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10.25.17
In the first line of Post Malone’s “Rockstar,” he raps about “f---in’ hoes” and “poppin’ pillies,” and he “just feels like a rockstar.”
The song carries themes of sex, drugs and rock ’n’ roll lifestyle. Post Malone talks about the rockstar life and how he lives that life as a rapper.
“Rockstar” is the No. 1 song on Billboard’s Hot 100 Chart, and it’s one of the many songs hitting the charts that contain sexual messages rather than the traditional love songs that were popular decades before, such as “My Guy” by Mary Wells, which peaked at No. 1 in 1964.
A lot of rap and hip-hop artists are trying to mimic the rockstar image that rose in the ’60s in the counterculture era, Scott Kutil, a junior studying music production and the recording industry, said. And for hip-hop and rap artists, that means rapping about sex as a status of power.
“We’re in another big counterculture alternative age as we rise to the era of hip-hop, where hip-hop is now poppin’,” Kutil said. “That kind of contests to why a lot of love music right now are all about that whole hookup, sex party kind of vibe.”
A study conducted in 2009 by Evolutionary Psychology suggests the traditional love songs about passion and longing have turned into songs about sex. Of the 174 top-10 hits of 2009, 92 percent of them contained lyrics that conveyed “reproductive messages.”
The soaring love ballads that were played on vinyls, tapes and CDs are now sparse. Streaming playlists are now filled with songs about sex, and songs about pining over someone are left in the era of Motown. That can be attributed to the trend that people want instant gratification, an idea that applies to sex without or before a relationship.
Kevin Pan / For The Post
“My favorite love song is “Home” by Edward Sharpe and the Magnetic Zeros because it’s a guy and girl and I like the different aspects they both portray.” Mackenzie Mercer, an undecided freshman.
Kevin Pan / For The Post
"‘I’ve Got You Under My Skin’ by Frank Sinatra. It’s a classic, me and my father used to listen to Frank Sinatra back in the day." Dan Bova, a senior studying business entrepreneurship.
Kevin Pan / For The Post
“Probably ‘Greatest Love Story’ by Lanco. I just like the sound of them and it’s super interesting and catchy.” Cassidy Book, a freshman studying communication.
Kevin Pan / For The Post
“I like Lee Brice, ‘I Don’t Dance.’ It’s a country song. It’s really cute, it just talks about how a guy is dancing with his wife and he doesn’t dance but because he loves her so much he’ll just do whatever she wants.” Mary Paulin, an undecided freshman.
Kevin Pan / For The Post
“My favorite love song? ‘Can’t Help Falling In Love’ by Elvis because it’s my boyfriend and I’s song.” Ashlin Smart, a sophomore studying education.
Kevin Pan / For The Post
“Probably ‘Love’ by Kid Cudi because it’s straight from the heart. It’s not necessarily a love song, it’s more about telling people it’s OK if you go through a breakup. Everyone goes through it and everyone’s going to find that one person. I share it with my girlfriend, he performed it the night I asked her out.” Bryan Dudash, a senior studying engineering technology and management.
Kevin Pan / For The Post
“Probably ‘Lucky’ by Jason Mraz and Colbie Caillat. It’s just a really good duet.” Jake Roush, a freshman studying sociology criminology.
Kevin Pan / For The Post
“My favorite love song would probably be ‘Over My Dead Body’ by Drake. I just really vibe with it, I don’t know why, it just connects to my soul and I’m not really a lovey-dovey type of person but I feel like it really just relates to me.” Micah Gundani, a freshman studying education.
Kevin Pan / For The Post
“Probably ‘Burn’ by Usher. It’s just a classic jam.” Adrian Dixon, a freshman studying psychology pre med.
Kevin Pan / For The Post
“I like ‘Chasing Cars’ by Snow Patrol. It’s just a good song.” Daniel Mondale, a freshman studying freshwater biology.
Akil Houston, an associate professor of cultural and media studies, remembers a time when people made mixtapes as a “demonstration of how much you cared.” If a person had an infatuation with someone, they would compile a list of love songs and give it to the person they liked.
That is not something that is popular in today’s society, Houston, a pop culture expert, said. Because songs often reflect what is socially happening in the world, differences in courting might play a role in what people sing about.
 “A lot of times (the songs) reflect whatever is going on in pop culture.”
Ray Charles’ “I Got a Woman” is a song that has changed in meaning over the years, Houston said. The song was featured on the 2005 collaboration “Gold Digger,” by Kanye West and Jamie Foxx.
Charles’ rendition is about a woman who gives him money and supports him in his endeavors, Houston said, but West and Foxx sang about a woman who is after their money.
With the current age being obsessed with instant gratification, Houston said the passionate love ballads have phased out.
“It’s interesting that you go from these moments of love, yearning and desire to just ‘I need to be sexually satisfied,’” he said.
During the Motown era, the songs were more “sensual,” Kutil, the president of Brick City Records, said. Motown was a record label created by in the early ‘60s that predominantly signed black artists and gave them a place to record. Some artists that came out of the Motown era include Smokey Robinson, Diana Ross, The Jackson 5 and Stevie Wonder.
When artists such as The Beatles came to the forefront in the mid ’60s, it gave way to a counterculture.
Meghan Rowe, a freshman studying journalism, listens to a lot of songs from the Motown era and in the R&B genre. In that era, she said societal norms censored the artists, so they had to be less obvious.
“They may be talking about sex, but it was … more discreetly talked about,” Rowe said. “They’re singing more to the person that they want or they like.”
People of that generation had a different way of love and how they showed it, Kutil said. People would go on a date and hang out, but nowadays it seems sex is the ultimate end goal.
“Now, you think about a date and if it doesn’t end in a hookup, it’s not a date, which is kind of crappy … but that’s just the way culture is right now,” Kutil said.
Because rap and hip-hop is popular right now, a lot of rappers are producing music about sex to “get people’s attention,” Katelyn Gilday, a senior studying community and public health, said.
“With our culture now, everyone is kind of more into sex because music is an influence. ... When people hear (those songs) they’re going to think, ‘Oh yeah, that’s OK,’” Gilday said. “They know people are going to listen to that. If they talk about yearning for love, I don’t think people are going to listen to that.”
Along with losing the feelings of love in songs, the focus on developing individual artist has become secondary to the lyrics, Houston said.
Every song about sex and love can be broken down into its parts and convey the same message, Houston said. It’s all about seeing someone a person wants, going after it and getting it. Record labels are more worried about producing people who can sell records than developing artists, he added, and right now, songs about sex sell.
“So if you know that the tweeners like a young girl who is just a little bit older than them, kind of can sing, but has a look, then let’s just find someone we can put into that box,” Houston said. “Talent can be secondary.”
Sex was still prevalent in songs before the turn of the 21st century, and they were not subtle about those messages, Houston said. Marvin Gaye’s “Sexual Healing” and “Let’s Get It On” in the ’70s and ’80s, respectively, communicate obvious messages of sex.
Though Gaye is known for his expressions of sex and love on those songs, his artistry shows through on his fifth studio album Here, My Dear, Houston said. The album focused on his divorce from Anna Gordy Gaye and was said to pay for expenses that came out of the divorce. The album explored love, Houston said — just in a different way.
“In that time, I think you had artists who could capture moments and convey certain feelings like that,” Houston said. “People still do concept albums, but I think the ability to speak to different emotions is something that kind of gets lost.”
Despite a loss of artistry and a focus on sex, there are still some artists who are able to examine all feelings of love.
On their most recent albums, Adele and Beyonce highlighted aspects of love, Houston said. Though Adele’s “Hello” was not the “typical radio love song,” it still talked of longing and heartache. Beyonce’s visual album Lemonade showed what self-love and self-care looks like, Houston added.
Kutil said artists like Ed Sheeran have been able to bring back traditional ideas of love. Sheeran had success with his song “Thinking Out Loud,” which reached No. 2 in January 2015, and his most recent release, “Perfect,” talks about him finding the love of his life.
When the counterculture phase dies down, Kutil said there will be a resurgence of singing about love and passion.
“You have those phases where there’s going to be new artists that are coming out that are doing (new) things,” Kutil said. “It really comes down to how long this surge of alternativeness lasts.”
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'Can't wait!' - Chelsea fans think they know what Gareth Southgate will do against Kosovo
The Chelsea winger could be set to make his second start for England
by Andy HaCallum Hudson-Odoi is among a handful of players who could come into England's line-up for the Euro 2020 qualifier against Kosovo on Sunday.
The Chelsea winger was an unused substitute during Thursday's 7-0 thrashing of Montenegro, a result which sealed England's place at the European Championship next summer.
Southgate is set to recall Raheem Sterling after dropping the Manchester City winger for his role in the training ground bust-up with Joe Gomez.
Nick Pope is also poised to start over Jordan Pickford, with Southgate wanting to hand the Burnley goalkeeper some crucial international experience.
Hudson-Odoi could also be set to come into the side ahead of Jadon Sancho, leaving the England boss with a tough selection headache.
But Southgate says he is delighted to be blessed with a group of young, hungry players - many of whom ply their trade for Chelsea.
“But I would imagine that Sven thought the same of Beckham, Scholes, Carrick, Gerrard, Lampard,” Southgate says. “If you’re the England manager, you’re normally in a reasonably privileged position. We have given young players their opportunity ahead of their time, which has given us a greater depth.
“There’s nothing better than seeing the smile on Tammy Abraham’s face when he gets his first goal for England [on Thursday]. That almost makes everything worthwhile.”
Chelsea fans took to social media before the game, with many believing Hudson-Odoi will indeed get his chance against Kosovo and win his third senior cap.
Hudson-Odoi was heavily linked with a move to Bundesliga giants Bayern Munich in the summer before agreeing a new five-year contract at Stamford Bridge.
But the 19-year-old's new deal has not stopped speculation surrounding the Chelsea winger, and he is reportedly on a list of four young targets being monitored by Madrid manager Zinedine Zidane and president Florentino Perez.
Spanish publication El Desmarque says both Zidane and Perez are admirers of England international Hudson-Odoi and are keeping tabs on the teenager ahead of a potential summer swoop.


