| psychological experiments and studies |
| magazines from American history |
| Supreme Court cases concerned with African-Americans |
| Romantic-era composers |
| African-American authors |
| programming terms |
| pre-1960s movies |
| Nobel Prize winners in physiology or medicine |
| popes |
| short story authors |
| rocks and minerals |
| Native American peoples |
| skyscrapers |
| 20th-century Middle Eastern leaders |
| Jewish lifecycle events |
| works of mystery and detective fiction |
| ancient Greek places |
| circuit components |
| Roman emperors |
| modern speeches |
| postmodern authors |
| African-American civil rights leaders |
| massacres |
| Arthurian characters |
| works of Russian short fiction |
| Dutch paintings |
| chemistry lab techniques |
| founders of religious traditions |
| active volcanoes |
| photography pioneers |
| mortal women in Greek myth |
| Charles Dickens novels |
| assassinations |
| rocket scientists |
| early 20th-century art movements |
| medieval battles |
| non-Shakespeare classical English dramas |
| ballets (part 2) |
| South American political leaders |
| schools of Western philosophy |
| ancient empires of the Mediterranean and Near East |
| ballets |
| Revolutionary War battles |
| mountains |
| scientific experiments |
| American third parties |
| space missions |
| video game series |
| economic concepts |
| Napoleonic battles |
| choreographers |
| classifications of mathematical functions |
| Secretaries of State |
| chemical elements |
| Shakespearean villains |
| European royal families |
| ancient Greek plays |
| works by Mozart |
| British reform movements |
| Greek mythological monsters |
| authors of speculative fiction |
| Supreme Court cases (part 2) |
| American composers |
| Mexican leaders |
| campaigns in the Pacific Theater of World War II |
| works by Ludwig van Beethoven |
| scientific scales |
| explorers |
| 20th-century African leaders |
| music theory terms |
| Chinese dynasties |
| distinctions among types of plants |
| British prime ministers |
| musicals (part 2) |
| moons |
| feminists |
| classic American television series |
| peoples of the early Middle Ages |
| North American rivers |
| battles of the ancient world |
| anthropologists |
| 20th-century physicists |
| western European rivers |
| ancient philosophers |
| aviators |
| World War II battles |
| African bodies of water |
| art museums |
| musicals (part 1) |
| deserts |
| mathematicians |
| sculptors |
| treaties |
| Supreme Court cases |
| operas |
| classes of particles |
| Asian rivers |
| New York Yankees |
| architects |
| 20th-century composers |
| Hindu gods and heroes |
| Trojan War heroes |
| tennis players |
| Latin American authors |
| Civil War battles |
| Jewish holidays |
| types of computation problems |
| quintuples |
| Olympics (pre-2000) |
| American warships |
| common mistakes (part 2) |
| religious texts |
| British monarchs |
| Norse gods |
| medieval Islamic dynasties |
| animal phyla |
| common mistakes |
| American plays |
| golfers |
| Russian tsars |
| psychologists |
| 20th-century paintings |
| elections |
| Egyptian deities |
| Japanese authors |
| Hockey Hall of Famers |
| Revolutionary War generals |
| organelles |
| kings of France |
| economists |


Titanic (James Cameron), 1997
Review from Cinephobia
It is with some surprise that I come to write this review. For, eight months after it opened, it's a little disorientating to remind myself that I actually did very much enjoy James Cameron's epic "Titanic." That's because, somewhere in that time period, the unrelenting crudfest that has surrounded this film has made me hate the film and everything about it.   And surely I can't be alone in wishing that Celine Dione's heart would just *stop*?
But I want to be objective. So, having acknowledged my qualms, I'll try to set them aside. And let's get at the question that the hype has all too often obscured: is the film any good? If so, why? And if not, why not?
The key to this question is to situate the film within the correct genre. James Cameron, let's remember, is an action director; he is probably America's most talented (Spielberg at his best is better, but Spielberg's problems of late are a whole different issue). He made "Aliens" (my vote as the best sci-fi actioner of the eighties), both "Terminator" films, "The Abyss," and "True Lies." He is not known as director of romances. True, one thing that was notable in his best films ("Aliens" and the first "Terminator") was an attention to character. But this was never a feature that could stand on its own, and until "Titanic" I don't think Cameron ever harboured illusions that it could. The beauty of Cameron's work was that he could create serviceable, yet affecting, emotional subplots and integrate them fully into the thrust of an action narrative (think how well Ripley's maternal instincts are played upon in "Aliens"). We must see the plot of Titanic in these terms. Do we really believe that Cameron conceived the love story between these two characters and then decided the perfect backdrop was the sinking of the Titanic? I don't. I'm pretty sure Cameron started with the thought of how exciting the sinking scenes would be and then grafted Jack and Rose into the events afterwards.
The film bears this out. As a good old fashioned disaster movie, "Titanic" is superb. The special effects, while not as flawless as some would have you believe, are extremely impressive. This is even truer when you consider how many involve water effects, which are notoriously difficult: they become expensive when done full scale (think "Waterworld") and tend to look very fake when miniaturised (think of the breaking dam in "Superman"). Cameron has the advantage of making his film after the rediscovery of the ship's wreckage. This has opened up a great deal of new information, which happily points to a sequence of events considerably more visually exciting than the old image of the ship sinking intact under the waves. And Cameron is ingenious at working his characters into the right places to witness all the best action. The imagery here is stunning, from the creepy bits as icy water chases rats through the hull to the showstopping final scenes as the stern sinks underwater. Cameron delivers all the highpoints like the skilled craftsman he is; the flawless framing, editing, and choreography of action should be taken up as an example within the industry (take note Michael Bay). These scenes get about an hour devoted to them, which is about the time the ship took to sink, but Cameron isn't after some "High Noon"-style exercise in real time editing.  On this level the film is an enormous achievement, and I came out of the cinema more excited than I have for quite a while. ("Face/Off" was probably the last film that gave me such a buzz).
Yet I quickly came to begrudge the film its success. Why? Because people seem to enjoy the film at a level on which it isn't any good. It was the love story that seemed to be drawing the repeat business, and the film's success at the Oscars was based, presumably, on the belief that the film was (or could be passed off as) a work of important, substantial, serious drama. After all, you don't give Oscars to action films (though they sometimes get nominations, as several of Spielberg's have). And this aspect of the film is a bare minimum effort: as I have suggested, it is subservient to the action. As drama in its own right it has considerably less integrity than Sarah Conner's plight in "The Terminator" or even Ripley's affecting angst in "Aliens." Essentially we have a familiar, hackneyed class conflict romance such as we have seen hundreds of times elsewhere, told with less flair than usual. Cameron doesn't add any surprising notes to the cliched story, and his representation of class oppression is very schematic (I'm inclined to think a British or Australian director might have brought out the logic of class with more subtlety and appreciation). Many of Cameron's character moments are oafishly clumsy and obvious (think of the business with the loogy, or the sequence with the Picassos, or the way Rose immediately spots the lifeboat shortage).
The performances are nothing to write home about. Kate Winslett is strong and well cast, but the flat material gives her no character nuances to try and draw out. Leonardo DiCaprio is all wrong: I've liked him in several previous roles (I thought he was excellent in Baz Luhrmann's superb "Romeo and Juliet"), but Jack is not his finest moment. DiCaprio looks all out of place in this time period, and there are none of the rough edges to his character that a life of poverty would likely produce (this is the kind of thing I mean when I say Cameron doesn't get the true workings of class). He's too unrelentingly perfect: it's like watching Winslett get seduced by a cherub. Billy Zane is a lot more entertaining, mainly because he seems to have realised how preposterous his part is and decided to ham it up. He has a blatant caddishness that is actually extremely funny: if Cameron was a little more appreciative of this we could have had an interesting film that explored Winslett's sly appreciation of his antisocial behaviour. But Cameron seems to take him seriously, and indeed seems to accept David Warner's henchman as a real character too. (Warner actually played the romantic lead in an earlier, less carefully marketed version of the story). Ultimately the stuntmen and CGI virtual people come off as more convincing: they at least know how to die.
So I can't go along with those who gave this film a Best Picture Oscar (against an unusually strong field, too) or think that it's the modern "Gone with the Wind." Let's just all get ourselves large screen TVs with hi-fi stereo sound and watch the final hour over and over again. Only then will sanity prevail.
_____________________ Review (C) 1998 Stephen Rowley. All rights reserved. Please do not reproduce in whole or in part without my permission.
I can be contacted at cinephobia@Not!yahoo.com (Remove the Not! for correct address).
For more reviews and essays, check out Cinephobia: http://www.werple.net.au/~lerowley/cinephob.htm
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


