CONFIG = os.path.join(TUTORIAL_DIR, 'tf/io_test.cfg' )
TRAIN_BATCH_SIZE = 50
TEST_BATCH_SIZE = 100
LOGDIR = 'log'
ITERATIONS = 5000
SAVE_SUMMARY = 20
SAVE_WEIGHTS = 100
# Check log directory is empty
train_logdir = os.path.join(LOGDIR,'train')
test_logdir = os.path.join(LOGDIR,'test')
if not os.path.isdir(train_logdir): os.makedirs(train_logdir)
if not os.path.isdir(test_logdir): os.makedirs(test_logdir)
if len(os.listdir(train_logdir)) or len(os.listdir(test_logdir)):
sys.stderr.write('Error: train or test log dir not empty...\n')
raise OSError
The top block defines a set of constants in capitalized letters. The bottom part is simply checking if the directories where we will store the network training logs are empty or not (so that we won't mix with the previous attempt). So what do the constants do?
TUTORIAL_DIR
... points to the top-level directory of the larcv-tutorial repostitory.TRAIN_IO_CONFIG
... a configuration file forlarcv_threadio
to read data for training.TEST_IO_CONFIG
... a configuration file forlarcv_threadio
to read data for testing.TRAIN_BATCH_SIZE
... a number of images (batch) to be used to calculate the average gradients for updating the network's weights.TEST_BATCH_SIZE
... a number of images to be used to calculate the average accuracy using test data set.LOGDIR
... the top-level directory to save the tensorboard logs.ITERATIONS
... the total number of steps (batches) to train the network.SAVE_SUMMARY
... a period in a training step count to save the log (tensorboard summaries).SAVE_WEIGHTS
... a period in a training step count to save the network's weights.
Configure data reader¶
We prepare two data reader instances: one for training and another for testing the network. Testing is not absolutely needed but we try here to just cover in this example. We don't go in details of how larcv_threadio
works here since there is a dedicated tutorial for that.
#
# Step 0: IO
#
# for "train" data set
train_io = larcv_threadio() # create io interface
train_io_cfg = {'filler_name' : 'TrainIO',
'verbosity' : 0,
'filler_cfg' : TRAIN_IO_CONFIG}
train_io.configure(train_io_cfg) # configure
train_io.start_manager(TRAIN_BATCH_SIZE) # start read thread
time.sleep(2)
train_io.next()
# for "test" data set
test_io = larcv_threadio() # create io interface
test_io_cfg = {'filler_name' : 'TestIO',
'verbosity' : 0,
'filler_cfg' : TEST_IO_CONFIG}
test_io.configure(test_io_cfg) # configure
test_io.start_manager(TEST_BATCH_SIZE) # start read thread
time.sleep(2)
test_io.next()
Defining a network¶
Let's construct a simple network for this exercise. We use 5x2 convolution layers with max-pooling operation followed after every 2 convolution layers except the last layer is average-pooling.
#
# Step 1: Define network
#
import tensorflow.contrib.slim as slim
import tensorflow.python.platform
def build(input_tensor, num_class=4, trainable=True, debug=True):
net = input_tensor
if debug: print('input tensor:', input_tensor.shape)
filters = 32
num_modules = 5
with tf.variable_scope('conv'):
for step in xrange(5):
stride = 2
if step: stride = 1
net = slim.conv2d(inputs = net, # input tensor
num_outputs = filters, # number of filters (neurons) = # of output feature maps
kernel_size = [3,3], # kernel size
stride = stride, # stride size
trainable = trainable, # train or inference
activation_fn = tf.nn.relu, # relu
scope = 'conv%da_conv' % step)
net = slim.conv2d(inputs = net, # input tensor
num_outputs = filters, # number of filters (neurons) = # of output feature maps
kernel_size = [3,3], # kernel size
stride = 1, # stride size
trainable = trainable, # train or inference
activation_fn = tf.nn.relu, # relu
scope = 'conv%db_conv' % step)
if (step+1) < num_modules:
net = slim.max_pool2d(inputs = net, # input tensor
kernel_size = [2,2], # kernel size
stride = 2, # stride size
scope = 'conv%d_pool' % step)
else:
net = tf.layers.average_pooling2d(inputs = net,
pool_size = [net.get_shape()[-2].value,net.get_shape()[-3].value],
strides = 1,
padding = 'valid',
name = 'conv%d_pool' % step)
filters *= 2
if debug: print('After step',step,'shape',net.shape)
with tf.variable_scope('final'):
net = slim.flatten(net, scope='flatten')
if debug: print('After flattening', net.shape)
net = slim.fully_connected(net, int(num_class), scope='final_fc')
if debug: print('After final_fc', net.shape)
return net
Build the network¶
Build the network and define loss, accuracy metrics and our solver. Any optimizer should work but you may have to tune the parameters by yourself. Here, we use RMSPropOptimizer
with base learning rate 0.0005
with no justification. Note we add minimal set of tensorflow variables into tf.summary to demonstrate later the tensorboard