Congratulations on reaching the final project of the Robotics Nanodegree!
Previously, you worked on the Semantic Segmentation lab where you built a deep learning network that locates a particular human target within an image. For this project, you will utilize what you implemented and learned from that lab and extend it to train a deep learning model that will allow a simulated quadcopter to follow around the person that it detects!
Most of the code below is similar to the lab with some minor modifications. You can start with your existing solution, and modify and improve upon it to train the best possible model for this task.
You can click on any of the following to quickly jump to that part of this notebook:
We have provided you with a starting dataset for this project. Download instructions can be found in the README for this project's repo. Alternatively, you can collect additional data of your own to improve your model. Check out the "Collecting Data" section in the Project Lesson in the Classroom for more details!
import os
import glob
import sys
import tensorflow as tf
from scipy import misc
import numpy as np
from tensorflow.contrib.keras.python import keras
from tensorflow.contrib.keras.python.keras import layers, models
from tensorflow import image
from utils import scoring_utils
from utils.separable_conv2d import SeparableConv2DKeras, BilinearUpSampling2D
from utils import data_iterator
from utils import plotting_tools
from utils import model_tools
In the Classroom, we discussed the different layers that constitute a fully convolutional network (FCN). The following code will introduce you to the functions that you need to build your semantic segmentation model.
The Encoder for your FCN will essentially require separable convolution layers, due to their advantages as explained in the classroom. The 1x1 convolution layer in the FCN, however, is a regular convolution. Implementations for both are provided below for your use. Each includes batch normalization with the ReLU activation function applied to the layers.
def separable_conv2d_batchnorm(input_layer, filters, strides=1):
output_layer = SeparableConv2DKeras(filters=filters,kernel_size=3, strides=strides,
padding='same', activation='relu')(input_layer)
output_layer = layers.BatchNormalization()(output_layer)
return output_layer
def conv2d_batchnorm(input_layer, filters, kernel_size=3, strides=1):
output_layer = layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides,
padding='same', activation='relu')(input_layer)
output_layer = layers.BatchNormalization()(output_layer)
return output_layer
The following helper function implements the bilinear upsampling layer. Upsampling by a factor of 2 is generally recommended, but you can try out different factors as well. Upsampling is used in the decoder block of the FCN.
def bilinear_upsample(input_layer):
output_layer = BilinearUpSampling2D((2,2))(input_layer)
return output_layer
In the following cells, you will build an FCN to train a model to detect and locate the hero target within an image. The steps are:
encoder_block
decoder_block
Create an encoder block that includes a separable convolution layer using the separable_conv2d_batchnorm()
function. The filters
parameter defines the size or depth of the output layer. For example, 32 or 64.
def encoder_block(input_layer, filters, strides):
# TODO Create a separable convolution layer using the separable_conv2d_batchnorm() function.
output_layer = separable_conv2d_batchnorm(input_layer, filters, strides)
output_layer = separable_conv2d_batchnorm(output_layer, filters, 1)
return output_layer
The decoder block is comprised of three parts:
def decoder_block(small_ip_layer, large_ip_layer, filters):
# TODO Upsample the small input layer using the bilinear_upsample() function.
upsampled_small = bilinear_upsample(small_ip_layer)
# TODO Concatenate the upsampled and large input layers using layers.concatenate
output_layer = layers.concatenate([upsampled_small, large_ip_layer])
# TODO Add some number of separable convolution layers
output_layer = separable_conv2d_batchnorm(output_layer, filters)
output_layer = separable_conv2d_batchnorm(output_layer, filters)
return output_layer
Now that you have the encoder and decoder blocks ready, go ahead and build your FCN architecture!
There are three steps:
def fcn_model(inputs, num_classes):
# TODO Add Encoder Blocks.
# Remember that with each encoder layer, the depth of your model (the number of filters) increases.
encoder1 = encoder_block(inputs,filters=64, strides=2)
encoder2 = encoder_block(encoder1, filters=256, strides=2)
encoder3 = encoder_block(encoder2, filters=512, strides=2)
#encoder4 = encoder_block(encoder3, filters=512, strides=2)
# TODO Add 1x1 Convolution layer using conv2d_batchnorm().
#conv_layer = conv2d_batchnorm(encoder4, filters=256, kernel_size=1
conv_layer = conv2d_batchnorm(encoder3, filters=512, kernel_size=1)
# TODO: Add the same number of Decoder Blocks as the number of Encoder Blocks
decoder1 = decoder_block(conv_layer, encoder2,512)
decoder2 = decoder_block(decoder1, encoder1, 256)
#decoder3 = decoder_block(conv_layer, encoder1, 1)
decoder3 = decoder_block(decoder2, inputs, 64)
#decoder4 = decoder_block(decoder3, inputs, 32)
#decoder4 = decoder_block(decoder3, inputs, 1)
x = decoder3
# The function returns the output layer of your model. "x" is the final layer obtained from the last decoder_block()
return layers.Conv2D(num_classes, 1, activation='softmax', padding='same')(x)
The following cells will use the FCN you created and define an ouput layer based on the size of the processed image and the number of classes recognized. You will define the hyperparameters to compile and train your model.
Please Note: For this project, the helper code in data_iterator.py
will resize the copter images to 160x160x3 to speed up training.
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
image_hw = 160
image_shape = (image_hw, image_hw, 3)
inputs = layers.Input(image_shape)
num_classes = 3
# Call fcn_model()
output_layer = fcn_model(inputs, num_classes)
Define and tune your hyperparameters.
learning_rate = 0.0002
batch_size = 40
num_epochs = 15
steps_per_epoch = 225 # 8991 training images, batch size 40, 8991/40 ~= 225
validation_steps = 61 # 2403 validation images, batch size 40, 2403/40 ~= 61
workers = 2
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
# Define the Keras model and compile it for training
model = models.Model(inputs=inputs, outputs=output_layer)
model.compile(optimizer=keras.optimizers.Adam(learning_rate), loss='categorical_crossentropy')
# Data iterators for loading the training and validation data
train_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join('..', 'data', 'train'),
image_shape=image_shape,
shift_aug=True)
val_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join('..', 'data', 'validation'),
image_shape=image_shape)
logger_cb = plotting_tools.LoggerPlotter()
callbacks = [logger_cb]
model.fit_generator(train_iter,
steps_per_epoch = steps_per_epoch, # the number of batches per epoch,
epochs = num_epochs, # the number of epochs to train for,
validation_data = val_iter, # validation iterator
validation_steps = validation_steps, # the number of batches to validate on
callbacks=callbacks,
workers = workers)
# Save your trained model weights
weight_file_name = 'model_weights'
model_tools.save_network(model, weight_file_name)
Now that you have your model trained and saved, you can make predictions on your validation dataset. These predictions can be compared to the mask images, which are the ground truth labels, to evaluate how well your model is doing under different conditions.
There are three different predictions available from the helper code provided:
# If you need to load a model which you previously trained you can uncomment the codeline that calls the function below.
weight_file_name = 'model_weights'
model = model_tools.load_network(weight_file_name)
The following cell will write predictions to files and return paths to the appropriate directories.
The run_num
parameter is used to define or group all the data for a particular model run. You can change it for different runs. For example, 'run_1', 'run_2' etc.
run_num = 'run_2'
val_with_targ, pred_with_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_with_targ', 'sample_evaluation_data')
val_no_targ, pred_no_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_non_targ', 'sample_evaluation_data')
val_following, pred_following = model_tools.write_predictions_grade_set(model,
run_num,'following_images', 'sample_evaluation_data')
Now lets look at your predictions, and compare them to the ground truth labels and original images. Run each of the following cells to visualize some sample images from the predictions in the validation set.
# images while following the target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','following_images', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# images while at patrol without target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','patrol_non_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# images while at patrol with target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','patrol_with_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
Evaluate your model! The following cells include several different scores to help you evaluate your model under the different conditions discussed during the Prediction step.
# Scores for while the quad is following behind the target.
true_pos1, false_pos1, false_neg1, iou1 = scoring_utils.score_run_iou(val_following, pred_following)
# Scores for images while the quad is on patrol and the target is not visable
true_pos2, false_pos2, false_neg2, iou2 = scoring_utils.score_run_iou(val_no_targ, pred_no_targ)
# This score measures how well the neural network can detect the target from far away
true_pos3, false_pos3, false_neg3, iou3 = scoring_utils.score_run_iou(val_with_targ, pred_with_targ)
# Sum all the true positives, etc from the three datasets to get a weight for the score
true_pos = true_pos1 + true_pos2 + true_pos3
false_pos = false_pos1 + false_pos2 + false_pos3
false_neg = false_neg1 + false_neg2 + false_neg3
weight = true_pos/(true_pos+false_neg+false_pos)
print(weight)
# The IoU for the dataset that never includes the hero is excluded from grading
final_IoU = (iou1 + iou3)/2
print(final_IoU)
# And the final grade score is
final_score = final_IoU * weight
print(final_score)


Bursa Malaysia remains in the red on profit-taking at midday
KUALA LUMPUR, Nov 8 — Bursa Malaysia ended the morning session in the red at midday today due to profit-taking in selected heavyweights.
At 12.30pm, the benchmark index was 1.68 points lower at 1,607.93 against yesterday’s close of 1,609.33 and moved between 1,606.02 to 1,611.34.
The key index opened 2.24 points lower at 1,607.09.
On the broader market, losers outpaced gainers 355 to 319 with 432 counters unchanged, 887 untraded and 16 others suspended.
Turnover amounted to 1.71 billion shares worth RM928.94 million.
An analyst said investors are currently taking the cue on the profit made over the week, as the local bourse has risen above 1,610 benchmarks.
“Besides profit-taking, they are also awaiting for key data which would be released next week including employment rate, industrial production data as well as 2019 third-quarter gross domestic product,” she said.
As for heavyweights, IOI Corporation was down one sen to RM4.33, Maxis and Sime Darby dipped five sen RM5.44 and RM2.30, Hong Leong Financial declined 20 sen to RM17.28 and Petronas Gas slipped six sen to RM16.54.
Of the actives, Sapura Energy was up two sen to 28 sen, newly debutant Spring Art and Trive Property and NETX were all flat at 25 sen, 1.5 sen and two sen respectively.
For top gainers, Southern Acids uptick 15 sen to RM3.80, UWC up 13 sen RM2.67, and Sarawak Consolidated gained up 10 sen to RM1.81 while for top losers, Nestle dipped 50 sen to RM146.40, United Plantation declined 88 sen to RM25.00 and Hong Leong Financial slipped 20 sen to RM17.28.
The FBM Emas Index was 5.68 points lower at 11,383.69, the FBMT 100 Index dipped 6.93 points to 11,195.11 and the FBM Emas Shariah Index decreased 11.39 points to 11,908.42.
The FBM Ace declined 25.71 points to 4,961.07 and the FBM 70 was 10.96 points firmer at 14,098.18.
Sector-wise, the Financial Services Index loss 5.23 points to 15,686.18, the Industrial Products & Services Index edged up 0.09 of-a-point to 154.98 and the Plantation Index lost 32.98 points to 6,865.77. — Bernama