_The_Best_Man_ (R) *** (out of ****)
If you ask me, it's impossible to not like a film that ends with the entire cast doing the electric slide to Cameo's '80s funk classic "Candy." But there's more to enjoy in _The_Best_Man_ than its infectious full company closing dance number. First-time writer-director Malcolm D. Lee's fresh and fun ensemble comedy is a classy effort that delivers the requisite doses of laughs and romance along with a fair amount of intelligence.
As the title implies, the central event in _The_Best_Man_ is a wedding, that between football star Lance Sullivan (Morris Chestnut) and his longtime love Mia (Monica Calhoun). The blessed event reunites a number of the couple's college friends, including Murch (Harold Perrineau Jr.), henpecked by longtime girlfriend Shelby (Melissa DeSousa); self-styled player Quentin (Terrence Howard); successful BET television producer Jordan Armstrong (Nia Long); and of course the best man himself, author Harper Stewart (Taye Diggs), who is shy to committing to long-suffering girlfriend Robin (Sanaa Lathan). He shakes up what could have been a smooth and blissful reunion when an advance copy of soon-to-be-published novel--not-so-loosely based on his and his friends' dirty little secrets--makes the rounds within this tightly-knit circle.
This is the film's one aggravating contrivance. Harper did not see any harm in airing his personal dirty laundry in the book because it was scheduled to be released after the big reunion at the wedding and hence wouldn't have to face everyone about it--or endanger Lance and Mia's impending nuptials. In the latter respect, it's understandable why he would not want his friends did not see the book until its release, but even once the two are wed, they and the others would eventually get upset with him anyway. And Harper is presented as someone who values friendship, not to mention a great writer; he is mentioned to be set for an appearance on _Oprah_ for his book. With the plot of his book so closely mirroring his real-life past, Harper doesn't appear to be that imaginative an author.
But one doesn't dwell on the issue since Lee's own writing and execution is energetic and entertaining. _The_Best_Man_ may be named for one character, but this is an ensemble film in the best sense, with all the characters each given their moments to shine and doing so brilliantly. In fact, Harper is perhaps the least interesting of the bunch; far more amusing are the smooth, smug womanizer Quentin and the shrewish Shelby, both played to perfection by Howard and DeSousa. This is not to say that the two most central players, Diggs and Long, fail to hold their own; blessed with talent and chemistry to match their good looks, they make for very captivating and likable leads.
As many times as I laughed during _The_Best_Man_, what I remember most clearly are the sweet and more serious moments. While the scandal of Harper's book is the engine of the film, its heart lies in love and fidelity--not just to lovers, but also to friends. The resolution to one of the plot threads may be a disappointment to some audiences, but it ends on the right note, and Lee must be commended for not going for the cheaply crowd-pleasing twist. In any event, any dissatisfaction is completely erased by the closing dance number--a perfect delight of a capper to a perfectly delightful film.
Michael Dequina<EMAIL_ADDRESS>|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>Mr. Brown's Movie Site: http://welcome.to/mrbrown CinemaReview Magazine: http://www.CinemaReview.com on ICQ: #25289934 | on AOL Instant Messenger: MrBrown23
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


Select running mode
This page allows you to select whether you are running the MWC Qt Wallet as a standard online hot wallet (the default choice and what most users will use), An "Online Node" which is used to submit transactions from cold storage, or a "Cold Wallet" which is used in conjunction with the "Online Node" and installed on a computer that never connects to the internet. Transactions are sent/received/finalized on the "Cold Wallet" but submitted with the "Online Node"
More details about Cold Wallet usecase you can find at our Cold Wallet Guide
https://github.com/mwcproject/mwc-qt-wallet/blob/2019_09_cold_wallet/DOC/cold_wallet.md
Any Questions
Email<EMAIL_ADDRESS>Twitter: @M_W_Coin
Discord: https://discordapp.com/invite/eUNwqf3
Telegram: https://t.me/joinchat/IJTzpk33q3uBnautDTC5Sw


Each ETF has been selected to balance considerations of low cost and high liquidity as a means to access each asset class. When making the selection for each ETF, we considered the following: expense ratio, bid-ask spread, assets under management, number of holdings, exchange rate hedging, and capital gains implications. Read more about our Investment Selection Methodology.
Stock ETFs
*US Total Stock Market
Vanguard U.S. Total Stock Market Index ETF (VTI)
*US Large-Cap Value Stocks
Vanguard US Large-Cap Value Index ETF (VTV)
*US Mid-Cap Value Stocks
Vanguard US Mid-Cap Value Index ETF (VOE)
*US Small-Cap Value Stocks
Vanguard US Small-Cap Value Index ETF (VBR)
Bond ETFs
*Short-Term Treasuries
iShares Short-Term Treasury Bond Index ETF (SHV)
*Inflation Protected Bonds
Vanguard US Short-Term Inflation-Protected treasury Bond Index ETF (VTIP)
*National Municipal Bonds
iShares National AMT-Free Muni Bond Index,ETF(MUB)


APP FEATURES
Barcode Scanner
Use the barcode scanner to automatically retrieve the name and picture of your food item.
Pantry
Organize your food items in ascending order of the expiration date.
Expired Page
Keeps all of your expired items up to 7 days. You also have the option to move them to your grocery list.
Grocery List
Create a shopping list from your expired items or manually add more, so you can have a reference when you go to the store.
APPS SCREENSHOT
GET THE APP
Download and start keeping track of your food today!
GET IN TOUCH
Please feel free to contact me if you have any questions.
Phone Number
(909)954-7470
Email Address
lafrias2@gmail.com


{% load user_templatetags %} {# Personal details #}
User: {{ application.user.username }}
My first (given) name(s): {{ application.first_names }}
My last (family) name(s): {{ application.last_name }}
Suffix (e.g., Jr.), if applicable: {{ application.suffix }}
{{ SITE_NAME }} e-mail: {{ application.user.email }}
Researcher's Category: {{ application.get_researcher_category_display }}
Organization Name: {{ application.organization_name }}
Job title or position: {{ application.job_title }}
City: {{ application.city }}
State/Province: {{ application.state_province }}
ZIP/postal code: {{ application.zip_code }}
Country: {{ application.get_country_display }}
Webpage: {{ application.webpage }}
Reference Category: {{ application.get_reference_category_display }}
Reference's Name: {{ application.reference_name }}
Reference's Email: {{ application.reference_email }}
Reference's Organization: {{ application.reference_organization }}
Reference's job title or position: {{ application.reference_title }}
Research Topic: {{ application.research_summary }}
Date of this agreement: {{ application.application_datetime }}


|
My Project
|
#include <ExternalTrackingIfc.h>
Public Attributes | |
| int | channel_number |
| int | satellite_id |
| double | pseudo_range |
| double | corrected_pseudo_range |
| double | carrier_phase |
| double | doppler |
| double | snr |
| double | frequency_lock |
| double | phase_lock |
| double | locktime |
| double | code_phase |
| gnss_t | gnss |
| signal_type_t | signal_type |
| channel_state_t | channel_state |
A measurement of a single tracking channel.
| double single_measurement_t::carrier_phase |
Carrier phase (cycles).
| int single_measurement_t::channel_number |
Unique ID of the channel.
| channel_state_t single_measurement_t::channel_state |
Current state of the tracking channel
| double single_measurement_t::code_phase |
Code phase (chips).
| double single_measurement_t::corrected_pseudo_range |
Pseudorange after applying satellite clock and atmospheric correction (m).
| double single_measurement_t::doppler |
Carrier doppler (Hz).
| double single_measurement_t::frequency_lock |
Frequency lock indicator [0..1].
| gnss_t single_measurement_t::gnss |
The GNSS (GPS, Galileo, GLONASS ...)
| double single_measurement_t::locktime |
Channel run time (s).
| double single_measurement_t::phase_lock |
Prequency lock indicator [0..1].
| double single_measurement_t::pseudo_range |
Code pseudorange (m).
| int single_measurement_t::satellite_id |
The satellite ID (PRN).
| signal_type_t single_measurement_t::signal_type |
The signal type (L1CA, L2CM, L5I, E1B, E5aI ...).
| double single_measurement_t::snr |
Signal to noise ratio (dB-Hz)


--- layout: post published: true title: "Anyone who thinks social media is a valid replacement for an RSS-reader, leave the room now" permalink: /2013/03/hitler-google-reader/ date: "2013-03-14 12:28:00" ---
I'm genuinely gutted about Google Reader shutting down. Time to find something else I guess. Or maybe build something myself? Gah.
And Orkut didn't get the chop? WTF?


iSpecies
A simple mashup of species information. Code on
github
.
Species
Find
Summary
Map
Images
Images from EOL
References
Data from CrossRef
Phylogeny
Data from TreeBASE
Phylogeny from Open Tree of Life


ATTN: Dear , Good day to you. I got your contact while searching for investment assistance in chamber of commerce in your country. I am Mr. Kenneth Malfred a 24 years old boy. The first son of (late) Dr. Godwin Malfred ,whom was managing director J & D Oil company in (MONROVIA) Liberia West Africa. But he was killed along side with my younger brother during the longing civil war by rebels and all our properties was totally destroyed. Before the death of my father, he deposited an amount of Eight million five hundred thousand American dollars (U.S. $ 8.500, 000) in a finance company I am saddled with the problem of securing a trust worthy foreign personality who will take me as a son, to help me transfer the money over to your country with the intention of investing it in a business under your directives and supervisions’ because I have no knowledge of investment until i finished up my Education . Please if you are so kind as to assist me I shall send to you all vital information’s for your confirmation regarding the holding company where the fund was lodged. I will giving any offer with every confidence on your acceptance to assist me as your son and manage the money in my behalf until such a time I am able to stand on my feet. I will be happy to hear from you. God Bless you as you consider my plea. Yours Truly Mr. Kenneth Malfred.


