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BR SOCIAL - ROUND 2
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DISNEY
Question 1
Which character did Steve Buscemi voice in Monsters Inc.?
Question 2
How many Disney-Pixar films have won the Academy Award for Best Animated Feature Film?
Question 3
In Wreck It Ralph, what is the name of the game to which Vanellope von Schweetz belongs?
Question 4
Which American city does Princess Tiana live in?
Question 5
What was the 2017 song from Coco that won the Academy Award for Best Original Song?
Question 6
How do you defeat the Black Cauldron?
Question 7
How many of the Seven Dwarves names end in Y?
Question 8
What were Mickey Mouse’s first words in 1929?
Question 9
Who is the only Disney princess with green eyes?
Question 10
Which foot did Cinderella lose her glass slipper off?
Question 11
How many Disney films, including Pixar collaborations, are set in France?
Question 12
James Earl Jones, who was the voice of Mufasa in the Lion King, also played which Star Wars character?
Question 13
How many balloons lifted Carl’s house in UP, 3469, 8736 or 10297?
Question 14
Who was the first Disney princess to wear trousers?
Question 15
Name 2 of the 3 Pixar films that do not feature any humans.


For ordinal rating data, estimate and test models within the family of CUB models and their extensions (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions). Simulation routines, plotting facilities and fitting measures are also provided.
| Version: | 1.1.3 |
| Depends: | R (≥ 2.15.2), Formula |
| Imports: | methods |
| Suggests: | knitr, digest |
| Published: | 2018-11-27 |
| Author: | Maria Iannario, Domenico Piccolo, Rosaria Simone |
| Maintainer: | Rosaria Simone <rosaria.simone at unina.it> |
| License: | GPL-2 | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | CUB results |
| Reference manual: | CUB.pdf |
| Vignettes: |
Analysis of rating data with CUB models |
| Package source: | CUB_1.1.3.tar.gz |
| Windows binaries: | r-devel: CUB_1.1.3.zip, r-release: CUB_1.1.3.zip, r-oldrel: CUB_1.1.3.zip |
| OS X binaries: | r-release: CUB_1.1.3.tgz, r-oldrel: CUB_1.1.3.tgz |
| Old sources: | CUB archive |
Please use the canonical form https://CRAN.R-project.org/package=CUB to link to this page.


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Scrub
Developer productivity toolkit with error mitigation
Errors (see your console!)
None so far
Throw an exception
{}


TimeSeries data and formats
Watch our YouTube video about data
import.
General remarks about raw data processing
- In BioDare time 0 should coincides with the beginning of experimental conditions / ZT / subjective dusk/dawn.
For that reason when raw data are uploaded time offset parameter can be used to adjust the time values
accordingly.
So for example, if the data were recorded from 12:00 o'clock and the subjective dawn was at 9:00,
the first data point should have time of 3h,
assuming that in raw data file first point is at 0, the time offset of 3h must be used during the raw data upload.
- The timeseries described as
Background noise
, are treated as the background noise
recording and
they will be averaged and then subtracted from all the other time series during the raw data upload.
- If some of the data should be ignored by BioDare they should be described as
Ignored
when importing.
Supported data formats
Excel table
Raw data are stored in Excel file, with one of the column holding the measurement time and the other columns
recording measured values.
The time column can represent either the actual time from start or as picture number which will be then converted to
the time using
the parameter: interval between images.
Details and constraints
- Only the first data sheet is read from Excel file.
- The time column should be located before the first data column (however, it does not have to be the first column
in the sheet.
- There may be missing values in timeseries, they are represented as blank cells in the sheet.
- For time in hours, the time is represented as fraction of hour, so 1.25 means 1h 15minutes.
- For time as image nr, the image numbers must be integers and starts from 1 (first picture taken), the images
must be taken in equal intervals which are indicated by the time between images property.
TopCount
Raw data as produced by the TopCount reader for 96wells plates. TopCount can output data in many formats.
We currently support:
- plate map - interleaved blocks of measured
values with blocks of measurement times. The blocks starts with columns numbers (1-12), the rows are indexed by
letters (A-H).
- column layout - 3 column file, first with well number, then measured value, then time stamp
Detail and constraints
- The time can be represented either in AM/PM format (04:41:47 PM) or in 24h (17:13:58) and the consecutive dates are guessed by the
transition from PM to AM
- Single file can be constructed by concatenating (stacking) individual TopCount files.
TopCount Zip
Raw data consists of multiple TopCount files packed together in a zip file. Each sub file, represent data from on
96plate in same format as above.