HomeSchool Dashboard
date_range
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12:02AM
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Marz The Hun
<EMAIL_ADDRESS>Brightness
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add
New Alarm
alarm
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Choose the Alarm Days
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Materialize Multiple Select
{% for alarm in alarms %}
{{ alarm.name }}
{{ alarm.time }}
{{ alarm.days }}
remove
edit
{% endfor %}
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--- tags: - earthquakes - geoscience author: display_name: Matt Peters email<EMAIL_ADDRESS>date: 2015-07-07 16:53:14 -0600 categories: [] EarthquakeEpicenters1850to2016: hub: name: Utah Earthquake Epicenters 1850 to 2016 item_id: 4c162e09b7b5436bae9a0e656b5343a7 updates: - November 2017 EarthquakeEpicentersMiningInduced1968to2016: hub: name: Utah Earthquake Epicenters Mining Induced 1928 to 2016 item_id: af0cbb8ecca942d58367f68bcd2643b6 updates: - November 2017 --- {% include abstract.html name="Earthquake Epicenters" stewards="UGS" abstract="The earthquake database consists of chronological list of earthquake origin time, location, and size for the Utah region." %}
Earthquake Epicenters 1850 to 2016 represents the modern epicenters of earthquakes in Utah.
The native spatial reference for this dataset is UTM Zone 12N, NAD83 (0.01 meter coordinate precision). There are no constraints or warranties with regard to the use of this dataset. Users are encouraged to attribute content to: Utah Geological Survey, State of Utah, SGID. Earthquake epicenters were created and are maintained by the Utah Geological Survey.
{% include contact.html subject=page.title contact=site.data.contacts.earthquakes %}
Earthquake Epicenters Mining Induced 1968 to 2016 represents the modern epicenters of earthquakes in Utah that are mining induced.


Type Function Library string.* Return value String Revision Current Public Release (2013.2076) Keywords string, gsub, sub, search See also string.find()
string.gmatch()
string.match()
Replace all occurrences of a pattern in a string.
string.gsub( s, pattern, repl [, n] )
s:gsub( pattern, replace [,n] )
String. The string to be searched.
String. A string specifying the pattern to match. See Lua String Manipulation.
String. If repl is a string, then its value is used for the replacement. The character % works as an escape character: any sequence in repl of the form %n, with n between 1 and 9, stands for the value of the n-th captured substring. The sequence %0 stands for the whole match. The sequence %% stands for a single %.
If repl is a table, then the table is queried for every match, using the first capture as the key; if the pattern specifies no captures, then the whole match is used as the key.
If repl is a function, then this function is called every time a match occurs, with all captured substrings passed as arguments, in order; if the pattern specifies no captures, then the whole match is passed as a sole argument.
If the value returned by the table query or by the function call is a string or a number, then it is used as the replacement string; otherwise, if it is false or nil, then there is no replacement (that is, the original match is kept in the string).
Number. Number of occurrences of pattern to replace.
print (string.gsub("Hello banana", "banana", "Corona user"))
print (string.gsub("banana", "a", "A", 2) -- limit substitutions made to 2
print (string.gsub("banana", "(an)", "%1-") -- capture any occurances of "an" and replace
print (string.gsub("banana", "a(n)", "a(%1)") -- brackets around n's which follow a's
print (string.gsub("banana", "(a)(n)", "%2%1") -- reverse any "an"s
print (string.gsub("Hello Lua user", "(%w+)", print) -- print any words found
print (string.gsub("Hello Lua user", "(%w+)", function(w) return string.len(w) end) -- replace with lengths
print (string.gsub("banana", "(a)", string.upper) -- make all "a"s found uppercase
print (string.gsub("banana", "(a)(n)", function(a,b) return b..a end) -- reverse any "an"s
print (string.gsub("The big {brown} fox jumped {over} the lazy {dog}.","{(.-)}", function(a) print(a) end )
print (string.gsub("The big {brown} fox jumped {over} the lazy {dog}.","{(.*)}", function(a) print(a) end )


About the M.I.T. (no affiliation) Grads
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Started with foundational HTML 5 and CSS.
Learned braded focused marketing strategies.
The joys of excel are endless.
Got into core coding conepts.
Started learning the fundamentals of code logic.
The most chalenging learning process I have taken on. Transformed the way I approach failure.
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// 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. // Copyright 2007 Google Inc. All Rights Reserved. /** * @fileoverview Newer versions of iPhoto include a Safari plugin which allows * the browser to detect if iPhoto is installed. Adapted from detection code * built into the Mac.com Gallery RSS feeds. * @see ../demos/useragent.html */ goog.provide('goog.userAgent.iphoto'); goog.require('goog.string'); goog.require('goog.userAgent'); (function() { var hasIphoto = false; var version = ''; /** * The plugin description string contains the version number as in the form * 'iPhoto 700'. This returns just the version number as a dotted string, * e.g., '7.0.0', compatible with {@code goog.string.compareVersions}. * @param {string} desc The version string. * @return {string} The dotted version. */ function getIphotoVersion(desc) { var matches = desc.match(/\d/g); return matches.join('.'); } if (goog.userAgent.WEBKIT && navigator.mimeTypes && navigator.mimeTypes.length > 0) { var iphoto = navigator.mimeTypes['application/photo']; if (iphoto) { hasIphoto = true; var description = iphoto['description']; if (description) { version = getIphotoVersion(description); } } } /** * Whether we can detect that the user has iPhoto installed. * @type {boolean} */ goog.userAgent.iphoto.HAS_IPHOTO = hasIphoto; /** * The version of iPhoto installed if found. * @type {string} */ goog.userAgent.iphoto.VERSION = version; })(); /** * Whether the installed version of iPhoto is as new or newer than a given * version. * @param {string} version The version to check. * @return {boolean} Whether the installed version of iPhoto is as new or newer * than a given version. */ goog.userAgent.iphoto.isVersion = function(version) { return goog.string.compareVersions( goog.userAgent.iphoto.VERSION, version) >= 0; };