, a dedicated monitoring/visualization tool for network training with tensorflow.
#
# Step 2: Build network + define loss & solver
#
# retrieve dimensions of data for network construction
dim_data = train_io.fetch_data('train_image').dim()
dim_label = train_io.fetch_data('train_label').dim()
# define place holders
data_tensor = tf.placeholder(tf.float32, [None, dim_data[1] * dim_data[2] * dim_data[3]], name='image')
label_tensor = tf.placeholder(tf.float32, [None, dim_label[1]], name='label')
data_tensor_2d = tf.reshape(data_tensor, [-1,dim_data[1],dim_data[2],dim_data[3]],name='image_reshape')
# Let's keep 10 random set of images in the log
tf.summary.image('input',data_tensor_2d,10)
# build net
net = build(input_tensor=data_tensor_2d, num_class=dim_label[1], trainable=True, debug=False)
# Define accuracy
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(net,1), tf.argmax(label_tensor,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
# Define loss + backprop as training step
with tf.name_scope('train'):
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=label_tensor, logits=net))
tf.summary.scalar('cross_entropy',cross_entropy)
train_step = tf.train.RMSPropOptimizer(0.00005).minimize(cross_entropy)
Defining tensorflow IO¶
In the next cell we define tensorflow's IO
merged_summary
... is tensorflow operation to create summaries to be written into a log file fortensorboard
.writer_train
... writes monitoring data for training data sample into a log file.writer_test
... is same aswriter_train
except it is for testing data sample.saver
... is a handle to store the state of the network = trained network variable values (weights, biases, etc.).
#
# Step 3: weight saver & summary writer
#
# Create a bandle of summary
merged_summary=tf.summary.merge_all()
# Create a session
sess = tf.InteractiveSession()
# Initialize variables
sess.run(tf.global_variables_initializer())
# Create a summary writer handle
writer_train=tf.summary.FileWriter(train_logdir)
writer_train.add_graph(sess.graph)
writer_test=tf.summary.FileWriter(test_logdir)
writer_test.add_graph(sess.graph)
# Create weights saver
saver = tf.train.Saver()
Train!¶
#
# Step 4: Run training loop
#
for i in range(ITERATIONS):
train_data = train_io.fetch_data('train_image').data()
train_label = train_io.fetch_data('train_label').data()
feed_dict = { data_tensor : train_data,
label_tensor : train_label }
loss, acc, _ = sess.run([cross_entropy, accuracy, train_step], feed_dict=feed_dict)
if (i+1)%SAVE_SUMMARY == 0:
# Save train log
sys.stdout.write('Training in progress @ step %d loss %g accuracy %g \n' % (i,loss,acc))
sys.stdout.flush()
s = sess.run(merged_summary, feed_dict=feed_dict)
writer_train.add_summary(s,i)
# Calculate & save test log
test_data = test_io.fetch_data('test_image').data()
test_label = test_io.fetch_data('test_label').data()
feed_dict = { data_tensor : test_data,
label_tensor : test_label }
loss, acc = sess.run([cross_entropy, accuracy], feed_dict=feed_dict)
sys.stdout.write('Testing in progress @ step %d loss %g accuracy %g \n' % (i,loss,acc))
sys.stdout.flush()
s = sess.run(merged_summary, feed_dict=feed_dict)
writer_test.add_summary(s,i)
test_io.next()
train_io.next()
if (i+1)%SAVE_WEIGHTS == 0:
ssf_path = saver.save(sess,'weights/toynet',global_step=i)
print('saved @',ssf_path)
# inform log directory
print()
print('Run `tensorboard --logdir=%s` in terminal to see the results.' % LOGDIR)
train_io.reset()
test_io.reset()
Checking log on the tensorboard¶
As the last line above says you can visualize your log using tensorboard. This command
tensorboard --logdir=log
on the terminal instantiates the tensorboard server and tells the localhost address to access through your web-browser. You can certainly ssh-tunnel to access the localhost of your remote machine to check it on your local machine's web-browser as well. For the above training, here's the screenshot of the loss and accuracy curve for train and test samples where the blue line represents metric measured on the training set and orange line is for the same on the test sample.
This notebook covered training convolutional neural networks to perform image classification of 5 LArTPC particles using a practice files. We encourage you to design your own network and train on our public dataset! We provide 50,000 entries of 5 particle images (10,000 per particle) for training and separate 40,000 for testing your network. When you are confident, try our data challenge, yet another set of 40,000 events without answers (i.e. no particle
information). Share your awesome result in the CSV format to us with your network architecture made available on a github repository.