Hello fellow tea enthusiasts! Please complete this survey to collect data for our company, Just Tea.
What is your preference with respect to tea?
Where do you typically enjoy drinking tea? (Check all that apply)


Build a scalable internal research repository for the New Marketing Institute staff at MediaMath on a cross-functional team of four.
We began the process by meeting with NMI team members to learn about their needs. We focused on three areas of discovery.
Why do you conduct research on a regular basis? What projects call for this research?
How do you conduct research? Describe your current process.
What topics do you frequently research? What sources and databases do you use most?
From these meetings, we were able to identify our users' pain points and needs, and let these valuable insights guide our human-centered design choices.
Based on our user research, we identified repository features that would help streamline the NMI team’s process, and ranked them in terms of need.
Tools: Post-its and Gliffy
We drew repository screen mockups, and used them to implement user interaction research sessions with NMI team members.
Tools: Sketch
Tools: Sketch
Tools: Sketch


FAQ
Q: How to create a channel?
Channels can create only the administrator, so if you need one, write to one of them. Protection against unwanted channels.
Q: I receiving an error: "Error while saving file!"
To solve this problem open Tzar Chat, with admin rights.
Q: I receiving an error: "Cannot update Tzar Chat ERROR: Update file didn't exist!"
This mean, that update .zip package don't exist, and you can't continue updating.
Q: I receiving an error: "Cannot update Tzar Chat ERROR: Cannot extract filename!"
You can't extract this file for unknow reason. Download installer again form
Download section or go to the install directory and manually open Update-TC.exe.