Dead Again (1991) Rated R for violence and language. ---------- Out of 4 stars: **** (4 Stars)
Starring: Kenneth Branagh, Emma Thompson, Derek Jacobi, Andy Garcia, Wayne Knight, Robin Williams.
Dead Again has one of the best endings to a film I have ever seen. Period. It ranks up there with CITIZEN KANE, THE USUAL SUSPECTS, and other films with surprise endings. However, while the resolution is surprising, the denouement is so well done that you may find yourself watching it over and over again. Director Kenneth Branagh, with his second feature, has proven that he is very capable of directing many different types of films, and at the same time, starring in them. No other director that I know of has starred in his own films, given credible performances, and at the same time, created a masterful work.
Dead Again has a very refreshingly original plot which allows the film to combine the style of the 40s with the frantic, quick-paced action of the 90s. The film noir moments are filmed using black and white, not only for effect, but to give the viewers a little clarity. Both actors, Emma Thompson and Kenneth Branagh, have dual roles and the color/no color contrast allows viewers to distinguish which time period the events are taking place in. The only problem with the film is the beginning which starts in the past, and then shifts to the present. It was a little confusing trying to figure out that Branagh was playing two roles. Fortunately, once the film gets on its feet, it never stops its relentless adrenaline-pumping run.
Most of the film takes place in the present day, with Kenneth Branagh portraying Mike Church, a private detective. He is asked to investigate the mysterious circumstances surrounding one Amanda "Grace" Sharp (Emma Thompson).  She locks the door to her room and puts a chair under the door handle for added protection. But what makes her think she is being stalked is one of the essential pieces of information Church must deal with. She has been having nightmares about a man on death row who stabs her to death with a pair of shears which he stole after his final haircut. Church suggests that she go to a hypnotist who can help her. Franklyn Madison (Derek Jacobi) is only a part-time hypnotist, but a hypnotist nonetheless. He considers that her problem may be something in her past, perhaps in a past life. Church, being the logical and sensible man that he is, thinks that Madison is crazy. Madison puts Sharp under, and she begins to tell the story of a married couple: Roman and Margaret Strauss (Branagh and Thompson).
I will tread lightly because most of the film hereafter is mysterious in its unfolding, and I may not reveal anything at all. I will say that the rest of the plot consists of surprises, plot twists, and uncertainty. The revelation of the Strauss' life begins to uncover events which may have led to Sharp's paranoia. This story begins to clarify why Sharp is so nervous, but suddenly twists arrive in the plot, and nothing is as it seems. Is someone actually trying to kill her? At the hands of a lesser director, nothing would have made sense. Branagh, being his second major film, is a powerful director, with traces of Hitchcock in his film. We may even have our next Hitchcock in Branagh. Unfortunately, Branagh has strayed from mainstream films, choosing to direct the brilliant HAMLET, a film most people tried to stay away from.
Watching Dead Again, I began to realize how much detail is involved in Branagh's work. The music is perfect (and won a Golden Globe nomination) and sets the right mood for the different time periods. Composed by Patrick Doyle, who also appears in the film in both timelines, the eerily enchanting score enhances the mystery as well as the tone. The ending is the best moment of the film. Many directors don't know how to end a film, or it isn't consistent with the rest of the picture. Branagh uses slow motion to enhance the final showdown, a film technique which, if used appropriately, can strengthen the suspense and climax. Dead Again is one of the few films which end with the climax, instead of a resolution. In my opinion, most resolutions are slow and distasteful. Sometimes a film will call for one, but after the climax, most audience members don't care what happens. Branagh wisely ends this film once the high point of the film is finished, leaving a sense of pleasure, instead of a sense of boredom.
Most of the praise should go to the director, but without the actors, there is no director. Kenneth Branagh is one of the only directors who can star in a film and direct it at the same time, creating a masterpiece (this does not include Mel Gibson). As an actor, Branagh is terrific. His dual role is handled extremely well, and without revealing too much, his portrayal of Mike Church has some hints as to the ending of the film. Certainly, only Branagh would be able to handle this kind of role while directing. Emma Thompson fairs better than Branagh, because she has a more complex role to play. Thompson maintains her nervous, but paradoxally trusting, character throughout the film. In her dual role, she does the same thing Branagh does, revealing small clues as to who is who and what is what. Derek Jacobi has a tough job, trying to rise to the level of the two leads. Surprisingly, he does so, and gives one of his best performances I have seen him give. Andy Garcia gives a good performance in his smaller role. Unfortunately, Garcia's role is a little too small, and his performance isn't as memorable as the rest of the cast. Also worthy of note is Wayne Knight (from SEINFELD), and Robin Williams, toned down from his normal frantic self.
Dead Again is rated R for violence and language. The screenplay, written by Scott Frank, is very intelligent and witty. The plot twists are amazingly detailed and impressive, and I wonder how the Academy forgot to nominate this screenplay. Maybe it was too original for their tastes (and SILENCE OF THE LAMBS was out that year). The cinematography, done by Matthew F. Leonetti, is quite impressive, especially his management of both time lines. Dead Again is a masterful piece of filmmaking with impressive sets, great acting, and some of the best direction of a film of this genre. Snubbed by the Academy, this film exhibits Branagh's multiple talents, and the ending is pure cinema. Films these days aren't made like this anymore.
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2019 AMAs to feature showstopping performances
by OTRCLOS ANGELES -- Taylor Swift is getting the most buzz heading into the 2019 American Music Awards.
There was speculation earlier about whether Taylor would be able to perform at the awards show amid the battle with her former record label, Big Machine Records. Much to the relief of her dedicated Swifties, Taylor will be taking the stage this Sunday. Not only will the pop star perform during the live show, but she will be crowned the Artist of the Decade.
Taylor has won more AMAs than anyone else in the last ten years. Having won 23 AMAs total, Taylor could break the record this year for most AMAs won of all time with five additional nominations.
The 2019 AMAs will feature other performances from legendary female artists as well. Shania Twain, the top-selling female country artist of all time, will perform a medley of her biggest hits. In celebration of the 25th anniversary of her first AMAs performance, seven-time AMA winner Tony Braxton will perform "Breathe Again" and "Unbreak my Heart."
Lovebirds Camila Cabello and Shawn Mendes will take the stage together to perform their chart-topping collaboration "Senorita."
Ke$ha will be back to the AMA stage for the first time since 2013. Selenators can also rejoice because Selena Gomez will perform the world television premiere of her new single in what will be her first live TV performance in two years.
Audiences will also be treated to hit performances from two of 2019's breakout artists: Lizzo and Billie Eilish. Plus, Halsey and Dua Lipa will return to the stage after their showstopping performances last year.
More performers include Christina Aguilera alongside A Great Big World, Green Day, Post Malone with Travis Scott, Thomas Rhett, and Ozzy Osbourne.
Plus, the Jonas Brothers are joining the party with a special performance from their sold out concert in Boston.
If you're watching on KABC/ABC7 in Los Angeles, catch a special edition of "On the Red Carpet at the AMAs" at 7 p.m. PT.
The 2019 American Music Awards air on Sunday, 8 p.m. ET/PT | 7 p.m. CT on ABC.


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Half-Past Seven Stories
by Robert Gordon Anderson
Story 16: "He That Took the City"
Additional Information
- Year Published: 1922
- Language: English
- Country of Origin: United States of America
- Source: Anderson, R.G. (1922). Half-Past Seven Stories. New York, NY: G.P. Putnam’s Sons.
-
Readability:
- Flesch–Kincaid Level: 5.9
- Word Count: 3,836
- Genre: Adventure
- Keywords: friendship
- ✎ Cite This
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Marmaduke trudged up the road. And the road went up, up, up the hill. First he thought that road was like a great worm, always squirming ahead of him, but then he decided that, although it twisted, it didn’t squirm , it was too still for that. After all, it was more like a ribbon, a wide brown ribbon, tied around the green shoulder of the hill.
He wondered where that ribbon road went—over the hill and far away—perhaps clear round the World! But, no, it couldn’t do that, for there was the Sea between, and it must stop at the Sea. Anyway, he would have liked to have travelled over it, to the very end, to see all the people and animals that walked over it, and the cities and churches that stood by its side.
But first he must find the Toyman. That is what he had come for. And the Toyman had just gone over that very road. Marmaduke had seen him from the valley below, his long legs climbing up that hill and the little boy had hurried after him, calling and calling.
“’Llo, Toyman, ‘llo, Toyman!” he shouted.
He heard an answer and put his hand to his ear to hear more clearly.
“’Llo, Toyman, ‘llo, Toyman!” came the mocking answer, faint and far-away.
But it wasn’t the Toyman. It was Echo, calling back from the hills.
Marmaduke had always wanted to meet Echo, but so far he never had. He thought she must be something like the Star-Lady, whom he had met, only not quite so bright. Her voice sounded a little sadder, too, like the Bluebird’s in the Fall when he says “Goodbye” to the fields and flies to the South. Often he had run after Echo, but he never could catch up with her, nor even see a glimpse of her silver and green dress. She always played Hide-and-Seek with him, and he was always “it.”
However, he didn’t worry long about friend Echo this morning. He was thinking of the Toyman. For the Toyman’s face had looked worried—far away and sad. It had looked somehow as Echo’s voice always sounded . What was it Mother had said? “Poor Frank!”—that’s what she called him; “he’s in trouble,” she had whispered to Father.
Marmaduke didn’t know what he could do, but he wanted to catch up with him, and put his hand in his, and tell him not to worry at all, and say, if he needed money he could have all there was in Marmaduke’s bank—every last penny, even the bright ones.
Across the road a big jack-rabbit jumped—jumped sping—sping—sping —like a toy animal made of steel springs. Wienerwurst ran after the rabbit, but his master didn’t stop to chase Jack. He was afraid if he wasted any time he would never catch up with the Toyman.
At last the ribbon road reached the top of the hill and wound along it a little way before it started twisting down the other side. For a moment Marmaduke’s eyes followed it down hill, and he wanted to follow it with his legs too, there were so many wonderful and mysterious places where it went, but just then he caught sight of the Toyman.
He was sitting right on the top of the hill, sitting with his chin in his hands, and his eyes on the West far away. And he said never a word.
So Marmaduke just stole up softly, and put his face against the Toyman’s, and sat down beside him.
And then the Toyman’s eyes came back from far away and looked down on the little boy and smiled again.
“Don’t you worry, Toyman,” the little boy said to him, “don’t you worry about anything . It’ll all come out in the wash.”
The Toyman didn’t ask what he meant by that, for he knew it was a proverb, a boy’s proverb that was as good as any King Soloman ever made.
“Sure, sonny,” he repeated, “it’ll all come out in the wash.” And he patted the hand beside him.
You see, Marmaduke never asked the Toyman what his trouble really was, or anything at all. And that is always the very best way—when a friend’s in trouble, don’t bother him with a lot of questions—and pester the life out of him—but just take his mind off his troubles by suggesting some nice game to play—like marbles or “Duck-on-the-Rock,” or going fishing, or something; and if you can’t do that, just sit beside him, “quiet-like,” and be his friend.
For a while they sat so, drinking in the cool air, and looking down at the valley, and the white houses, and red barns, and the yellow haystacks, and the horses and people like ants crawling here and there. There were two ribbons in the valley now, one brown and one silver, the Road and the River. And from the Church with the Long White Finger Pointing at the Sky, came the sound of bells—pealing —pealing—up the hill to the Sky.
All else was still. But after they had listened for a while they discovered that it wasn’t so still as it had seemed. Every bird and insect, each leaf and blossom, was busy, preparing its dinner, or else just growing. A twig rustled as a little garter snake squirmed into the thicket. A little gray nuthatch looked for its lunch on a locust tree, crawling over the trunk head-downwards, while, on a branch overhead, a crested flycatcher perched watching, watching, then all-of-a-sudden swooped down and pounced on a fly, swallowed him, flew back to its perch, and watched again.
In the tall grasses which rose like a miniature forest around his head, green katydids jumped, as spry as monkeys. And, as he lay on his back, he could see, way up in the middle of the sky, and right on a line with his eye, Ole Robber Hawk himself, or else one of his relatives or friends. He was brown, of course, but against the blue of the sky he looked like a little black speck with a couple of thin wavy lines for wings.
There was music, too, for a woodthrush sang, oh ever so sweet, and the oriole whistled as clear as a flute, while a locust rattled away like the man who plays the drum and all the noisy things in the theatre-orchestra. But, busiest of all, at his feet an army of black ants hurried around a little hole in the ground, seeming quite as big as the people and horses in the valley below.
“It’s just like a little city here, isn’t it, Toyman?” Marmaduke said, “all the katydids, and bugs, and snakes, and things, workin’ an’ workin’ away.”
“Yes,” said the Toyman, as they watched Robber Hawk swing round and round in the sky, “how any one can feel lonely in the country I can’t see. I can understand it in the city, where you can’t speak to a soul without his putting his hand on his watch, but here there’s always a lot of folks with beaks and claws and tails, and all kinds o’ tongues an’ dialecks, that you don’t need any introduction to, to say ‘howdy!’”
But Marmaduke remembered that morning and how the Toyman had seemed in trouble. He had certainly looked lonely when Marmaduke and Wienerwurst had found him sitting up there on the hill, and the little boy couldn’t help asking,—“Don’t you ever feel lonely?
For the first time that afternoon the Toyman, who had been so worried, laughed his old hearty laugh, and Echo sent it back from her cave in the hill.
“No!” said he, “I don’t want any ole wife. Like as not she’d talk me to death. Besides I don’t feel lonely when you’re along, little fellow.”
The little boy felt very happy over that, but, for some reason or other, he felt quite embarrassed, too. Often, when he felt happiest, he couldn’t put his happiness into words—he just couldn’t talk about the particular thing that was making him happy. And, strange to say, he would usually talk about something quite different. So he said,—
“Let’s see your knife.”
The Toyman took it out. It was a beauty, too, with five blades, all of different sizes, and a corkscrew.
Marmaduke tried to open one of the blades, but he couldn’t, they were too strong for his fingers.
So the Toyman took it.
“Which shall it be?” he asked.
“The very biggest,” came the answer, “and oh, Toyman, let’s play ‘Mumbledy Peg!’”
“A galoochious idea!” exclaimed the Toyman, “how did you ever think of it?”
“Oh!” said Marmaduke, “I thought of it—just like this”; and he snapped his fingers to show just how quick. “But pshaw! I could think of lots more galoochious than that.” Then he added in delight,—“The one who loses has to pull the peg out of the ground with his teeth.”
Meanwhile the Toyman was driving that peg into the ground. When it was in so far that it seemed as if no Thirty White Horses could ever pull it out, they began the game—the famous game of Mumbledy Peg.
First, Marmaduke put the knife in the palm of his right hand and made that knife turn a somersault in the air. And it landed right on the blade point and stuck upright in the ground.
Then, taking the knife in the palm of his left hand, he made it turn another somersault in the air. Again it landed on the point of the blade and stuck in the ground, quivering deliciously.
“Neat work!” said the Toyman. Probably he said it too soon, for on the very next try Marmaduke missed, and the Toyman had his turn.
He took the knife and got just as far as Marmaduke with his tricks, then he missed, too.
So Marmaduke took another turn and clenched his right fist tight shut, and threw the knife in the air from that, and it turned another somersault clean, and landed straight up in the ground. And he did the same with his left hand clenched. He was getting on famously!
The next trick in the game of Mumbledy Peg was to twirl the knife from the tip of the first finger, then from the second, and so on. When Marmaduke tried it from the third finger, the knife fell on its point, quivered feebly as if it were sick, then fell over on its side, only part way up in the air.
“Can you get two fingers under it—between the blade and the ground?” said the Toyman eagerly. “If you can, it’s all right.”
“You try?” said Marmaduke.
“What—with these fingers?” laughed the Toyman, “you’d better try yours—you’d have more of a chance.”
So Marmaduke tried, and just managed to squeeze his two smallest between the blade and the ground. But when he tried twirling it from his last finger he failed. The knife fell over on its side, and he couldn’t squeeze any two of his fingers, even the smallest, between the grass and the blade.
“Oh dear!” he exclaimed, “I always miss with my ‘pinky.’”
However, the Toyman missed with his fourth finger, and Marmaduke was still ahead.
“I’m off my game,” the Toyman explained a little later, as he threw the knife over his left shoulder and failed, “and you’re in rare form!”
Now this was strange, for the Toyman was so good at work and games and everything, but I’m thinking it was like that time they played marbles—he did it on purpose, just to let the little boy have the fun of winning. That would have been like the Toyman.
Anyway, the last time Marmaduke threw the knife through the air, and it made its last somersault and stuck up in the ground, straight as straight as could be and quivering like a jews-harp, the Toyman said,—
“Congratulations, ole man, you’ve won!”
And somehow Marmaduke liked to be called “ole man,” and felt quite as proud over that as over winning the game.
Now the Toyman had to get down on his hands and knees and try to pull the peg out of the ground with his teeth. And oh, what a time he made of it, growling like a dog over a bone, all for the fun of the thing, until Marmaduke shouted in glee and Echo answered back from her cave again.
So for a long time they played Mumbledy Peg on the hill, while the shadows grew longer and longer on the grass at their feet. Then they stopped to rest and sat quiet “for a spell.”
Opposite them, in the West, were other hills, higher ones too, rising way up in the sky. And far above them curled great white clouds, standing still as still could be.
For a long while they watched those clouds, the man and the boy, then Marmaduke said,—
“I wonder if you see what I see.”
“What do you see, Sonny?” the Toyman replied.
“A great big city—look, there it is!” And the little boy pointed straight at the clouds.
“Why, to be sure!” exclaimed the Toyman, “There it is, an’ it looks mighty pretty. But just what do you make out?”
“Well!” replied Marmaduke, squinting his eye thoughtfully, “I see a big wall and towers on it—a whole lot of towers. There’s about fifty, I guess.”
The Toyman squinted too, and pointed his brown finger at the clouds, counting slowly under his breath.