Notebooks
Story: Poisons and a description of gathering them
Poisons and a description of gathering them
Title
Poisons and a description of gathering them
Collection
Summary
Tamme and !nanni name and describe poisons used by the !kun, most of them taken from trees and plants. There are many poisons. The poisons are prepared (often boiled in a pot) and put onto arrows to shoot game. The people also gather caterpillars called Kai from large trees, with which they make poison. !nanni describes one of these excursions to gather this poison by his people. His father and grandfather (Karu) were on this excursion, and Karu was injured during the outing.
Comments
1) p.9581opp: Tamme heard this (on the opposite page) from his father (||namme) and observed it himself, 2) p.9582v: a note on how certain animals sleep: the hyena sleeps soundly but with its ears and the jackal sleeps with its eyes but not its ears, 3) p.9583v: Tamme says that people become intoxicated when they eat a cat's flesh, 4) The term of address for a young male child, 5) What the !kun call each other and their in-laws by way of showing respect (≠khumm). They refrain from saying people's names (little or great). Men appear to respect women (including their daughters) and vice versa, 6) This story is found in Book XI & XII-8
Contributors
Date
26 August 1880 (started)
Categories
Custom and daily life, History (personal), Plants and animals, Artefact and dress
Keywords
poisons (used on arrows) , poisons (the gathering of) , poisons (used by the !kun to shoot game) , poisons (a description of various) , poisons (the names of plants and trees used) , poisons (the preparation of) , poisons (Kai caterpillar used) , poisons (how !nanni's people gather them) , poisons (!nanni and Tamme's descriptions of those used by the !kun) , !nanni (how his relations gathered Kai for poison) , !nanni (the names of his relations) , !nanni (his personal history) , gathering (poison) , gathering (a description of) , gathering (what dress is worn for) , gathering (the skins worn for) , gathering (and a story of !nanni's people) , names (of !nanni's relations) , names (of a caterpillar used for poison) , names (of plants and trees used as poison) , personal history (!nanni and his family gather and prepare poisons) , personal history (Karu is injured while out gathering poison) , arrow (poison gathered and prepared by the !kun) , arrow (making of poisons for) , arrow (poisons used by the !kun) , arrow (used by the !kun for shooting game) , dress (worn by the !kun to gather poison) , dress (skins worn for gathering poisons) , caterpillar (!kun name of) , caterpillar (called Kai) , caterpillar (used by the !kun to make poison) , caterpillar (gathered by !nanni's people) , game (the making of poisons for shooting) , game (and poisons gathered and prepared by !nanni's people) , Karu (!nanni's paternal grandfather) , Karu (injured while out gathering poison) , hunting (poisons gathered and prepared by !nanni's people to shoot game) , hunting (arrow-poisons used by the !kun) , pot (poisons boiled and prepared in one) , pot (and poisons gathered and prepared by the !kun) , skins (worn by the !kun for gathering poisons) , plants (and trees used for making poisons by the !kun) , plants (!kun names of) , trees (and plants used for making poisons by the !kun) , trees (from which Kai caterpillars are gathered to make poison) , trees (!kun names of)
Story Pages
9581-9598