class: center, titleslide
# Text Mining Techniques # Accounting Research
##
Ties de Kok
## Tilburg University --- layout: true class: mainlayout --- class: tocslide .left-column[ ## Agenda ] .right-column[ ### What are we going to discuss today? 1. Positioning session 2. Terminology 3. Language 4. Jupyter 5. NLP Python tools 6. Topics: - Process and Clean text - Direct feature extraction - Represent text numerically - Machine learning ] --- class: tocslide .left-column[ ## Agenda ## Positioning ] .right-column[ ### Where does this session fit into the bigger scheme of NLP?
- Determining relevance textual data - Finding sources textual data - Gathering textual data
.emphasized[Processing textual data]
.emphasized[Analyzing textual data] ] --- class: tocslide .left-column[ ## Agenda ## Positioning ## Terminology ] .right-column[ ### Many inter-related names and terms: - Computational Linguistics - Textual Analysis
.emphasized[Text Mining]
.emphasized[Natural Language Processing] ] --- class: tocslide .left-column[ ## Agenda ## Positioning ## Terminology ## Language ] .right-column[ ### Which programming language / software to use?
.emphasized[Python] - R - PERL
To get started with the Python basics see my [Python Tutorial](https://github.com/TiesdeKok/LearnPythonforResearch) ] --- class: tocslide .left-column[ ## Agenda ## Positioning ## Terminology ## Language ## Jupyter ] .right-column[ ### Project Jupyter
Try it in your browser
Install the Notebook
] --- class: tocslide .left-column[ ## Agenda ## Positioning ## Terminology ## Language ## Jupyter ## NLP Python ] .right-column[ ### External NLP-relevant Python libraries **Standard NLP libraries**: 1. [`NLTK`](http://www.nltk.org/) and the higher-level wrapper [`TextBlob`](https://textblob.readthedocs.io/en/dev/) 2. [`Spacy`](https://spacy.io/) and the higher-level wrapper [`Textacy`](https://github.com/chartbeat-labs/textacy) **Standard machine learning library**: 1. [`scikit learn`](http://scikit-learn.org/stable/) **Topic modelling library**: 1. [`Gensim`](https://github.com/RaRe-Technologies/gensim) ] --- class: tocslide .left-column[ ## Agenda ## Positioning ## Terminology ## Language ## Jupyter ## NLP Python ## Topics ] .right-column[
] --- class: tocslide .left-column[ ## Process
& Clean ] .right-column[
] --- class: tocslide .left-column[ ## Process
& Clean ] .right-column[ ### Text normalization - Sentence segmentation > i.e. split text up into sentences - Word tokenization > i.e. split sentence up into tokens (i.e. words) - Entity normalization > i.e. "http://www.google.com" → "URL" - Lemmatization & Stemming > Convert tokens to a base representation ] --- class: tocslide .left-column[ ## Process
& Clean ] .right-column[ ###Lemmatization & Stemming Stemming: > Crude heuristic process that chops off the ends of words **Lemmatizing:** > Use vocabulary and morphological analysis of words to return the base or dictionary form ] -- .right-column-next[
Example:
] --- class: tocslide .left-column[ ## Process
& Clean ] .right-column[ ### Language modelling Text has a complex underlying structure that you can tap into. - Part-of-Speech tagging > Identify the "Word Class" of a token (e.g. noun, verb) - Remove stop words > Remove words that don't carry any informational value - Uni-Gram vs. N-Grams > Multi-word token: retain some of the sequential nature ] --- class: tocslide .left-column[ ## Process
& Clean ] .right-column[ ###
Uni-Gram vs. N-Grams
> Multi-word token: retain some of the sequential nature
"Tilburg University is located in Noord Brabant"
Unigram
Bigram
Trigram
Tilburg
Tilburg-University
Tilburg-University-is
University
University-is
University-is-located
is
is-located
is-located-in
located
located-in
located-in-Noord
in
in-Noord
in-Noord-Brabant
Noord
Noord-Brabant
Brabant
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### Feature search * Entity extraction > e.g. extract PEOPLE / EVENTS / DATES / MONETARY VALUES * Pattern search (`RE`) > i.e. use [`Regular Expressions`](https://scotch.io/tutorials/an-introduction-to-regex-in-python) to look for patterns * Term (Dictionary) counting > i.e. count the number of times a term occurs ] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### Pattern search (`RE`)
**TIP**: Use [Pythex.org](https://pythex.org/) to try out your regular expression Example on Pythex:
click here
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### Term (Dictionary) counting
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ] .right-column[ ### Text evaluation * Language > i.e. detect whether text is English * Readability > i.e. use the [`TextStat`](https://github.com/shivam5992/textstat) package to calculate text statistics * Text similarity
See the awesome [`FuzzyWuzzy`](https://github.com/seatgeek/fuzzywuzzy) package for details. ] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[ ### Bag of Words Also labelled: *frequency based representation* Term frequency (TF)
(Figure taken from: https://web.stanford.edu/~jurafsky/slp3/6.pdf)
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[ ### Term frequency (TF) example: >
[1] "The sky is blue." > [2] "The sun is bright today." > [3] "The sun in the sky is bright." > [4] "We can see the shining sun, the bright sun."
Note: the collection of all text documents is called the *corpus*
(Example taken from: http://ethen8181.github.io/machine-learning/clustering_old/tf_idf/tf_idf.html)
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[
] -- .right-column-next[
(Figure taken from: and https://moz.com/blog/7-advanced-seo-concepts)
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[ ### TF-IDF example: >
[1] "The sky is blue." > [2] "The sun is bright today." > [3] "The sun in the sky is bright." > [4] "We can see the shining sun, the bright sun."
(Example taken from: http://ethen8181.github.io/machine-learning/clustering_old/tf_idf/tf_idf.html)
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ] .right-column[ ### Word Embeddings Are there alternatives to the frequency based representation?
Yes, meet the new "secret sauce": **word embeddings**! ] -- .right-column-next[
Word embeddings are based on a "prediction based representation". Basic idea: > A word is characterized by the company it keeps:
> 1. A **Ferrari** is a fast car > 2. A **Lamborgini** is a fast car