In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.
Run the following cell to download the CIFAR-10 dataset for python.
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10_location = '/input/cifar-10/python.tar.gz'
if isfile(floyd_cifar10_location):
tar_gz_path = floyd_cifar10_location
else:
tar_gz_path = 'cifar-10-python.tar.gz'
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = block_num
if not isfile(tar_gz_path):
with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
urlretrieve(
'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
tar_gz_path,
pbar.hook)
if not isdir(cifar10_dataset_folder_path):
with tarfile.open(tar_gz_path) as tar:
tar.extractall()
tar.close()
tests.test_folder_path(cifar10_dataset_folder_path)
The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named data_batch_1
, data_batch_2
, etc.. Each batch contains the labels and images that are one of the following:
Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the batch_id
and sample_id
. The batch_id
is the id for a batch (1-5). The sample_id
is the id for a image and label pair in the batch.
Ask yourself "What are all possible labels?", "What is the range of values for the image data?", "Are the labels in order or random?". Answers to questions like these will help you preprocess the data and end up with better predictions.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import helper
import numpy as np
# Explore the dataset
batch_id = 1
sample_id = 200
helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)
def normalize(x):
"""
Normalize a list of sample image data in the range of 0 to 1
: x: List of image data.  This time, you'll implement the one_hot_encode
function. The input, x
, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to one_hot_encode
. Make sure to save the map of encodings outside the function.
Hint: Don't reinvent the wheel.
def one_hot_encode(x):
"""
One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
: x: List of sample Labels
: return: Numpy array of one-hot encoded labels
"""
#DONE
nclasses = 10
ohv = []
ohv = np.zeros((len(x), nclasses), dtype = np.int)
for i in range(len(x)):
ohv[i,x[i]] = 1
return ohv
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_one_hot_encode(one_hot_encode)
As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset.
Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)
This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle
import problem_unittests as tests
import helper
# Load the Preprocessed Validation data
valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))
For the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.
Note: If you're finding it hard to dedicate enough time for this course each week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages to build each layer, except the layers you build in the "Convolutional and Max Pooling Layer" section. TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.
However, if you would like to get the most out of this course, try to solve all the problems without using anything from the TF Layers packages. You can still use classes from other packages that happen to have the same name as ones you find in TF Layers! For example, instead of using the TF Layers version of the
conv2d
class, tf.layers.conv2d, you would want to use the TF Neural Network version ofconv2d
, tf.nn.conv2d.
Let's begin!
The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions
neural_net_image_input
image_shape
with batch size set to None
.name
parameter in the TF Placeholder.neural_net_label_input
n_classes
with batch size set to None
.name
parameter in the TF Placeholder.neural_net_keep_prob_input
name
parameter in the TF Placeholder.These names will be used at the end of the project to load your saved model.
Note: None
for shapes in TensorFlow allow for a dynamic size.
import tensorflow as tf
def neural_net_image_input(image_shape):
"""
Return a Tensor for a batch of image input
: image_shape: Shape of the images
: return: Tensor for image input.
"""
# DONE
return tf.placeholder(tf.float32,(None, *image_shape), name = 'x')
def neural_net_label_input(n_classes):
"""
Return a Tensor for a batch of label input
: n_classes: Number of classes
: return: Tensor for label input.
"""
# DONE
return tf.placeholder(tf.float32,[None, n_classes], name = 'y')
def neural_net_keep_prob_input():
"""
Return a Tensor for keep probability
: return: Tensor for keep probability.
"""
# DONE
return tf.placeholder(tf.float32, name = 'keep_prob')
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tf.reset_default_graph()
tests.test_nn_image_inputs(neural_net_image_input)
tests.test_nn_label_inputs(neural_net_label_input)
tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)
Convolution layers have a lot of success with images. For this code cell, you should implement the function conv2d_maxpool
to apply convolution then max pooling:
conv_ksize
, conv_num_outputs
and the shape of x_tensor
.x_tensor
using weight and conv_strides
.pool_ksize
and pool_strides
.Note: You can't use TensorFlow Layers or TensorFlow Layers (contrib) for this layer, but you can still use TensorFlow's Neural Network package. You may still use the shortcut option for all the other layers.
def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):
"""
Apply convolution then max pooling to x_tensor
:param x_tensor: TensorFlow Tensor
:param conv_num_outputs: Number of outputs for the convolutional layer
:param conv_ksize: kernal size 2-D Tuple for the convolutional layer
:param conv_strides: Stride 2-D Tuple for convolution
:param pool_ksize: kernal size 2-D Tuple for pool
:param pool_strides: Stride 2-D Tuple for pool
: return: A tensor that represents convolution and max pooling of x_tensor
"""
# DONE
input_shape = x_tensor.get_shape().as_list()
conv_strides_4D = [1, *conv_strides, 1]
pool_strides_4D = [1, *pool_strides, 1]
pool_ksize_4D = [1, *pool_ksize, 1]
weights = tf.Variable(tf.truncated_normal(shape=[*conv_ksize, int(input_shape[3]), int(conv_num_outputs)],mean=0, stddev=0.1))
bias = tf.Variable(tf.zeros(conv_num_outputs))
conv_layer = tf.nn.conv2d(x_tensor, weights, conv_strides_4D, padding='SAME')
conv_layer = tf.nn.bias_add(conv_layer, bias)
conv_layer = tf.nn.relu(conv_layer)
conv_layer = tf.nn.max_pool(conv_layer, pool_ksize_4D, pool_strides_4D, padding='SAME')
return conv_layer
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_con_pool(conv2d_maxpool)
Implement the flatten
function to change the dimension of x_tensor
from a 4-D tensor to a 2-D tensor. The output should be the shape (Batch Size, Flattened Image Size). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
def flatten(x_tensor):
"""
Flatten x_tensor to (Batch Size, Flattened Image Size)
: x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
: return: A tensor of size (Batch Size, Flattened Image Size).
 Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
def fully_conn(x_tensor, num_outputs):
"""
Apply a fully connected layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
 Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
Note: Activation, softmax, or cross entropy should not be applied to this.
def output(x_tensor, num_outputs):
"""
Apply a output layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
 The function takes in a batch of images, x
, and outputs logits. Use the layers you created above to create this model:
keep_prob
. def conv_net(x, keep_prob):
"""
Create a convolutional neural network model
: x: Placeholder tensor that holds image data.
: keep_prob: Placeholder tensor that hold dropout keep probability.
: return: Tensor that represents logits
"""
# DONE
"""
1st Convolutional/max pool layer
"""
conv1_num_outputs = 64
conv1_ksize = (3,3)
conv1_strides = (1,1)
pool1_ksize = (2,2)
pool1_strides = (2,2)
conv1 = conv2d_maxpool(x, conv1_num_outputs, conv1_ksize, conv1_strides, pool1_ksize, pool1_strides)
# I added to each conv layer an extra step to perform local normalization (idea taken from section 3.3 in paper:
# Krizhevsky et al., ImageNet classification with deep convolutional neural networks (NIPS 2012).)
#I also took some ideas from: https://arxiv.org/pdf/1608.06037.pdf
conv1 = tf.nn.lrn(conv1)
"""
2nd Convolutional/max pool layer
"""
conv2_num_outputs = 128
conv2_ksize = (2,2)
conv2_strides = (1,1)
pool2_ksize = (2,2)
pool2_strides = (2,2)
conv2 = conv2d_maxpool(conv1, conv2_num_outputs, conv2_ksize, conv2_strides, pool2_ksize, pool2_strides)
conv2 = tf.nn.lrn(conv2)
# I decided to add this dropout here to reduce overfitting... even if I have te maxpool,
#I observed an improvement i nthe results.
conv2 = tf.nn.dropout(conv2, keep_prob)
"""
3rd Convolutional/max pool layer
"""
conv3_num_outputs = 256
conv3_ksize = (2,2)
conv3_strides = (1,1)
pool3_ksize = (2,2)
pool3_strides = (2,2)
conv3 = conv2d_maxpool(conv2, conv3_num_outputs, conv3_ksize, conv3_strides, pool3_ksize, pool3_strides)
conv3 = tf.nn.lrn(conv3)
# DONE
x_flat = flatten(conv3)
# DONE
"""
1st Fully connected with Dropout layer
"""
fc1_num_outputs = 1024
fc1 = fully_conn(x_flat, fc1_num_outputs)
fc1 = tf.nn.dropout(fc1, keep_prob)
"""
2nd Fully connected with Dropout layer
"""
fc2_num_outputs = 1024
fc2 = fully_conn(fc1, fc2_num_outputs)
fc2 = tf.nn.dropout(fc2, keep_prob)
"""
Output layer
"""
n_classes = 10
logits = output(fc2, n_classes)
# TODO: return output
return logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
##############################
## Build the Neural Network ##
##############################
# Remove previous weights, bias, inputs, etc..
tf.reset_default_graph()
# Inputs
x = neural_net_image_input((32, 32, 3))
y = neural_net_label_input(10)
keep_prob = neural_net_keep_prob_input()
# Model
logits = conv_net(x, keep_prob)
# Name logits Tensor, so that is can be loaded from disk after training
logits = tf.identity(logits, name='logits')
# Loss and Optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
tests.test_conv_net(conv_net)
Implement the function train_neural_network
to do a single optimization. The optimization should use optimizer
to optimize in session
with a feed_dict
of the following:
x
for image inputy
for labelskeep_prob
for keep probability for dropoutThis function will be called for each batch, so tf.global_variables_initializer()
has already been called.
Note: Nothing needs to be returned. This function is only optimizing the neural network.
def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
"""
Optimize the session on a batch of images and labels
: session: Current TensorFlow session
: optimizer: TensorFlow optimizer function
: keep_probability: keep probability
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
"""
# DONE
session.run(optimizer, feed_dict={
x : feature_batch,
y: label_batch,
keep_prob: keep_probability
})
pass
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_train_nn(train_neural_network)
Implement the function print_stats
to print loss and validation accuracy. Use the global variables valid_features
and valid_labels
to calculate validation accuracy. Use a keep probability of 1.0
to calculate the loss and validation accuracy.
def print_stats(session, feature_batch, label_batch, cost, accuracy):
"""
Print information about loss and validation accuracy
: session: Current TensorFlow session
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
: cost: TensorFlow cost function
: accuracy: TensorFlow accuracy function
"""
# Done
loss = session.run(cost, feed_dict={
x : feature_batch,
y: label_batch,
keep_prob: keep_probability
})
accuracy = session.run(accuracy, feed_dict={
x : valid_features,
y: valid_labels,
keep_prob: keep_probability
})
print('\nLoss: {:>.6f} --------- Accuracy: {:.6f}%\n'.format(loss, accuracy*100))
pass
Tune the following parameters:
epochs
to the number of iterations until the network stops learning or start overfittingbatch_size
to the highest number that your machine has memory for. Most people set them to common sizes of memory:keep_probability
to the probability of keeping a node using dropout# TODO: Tune Parameters
epochs = 70
batch_size = 256
keep_probability = 0.5
# Note: 60 epochs might be enought seeing the results,
# but takes like 1 hour to re-run so I'll stay with this answer
# (I tested with 30, 40, 50, 70 and 100 and endless combinations of filters and max-pooling)
Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
print('Checking the Training on a Single Batch...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
batch_i = 1
for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')
print_stats(sess, batch_features, batch_labels, cost, accuracy)
Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
save_model_path = './image_classification'
print('Training...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
accuracy_array = []
# Training cycle
for epoch in range(epochs):
# Loop over all batches
n_batches = 5
batch_accuracy = 0.
for batch_i in range(1, n_batches + 1):
for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')
print_stats(sess, batch_features, batch_labels, cost, accuracy)
# Save Model
saver = tf.train.Saver()
save_path = saver.save(sess, save_model_path)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import tensorflow as tf
import pickle
import helper
import random
# Set batch size if not already set
try:
if batch_size:
pass
except NameError:
batch_size = 64
save_model_path = './image_classification'
n_samples = 4
top_n_predictions = 3
def test_model():
"""
Test the saved model against the test dataset
"""
test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load model
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
# Get Tensors from loaded model
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
test_batch_acc_total += sess.run(
loaded_acc,
feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})
test_batch_count += 1
print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))
# Print Random Samples
random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
random_test_predictions = sess.run(
tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)
test_model()
You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN. Pure guessing would get you 10% accuracy. However, you might notice people are getting scores well above 80%. That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_image_classification.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