“Where?” asked the Toyman, craning his neck.
Marmaduke pointed at the Cloud City.
“There—just behind the biggest tower.”
“Just a little to the right, you mean?” again asked the Toyman, trying hard to see so as not to miss anything in that wonderful city. Then he added,—”oh, I get it now—it’s got a gold cross on it an’ little diamonds at the tips. My! How they shine in the sun.”
Then Marmaduke put in,—
“An’ there’s flags on the towers, red, yellow and blue—”
“How nice they look!” the Toyman murmured, “All a wavin’ in the wind.”
“And there’s soldiers in the streets, with helmets on their heads, an’ spears, an’ things—”
“You bet—an’ you kin hear the silver shoes of their horses on the cobbles—”
“What kind of cobbles?”
The Toyman thought a moment—
“Oh, let me see—wh-h-y, I’d say they were all cut outo’ agate like your shooters—leastways they look like that at this distance. An’ the sidewalks, of course, are of gold—a blind man could tell that—”
“What else?” demanded Marmaduke, a little out of breath, and dazzled by all this sudden glory.
“Oh, a lot else—” the Toyman replied, “for one thing, the door-knobs in all the castles are silver—but then that’s nothin’—silver’s so common even their frying-pans are made outo’ that. But you ought to see their lamp-posts in the street. Their poles are built of ivory from the tusks of elephants of the first water; an’ the glass on top is nothing but rubies—”
“Whew!” exclaimed Marmaduke, “That’s a great city.”
“Yes,” added the Toyman, “it’s a great city.”
So for a little while they watched that great Cloud City with all its towers, and flags and banners waving in the wind; and heard the horses prance over the bright cobbles, and the glorious music coming from out the great church doors. Suddenly Marmaduke asked,—
“Do you ‘spose we could take that city?”
“‘Spose!” exclaimed the Toyman, “why, I’m sure of it.  And he put his hands to his lips and hallooed through them as through a trumpet, Echo answering back as if she had a trumpet, too.

And he made all the motions of mounting a horse himself, and calling, “Charge!” to the soldiers. It was a beautiful game, and so real that Marmaduke felt he was actually flying through the air on a winged horse, at the head of a mighty column of soldiers, straight towards the Cloud City.
But alas! They didn’t take that city, for, as they came near it, a horn sounded from the valley below. They turned back to look and there far, far beneath them, they saw the White House with the Green Blinds By the Side of the Road, and Mother standing by the door. She looked ever so tiny, and she was blowing that horn over and over to call them to supper. They reined in their horses to listen, for they knew what they would hear in a minute. Yes, there it came, that other horn—it was Echo’s. And when they turned in their saddles to look at the Cloud City again, it had vanished—vanished at the sound of the horn, with all their horses and men.
[Illustration: “So, for a little while, they watched that great Cloud City, with all its towers and flags and banners waving in the wind.”]
“Oh dear!” said Marmaduke, when he found himself on the hill once more, the game all over and ended, “she’s always mocking us an’ spoiling things, that Echo.
“We had a lesson ‘bout that, in Sunday school today,” Marmaduke told him, “all about ‘he who taketh the city.’ But the teacher said ‘he who conquers his spirit is greater’n he who taketh the city.’ How can you conquer a spirit, Toyman, when you can’t see it? Did you ever conquer your spirit?”
The Toyman looked very sober for a while, as they rose and turned their faces towards the road and the valley.
“Yes,” he said, “that’s what I’ve been trying to do all day. I had some trouble an’ temptation, an’ it was getting the best of me. You know, something bad in me that was tellin’ me to do things I’d oughtn’t to. I tried hard to get my fingers around that bad spirit an’ throw him out by his heels. That’s why I came up here on the hill to fight it out. You’ll understand some day—when you’re older.”
But, strange to say, the little boy thought he understood even then—at least part of it.
“Have you conquered it, Toyman?” he asked at last.
“I think so,” the Toyman answered slowly—“leastways I hope so.”
“And when did you conquer it?” the little boy prattled on.
The Toyman thought for a moment.
“When you just crep’ up behind me, so still an’ quiet, an’ put your face against mine.” And at that the Toyman hugged him again. “No, I guess we won’t take that city tonight—we’ve done a better job.”
As they walked to the brown ribbon road again, and over the hill to the valley, the sun was setting. They could see it perched like a gold saucer on the top of the hill, or like the shield of one of their soldiers. Gold bit by gold bit it sank below. Then it went altogether, out of sight, but the Cloud City came back again just for a moment, and a rosy light shone upon that Cloud City and all its banners, and towers, and spires.
Then suddenly it faded quite away. And the little boy and the Toyman walked home through the night, but they whistled together as they went.