Explore Twitter data
on the current
European Elections
in real time.
This tool visualizes data at the aggregate level from tweets on the current European Elections and allows anyone to explore
(dis)similarities between countries on political orientation, as well as the relative share of voice on many hot topics around Europe!
Many new charts and insights are currently under development — check back frequently!
The data is based on a sample of all Tweets, and automatically updated approximately every 15 minutes. There is no need to refresh the
page.
This project is a student initiative of Tilburg School of Economics and Management, developed by
Andrea Antonacci (Master of Science student in Marketing Management at Tilburg
University), Marco De Lucchi (Web developer), and mentored by
Hannes Datta (Associate Professor at Tilburg University).
Special thanks go to Enrico Giulini for his helpful
contribution, and to faculty at Tilburg School of Economics and Management for providing feedback.
The project’s infrastructure is supported with research funds from Tilburg School of Economics and Management in the interest of
encouraging entrepreneurship among students, and to generate a large body of data for students’ to write their Master theses on.
Our current set of keywords is available for download here.
To compute an average political orientation score per country shown in the first chart, we assigned a political orientation score to
each national party, ranging from 1 (far left) to 5 (far right).
This classification is purely based on our own research effort on national parties. We classified parties as far left/right when they
showed (extreme) political positions (e.g., anti-immigration, anti-establishment, anti-élite attitudes, populism, euroscepticism,
etc.). Please, reach us if you do not agree with our current classification of a party.
Our current political orientation classification is available for download
here.
Please, notice that we do not do text mining of tweets. Instead, we simply count the number of tweets that contain a certain keyword
of interest. Despite being less accurate, this approach turned out to be the most appropriate for real-time data visualization.
In our data, we do not observe a user’s country of origin; we only observe the language of a Tweet, which we use as a proxy for a
user’s nationality. As languages do not directly correspond to countries (e.g., in some countries, multiple languages are spoken), we
have decided to drop the following countries in certain cases: Belgium, Croatia, Luxembourg, Malta.
Instead, in some other charts, we aggregate countries speaking the same language (e.g., Austria and Germany, Greece and Republic of
Cyprus, Ireland and United Kingdom).
The information contained in this website is for general information purposes only. While we endeavor to keep the information
up-to-date and correct, we make no representations or warranties of any kind, express or implied, about the completeness, accuracy,
reliability, suitability or availability with respect to the website or the information, services, or related graphics contained on
the website for any purpose. Any reliance you place on such information is therefore strictly at your own risk.
In no event we will be liable for any loss or damage including without limitation, indirect or consequential loss or damage, or any
loss or damage whatsoever arising from the use of the information contained in this website.
Feel free to reach us at<EMAIL_ADDRESS>with any question, feedback, suggestion, or if you believe we have misclassified any keyword.
For more information on Election Stats, please contact the Tilburg University press office at tel. +31 13 466 4000 / persvoorlichters@tilburguniversity.edu.