Converts HDF5 data to use desired data type, scaling and encoding (ODIM gain, offset, undetect and nodata values). More...
#include <DataConversionOp.h>
Public Types | |
| typedef SrcType< M const > | src_t |
| typedef DstType< M > | dst_t |
Public Member Functions | |
| DataConversionOp (const std::string &type="C", double gain=1.0, double offset=0.0, double undetect=0.0, double nodata=255.0, std::string copyGroupSuffix="") | |
| void | processH5 (const Hi5Tree &src, Hi5Tree &dst) const |
| Ensures data to be in standard type and scaling. Makes a converted copy if needed. More... | |
| virtual void | processDataSet (const DataSet< src_t > &srcSweep, DataSet< dst_t > &dstProduct) const |
| void | processImage (const PlainData< src_t > &src, drain::image::Image &dst) const |
| Converts src to dst such that dst applies desired gain, offset, undetect and nodata values. | |
| void | processImage (const ODIM &odimSrc, const drain::image::ImageFrame &src, const ODIM &odimDst, drain::image::Image &dst) const |
| Converts src to dst such that dst applies desired gain, offset, undetect and nodata values. More... | |
| void | traverseImageFrame (const ODIM &odimSrc, const drain::image::ImageFrame &src, const ODIM &odimDst, drain::image::ImageFrame &dst) const |
| void | setGeometry (const M &srcODIM, PlainData< dst_t > &dstData) const |
Static Public Member Functions | |
| static PlainData< DstType< M > > & | getNormalizedData (const DataSet< src_t > &srcDataSet, DataSet< dst_t > &dstDataSet, const std::string &quantity) |
Protected Attributes | |
| std::string | copyGroupSuffix |
| Suffix for trailing path element ("/data") for storing the original. | |
Converts HDF5 data to use desired data type, scaling and encoding (ODIM gain, offset, undetect and nodata values).
| M | - ODIM type |
|
virtual |
SWAP & delete
Ensures data to be in standard type and scaling. Makes a converted copy if needed.
Usually, the operator does not need groups sorted by elevation.
| void processImage | ( | const ODIM & | odimSrc, |
| const drain::image::ImageFrame & | src, | ||
| const ODIM & | odimDst, | ||
| drain::image::Image & | dst | ||
| ) | const |
Converts src to dst such that dst applies desired gain, offset, undetect and nodata values.
Sometimes this is applied directly (for alpha channel ops).
| void traverseImageFrame | ( | const ODIM & | odimSrc, |
| const drain::image::ImageFrame & | src, | ||
| const ODIM & | odimDst, | ||
| drain::image::ImageFrame & | dst | ||
| ) | const |
Checks 'undetect' first because 'undetect' and 'nodata' may be the same code


You can use CMake variables in the following Build and Build Outputs sections. The CMake variables listed in the table below might be useful. Some of the variables are created by this tool. You can use these variables to customize the build.
| Variable Name | Description |
|---|---|
| CMAKE_BUILD_TYPE | Specifies the build type (Debug, Release, etc) |
| THIRD_PARTY_INSTALL_PREFIX | The directory where all third party projects should install their outputs |
| SOURCE_DIR | The root directory where this project was downloaded/extracted to |
| PREFIX | A platform-dependent prefix. On Unix this is lib, on Windows this is blank |
| EXTENSION | A platform and library type dependent suffix. On unix this is either .a or .so, On windows this is always .lib |
Directory where CMake will attempt to find the library file: {{ lib.outputDirectory }}/{{ '${PREFIX}' }}{{lib.name}}.{{'${EXTENSION}' }}
Changes are saved automatically
Back


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Or log in to Blue Access for MembersSM; to:
- Estimate the cost of procedures, treatments and tests based on provider and your plan
- See what other patients have to say about a provider, or add your own review
- View quality, certifications and recognitions for doctors
- See if a Blue Distinction® Center for Specialty Care is available near you
Choosing a Hospital
We understand that finding reliable care is important. Thats why we created the Blue DistinctionSM program, which identifies hospitals and medical facilities that meet high standards.
There are two types of Blue Distinction recognitions:
- Blue Distinction Centers: Hospitals that have proven their ability to meet high standards of specialty care.
- Blue Distinction Centers+: Hospitals that are recognized for meeting high standards of care and for delivering cost-effective care.
You can find more information about this program under the Doctors and Hospitals section of your Blue Access for Members account or use our Provider Finder to search for a Blue Distinction Center near you.


%UseLnInsteadOfLog
yCF=RandomInteger{}(1,4);
xCF=RandomInteger{}(2,4);
xSolution= RandomInteger{}(2,10);
ySolution=xSolution+ RandomInteger{}(1,10);
ySide= yCF y;
xSide= xCF x;
xLength=(x=xSolution; xSide)_2;
yLength=(y=ySolution; ySide)_2;
DiagonalSquared=(xLength-xSolution)^2+ (yLength-xSolution)^2;
thePerimeter = 2(xLength+yLength);
thePoly=Polynomialize( (xSide-x)^2+(ySide-x)^2);
theUnit=xSolution/5;
theUnitDiag=theUnit * sqrt(1/2);
yViaX=(thePerimeter - 2xSide)/(2 yCF);
polyInX= Polynomialize( (y=yViaX; thePoly)_2-DiagonalSquared);
xOne= SolveFor{}(x, polyInX)_1;
xTwo= SolveFor{}(x, polyInX)_2;
yOne=(x=xOne; yViaX)_2;
yTwo=(x=xTwo; yViaX)_2;
1/((yOne>xOne) +(yTwo>xTwo)-2);
PlotRemoveCoordinateAxes {}0+
PlotPath(
( (0,0),
(xLength,0),
(xLength,yLength),
(0,yLength),
(0,0)
) , black
)
+
PlotPath(
( (xSolution,0),
(xSolution,xSolution),
(0,xSolution)
) , blue
)
+
PlotPath(
( (xLength-theUnit,0),
(xLength-theUnit,theUnit),
(xLength,theUnit)
) , gray
)
+
PlotPath(
( (xSolution-theUnit,0),
(xSolution-theUnit,theUnit),
(xSolution,theUnit)
) , gray
)
+
PlotPath(
( (theUnit,xSolution),
(theUnit,xSolution-theUnit),
(0,xSolution-theUnit)
) , gray
)
+PlotSegment( (xSolution, xSolution), (xLength, yLength), red,3)
+PlotLabel((-theUnitDiag, -theUnitDiag),"D")
+PlotLabel((xSolution, -theUnit),"G")
+PlotLabel((xLength+theUnitDiag, -theUnitDiag),"C")
+PlotLabel((xLength+theUnitDiag, yLength+ theUnitDiag),"B")
+PlotLabel((-theUnitDiag, yLength+ theUnitDiag),"A")
+PlotLabel((-theUnit, xSolution),"E")
+PlotLabel((xSolution+theUnitDiag, xSolution+theUnitDiag),"F")
+PlotLabel((xSolution/2, -theUnit),"x")
+PlotLabel(( -theUnit,xSolution/2),"x")
+PlotLabel(( xLength/2, yLength+theUnit), xSide)
+PlotLabel(( xLength+theUnit, yLength/2), ySide)
+PlotViewRectangle((-xLength/5,-yLength/5),(xLength*1.2, yLength*1.2))
\(ABCD\) is a rectangle in which \(AB=xSide\) \(cm\) and \(BC=ySide\) \(cm\).
The point \(E\) is on \(DA\), the point \(G\) is on \(DC\) and the point \(F\) is chosen so that
\(DEFG\) is a square of side \(x\) \(cm\), as indicated on the diagram.
The length of the segment \(BF\) is \(sqrt(DiagonalSquared)\) \(cm\).
- Show that \(thePoly=DiagonalSquared\).
- Given that the perimeter of the rectangle \(ABCD\) is thePerimeter \(cm\), find the values of \(x,y\).
- Enter your answer in the format \(x=?cm{\color{red}{,}}y=?cm\). Do not forget the comma and the measurement units.
Answer:
(p=MatchesPattern{}( AlgebraAnswer, (x={{b}}, y={{c}}));
q=MatchesPattern{}( AlgebraAnswer, (y={{c}}, x={{b}}));
if (p==0 and q==0, 0,
( p; q;
( (Polynomialize(b-(xSolution cm))==0) and (Polynomialize(c-(ySolution cm))==0))
)_3
)
)_3
EnsureExpressionDependsMandatoryVariables(AlgebraAnswer,(x,y ,cm));
if (
MatchesPattern{}( AlgebraAnswer, (x={{b}}, y={{c}}) )==0
and
MatchesPattern{}( AlgebraAnswer, (y={{c}}, x={{b}}) )==0
,
"
Your answer does not match the pattern \(x=a, y=b\).
Please do not forget the comma."
,
"");
(x=xSolution cm, y=ySolution cm)


Git init
Git init is the act of registering this current code base.
Git is a version control system; you can think of it as a series of snapshots (commits) of your code base. With the creation of each snapshot you build out a whole application, seeing the growth of a full app. During this time you can make branches to experiment and come back to a previous snapshot you took or merge those experiments into your app.
Github is a code hosting platform for version control and collaboration. It lets you and others work together on projects from anywhere using those code snapshots that you took during development.
Git init
Git init is the act of registering this current code base.