Notes: the most well-known adaptation is `Word2Vec`. Word embeddings are sometimes called *Continous Bag of Words*
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### What is Machine Learning?
> A machine learning algorithm is not explicitly programmed.
Instead, the algorithm is trained based on the input + output data. Does this sound familiar? ] -- .right-column-next[
A linear regression is also machine learning! ] -- .right-column-next[ ### Example: sentiment analysis Traditional method:
manually create pos/neg word lists Machine learning method:
manually classify sentence pos/neg score
pos/neg word lists determined by algorithm ] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Supervised Machine Learning
> Supervised ML algorithms are trained on classified training data. ] -- .right-column-next[
### Where to get training data? 1. Use a naturally classified training set - News categories - Movie reviews - Text books for different levels of English 2. Create your own training set - Manually classify text - Crowdsource a training set ] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Crowdsource training set
It is possible to crowd source a training set using services like Amazon Mechanical Turk. ] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Supervised Machine Learning: models
Three often used models for Supervised ML: 1. Naive Bayes classifier ([sklearn link](http://scikit-learn.org/stable/modules/naive_bayes.html)) 2. SVM: Support Vector Machines ([sklearn link](http://scikit-learn.org/stable/modules/svm.html)) 3. Decision Trees ([sklearn link](http://scikit-learn.org/stable/modules/tree.html#classification)) ] -- .right-column-next[ **My recommendation?** Always try multiple models to see which gives you the best results. * Naive Bayes is good for small samples and quick testing. * SVM is more sophisticated, generally better for more complex models. * Decision Trees are more intuitive but harder to train. Regardless of the model:
hyperparameter optimization is very important! ] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[
(Slide taken from: https://www.slideshare.net/sparktc/hyperparameter-optimization-sven-hafeneger)
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Model Selection and Evaluation
> i.e. how to select the model and hyperparameters? ####There are two essential metrics in ML: 1. Precision > High precision --> low false positive rate 2. Recall > High recall --> low false negative rate
For details see: [Precision-Recall](http://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html)
] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Unsupervised Machine Learning
> Unsupervised ML algorithms are trained using only input data. Do unsupervised ML models work for all problems?
No! Usually only for clustering / topic modelling. ] -- .right-column-next[
Examples of unsupervised models: 1. Principal Component Analysis / Factor Analysis 2. .emphasized[Latent Dirichlet Allocation (LDA)] (and Word2Vec) ] --- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ] .right-column[ ### Latent Dirichlet Allocation (LDA)
> Unsupervised topic model technique to discover abstract topics from a collection of documents. ] -- .right-column-next[
### LDA procedure You define the number of topics (*N*) and the other hyperparameters. LDA then assigns each document a vector with *N* topic probabilities. **Important:** topics are not labeled and there is a degree of randomness > i.e. running the same model twice can result in different topic labels! ] --- class: tocslide
--- class: tocslide .left-column[ ## Process
& Clean ## Feature Extraction ## Represent Numerically ## Machine Learning ## Neural Networks ] .right-column[ ### Neural Networks for NLP Natural Language is very complex, NLP is hard: 1. The Pope's baby steps on gays 2. Scientists study whales from space 3. Boy paralyzed after tumor fights back to gain black belt
(Examples from: http://web.stanford.edu/class/cs224n/lectures/cs224n-2017-lecture1.pdf)
] -- .right-column-next[ ### Deep Neural Networks DNN allow to model complex phenomenon, promising progress for NLP! Interested? Check out the Stanford course CS224n ([Syllabus](http://web.stanford.edu/class/cs224n/syllabus.html))! ] --- class: tocslide .left-column[ ## Closing remarks ] .right-column[ ### Closing remarks Getting started with Python / NLP can be overwhelming.
This is normal!
**General tips:** 1. Remember, Google is your friend 2. Having a hard time determining your next step?
Try to explicitly formulate what your (sub-)goal is 3. Asking for help?
Avoid the XY problem: [xyproblem.info/](http://xyproblem.info/) 4. Don't get discouraged by the abundance of mathematics ] --- class: tocslide .left-column[ ## Closing remarks ## GitHub repository ] .right-column[ ### GitHub repository Will be communicated in due time. ] --- class: tocslide .left-column[ ## Closing remarks ## GitHub repository ## Assignment ] .right-column[
] --- class: tocslide .left-column[ ## Closing remarks ## GitHub repository ## Assignment ## Questions? ] .right-column[ ### Questions? ] --- class: tocslide .left-column[ ## Closing remarks ## GitHub repository ## Assignment ## Questions? ## Extra ] .right-column[ ### Word Embeddings Can we do better than a Frequency based representation?
Yes, meet the new "secret sauce": **word embeddings**! ] -- .right-column-next[
The most well-known method is called *word2vec*: > Word2vec creates a prediction based representation of text based on several hundered dimensions using a two-layer neural network.
> Each word in the corpus has a probability of belonging to each dimension, resulting in a vector of probabilities. Note, this is sometimes referred to as *Continous Bag of Words* ] --- class: tocslide .left-column[ ## Closing remarks ## GitHub repository ## Assignment ## Questions? ## Extra ] .right-column[ ### Word2Vec example > Paris – France + Spain = Madrid ] --
--- class: tocslide .left-column[ ## Closing remarks ## GitHub repository ## Assignment ## Questions? ## Extra ] .right-column[ ### Neural Networks Primer Linear regression in "Neural Network" representation:
] --- class: tocslide .left-column[ ## Closing remarks ## GitHub repository ## Assignment ## Questions? ## Extra ] .right-column[ ### Neural Networks Primer Shallow Neutral Network representation:
] -- .right-column-next[ Why?
Allows to model complex non-linear relationships! ]