--- layout: post title: "Teaching Machines Reviewed in Inside Higher Ed" date: 2022-05-25 15:00:00 +0000 tags: image: https://s3.amazonaws.com/audreywatters/2017-11-03-news.jpg imagecredits: https://www.flickr.com/photos/62693815@N03/6277209256 ---
Joshua Kim has reviewed Teaching Machines in his column on Inside Higher Ed:
In Teaching Machines, Watters has provided a sturdy historical foundation for her long-running (and highly influential) skepticism of the efficacy of most educational technologies. Teaching Machines is a book, and Watters is a scholar, that both deserve a central place in our academic conversations about the future of teaching and learning.


{% extends 'base.html' %} {% import 'bootstrap/wtf.html' as wtf %} {% block content%}
Comment {{ pitch.title }}
{{ justone.comment }}
Comment By {{ justone.users.username|capitalize }}
No comments Yet


|
Hi <Admin>,
Greetings.. !! You have been added as an Admin in Recruitment Portal of Quantium We have created an account for you to handle our hiring process through which you can add questions, create jobs, add candidates ,create tests for selected Candidates and evalulate the test results. Please log on to https://www.quantiumrecruitment.com using the social login associated with your emailId.
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hn2016_falwa: Finite-amplitude local wave activity¶
hn2016_falwa is a python library that provides tools to measure and study life cycle of large-scale extreme weather events. It implements the finite-amplitude local wave activity and flux diagnostic introduced in:
To users¶
If you are interested in using the package, please leave your contact here such that I can keep you updated of any changes made.
Installation¶
This current version works in both Python 2.7 and 3.6. Note that from v0.3.0 onword, some functions are having backend in Fortran. You will need a fortran compiler (e.g. gfortran) to implement the installation.
Dependencies include Numpy, Scipy and optionally Matplotlib. Since the package is still being actively developed, please use the develop mode for installation:
git clone https://github.com/csyhuang/hn2016_falwa.git
cd hn2016_falwa
python setup.py develop
To incorporate updates, pull the new version of the code by:
git pull
Developer v.s. Object-oriented Interfaces¶
There are two interfaces for this library. One is the developer interface; the other is the object-oriented interface, which is a wrapper for the basis functions in the developer interface.
The developer interface contains separate functions that users can alter the inputs more flexibly. Functions are added upon users’ request on new functionalities to test hypotheses (also see the test branch). The developer interface consists of 4 types of functions:
- The basis functions are smallest unit of functions that make up the wrapper functions and object-oriented interface.
- The wrapper functions implement particular analysis tasks for published work/manuscripts in preparation
- The utility functions compute general quantities, such as static stability or quasi-geostrophic potential vorticity that are not specific to the finite-amplitude wave theory.
- The beta-version functions include utilities that are not fully documented but has been used in research.
The object-oriented interface is an easy-to-use interface that takes in the climate field and coordinates as the attributes of an object, and implement the wrapper functions above as methods.
Issues Reporting¶
Please make inquiries about / report issues / with the package and suggest feature extensions on the Issues page.
If you need help analyzing output from particular model/analysis with our techniques, feel free to email me<EMAIL_ADDRESS>with sample datasets and I can configure the code for you.


Primary
Clear
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Secondary
Solid
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Tertiary
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Warning
Solid
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Solid
Danger
Outline
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Outline
Light
Clear
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Medium
Solid
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Dashboard¶
In the dashboard, the environment states are visualized during an episode. The plot is updated simultaneously to the run time of the control. The input voltage is drawn in magenta and the other state variables are plotted in blue. References are shown in green. Furthermore, nominal limits (dotted-yellow) and technical limits (dashed-red) are depicted. The plots can be used to analyze the behaviour of the controller.
-
class
gym_electric_motor.envs.dashboard.
MotorDashboard
(variables, tau, low, high, episode_length, safety_margin, with_ref, update_cycle=None)[source]¶ Visualization of the variables of the motor in graphs. This can be the angular velocity omega, torque phase voltages, phase currents, supply voltage and the rotor angle at the PMSM.
In the table are all variables given that can be shown for a specific motor.
variable extex shunt permex series PMSM omega x x x x x torque x x x x x u_sup x x x x x i x x i_e x x u x x x u_e x u_a x x u_b x u_c x i_a x x x i_b x i_c x epsilon x Constructor of the dashboard.
Parameters: - variables – Names of the variables that shall be shown on the dashboard
- tau – system time constant
- low – Array of lowest possible values for the variables in the same order as the variables
- high – Array of highest possible values for the variables in the same order as the variables
- episode_length – episode length of the system
- safety_margin – Ratio between maximal and nominal power of the motor parameters.
- with_ref – Array of Booleans indicating if the variables have a reference that has to be plotted
- update_cycle – Number of steps after which the dashboard shall be updated. | Updating every cycle leads to a very low speed.


[filmcritic.com presents a review from guest writer Aileo Weinmann. You can find the review with full credits at http://www.filmcritic.com/misc/emporium.nsf/2a460f93626cd4678625624c007f2b46/8323d4a70a47a4a3882568260009e241?OpenDocument
LIGHT IT UP smolders and spits out some smoke, but no fire. The film treads lightly on the volatile crossroads where disenchanted inner city youth meet the desperate state of urban public education. A la HIGHER LEARNING, the prevailing simplification of issues concludes with empty and confusing platitudes in answer to some complex stuff.
Queens, New York – it's about as bad as it can get at Lincoln High. Almost nobody has textbooks, snow is blowing through broken windows in dilapidated classrooms, and student favorite Mr. Knowles (Judd Nelson – THE BREAKFAST CLUB, FROM THE HIP) was just fired for no good reason by heavy-handed Principal Armstrong. When Officer Dante Jackson (Forest Whitaker – THE CRYING GAME, JASON'S LYRIC) struggles to detain gifted student artist `Ziggy' Malone on bogus charges, Lester Dewitt (Usher Raymond) takes matters into his own hands by seizing the Officer's gun and taking Jackson hostage. Now barricaded against the New York Police Department, the basketball star Lester, the artist `Ziggy,' along with a student council member, a punk-rocker, a hustler, and a gang banger have to figure out what they are standing for.
Lester and the other students ultimately attempt to use their newfound leverage to demand improvements for a decent education. They use e-mail to rally support from the community and dispel allegations by the media that they are nothing but violent gang vigilantes. As negotiations fail, the stakes for Lester, the other students, and Officer Jackson escalate. The dramatic climax centers around whether loss of life can be avoided as the NYPD closes in without meeting Lester's conditions.
Some may contend that LIGHT IT UP does well to leave its audience frustrated with incomplete answers; after all, there are no easy solutions to the problems it examines. On the other hand, it seems irresponsible to present such radical actions and then gloss over the consequences in this incomplete fashion. Call me old school, but I'd rather `light it up' with a pint of JD than a ticket to this show – at least a hangover usually has a few decent stories to go with it.
2.5 stars
-- Christopher Null -<EMAIL_ADDRESS>- http://www.filmcritic.com Check out the G-Rex Personal Shopper at http://www.g-rex.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