An extension of the bushing example which links the chart to a data table. When hovering over the table, that row of data is highlighted in the chart. When brushing, the data in the table is updated.
The table is divgrid, a simple reusable chart that follows general update pattern I.
Divgrid isn't fast when updating with hundreds or thousands of rows, so only the first 10 rows of the data are shown. For better performance, use a more sophisticated data grid like SlickGrid.
There is an old example of SlickGrid with parallel coordinates. In that version the SVG is a bottleneck. An example of SlickGrid with this library should appear in the next few weeks (Nov-Dec, 2012).


Just after the climactic moment in "Dancing At Lughnasa," the five Mundy sisters stand facing one another, breathless and yet strangely invigorated. They look at each other in silence, perhaps sensing for the first time how much they need each other. Or maybe on a subconscious level they each know it's only a matter of time before their way of life comes to an end. "Lughnasa," adapted from Brian Friel's acclaimed play, so beautifully captures the details of its time and place and its characters' lives that it's difficult to resist being drawn in. Director Pat O'Connor did a similarly fine job evoking 1950s Irish life in the lovely sleeper "Circle of Friends," and here he recreates 1936 Ireland, specifically rural Donegal, where the Mundys live quietly on a tiny farm that has probably seen better days. The same can be said for most of the women. Self-righteous schoolteacher Kate (Meryl Streep) rules the roost, ordering her siblings around like servants. Her sole delight seems to come from seeing others take a fall: "Her dancing days will be over," she smirks, when a snooty former student is forced into marriage. Clownish Maggie (Kathy Burke) does what she can to inject a little levity into the house. Quiet Agnes (Brid Brennan) looks after the mentally challenged Rose (Sophie Thompson); the youngest sister Christina (Catherine McCormack) looks after her eight-year-old son Michael (Darrell Johnston), the result of her affair with a devil-may-care Welshman named Gerry (Rhys Ifans), who drops by every 18 months or so. None of the sisters have had much luck with love or money; their only luxuries are a bicycle and a wireless set that occasionally picks up a bit of music. When brother Jack (Michael Gambon) returns to Donegal after doing missionary work in Uganda and announces he thinks he's come home to die, Maggie gasps, "Jesus, don't! We can't afford to bury you." Jack's reappearance is just the first harbinger of changes for the Mundys. He went to Africa as a Catholic priest but has since become obsessed with the rituals and practices of the natives, much to the dismay of Kate. Instead of saying Mass at the local church, Jack would rather investigate the celebrations of the pagan Feast of Lughnasa, in which drunken participants leap over bonfires and dance. Dancing becomes a recurring motif in the film as the only way the women can escape the drudgery of their day-to-day routines. Small wonder Kate and Agnes look on with just a touch of jealousy as Gerry waltzes with Christina; the only thing better than dancing is dancing with someone else. With very little in the way of plot, "Lughnasa" must rely on character development for its drama. In lesser hands, Kate might have come off as a stern, humorless old maid, but instead we can see how much she really cares about keeping her family together and holding on to their home. As expected, Streep's accent is flawless but what's even more impressive is how she brings out Kate's hidden colors, which this woman has cloaked in multiple layers of acid and ice. Burke's buoyant Maggie, McCormack's tolerant Christina and Brennan's timid Agnes also reveal unexpected personality traits as "Lughnasa" progresses. The screenplay falters only in a slightly melodramatic episode involving Rose's infatuation with an unsuitable man; that O'Connor intercuts scenes of a fox raiding a henhouse into this sequence doesn't help matters. "Lughnasa" is much more effective at showing how the Mundys manage to survive their many disputes and differences and find some peaceful common ground. Like most families, they're often so busy worrying about the future that they don't realize how happy they should be. They may not be wealthy or blessed with much in the way of material possessions, but the women of "Lughnasa" prove that sisterhood can truly be powerful.
James Sanford
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


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| {{asset.name.replace('.zip','')}} |
Newer version installed
Installed
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AMAZING PEOPLE ARE MAKING AMAZING DESIGNS IN A FUN ENVIRONMENT
When you work with us, you are part of a team of committed collaborators. All aspects pf our software development process are intergrated, interactive and agile
Our approach unifies design, development and product management to create exceptional products
We start each project by understanding your business goals, the user's needs and the product requirements
Then we help translate them into effective and elegant solutions.
Before we sign a contract or write a line of code, our time will spend the neccesary time needed to understand your product vision.
The outcome of this scoping session is proposal or high-level statement of work.
Basing on your recquirements, we can provide a complete offering or just the pieces you need.
Our design practice offers a full range of services including brand strategy, interaction and visual design abd user experience testing.
Throughout your project, our designers create and implement visual design and workflows, solicit user feedback and work with you to make sure whatget built is what you needed.
All engineers are fluent in the latest enterprise, mobile and web development technologies.
They collaborate with your team to write, and improve code on a daily basis using provwn practices such as test-driven development and pair programming.
Planning and development is iteractive. Because we are constantly coding and testing the products we build are always ready to go live.
This iteractive process allows for changes as business recquirements evolve.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Makes 4
This is an Indian version of Scotch eggs, rolled in a crunchy sesame seed coating.
Bring a large pan of water to the boil and cook 4 of the eggs for 4-5 minutes until they are medium- to hard-boiled. Drain and cool under cold running water (this will prevent the yolks from discolouring). Peel and set aside. Beat the remaining egg in a bowl and set aside.
In a bowl, mix together the minced beef, garlic, ginger, spices, cornflour and egg yolk until well combined. Season, to taste, with salt.
Divide the beef mixture evenly into 4 portions. Roll each portion into a ball, then flatten out to form a thin patty. Wrap each patty around the peeled boiled eggs, smoothing out the join and making sure there is no egg left exposed.
Roll the scotch eggs in the flour, then dip in the beaten egg and roll in the sesame seeds.
Half-fill a frying pan with the vegetable oil and heat until hot. (CAUTION: hot oil can be dangerous. Do not leave unattended). Fry the nargis kebabs in small batches for 4-5 minutes, turning the eggs occasionally, or until golden-brown all over and cooked through. Remove from the oil with a slotted spoon and drain on kitchen paper.


Call me + 225 07 500630 Contacts email me<EMAIL_ADDRESS>GREETINGS, Based on the information and recommendation I got about quick growth of business, investment and commerce in your country, I deemed it necessary to write and inform you about my interest to invest in any viable venture in joint partnership with you. I am Mrs Rita Abed the first wife of king George Abed K.S.M the king of mende tribe in Sierra-Leone. On the 19th May, 2004 my husband was murdered by some rebel group on the accusation that he is a great sponsor and in support to restore the democratically elected President of Alhadji Tejan Kabba. Almost half of the palace was burnt.Fortunately I was not in the place when they raided the palace my husband married 4 wives with 16 children two of the wives, with my 3 children where murdered in cold blood while my second son escape the incident with bullet wounds on his right leg at the moment where the remaining families are I don?t know their way about whether they are alive or not. Right now I am left alone with only one son since the sudden assassination of my husband the family has been displaced and totally disorganized left with noting. Early that year befor my husband was Late husband was murdered he came back from travel and gave me some document to keep for him as usual he told me that he deposited 18.5 million USD in a trunk box with private security company in Abidjan Côte d?Ivoire as a family treasure. So I decided to go and search for the documents where I use to keep them I discovered the documents covering the deposit he made with the company and some other documents like his will and other documents for his business. As my town has been a serious war zone, I made arrangement with two peace keeping force to help me and my son move out of the country, however we moved out of Sierra-Leone successfully, to Abidjan Côte d?Ivoire where we are now. I have gone and conform the deposit with the company with all the documents all the documents are with me here. Now I wish to transfer these funds abroad and enter into a business venture as the country (Côte d?Ivoire) is no longer in peace since they conducted their Presidential election on the 22nd October 2004 the country has witness all sort of political unrest ranging from ethnic clash which resulted to killing of innocent citizens and strangers people are living in fears as the country is no longer that peaceful nation as it was before due to this reason that I have decided to contact you, to assist me to lift this my consignment out of this country. Please I need your assistance. Waiting for your urgent reply.Thanks and God bless. Best<EMAIL_ADDRESS>Mrs Rita Abed.+22507500630 _________________________________________________________________ MSN Messenger: appels gratuits de PC à PC ! http://www.msn.fr/msger/default.asp


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Feedback time!
03/08/2015
Pairing
Pairing has been a bit anxiety inducing for me. I just anticipate being judged for my lack of knowledge, particularly if I'm feeling behind at that moment. But I've found that during a pair, I learn the most and am absolutely more productive. Occasionally my pair and I are not terribly productive but this is usually for lack of preparedness. But what is always rewarding is when I go into a pair prepared, but anxious, and yet I perform really well throughout the pair and end up feeling super confident. I just always go into a pair hoping I can give the other person as much as I always get out of pairing from them.
Feedback
Oddly, I feel much less anxious about getting feedback from pairing sessions. You would think it would be the most obvious thing to be anxious about: hearing exactly, honestly how the other person felt you handled the pair. Yet, I usually feel I have a good idea of how I did for myself immediately after. It turns out I'm more anxious about how I feel the pair went. It also is interesting to see their perspective since I find I'm much harder on myself than anyone else is. But I do enjoy getting constructive, actionable feedback. It gives me something to grow with and work on.
Writing feedback has been interesting. I found I end up wanting to spend most of the paragraph giving myself feedback and in turn apologizing to the other person for my failures and flaws than actually giving them feedback on how they did. It's something I recognized right away and try to curb. It's just not useful! And that's something I'm learning as well; focussing so much on my faults is just counter-productive. I build too many silly barriers emotionally for myself and I need to break through!