Preface
The tengwar alphabet was created by Fëanor, as told to us by J.R.R. Tolkien.
The poems and pictures in the Tengwa ABC are original work by Chaered (and others where noted), first posted on the Vinyë Lambengolmor forum on Discord. It is published as “creative commons with attribution and share-alike” (CC-BY-SA), meaning you are free to use and reproduce it but must include a reference to the original source.
The original text of the poems is also part of the sample text set in https://quettali.org/alcar/text/abc, and we will keep an errata list for this book available there too.
The aim of this volume is to provide some entertainment and study material for those wishing to study Quenya. So enjoy, dear reader!
[Note: the original version of this document includes artistic pictures for each of the Tengwar to illustrate the poems. They have been omitted from this version to save space, but can seen in the original above, as well as in the PDF version which can be found at https://quettali.org/book/abc-book.pdf as of July 2023.]
The Tengwar
Fëanor’s craft,
on paper draft
with lines and curves,
our words preserves
curwë Feänáro
hyalinenna móro
telqui, lúvar, tehtar
quetië san rehtar
The craft of Fëanor
ink on paper
stems, bows, marks
thus they save/preserve words
Tinco (metal)
A sturdy metal will endure:
On hooves, it keeps the footing sure;
For cooks, sharp blades trump all the rest.
When smiths forge tools, they are the best
i-anturca TINCO vorta
pattalessë saipo norta
maxar-massë maica mahta
tamo maca, ilya lahta
The very hard metal preserves;
on the hoof, a boot rides;
in the hand of a cook, a blade wields;
a smith forges, each excels.
Parma (book)
Book and scroll sit on the shelf.
To us they are joy itself.
Oh such tales and lore in prose!
What to pick for my repose?
ela PARMA ar teccelma
palustassë, alasselma
ecë henta nyarvë, ñolë;
mana napuvan pó mólë?
Behold, a book and/beside a scroll
on the shelf, our happiness
(one) could read fiction, study;
What will I pick after work?
Calma (lamp)
The lamp so bright,
a bloom that burns;
lifts gloom with light
till dawn returns.
i calima CALMA
fá vanya vë alma
i lómessë laurë
tenn' entulë aurë
The bright lamp,
a flame beautiful like a flower;
in the gloom is golden
until the day returns.
Quessë (feather)
The birds have a feather,
where others a hair;
they fly up together
to greet the sky fair.
i aiwin i QUESSË
minomë findessë
amaviluvantë
menel suilantë
The birds have the feather
instead of head-of-hair;
they will fly up,
the sky greets.
Ando (gate)
This sturdy gate set in a hedge
drives ’tween the in and out a wedge:
it bids a friend a welcome stay
and tells a foe to go away.
ara hahta ANDO
tana tanwa lando
tar naþissë málo
leltas cotto talo
Beside a hedge, a gate
draws a sign of a boundary:
thither it welcomes a friend,
it sends an enemy thence.
Umbar (fate)
Our story has ended,
our future in gloom;
to turn into fairies,
oh sneer of the doom!
lúmelva avánië
tuluryalva lumba
na naucor rámaiti
venya faica UMBAR
Our time has gone,
our future is gloomy;
to be winged dwarves:
our contemptible doom.
Anga (iron)
The dwarves find red rock and then toil
to make it red-hot as to boil
and drain it of its iron-blood,
then leave the rest as soulless mud.
Casari rusca ondossë tas cesta
úranen laucata tér velis esta
ANGA ve sercë ondollo suryaila
loxo ve loico luppollo hehtaila
Dwarves (the) red (stuff) in the rock here seek,
with fire heat (it) so it boils out:
iron, like blood from the stone, flowing;
mud, like the corpse of the lumps/heaps, remaining.
Ungwë (spiderweb)
The spider sure a home can weave,
so cozy you would never leave.
To flying guests it’s so polite, says:
Come in, and we’ll have a bite.

Þúlë (spirit)
Into wintry air I stride
through the door, from the inside;
just so, breath escapes the lips
that were keeping it in grips.
hrívëa vilyassë
lenwetan findassë
tiër ÞÚL’ nurnonna
uþë laqui yonna
in winter’s air
I exit a door;
likewise breath into the dark [air]
escapes, if not enclosed.
Formen (north)
North in the sky we sense these lines
Where now the Silver Sickle shines.
Aule and Varda made their stands
and placed it up to ward our lands.
FORMEN menelessë
Valacirca essë
yo Aulë yo Varda
maitaner or arda
North (part) of the firmament:
Valacirca (is the) name,
both Aulë and Varda
made it above the Earth.
Harma (treasure)
This grab-bag of loot,
it shines in the sun.
The dragon’s afoot,
stop counting, and run!
haurallo si HARMA
alcaila nu arma
á norë, vá tocë
tas tensi i lócë!
This treasure from a hoard
glittering in a ray of sunlight
Run, don’t appraise
The dragon is already there!
Hwesta (breeze)
The breeze once more
blows from the shore,
makes this ship go
where rivers flow.
i nórima HWESTA
en-váya va hresta
ar nisteä cirya
yas sírë oi sirya
The strong breeze
again-blows from shore
and pushes a ship
where a river ever flows.
Anto (mouth)
Above the tongue-cave, as to guard
our mouth, the nose checks for what’s barred;
permitting passage only if
you pass its scrutinizing sniff.
lambo martaman to
nengwa alya ANTO
nustenen þantaila
mittarë cé saila
Above the residence of (the) tongue,
a nose helps the mouth
deciding with sense-of-smell
(the) entering, if wise.
Ampa (hook)
A hook here holds
firm in its grasp
a loop or folds.
It’s called a clasp.
AMPA ve quárë
napa ascarë
i quint harpessë
tancal i essë
A hook, like a fist
easily holds
the loop on the garment;
clasp is the name.
Anca (jaws)
The lion jaws wide,
huge fangs that can crunch!
But no need to hide:
it’s naptime, not lunch.
i ANCA o raura
carcara ar haura
mal áva tye rucë
vin háca, lá tyucë
The jaw from a lion,
toothed and huge;
but don’t you fear
he’s yawning, not chewing.
Unquë (hollow)
This helmet-hole lays bare
the brigand’s head of hair.
Perhaps its latest trim
was by an axe’s rim?
i UNQUË castolossë
apanta find' arpossë
yas hyaldava i címa
tar mittanë i síma
The cavity in the helmet
reveals the hair on the thief;
where of an axe, the blade-edge
entered thither into the mind.
Númen (west)
From the western sea,
waves forever more
roll in, come to see
and greet the sandy shore.
NÚMEN-eäreno
falmali en-oio
i litsenna sesta
inte, suilar hresta
Of the west-sea
some waves ever and again
onto the sand put
themselves, greet the shore.
Malta (gold)
The heavy gold,
it can feel cold.

His deeds are grim,
beware and flee!
ú merit qui ÑWALMË
va Melcor á norë
qui tye remuvaina
en oluvat orco
If you don’t want the torment,
run away from Melcor.
If you’ll be captured
soon you’ll become an orc.
Órë (heart)
When torn apart
by doubt or fear,
heed what your heart
bids you to hear.
quië attindossë
hya ñwalitye þossë
á ÓRË-tyan lasta
ortauvasses casta
Whenever in doubt
or you suffer from fear,
listen to your heart
it will lift [that] up.
Vala (angelic power)
The valar sing the world to be
Oh let us praise their deeds we see
As Ulmo, Lórien, Manwë fine
in concert with the maiar shine
Ilya VALA liru 'n Arda
laituvalvë ilya carda Ulmo,
Lórien ar Manwë
as i maiar im' ósanwë
Each vala sings the Earth.
We will praise each deed
Ulmo, Lórien and Manwë
with the Maiar in communion.
Anna (gift)
Beloved Yavanna,
your name contains ANNA;
how strange is this letter
without its garland.
i tengwa ANNA
nemë napanna
mal manen ú so
tecë Yavanna?
The letter ANNA (gift)
seems an additional [letter]
but how without it
to write “Yavanna” (fruit-giver)?
Vilya (air)
The bees fill honey-scented air
and follow trails to flowers where
the sweetness lures them, on this trek
from hive through skies out here and back.
nieréli quantar VILYA
losseli telyantar ilya
vilya i lissenna tië
etta vilya níþa sië
Some bees fill (the) air
inflorescences lure each one.
Air is the road to honey
therefore (the) air smells so sweet.
Rómen (east)
From Cuiviénen came the elves,
who Quendi count among themselves,
to travel past the eastern sea,
walk west, where Valinor might be.
Cuiviénenello eldar
Quendion i vanimeldar
haila RÓMEN-eär lelyar
Valinorenna estelyar
From Cuiviénen, elves
beautiful ones from (among the) Quendi
travel beyond the east-sea
hoping towards Valinor.
Arda (region)
From west to the east,
from north to the south;
Manwë rules Arda -
that is our house.
númello rómenna,
formello hyarmenna:
ela! - venya marda
na Manweva ARDA
From west to east
from north to south:
behold! - our home
is Manwë’s Arda.
Lambë (tongue)
The frog it has spoken,
lured flies with its croaking;
the last thing they’ll see
its mouth closing will be.
i quáceo LAMBË
telyanta sí tambë
ceþula pupsonna
tenn' anto en-yonna
The frog’s speech/tongue
lures now so
a curious fly
until (the) mouth (is) closed-again
Alda (tree)
This tree in the wood,
so long it has stood
in brown and in green
draws the letter here seen.
si ALDA tauressë
ná tengwava essë
i laica ar varnë
andavë termarnë
This tree in the forest
is a tengwar’s name which,
brown and green
has stood a long time.
Silmë (starlight)
Elves have affection
to starlight's perfection.
Behold these silvery gleams
on the lakes: exaltation!
Eldar samir nilmë
an telperin SILMË
elion, fainailë
angalailininnar
Elves have affection
to silver starlight
of the stars emitting
light to the Mirrormeres.
Essë (name)
What do you cry out in need?
Your friend’s name, aloud, indeed!
True name, nickname, nom de guerre:
call one, and they’ll soon be there!
mana carityë mi hranga lumessë?
haþeä tyen málotyava cilmessë!
hya senya an-ESSË ya renis o néþë -
úlumë úvatyë mi naica eressë
What do you do in a hard time?
The nickname of your friend helps you!!
or his given-name that he remembers from youth -
you'll never be in a bitter solitude!
Hyarmen (south)
Seagulls fly throughout the south
overhead the river’s mouth
to the water, waves and foam,
serenade the sea they roam.
HYARMEN yasse vilir
maiwi or i etsir
an falastala nén
lirila earen
South where fly
gulls over the mouth of a river
to foaming water
singing for the sea.
Yanta (bridge)
A bridge can cross a torrid stream
that else unfordable may seem.
The only path I seek to find
is to your inner heart and mind.
YANTA langa sír
lá síry' avanir
apa céþan rië
endatyanna tië
A bridge crosses a river
across the river’s unwill
but I am searching only
a road to your heart.
Úrë (heat)
Heat our faces
by sun’s graces;
past cold night
comes warmth and light.
ÚRË canwalvannar
Anar lelta annar
apa ringa lómë
sí ná aurë-nómë
Heat on(to) our faces,
the sun giving gifts;
after cold night
now is a place of sun-warmth.
Halla (tall)
From atop this soaring tree
looking out what do you see?
Far beyond the canopy
of green, all and infinity.
HALLA ald' alalla.
aldingallo palla
taurë ecë tuvë
imbaro ilúvë
A tall tree keeps growing.
From the tree-top, far beyond
(the) forest, one can see
the whole of the world.
Telco (stem)
A lone-legged tray
for fruit display
can also bear
a vowel there.
aiya erinqua
TELCO yávinqua
lá ecë hehta
nu ómatehta
Behold a lone
(but) fruitful leg.
You cannot omit
it under a vowel.
Ára (dawn)
Laurelin as dying deed
bore the Sun, a cherished seed.
To see the east in dawn aglow
good Ariën, our thanks we show!
Laurelin yavane telda
yáva: i Aran ammelda.
cenile rómen nu ÁRA
hentalvë Ariën mára
Laurelin bore at last
a fruit: the much-loved Sun.
Seeing the east under (the) dawn
we thank good Ariën.
Acknowledgements
As the editor and main author of this slim volume, let me say many thanks to those who helped to make it possible!
The one named Ránatar/Maivandion on the VL (Vinyë Lambengolmor) channel on Discord contributed: the picture for the opening poem, the poems and the picture idea for: umbar, ñoldo, anna, arda, and silmë; and the Quenya poem and the picture idea for: ñwalme and essë.
The one named Tecinde/Accalletir on VL contributed the picture for malta; and the picture idea, the picture and the poems for hyarmen.
Advice, some visual elements, corrections and encouragement were generously provided by multiple users on VL, chiefly (in no particular order) Paul Strack, Röandil, Gilruin, Luinyelle/ Ellostiel, Orondil, Elaran, Parmandil, and Valerie/Curulócë/Lômilôkhi.
© 2023, Chaered.
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.