---
layout: sidebar
display-title: false
---
{% comment %}
Slate by Pixelarity
pixelarity.com |<EMAIL_ADDRESS>License: pixelarity.com/license
Name:
Sidebar layout
Description:
A layout where content is displayed alongside the sidebar.
Allows defining the sidebar orientation on each page.
Example:
---
layout: sidebar
sidebar_align: right
---
{% endcomment %}
{% include components/navtiles.html customtag='tiles-children-young-people' title='Children & Young People' customcss='gateway' %}
{% include components/navtiles.html customtag='tiles-fertility-pregnancy' title='Fertility & Pregnancy' customcss='gateway' %}
{% include components/navtiles.html customtag='tiles-emotional-wellbeing' title='Emotional Wellbeing' customcss='gateway' %}
{% include components/navtiles.html customtag='tiles-general' title='Adult Health' customcss='gateway' %}
{{content}}


What is this?
This is a Capture The Flag (CTF) game that is designed to give you hands on experience with application security by challenging you to exploit vulnerabilities in an intentionally insecure application.
How do I check my score and where I stand?
Got to the Scoreboard tab to check out every participant's score.
How to I register for this CTF?
Go to Register. Register an account by entering your details.
What happens after I register?
After you register, we will create an insecure web application for you.


Before Change
def _update_magic_objects(self, level):
objects = collect(level+1)
contained_objects = set()
valid_refs = set()
for obj in objects:
if obj.invalidates_magic_network:
valid_refs.add(weakref.ref(obj))
for contained in _get_contained_objects(obj):
contained_objects.add(contained)
// check whether we should restart time, continue time, or raise an
// error
inter = valid_refs.intersection(self._previous_refs)
if len(inter) == 0:
// reset time
self.t_ = 0.0
elif len(self._previous_refs) == len(valid_refs):
After Change
for obj in objects:
for contained in _get_contained_objects(obj):
contained_objects.add(contained)
objects |= contained_objects
// check whether we should restart time, continue time, or raise an
// error
some_known = False
some_new = False
for obj in objects:
if obj._network == self.id:
some_known = True // we are continuing a previous run
elif obj._network is None and obj.invalidates_magic_network:
some_new = True
// Note that the inclusion of objects that have been run as part of
// other objects will lead to an error in `Network.before_run`, we
// do not have to deal with this case here.
if some_known and some_new:
raise MagicError(("The magic network contains a mix of objects "
"that has been run before and new objects, Brian "
"does not know whether you want to start a new "


Microsoft loves Linux.
Who is this Microsoft? and what did you do to telnet?.. Gopher must've ate it. Open Source for life!
Those fellows who work on PowerShell must be crazy! Who'd want port 22 open on a Windows box?
Me.
No more nc.exe bro dawg! Captn_Jack and his fellow RedHat wearing brethren can finally manage Windows and Linux over thy administrator controlled port.
Supercilious really...
But why is SSH coming? I mean it's only 30 years in the making...
Well here is what the PowerShell Team has to say:
Finally, I'd like to share some background on today’s announcement, because this is the 3rd time the PowerShell team has attempted to support SSH. The first attempts were during PowerShell V1 and V2 and were rejected. Given our changes in leadership and culture, we decided to give it another try and this time, because we are able to show the clear and compelling customer value, the company is very supportive. So I want to take a minute and thank all of you in the community who have been clearly and articulately making the case for why and how we should support SSH!
Emphasis put on the key point.
Trust me, I'm a professional.