Music to our ears. From simple rentals for party music or a presentation to large concert and dance party setups our equipment and staff will ensure your event is loud and clear and you don't have to worry about a thing.
 From a simple colored lighting that will spruce up any room to fully controlled lighting rigs we have the equipment and the know how to make your event spectacular.
Let your next event be seen like never before. Our knowledgable video staff is able to help you create a professional look and feel for a fraction of the cost. With worldwide streaming you can be sure your event will be seen and remembered by hundreds of people.
General Manager
Operations Coordinator
Storeroom Manager
We are proudly run by a team of Michigan Technological University students who are dedicated to the success of your event. We're pretty cool people too, so check us out!


--- layout: default title: IceCTF title_label: hide post_scripts: - /js/registration.js startup_functions: ---
The competition hasn't started yet, but while you wait, you can take a look at our FAQ, and at the sample challenges to get ready!


The Board’s eleven-person elected board of trustees is responsible for approving the policies and bylaws governing the Board’s operations. The board of trustees also ensures that the quality of education in Waterloo Region is maintained, and the educational goals and needs of all students are met.
It is the responsibility of trustees to attend Committee of the Whole and Board meetings. Committee of the Whole meetings are held on the second and third working Mondays of each month. Board meetings are held on the last working Monday of each month where the Board ratifies the action taken at Committee of the Whole meetings in previous weeks.
The specific list of trustee responsibilities is available here:


Class ContactResponse
Class ContactResponse
- SMSApi\Api\Response\AbstractResponse implements SMSApi\Api\Response\Response
- SMSApi\Api\Response\ContactResponse
Namespace: SMSApi\Api\Response
Package: SMSApi\Api\Response
Located at Api/Response/ContactResponse.php
Package: SMSApi\Api\Response
Located at Api/Response/ContactResponse.php
public
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GENDER_FEMALE
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"female"
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GENDER_MALE
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"male"
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$obj
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Bicentennial Man (1999) 1 1/2 stars out of 4. Starring Robin Williams, Sam Neill, Embeth Davidtz, Oliver Platt and Hallie Kate Eisenberg. Directed by Chris Columbus.
It is in the nature of our species to continually try to better ourselves, to evolve.
And you can't fault a person for doing what comes naturally.
So, I really can't be upset with Robin Williams who seems to have turned his back on comedy to strive to become an Actor.
After all, we all want to improve, to continually grow ... Ah, the hell with it. Who am I kidding? Just call me a selfish s.o.b. I want my old Robin Williams back, the one who kept me laughing for nearly two hours in concert back in Columbus, Ohio, about 17 years ago, the Robin Williams from "Good Morning Vietnam" and "Aladdin."
I want the spontaneous Robin Williams who appears on talk shows or performs during Comic Relief.
This new Robin Williams is creepy. Worse, he's kind of dull and boring. I'm tired of this touchy-feely guy who began emerging in "Good Will Hunting" and has invaded my multiplex in "Patch Adams," "When Dreams May Come," "Jakob the Liar" and now "Bicentennial Man."
He has become the cliche of the comedian wanting to play Hamlet.
Maybe this is what attracted him to "Bicentennial Man," an artificial weeper that is overly schmaltzy and ponderous.
In the movie, based on a short story by science fiction legend Isaac Asimov, which was later expanded into the novel, "The Positronic Man," by Robert Silverberg, Williams plays Andrew, a robot who continually strives for humanity. How anyone can see this as an improvement is beyond me, but after all, it is science fiction.
We first meet Andrew at the home of Sir (Sam Neill), his wife, Ma'am (Wendy Crewson) and their daughters, Miss (Undze Letherman) and Little Miss (Pepsi spokeskid Hallie Kate Eisenberg, who already is beginning to wear out her welcome).
At first considered merely a "household appliance," Sir sees a creative spark in Andrew and encourages him to continue his explorations.
As the decades pass - the movie covers 200 years - Andrew continues to grow and evolve.
But then he falls in love with Portia, the great-granddaughter of Sir and the granddaughter of Little Miss.
Both Little Miss as an adult (as well as an old woman) and Portia are played by Embeth Davidtz, best remembered as Ralph Fiennes Jewish maid in "Schindler's List."
So, more than halfway through the movie, directed by Chris Columbus, who has done better work, we are now involved in a robot/girl love story and all the cliches that entails.
The real stars of "Bicentennial Man" are the makeup and special effects people who created Williams' robot look. Unfortunately, later in the movie he is given a human face that looks like an old, tired Robin Williams.
People are going to flock to "Bicentennial Man" no matter what I or any other critic says. It's inoffensive and the kids will like it, though the younger ones may become restless because of it's nearly two-hour-and-20-minute running time.
But it is a rather weak movie, rife in self-important sounding dialogue and situations that don't ring true.
I just wish Robin Williams would quit wasting his time and devolve. Who says moving forward is always progress?
Bob Bloom is the film critic at the Journal and Courier in Lafayette,IN. He can be reached by e-mail at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>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


Рейтинг хендлерів
Dynamic tabbed interfaces.
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Order
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Client
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Amount
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Date
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Status
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|---|---|---|---|---|---|---|
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Spotify
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$9.00 | 05 May | Rejected | ||
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FR002 |
Dropbox
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$257.93 | 12 May | Completed | |
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FR003 |
Google
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$441.99 | 15 May | Completed | |
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FR004 |
FrontApp
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$99.00 | 01 Jun | Pending | |
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FR005 |
Slack
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$14.89 | 02 Jun | Rejected | |
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FR006 |
Spotify
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$9.00 | 05 Jun | Completed | |
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FR007 |
Htmlstream
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$1,579.99 | 15 Jun | Completed | |
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FR008 |
GitHub
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$235.85 | 16 Apr | Rejected | |
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FR009 |
Dropbox
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$12.31 | 12 Apr | Completed | |
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FR0010 |
Google
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$891.00 | 29 Apr | Completed | |
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FR0011 |
Atlassian
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$321.11 | 30 Apr | Pending | |
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FR0012 |
Slack
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$16.29 | 02 Mar | Rejected | |
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FR0013 |
Spotify
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$4.00 | 01 Mar | Completed | |
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FR0014 |
Htmlstream
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$3,462.00 | 23 Mar | Completed | |
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FR0015 |
Spotify
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$9.00 | 27 Mar | Rejected | |
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FR0016 |
Dropbox
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$196.73 | 04 Feb | Completed | |
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FR0017 |
Google
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$235.99 | 18 Feb | Completed | |
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FR0018 |
FrontApp
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$34.00 | 21 Feb | Pending | |
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FR0019 |
Slack
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$72.89 | 28 Feb | Rejected | |
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FR0020 |
Spotify
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$9.00 | 05 Jan | Completed | |
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FR0021 |
Htmlstream
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$7,485.00 | 15 Jan | Completed | |
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FR0022 |
Slack
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$76.00 | 22 Jan | Rejected | |
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FR0023 |
Spotify
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$12.00 | 18 Jan | Completed | |
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FR0024 |
Htmlstream
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$87.43 | 30 Jan | Completed |


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Replace all fasteners from imperial to metric.
from api import Api, PartsOccurrences, PartElement
from sources import screwlib_path
def main():
session = Api()
print("* Author: recs")
print("* Last update: 2020-00-00")
session.check_valid_version('Solid Edge ST7','Solid Edge 2019')
assembly = session.active_document()
print("* part-number: %s\n" % assembly.name)
Check if part is sheetmetal or other type of part.
assert assembly.name.endswith(".asm"), "This macro only works on .asm not %s" %assembly.name[-4:]
parts = PartsOccurrences(assembly)
Display quantity of parts in the assembly.
quantites(parts.count_fasteners, parts.count_imperial, parts.count_metric)
Replace screws.
for part in parts.screws():
screw = PartElement(part)
print(screw.name)
replaced_part = screw.replace_metric()
print(replaced_part)
TODO: Reprint quantity after the process done.
quantites(parts.count_fasteners, parts.count_imperial, parts.count_metric, state="(Changed state)")
def quantites(total, count_imperial, count_metric, state="(Current state)"):
TODO: number of screws, nuts, washers
print("{}".format(state))
print("Total number of fasteners: {}\n".format(total))
print("...............- imperial: {}\n".format(count_imperial))
print("...............- metric : {}\n".format(count_metric))
Bolts converts screws,nuts,washers from imperial to metric, (Press y/[Y] to proceed.):
def confirmation(func):
response = raw_input(
)
if response.lower() not in ['y']:
print('Process canceled')
sys.exit()
else:
func()
if __name__ == "__main__":
confirmation(main)