A single PA view of the left hand and wrist was obtained for determination of bone age. A single PA view of the right hand and wrist was obtained for determination of bone age. Note that the standards are based on left hand radiographs.
None.
Sex: Male Female
Chronological age: (years + months)
Bone age: (years + months)
Standard deviation: months
Normal bone age, within 2 standard deviations of chronological age. Advanced bone age, more than 2 standard deviations above chronological age. Delayed bone age, more than 2 standard deviations below chronological age.


Particle QML Type
Represents particles manipulated by emitters and affectors More...
| Import Statement: | import QtQuick.Particles 2.0 |
Properties
- alpha : real
- autoRotate : bool
- ax : real
- ay : real
- blue : real
- currentSize : real
- endSize : real
- green : real
- initialAX : real
- initialAY : real
- initialVX : real
- initialVY : real
- initialX : real
- initialY : real
- lifeLeft : real
- lifeSpan : real
- red : real
- rotation : real
- rotationVelocity : real
- startSize : real
- t : real
- update : bool
- vx : real
- vy : real
- x : real
- xDeformationVectorX : real
- xDeformationVectorY : real
- y : real
- yDeformationVectorX : real
- yDeformationVectorY : real
Detailed Description
Particle elements are always managed internally by the ParticleSystem and cannot be created in QML. However, sometimes they are exposed via signals so as to allow arbitrary changes to the particle state
Property Documentation
ImageParticle can draw colorized particles. When it does so, alpha is used as the alpha channel of the color applied to the source image.
Values are from 0.0 to 1.0.
If autoRotate is true, then the particle's rotation will be set so that it faces the direction of travel, plus any rotation from the rotation or rotationVelocity properties.
ImageParticle can draw colorized particles. When it does so, blue is used as the blue channel of the color applied to the source image.
Values are from 0.0 to 1.0.
The size in pixels that the particle image is at the end of its life. If this value is less than 0, then it is disregarded and the particle will have its startSize for the entire lifetime.
ImageParticle can draw colorized particles. When it does so, green is used as the green channel of the color applied to the source image.
Values are from 0.0 to 1.0.
The x acceleration of the particle at the beginning of its lifetime.
The method of simulation prefers to have the initial values changed, rather than determining and changing the value at a given time. Change initial values in CustomEmitters instead of the current values.
The y acceleration of the particle at the beginning of its lifetime.
The method of simulation prefers to have the initial values changed, rather than determining and changing the value at a given time. Change initial values in CustomEmitters instead of the current values.
The x velocity of the particle at the beginning of its lifetime.
The method of simulation prefers to have the initial values changed, rather than determining and changing the value at a given time. Change initial values in CustomEmitters instead of the current values.
The y velocity of the particle at the beginning of its lifetime.
The method of simulation prefers to have the initial values changed, rather than determining and changing the value at a given time. Change initial values in CustomEmitters instead of the current values.
The x coordinate of the particle at the beginning of its lifetime.
The method of simulation prefers to have the initial values changed, rather than determining and changing the value at a given time. Change initial values in CustomEmitters instead of the current values.
The y coordinate of the particle at the beginning of its lifetime.
The method of simulation prefers to have the initial values changed, rather than determining and changing the value at a given time. Change initial values in CustomEmitters instead of the current values.
The time in seconds that the particle has left to live at the current point in time.
ImageParticle can draw colorized particles. When it does so, red is used as the red channel of the color applied to the source image.
Values are from 0.0 to 1.0.
Degrees clockwise per second that the particle image is rotated at while alive.
Inside an Affector, the changes made to the particle will only be applied if update is set to true.
The x component of the deformation vector along the X axis. ImageParticle can draw particles across non-square shapes. It will draw the texture rectangle across the parallelogram drawn with the x and y deformation vectors.
The x component of the deformation vector along the X axis. ImageParticle can draw particles across non-square shapes. It will draw the texture rectangle across the parallelogram drawn with the x and y deformation vectors.
The y component of the deformation vector along the X axis. ImageParticle can draw particles across non-square shapes. It will draw the texture rectangle across the parallelogram drawn with the x and y deformation vectors.
The y component of the deformation vector along the Y axis. ImageParticle can draw particles across non-square shapes. It will draw the texture rectangle across the parallelogram drawn with the x and y deformation vectors.


Material for Final Exam
Prob140 Spring 2017
A. Adhikari
Probability
- Chapter 1: Spaces, events, basic counting, exponential approximation
- Chapter 2: Addition and multiplication rules
- Chapter 5: Unions and intersections of several events
- Section 8.1, 8.2: Bayes’ rule
- Section 11.1: Probabilities by conditioning and recursion (discrete)
- Section 22.1: Probabilities by conditioning on a continuous variable
Distribution
- Chapter 3: Intro; equality versus equality in distribution
- Chapter 4: Joint, marginals, conditionals, independence (discrete case)
- Section 8.3: Random permutations and symmetry
- Sections 17.1, 17.2: Density
- Section 17.1 and Lab 9: CDF and inverse CDF
- Sections 18.2, 18.3: Density of a transformation
- Chapter 19: Joint, marginal, and conditional densities; independence
- Section 15.1, Homework 8, Section 24.1: Correlation
- Section 16.3: Central Limit Theorem
- Section 21.1: Convolution formula for the density of a sum
- Sections 21.2, 21.3: Moment generating functions, sums, and the CLT
Expectation
- Chapter 9: The crucial properties (discrete case) including method of indicators and expectations of functions
- Lab 4: Tail sum formula
- Section 10.3: Markov’s bound
- Section 11.2, 11.3: Expectation by conditioning (discrete case)
- Section 17.3: Expectation using densities
- Section 19.1: Expectation using joint densities
- Section 21.2: Moment generating functions
- Section 21.4: Chernoff bound
- Section 23.3: Conditional expectation as a least squares predictor
Variance
- Chapter 10: Intro, linear transformations, least squares, Chebychev’s bound
- Chapter 15: Covariance; variance of a sum
- Lab 12: Application of method of indicators
- Chapter 23: Conditional variance, mixtures, variance by conditioning, least squares
Classical Inference
- Sections 16.4, 16.5: IID sample mean; confidence interval for population mean
- Section 20.3: Maximum likelihood
- Section 20.5: Chi-squared statistic and goodness of fit
- Section 23.3: Least squares prediction and simple linear regression
Random Counts
- Sections 9.1, 10.1: Uniform on 1, 2, …, n
- Sections 6.1, 6.2, 6.3, Chapter 7, 9.2, 15.2, 20.3: Bernoulli, binomial and multinomial
- Sections 8.4, 9.2, 15.3, 15.4: Hypergeometric
- Section 6.4, Chapter 7, Sections 9.1, 9.3, 10.1, 20.3 and related homework: Poisson
- Homework 4: Geometric
- Section 17.3, 18.2: Expectation, variance, CDF, density
Beta
- Section 19.4: Integer parameters; uniform order statistics
- Chapter 22: Beta-binomial
Normal
- Section 16.3: CLT
- Sections 16.4, 16.5: Normal confidence intervals
- Section 18.2: Normal densities
- Chapter 20: Independent normal variables, linear combinations, squares, Rayleigh, chi squared
- Section 21.3: Normal MGF, sums, CLT
- Chapter 24: Bivariate normal, regression, independence, linear combinations
Gamma
- Section 18.1: Exponential
- Homework: Gamma function, gamma density, mean, variance
- Sections 20.4, 20.5: Chi squared
- Sections 20.5, 21.2: Sums of independent gammas with the same rate
Omitted from Final
- Sections 6.3, 10.4
- Chapters 12, 13, 14
- Sections 16.1, 16.2, 17.4, 24.5, 24.6