{%extends 'header.html'%} {%block content%}
| Student ID | Student Name | Parent Name | Parent Phone Number | Parent Email | Address | Birthday | Edit | Delete | {%for student in students%}
|---|---|---|---|---|---|---|---|---|
| {{student['studentid']}} | {{student['studentname']}} | {{student['parentname']}} | {{student['parentphone']}} | {{student['parentemail']}} | {{student['address']}}, {{student['town']}} | {{student['birthday']}} | Edit |


Temporary project
This project is currently marked as temporary and due for deletion in {{ project.scheduled_deletion_time|naturaltime }}.
{% if user.is_anonymous %} {% url 'ui-users-signin' as signin_url %} {% url 'ui-users-signup' as signup_url %} {% blocktrans with current_url=request.path %}Please sign in with an existing account, or sign up for a new account, to save this project.
{% endblocktrans %} {% else %}Use the form below to save the project (with a new name and owning account it you like).
{% endif %}


Facebook Privacy Watcher & Analyzer
FPWA (Facebook Privacy Watcher and Analyzer) is a browser extension (i.e. plug-in) for Google Chrome. It collects data about the privacy settings as well as about behavior of Facebook users. It also helps users to customize their privacy settings as well as to know their behavior on Facebook.
In addition, a mobile application for Android operating system with similar functionality is on Google Play store. To see how it works, click here.
FPWA (Facebook Privacy Watcher and Analyzer) is a browser extension (i.e. plug-in) for Google Chrome. It collects data about the privacy settings as well as about behavior of Facebook users (for more details click here). It also helps users to customize their privacy settings on Facebook.
FPWA collects the following data:
Hash value of the Facebook ID.
Counter (including timestamps) indicating how often the plug-in was activated.
The visibility of each profile field before the first usage of the plug-in.
The visibility of each profile field after using the plug-in.
The type and visibility of timeline entries.
Number of friends.
Number of photos and labels.
Number of likes (per item, per type).
Number of comments (per item, per type).
Countries from which we receive the feedbacks.
The time users invest to examine newsfeed posts, w.r.t content creation, composition, and consumption.
Demographical data, e.g. the gender and age of the user.
The usage pattern w.r.t. functionalities.
The use of third partly apps.
Communication partners w.r.t to the social graph distance.
The content shared by users (type of content, when it has been created or watched, size).
Attention that users pay to Facebook (by measuring durations and frequencies of sessions).
The popularity of different Facebook functionalities and apps (and what is the dominating function).
Communication patterns.
Dynamics in user behavior.
The percentage of users who leave Facebook without logging out or closing Facebook browser tab.
Authors of the newsfeed content.
Fraction of friends contirbutes to the newsfeed.
The percentage of friends whos posts appear in newsfeed.
How long does it take newsfeed post to be displayed by user?
How long different types of newsfeed entries stay in the viewport of the browser before being clicked?
How many newsfeed entries have been viewed on average per day?
What are the types of accessed content?
Statistics about visited pages.
Your data will be collected without your identity (e.g. we know that "someone" liked some post, but we cannot know who did that).
Furthermore, your data is accumulated with the data of other participants. The collected data will be encoded using hash algorithms (i.e. your data is not reversible) which means that the database of FPWA plugin will contain just the hashed data.
Our source code is available online.
We need (and use) your data only for academic/research purposes. In particular the objective of our study is twofold:
First, evaluating data exposure in Facebook.
The huge set of functionalities Facebook offering to their users attracts lots
of people from different age rages to join Facebook. This will make them
vulnerable to different kinds of risks. Protecting personal data means a lot to most Facebook users.
Therefore, we provide a simple (color-based) mechanism that will help our volunteers to set their
privacy settings in a way that limit private data exposure.
Second, understanding user behavior in Facebook.
User behavior in Facebook is a critical issue for different parties. For example, authors of alternative online
social networks architectures to make well-founded design choices, and Facebook itself,
because understanding user behavior impacts their design choices in Facebook, and help them provide
the most appropriate functions and features to that user. In addition to that, user behavior in
Facebook may concern the user itself, for example, the user might be eager
to know statistics about his/her usage of Facebook because he could spend a lot
of his time on Facebook at the expense of his duty and responsibilities in the
life. These statistics may include: the most Facebook functionalities used,
the time user spend on Facebook, or even the time user spend on a specific
functionality, and much more of statistics that can be measured.
Note: Our study is an extension of a previous work.
First of all, FPWA helps you to better customize your privacy settings in Facebook. It provides an innovative interface to overlook and change privacy on Facebook easily. The basic idea is that people can gather information more easily when it is associated with colors. In the profile page of Facebook and were linked with colors. The idea is inspired from FPW.
Second, once we collect sufficient amount of data about you and other volunteers, you will be able to see information and statistics about your behavior in Facebook. Furthermore, you can compare your behavior to the behavior of other volunteers. In addition, more features are being developed.
Recently, most people tend to use smart devices to reach their Facebook account. From that, the need for smart devices application was emerged.
All you need is to login your Facebook account from our application, and then continue your usual behavior on the original Facebook application.
The application is collecting the Activity log in your Facebook account. it collects your actions that happened before 3 days, you can view the collected data before sending to the server. For more technical details, click here.
Your privacy data is completely annonymized, every data element (your id, group id, page id ...etc.) is hashed to annonymize your identity as well as in the Chrome browser plugin.
Dr. Hani Salah Prof. Dr. Thorsten Sturfe