Task Name: Task Finished? Due Date: Time: Repeat: No repeat Once a Day Once a Week Once a Month Once a Year List: Default Personal Shopping Wishlist Work Save Cancel New List


|
Tarot CardsMadame Sosostris, famous clairvoyante,Had a bad cold, nevertheless Is known to be the wisest woman in Europe, With a wicked pack of cards.  Look!) Here is Belladonna, the Lady of the Rocks, The lady of situations. Here is the man with three staves, and here the Wheel, And here is the one-eyed merchant, and this card, Which is blank, is something he carries on his back, Which I am forbidden to see. I do not find The Hanged Man. Fear death by water. — T.S. Eliot, "The Wasteland" Most people associate tarot cards with vague images of grim-faced gypsies making ominous pronouncements. However, it would be more correct to imagine grim-faced Italians making ominous pronouncements.Tarot cards, as best anyone can tell, originated in Italy around the 15th century. However, the phrase “as best as anyone can tell" encompasses a pretty large body of conflicting opinions. Here’s what we know for sure: Renaissance Italians played a card game called tarrochini, which involved a deck of cards that looks a lot like what most people would call a “tarot deck” today. The cards were used for a vaguely bridge-like game by the same name, as well as (more discreetly) being used for fortune-telling. Everyone is pretty sure that these cards didn't just spring into existence fully formed and overnight, but no one has any really credible evidence to suggest its precise previous lineage. The art of divination, or fortune-telling, certainly dates back much further than the 15th century. The Chinese were among the earliest innovators of divination, employing a ancient system of sticks and coins known as the I-Ching. By 1000 A.D., the Chinese were using cards for divination, both within and without the I-Ching framework. The I-Ching was less of a fortune-telling device than a sort of metaphysical “Dear Abbey” — readings tend to offer advice and guidelines for right living rather than “you will meet a tall dark stranger.” (This is, of course, a gross and insulting simplification of what the I-Ching actually does and is, but then this article is about tarot cards.) Other fortune-telling traditions date back as far as the ancient Egyptians. Dice, goat innards, cloud shapes and voices in the head have been used to foretell the future for as long as there has been a future. The key attribute of a divination tool is that it needs to be random but still suggest a pattern. Once people started making decks of cards, they were worked into the mix. Certain brands of fuzzy, white-light New Agers like to claim that tarot dates back to ancient Egypt, the Knights Templar, Atlantis, ancient alien charioteers, etc., etc. These claims have various levels of credibility, depending on largely on what you're willing to believe. Eventually, tarot cards did find their way into the hands of the gypsies they are so famously associated with, but that didn't happen until the 20th century. Origins aside, the tarot uses an extremely complex series of images and attributes to decode the currents of past, present and future. Based in equal parts on hermetic magical traditions, astrology and Judeo-Christian mythology, the web of images and allusions lends itself to the interpretative imagination. The actual tarot deck is traditionally made up of 22 trump cards known as the Major Arcana. These are the images that most people associate with tarot, such as The Magician, the High Priestess, the Hanged Man, the Wheel of Fortune and Death. The structure of the major arcana appears to have been based on the Tree of Life diagram from the Kaballah, a collection of Jewish mystical-occult teachings. The Minor Arcana are more or less the same as a modern deck of playing cards, with four suits, numbered cards (from Ace to 10) and face cards. The suits are cups, coins, swords and staves, which correspond to hearts, diamonds, spades and clubs. The face cards include the king, queen, knight (equivalent to the jack in a modern deck) and the page. Each suit corresponds to one of the traditional alchemical elements — earth, air, fire and water, which are also used to align the cards with astrological symbols. Some tarot decks have little vignettes pictured on the minor arcana, others arrange symbols in various geometric patterns. The images are based along Jungian archetypes, designed to be evocative and to have certain universal connotations (universal to Western culture, anyway). The most famous traditional Tarot deck was designed by A.E. Waite, leader of the Golden Dawn, an offshoot of Freemasonry. Other famous decks were designed by leading occult practitioners like Aleister Crowley. In recent years, the American obsession with all things "New Age" (which generally means really old things that have been completely reinvented by flaky poseurs) has led to a proliferation of tarot and fortune-telling decks, found as easily at Barnes & Noble as in a dusty occult shop. Modern tarot deck designs range from unimaginative tracings of the Waite deck to naked porno decks (including the aptly named "Tarot of the Erotic Garden of Penises," pictured below), and a wide range of happy-sunshine-bliss-bliss products that can feature fairies, dragons, elves, Wiccans, samurai, druids, angels and/or baseball players.
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ON THE 2030 AGENDA AND SDGs (SUSTAINABLE DEVELOPMENT GOALS)
The 2030 Agenda for Sustainable Development, adopted in September 2015, is a comprehensive plan of action for the world in a 2030 perspective, established by UN via negotiations amongst its member states. The adoption of the 2030 Agenda is an unprecedented event in the history of mankind. All 193 UN member states committed themselves to working for the realisation of 17 Sustainable Development Goals – SDGs. The Goals are focused on ensuring decent lives, peace and economic progress for all people in the world while taking care of the natural environment and combating climate change.
The 2030 Agenda is focused on five areas of critical importance for humanity and the planet (so-called 5Ps):
People are at the centre of the 2030 Agenda. Eradication of poverty and hunger amongst them, fulfilment of their individual potential in dignity and equality as well as life in healthy environment are major challenges and prerequisites of sustainable development.
Planet should serve the needs of present and future generations. The rational use of natural resources, sustainable consumption and production, inclusive economic growth as well as climate action will help protect the planet from degradation and ensure intra- and intergenerational solidarity.
Prosperity should be provided to everyone through common improvement of life quality. Universal access to quality education, medical service as well as infrastructure in connection with environment-friendly economic and technological progress will enable the enjoyment of prosperous and fulfilling life.
Peace remains on guard of the 2030 Agenda realisation. The achievement of the Goals is possible only in societies free from violence and fear, based on tolerance and inclusion.
Partnership entails the necessity of mobilization of many environments. Global cooperation among all the countries, co-action of the government, business and civil society, in the spirit of solidarity with the poorest and most vulnerable, will enable the realisation of the ambitious vision of the 2030 Agenda.
Sustainable development in EU and OECD
EU’s engagement with sustainable development
The course of action for sustainable development in EU was outlined for the first time in the EU Sustainable Development Strategy (EU SDS), adopted in 2001 and revised in 2006. The EU SDS goals were monitored by a relevant set of sustainable development indicators (SDIs).
Joining the global initiative of the 2030 Agenda, the European Commission issued the communication ‘Next steps for a sustainable European future’ in 2016. The document declared integration of SDGs in EU policy framework and regular reporting of the progress in SDGs implementation. Taking into account that EU development priorities were determined in i.a. Europe 2020 Strategy, 10 Priorities of the European Commission and Circular Economy Package, a special set of 100 indicators was adopted for monitoring the Sustainable Development Goals, different from the UN set of SDG indicators and consistent with challenges faced by EU member states. The set fully relies on the statistical resources of the European Statistical System.
OECD’s engagement with sustainable development
The Organisation for Economic Co-operation and Development also takes an active part in the sustainable development initiatives, including the 2030 Agenda realisation. OECD supports its member states in SDGs implementation through i.a. assistance with domestic resource mobilisation as well as policy review and its adjustment to specific Goals. Among the most important OECD’s initiatives are Better Life Initiative as well as Green growth and Sustainable development, both adopted in 2011.
Better Life Initiative – OECD’s project promoting better policies in line with the phrase ‘Better policies for better lives.’ The initiative engages the political world into actions aimed at fostering well-being, and enhances social engagement. Development of Better Life Index, enabling a comprehensive view of societal well-being issue, was part of the project. Results of the latest research on life quality are presented in successive editions of the thematic publication How’s life? Measuring Well-being.
Green growth and Sustainable development – actions towards policy enhancement for economic growth, while preserving natural environment assets and developing environment-friendly infrastructure. OECD’s activity in the area of green growth is based on the Green Growth Strategy, adopted in 2011. Progress made by individual countries in implementing economic and environmental priorities, outlined in the Strategy, is presented in the successive editions of the publication Towards Green Growth? Tracking Progress.
OFFICIAL STATISTICS FOR SDGs
The task of official statistics, indicated in the 2030 Agenda, is to monitor progress in achieving the Sustainable Development Goals. The monitoring is carried out on the three following levels:
• global – this level is coordinated by The United Nations Statistical Commission;
• world regions – the responsibility for them lies with the UN regional commissions (as for Poland, it is the United Nations Economic Commission for Europe – UNECE);
• national – national statistical offices are accountable for this level (in Poland, it is Statistics Poland – GUS).
In order to assess progress on the global and regional level, a set of so-called global (SDG indicators). is used. Indicator values for the whole world and its regions are calculated by international organizations, mainly on the basis of official statistical data obtained from individual countries. On the national level, instead of global indicators, countries can use their own sets of indicators for monitoring areas and issues of greatest relevance to them. This approach has been adopted in Poland (see – – the indicator set monitoring national priorities).
Progress made on the global and regional scale is presented in annual UN reports. In addition to that, a global database. is available for users searching for detailed information on the world, regions and individual countries. Actions undertaken by individual countries are monitored within (Voluntary National Reviews – VNR), conducted every year in July at the UN High-Level Political Forum in New York. In 2018, Poland conducted its first national review. As an institution responsible for indicator framework and provision of necessary statistical information, Statistics Poland took part in preparing a report on Poland’s progress.
Monitoring progress in the realisation of the 2030 Agenda is a great challenge for official statistics. It requires coordination of many institutions’ activities and cooperation between the international and national level. So as to support statistical institutions in the UNECE region, the Steering Group on Statistics for Sustainable Development Goals was established. Since 2018, the Group has been co-chaired by Poland (GUS). One of the Group’s greatest achievements is preparation of (Road Map on statistics for Sustainable Development Goals), which facilitates organization of the 2030 Agenda monitoring.
PUBLICATIONS
A picture of Poland. Statistics for SDGs
In 2015, Poland became one of 193 countries that signed the UN Resolution on the 2030 Agenda for Sustainable Development. In July 2018, Poland is presenting for the first time its advancement towards reaching the Sustainable Development Goals (SDGs) under Voluntary National Review (VNR) at the UN High Level Political Forum. On this occasion, Statistics Poland, as the coordinator of the SDGs monitoring process in Poland, has prepared the publication A picture of Poland. Statistics for SDGs, , which aims to introduce foreign readers to a picture of our country and the role that Poland has to fulfill in achieving the Goals of the 2030 Agenda.
The information set out in the publication was inspired by the development priorities for the country, indicated in the document Implementation of Sustainable Development Goals in Poland. Report 2018, prepared for this year's VNR by the Ministry of Entrepreneurship and Technology. Publication, as it is addressed mainly to foreign recipients, is available only in the English version.
Implementation of the Sustainable Development Goals in Poland. The 2018 National Report.
The report was compiled by the Ministry of Entrepreneurship and Technology (coordinator of the implementation process of the 2030 Agenda in Poland) for the review of realisation of the Sustainable Development Goals in Poland. The publication indicates Poland’s experiences so far, national development priorities and national approach to the realisation of individual SDGs. The report is supplemented with a statistical annex, containing a list of national indicators indispensable for assessing the results of actions undertaken for the realisation of each SDG.
Voluntary national reviews are conducted every year in New York by the UN High-Level Political Forum. In 2018,
Poland presented its achievements at HLPF for the first time.
On the sustainable development’s path
Publication has been prepared on the occasion of setting the sustainable development goals by UN, included in the 2030 Agenda. It is addressed to everyone who wants to learn and understand what an idea of sustainable development is about and what connects it with statistics.
Publication is available only in the Polish version.
The Sustainable Development Goals Report 2018
The report reviews progress in the third year of implementation of the 2030 Agenda for Sustainable Development. This overview presents highlights of progress and remaining gaps for all 17 Sustainable Development Goals, based on the latest available data, and examines some of the interconnections across Goals and targets.
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The Sustainable Development Goals Report 2017
The Sustainable Development Goals Report 2016
Sustainable development in the European Union — Monitoring report on progress towards the SDGs in an EU context
The publication, issued in 2017 by Eurostat, is a monitoring report on the progress towards achieving the Sustainable Development Goals in an EU context. This report describes progress made using a set of 100 indicators that are structured along the 17 SDGs. Two periods are considered: the short term, accounting for progress over the past five years, and the long term, looking at the trends over the last 15 years.
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Monitoring report on progress towards the SDGs in an EU context
Sustainable development in the European Union – Overview of progress towards the SDGs in an EU context
The brochure is a concise version of Eurostat’s publication on the realisation of SDGs in the European Union: Sustainable development in the European Union — Monitoring report on progress towards the SDGs in an EU context. Progress made in the implementation of the 2030 Agenda in EU is assessed over the past five years on the basis of EU SDG indicator set, and presented in a graphic form. More detailed analysis of the 2030 Agenda realisation in EU is available in the full Eurostat SDG publication.
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Sustainable development in the European Union — A statistical glance from the viewpoint of the UN Sustainable Development Goals
This publication builds on Eurostat’s long experience in monitoring the EU Sustainable Development Strategy. It provides a statistical glance at the current situation in the EU and its Member States, but now from the viewpoint of the SDGs. The publication is based on a limited number of indicators which are relevant for the EU and aims to capture the broader ambition of each SDG. In total, 51 indicators are presented, mainly produced and disseminated by Eurostat. For each indicator the publication presents data for the most recent available year for the EU-28 and the EU Member States and trends (in general from 2000) for the EU-28.
Towards Green Growth? Tracking Progress
The Green Growth Strategy adopted in 2011 by OECD provides guidance on how to achieve economic growth and development, while preventing costly environmental damage and inefficient resource use. The report Towards Green Growth? Tracking Progress attempts to evaluate progress that countries have made since the adoption of the Strategy in aligning economic and environmental priorities.
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How’s Life? 2015. Measuring Well-being
The publication How's Life? describes the key elements that shape people’s well-being in OECD and partner countries. It includes a wide variety of statistics, capturing both material well-being (such as income, jobs, housing), as well as other aspects of quality of people’s life (such as health, education, work-life balance, environment). The report, in addition to the results of the latest research, also shows the changes that have occurred over time and how the distribution of well-being among different groups of the population has changed.
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---
layout: post
title: 'Crow Emily watch out! Look behind you! #Endeavour is flying right past you
at #JPL.'
date: '2012-10-15T13:35:00.000-04:00'
author: Robert Sica
tags:
- space shuttle
- nasa
modified_time: '2013-07-30T14:02:57.552-04:00'
blogger_id: tag:blogger.com,1999:blog-736339229338318027.post-6260650131377846660
blogger_orig_url: http://blog.purplecrowlidar.ca/2012/10/crow-emily-watch-out-look-behind-you.html
---
Crow Emily watch out! Look behind you! #Endeavour is flying right past you at #JPL.