KINIT
Section: User Commands (1)Index Return to Main Contents
NAME
kinit - obtain and cache Kerberos ticket-granting ticketSYNOPSIS
- kinit
-
[-V]
[-l lifetime] [-s start_time]
[-r renewable_life]
[-p | -P]
[-f | -F]
[-a]
[-A]
[-C]
[-E]
[-v] [-R]
[-k [-t keytab_file]] [-c cache_name]
[-n]
[-S service_name][-T armor_ccache]
[-X attribute[=value]]
[principal]
DESCRIPTION
kinit obtains and caches an initial ticket-granting ticket for principal.OPTIONS
- -V
- display verbose output.
- -l lifetime
-
requests a ticket with the lifetime
lifetime.
The value for
lifetime
must be followed immediately by one of the following delimiters:
s seconds m minutes h hours d days
as in "kinit -l 90m". You cannot mix units; a value of `3h30m' will result in an error.
If the -l option is not specified, the default ticket lifetime (configured by each site) is used. Specifying a ticket lifetime longer than the maximum ticket lifetime (configured by each site) results in a ticket with the maximum lifetime.
- -s start_time
- requests a postdated ticket, valid starting at start_time. Postdated tickets are issued with the invalid flag set, and need to be fed back to the kdc before use.
- -r renewable_life
- requests renewable tickets, with a total lifetime of renewable_life. The duration is in the same format as the -l option, with the same delimiters.
- -f
- request forwardable tickets.
- -F
- do not request forwardable tickets.
- -p
- request proxiable tickets.
- -P
- do not request proxiable tickets.
- -a
- request tickets with the local address[es].
- -A
- request address-less tickets.
- -C
- requests canonicalization of the principal name.
- -E
- treats the principal name as an enterprise name.
- -v
- requests that the ticket granting ticket in the cache (with the invalid flag set) be passed to the kdc for validation. If the ticket is within its requested time range, the cache is replaced with the validated ticket.
- -R
- requests renewal of the ticket-granting ticket. Note that an expired ticket cannot be renewed, even if the ticket is still within its renewable life.
- -k [-t keytab_file]
- requests a ticket, obtained from a key in the local host's keytab file. The name and location of the keytab file may be specified with the -t keytab_file option; otherwise the default name and location will be used. By default a host ticket is requested but any principal may be specified. On a KDC, the special keytab location KDB: can be used to indicate that kinit should open the KDC database and look up the key directly. This permits an administrator to obtain tickets as any principal that supports password-based authentication.
- -n
- Requests anonymous processing. Two types of anonymous principals are supported. For fully anonymous Kerberos, configure pkinit on the KDC and configure pkinit_anchors in the client's krb5.conf. Then use the -n option with a principal of the form @REALM (an empty principal name followed by the at-sign and a realm name). If permitted by the KDC, an anonymous ticket will be returned. A second form of anonymous tickets is supported; these realm-exposed tickets hide the identity of the client but not the client's realm. For this mode, use kinit -n with a normal principal name. If supported by the KDC, the principal (but not realm) will be replaced by the anonymous principal. As of release 1.8, the MIT Kerberos KDC only supports fully anonymous operation.
- -T armor_ccache
- Specifies the name of a credential cache that already contains a ticket. If supported by the KDC, This ccache will be used to armor the request so that an attacker would have to know both the key of the armor ticket and the key of the principal used for authentication in order to attack the request. Armoring also makes sure that the response from the KDC is not modified in transit.
- -c cache_name
-
use
cache_name
as the Kerberos 5 credentials (ticket) cache name and location; if this
option is not used, the default cache name and location are used.
The default credentials cache may vary between systems. If the KRB5CCNAME environment variable is set, its value is used to name the default ticket cache. Any existing contents of the cache are destroyed by kinit.
- -S service_name
- specify an alternate service name to use when getting initial tickets.
- -X attribute[=value]
-
specify a pre-authentication attribute and value to be passed to
pre-authentication plugins. The acceptable attribute and
value values vary from pre-authentication plugin to plugin.
This option may be specified multiple times to specify multiple
attributes. If no value is specified, it is assumed to be
"yes".
The following attributes are recognized by the OpenSSL pkinit pre-authentication mechanism: X509_user_identity=value specify where to find user's X509 identity information X509_anchors=value specify where to find trusted X509 anchor information flag_RSA_PROTOCOL[=yes] specify use of RSA, rather than the default Diffie-Hellman protocol
ENVIRONMENT
Kinit uses the following environment variables:- KRB5CCNAME
- Location of the Kerberos 5 credentials (ticket) cache.
FILES
- /tmp/krb5cc_[uid]
- default location of Kerberos 5 credentials cache ([uid] is the decimal UID of the user).
- /etc/krb5.keytab
- default location for the local host's keytab file.
SEE ALSO
klist(1), kdestroy(1), kerberos(1)
Index
This document was created by man2html, using the manual pages.
Time: 05:29:05 GMT, December 24, 2015


Ramirez has 6 3s, leads No. 23 Arkansas women by Stony Brook
by Associated PressFAYATTEVILLE, Ark. (AP) — Amber Ramirez hit six 3-points and scored 26 points, Chelsea Dungee added 19 points and No. 23 Arkansas rolled to an 88-58 win over Stony Brook on Sunday.
Ramirez had half of her 3-pointers in a string when the Razorbacks scored on eight straight possessions, including six straight behind the arc. Ramirez hit two 3-pointers to put Arkansas up 36-25 and after the Seawolves hit a 3, Ramirez, Makayla Daniels, Ramirez and Dungee knocked down triples in a 15-0 run that made it 51-28 just before halftime.
Ramirez, a redshirt junior, had a 33-point game two years ago as a sophomore at TCU, hitting 11 of 17 behind the arc. Makayla Daniels had 13 points for the Razorbacks (4-0), who went 9 of 24 behind the line and 27 of 37 at the foul line.
Anastasia Warren led Stony Brook (3-1) with 15 points. The Seawolves were 3 of 19 from distance and shot 26% overall while going 15 of 23 from the line. Although they lost the rebounding battle 49-45 they grabbed 21 offensive rebounds.
_____
More AP women’s basketball: https://apnews.com/Womenscollegebasketball and https://twitter.com/AP_Top25


JSemcor supports all currently available versions of Semcor. No version of Semcor is included with the JSemcor distribution; Semcor must be downloaded separately from Rada Milhacea’s website at http://www.cs.unt.edu/~rada/downloads.html.
The freely available version of JSemcor is licensed for use for non-commercial purposes only, as long as proper acknowledgment is made. Details can be found in the license, which is included with the distribution. The copyright on the software is owned by MIT; if you wish to use the software for commercial purposes, please contact the MIT Technology Licensing Office for more information on how to obtain a commercial license.
The main interface for accessing concordance data is the {@link edu.mit.jsemcor.main.IConcordanceSet} interface. The distribution comes with a single default implementation of this interface, the {@link edu.mit.jsemcor.main.Semcor} class. In the simplest case, where you are using data files on the same filesystem as your Java program, you can instantiate the {@link edu.mit.jsemcor.main.Semcor} class with a single argument, a Java {@link java.net.URL} object that points to the directory where the Semcor concordance data files are located.
An example of this can be found in below, in the form of a Java method testSemcor(). In that method, the first block of two lines (4-5) deals with constructing a {@link java.net.URL} object that points to the Semcor data files. The base Semcor directory is the directory that contains the subdirectories brown1, brown2, and brownv. In this example, it is assumed that the Semcor zip file was unzipped to the location “C:\Semcor\”. This may be different on your system depending on where you choose to put your Semcor files. The second block of code, two lines long (8-9), constructs an instance of the default Semcor object, and opens it by calling the open() method. The next block of lines (12-14) retrieves the first context file from the concordance by name, and retrieves the first sentence of that context file by direct access. Following that, lines 17-23 comprise a simple for loop searches for the first wordform in that sentence that has an assigned sense. Once that word form is found, some salient characteristics are printed to the console on lines 26-28. The text following the example code shows the console output of the method.
Sample Code:
1 public void testSemcor() throws IOException { 2 3 // construct the URL to the Semcor directory 4 String path = "C:/Semcor/"; 5 URL url = new URL("file", null, path); 6 7 // construct the semcor object and open it 8 IConcordanceSet semcor = new Semcor(url); 9 semcor.open(); 10 11 // look up first sentence of first context 12 IConcordance concord = semcor.get("brown1"); 13 IContext context = concord.getContext("br-a01"); 14 ISentence sentence = context.getSentences().get(0); 15 16 // find first word with non-null sense 17 IWordform wordform = null; 18 for(IWordform wf : sentence.getWordList()){ 19 if(wf.getSemanticTag() != null){ 20 wordform = wf; 21 break; 22 } 23 } 24 25 // print it 26 System.out.println("Text = " + wordform.getText()); 27 System.out.println("POS = " + wordform.getPOSTag().getValue()); 28 System.out.println("Sense Key = " + wordform.getSemanticTag().getSenseKeys().get(0)); 29 }
Sample Code Output:
1 Text = Fulton County Grand Jury 2 POS = NNP 3 Sense Key = group%1:03:00::


Contact name
City
Country
Is Promoted
Maria Andres
Berlin
Germany
Ana Trujillo
Mexico D.F.
Mexico
Antonio Moreno
Mexico D.F.
Mexico
Thomas Hardy
Lond
UK


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.apache.hivemind.internal.ser.ServiceSerializationHelper
public class ServiceSerializationHelper
Class used to hold a global instance of
ServiceSerializationSupport
, so that
ServiceToken
s may locate them.
| Constructor Summary | |
|---|---|
ServiceSerializationHelper()
|
| Method Summary | |
|---|---|
static ServiceSerializationSupport |
getServiceSerializationSupport()
Returns the previously stored SSS. |
static void |
setServiceSerializationSupport(ServiceSerializationSupport serviceSerializationSupport)
Stores the SSS instance for later access; if an existing SSS is already stored, then an error is logged (should be just one SSS per class loader). |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public ServiceSerializationHelper()
| Method Detail |
|---|
public static ServiceSerializationSupport getServiceSerializationSupport()
ApplicationRuntimeException
- if no SSS has been stored.public static void setServiceSerializationSupport(ServiceSerializationSupport serviceSerializationSupport)
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


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