The weather has been pretty warm this fall, although there have been a few cold days. Shane is glad that he can still go out and play a lot and it's not too cold when he walks to school. It stayed warm long enough for the stone cleaners to finish cleaning the outside of Shane's house, but the chimneys will have to wait until Spring. Shane likes to help clean inside the house. He especially likes to help vacuum the kitchen.
Shane made some picture frames for holiday gifts.
Grandma Connie and Papa Morris came to visit for Thanksgiving. We went to some fun places with them like the Carnegie Museum and the playground.
The season of winter holidays and holiday parties has begun. Shane got to go to Mom's poster fair and then to the INI holiday party. He also attended several other CMU holiday parties. Great Grandma Gertie and Great Grandpa Harry came to visit with Grandma Judy and Papa Mike during Hanukkah. Shane got to try out his new snow shovel during our brief blast of winter. Then we went to Maryland. Shane had a great time with Cousin Justin and he got to ride the Metro. Somehow we got everyone to look at the camera at the same time for a big family portrait, although the photographer tried to cut off the top of Uncle Jeff's head. Shane managed to get a big cut on his face after he and Justin wacked each other with books, but Mom managed to camouflage it so you can't see it in the family portrait. When we came back to Pittsburgh the snow was gone and the weather was warm again so we played outside a lot. Shane put together his new tool bench and showed Maya how to use his tools. Vacation has been fun, but Shane can't wait to go back to school.
Shane and Maya helped Mom and Dad make a chocolate souffle cake for New Year's Eve. After Shane ate his cake he went night night.
Privacy policy
This page maintained by Shane's Mom


My Web Dvelopment starting point
How you do anything is how you do everything.
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Assessment 7.9: Data Visualization
Languages
Primary Goals
Demonstrate understanding of available public data and data visualization tools for HTML and JS by creating a simple visualization.
Overview
In this project, you will build a visualization with chart.js and/or d3 to practice finding and visualizing data. This should take you about 1 hour.
Project Requirements
- [ ] You must choose a public data set.
- [ ] You must use chart.js or d3 to create a chart or graph to visualize 1 or 2 aspects of the data.
- [ ] Either your data or your vizualization method should be different than what you made in the topic exercise earlier.
- [ ] You may put the new visualization in a new project, or an existing project.
- [ ] You must have a short explanation under your data vizualization of how to read it.
- Bonus points if your data AND your vizualization method are be different than what you made in the topic exercise.
- Bonus points if the vizualization shape or color is relevant to the subject of the data.
Submitting your project
- If your project is a stand-alone data visualization, create a URL for it with GitHub pages. If it is part of a larger project, be sure to submit the url along with an explanation of where to look for it.
- Submit your project URL to your Program Manager.


Chef Workstation packages all the tools necessary to be successful with Chef Infra and InSpec.
Test Kitchen is an open source integration tool pioneered by Chef for developing and testing infrastructure code and software on isolated target platforms.
Chef Supermarket is the site for community cookbooks. It provides an easily searchable cookbook repository and a friendly web UI.
Chef Cookstyle is a code linting tool that helps you write better Chef Infra cookbooks by detecting and automatically correcting style, syntax, and logic mistakes in your code.