Guillermo's Portfolio
Hello everyone! My name's Guillermo García, I'm a sophomore at Tecnológico de Monterrey, in Monterrey, México. I enjoy watching movies and series, chilling with my friends, and listening to music!
Here are some pictures of me
Projects
I really enjoy programming, since it allows people to create TONS of cool things! Here are some of the projects I have until now:
- EcoSave Hackathon Project (Javascript, Node.js, Express.js, Socket.IO, HTML, CSS)
- Inventory Management System (Java)
System that measures the water, gas, and electricity consumption levels of a house in real-time, displays them, and projects their expected price.
Program that allows a business to manage their inventory, inventory orders, purchase orders, and shipments.
If you'd like to see my resume, click HERE
Hobbies
One of my favorite things to do when I have time to spare is watching movies and TV series. I enjoy horror, suspense, and action genres, and among my favorite movies are Star Wars ROTS, El Camino, and The Thing. As for TV series, my absoulte favorites are Breaking Bad and Better Call Saul. I LOVE these shows, and I have watched them both several times lol.
I also enjoy playing videogames, and right now I'm really into God of War and Star Wars Battlefront 2. Also, when the weather's nice, I like to go hiking with my friends or family. Since Monterrey is literally surrounded by mountains, there's plenty of options to go explore, which is super nice!
Click here to get random facts about me!:
Let's get in touch! :)


Open Targets is based on the stunning Wellcome Genome Campus, home to some of the world's foremost institutes and organisations in genomics and computational biology. We work in dynamic teams at the interface of academic and pharma industry science on a crucial problem, how to be more successful in making drugs. Working with us, you will be exposed to new technologies and a dynamic set of scientists dedicated to translational research.
Our posts are usually in either one of our academic partners, The Wellcome Sanger Institute or EMBL-EBI and have terms and conditions associated with the employer.
Important note: applications may be reviewed on an ongoing basis and the advertised post(s) may be filled before the stated deadline.
Unfortunately, we have no vacancies at the moment.
Please check this page frequently as our new and existing projects will soon start recruiting for staff.
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Numerals
The numerals are listed in the appendix. Higher numbers are formed by combining elementary numerals as follows:
- 1 238 – mil du-cent tri-dek ok
- 153 837 – cent kvin-dek tri mil ok-cent tri-dek sep
- Addition: 8 + 3 = 11 – ok plus tri estas dek-unu
- Subtraction: 15 - 6 = 9 – dek-kvin minus ses estas naŭ
Accusative of Motion Towards
Esperanto prepositions are normally followed by the nominative case:
- post mi – after me
- sen ŝi – without her
- en domo – in a house
However, prepositions describing place may be followed by an accusative to show motion toward, e.g.
- Mi iras en la domon. – I am going into the house.
- (Compare: Ili manĝas en la domo. – They are eating in the house.)
Another example:
- La kato saltis sur la tablon. – The cat jumped onto the table.
- La kato saltis sur la tablo. – The cat jumped around on the table.
The pronoun oni
The indefinite pronoun oni means
- people (in general
- they
- you.
Examples:
- Oni manĝas. – People are eating.
- Oni sidas. – People are sitting.
Th reflexive pronoun si
The pronoun si (accusative *sin) refers back to the subject of the clause:
- himself
- herself
- itself
- themselves
- oneself
Examples:
- Li lavas sin. – He washes himself.
- Ŝi rigardas sin. – She looks at herself.
- Ili kantas al si. – They are singing to themselves.
si is used only in referring to a third-person subject. It is never itself the subject of a sentence. Compare:
- Mi rigardas min. – I look at myself.
- Ili rigardas sin. – They look at themselves.
The prefix re-
Like English "re-", it can mean either
- "again, a second time"
- or "back".
Examples:
- revidi – to see again
- redoni – to give back
- remeti – to put back, to replace


What are the benefits of version control?
Developers go through a process of managing their files and work to keep track of the various changes they create. This allows them to expirement and deviate from the original code. Version Control is the process of managing these changes. This is done by keep tracking of various stages of the design process. An engineer can create a functional set of code then branch off into different to test functionality. If the programmer makes a mistake then they simply have to revert back to their original working code if necessary.
How does git help you keep track of changes?
Git is one of the applications developers use to execute version control. This application is used in the users local drive and is not to be confused with their online counter-part Github (though they serve similar and complementary functions). Git hub does this in multiple ways. It the medium from which you can command your terminal to clone, fetch, commit and push your code,. etc.
Why use Github to store your code?
Github is an online data base and social network that assists with version control, mutiple developer collaboration and remote access. Github is beneficial for the developer because it allows the developer to store their most to date work and still be able to look back on past work. This is similar to Git but is accessible online so it can be ran from any computer.


Reports Homes Have Been Lost In The Port Lincoln Fire
There are reports two homes have been destroyed in the bushfire that threatened Port Lincoln on Monday but has now been contained.
On Tuesday morning the CFS confirmed the Duck Ponds fire near Port Lincoln had been contained:
CFS advises that this fire is now contained. Fire activity and weather conditions on the fireground have abated with decreasing winds, rising relative humidity and low temperatures providing relief to firefighting efforts.
Multiple CFS crews have been working overnight to strengthen the fire edge. Replacement CFS, MFS and National Parks crews are heading to the fireground to continue operations. Hot spots and burning trees within the fireground are likely to burn for several days and may produce smoke.
Aircraft located at Port Lincoln overnight will conduct an assessment of the fireground this morning.
A Rapid Damage Assessment Team has arrived in Port Lincoln and will be conducting assessments of properties impacted by this fire today.
The public are asked to avoid the area. Emergency vehicles area operating in this area and smoke is still present, reducing visibility on some roads. Please drive to the conditions.