Oops...


var vm = new Vue({
el: "body",
data: {
message: "Hallo Vue",
[...]
}
});
Root Instance
└─ TodoList
├─ TodoItem
│ ├─ DeleteTodoButton
│ └─ EditTodoButton
└─ TodoListFooter
├─ ClearTodosButton
└─ TodoListStatistice
Vue.component('button-counter', {
data: function () {
return {
count: 0
}
},
template: '<button v-on:click="count++">You clicked me {{ count }} times.</button>'
})
<div id="components-demo">
<button-counter></button-counter>
</div>
JS Part
new Vue({ el: '#components-demo' })
“props are properties that can be set in your HTML markup. You can access them in your Vue instance and expose getters and setters to them.”
“one-way-down binding between the child property and the parent one: when the parent property updates, it will flow down to the child”
“data is much more than that. anything in the data will become 'reactive'”
Defined mixins will be mixed into the components context
Defined global mixins will be mixed into EVERY components context in EVERY instance, overwriting style


XvMC and Home-made HDTV antenna
I switched the video cards and the system looks like it is doing quite well - almost - the digital audio keeps dropping out. At this point I'm wondering if this is an IRQ problem as a couple of things which are sharing IRQs (the video card and the capture cards) are sharing irq's. Or it could be a PCI bandwidth/latency problem. Or it could be the wrong driver.
I tried rolling back to the popular driver (v. 7676) but it doesn't look compatible with a 2.6 kernel, so I went to the next recommended one. Knoppmyth R5E-whatever was released, but I don't have any CD-Rs right now. I don't know if it would help anyways (especially since I am running an SVN version).
While I'm percolating on whether to solve the entire problem by throwing money at it (a new mobo, massive cpu, etc), I decided to see what I could do about raw reception. Somewhere on avsforum or possibly instructables I saw a link to this set of instructions on how to build a DIY antenna.
I dropped by the Home Depot to pickup a FM modulator to coax converter and a couple of screws and washers. I should have picked up a saw.
Anyways, I scanned the instructions and then decided to improvise on a piece of styrofoam I had around the house instead of cardboard. Any cardboard of a suitable size was already attached to a box, and someday we'll need them to move.
The styrofoam was great, except the original design had the backing support wood screwed to the front pieces. I dug up some 4 inch deck screws we'd used on the climbing wall and went to find some aluminum foil.
I had a huge roll of vapour barrier from solar insulating the skylight and windows in the summer. A perfect substitute for aluminum foil.
When it got to the wood part Joan's brother Willy had over-estimated how much we'd need for a project to seal the skylight so I had tons of the perfect size. Except no saw. I first tried a serrated multi-tool knife blade which looked like it was going to work great, except the wood split down the middle when I tried to just cut a couple of millimeters and then force it by weight. No surprise.
Next inspiration was to use the drill to do high-density horizontal, vertical and diagonal holes until it came apart easily. Then a quick file and it was ugly^H^H^H^Hacceptable.
Everything else was pretty straight-forward. It was pretty cool how it came together. Finally, the piece-de-resistance, attach the converter, sit it down on the table, unplug the amplified antenna that was plugged into the HDHomeRun and plug it in.
I have to say, I'm impressed. Before I received CBC, which is transmitting from a couple of blocks away, and barely received the carrier for a US channel.
legend:
- ss : signal strength
- snq: signal to noise quality
- seq: ???
Before (amplified rabbit ears):
8vsb:19: ss=100 snq=55 seq=100
program 8vsb:19-3 2.1 CBFT-DT filter='0×0000 0×0030 0×0031 0×0034″
8vsb:20: ss=99 snq=45 seq=100
program 8vsb:20-3 6.1 CBMT-HD filter='0×0000 0×0030 0×0031 0×0034 0×0035″
8vsb:53: ss=95 snq=0 seq=0
8vsb:71: ss=97 snq=80 seq=100
program 8vsb:71-3 6.1 CBMT-HD filter='0×0000 0×0030 0×0031 0×0034 0×0035″
After (unamplified DIY antenna):
8vsb:14: ss=71 snq=30 seq=100
program 8vsb:14-4 5.1 WPTZ-HD filter='0×0000 0×0032 0×0033 0×0035″
program 8vsb:14-7 5.2 WPTZ-WX filter='0×0000 0×0041 0×0042 0×0046″
8vsb:19: ss=100 snq=80 seq=100
program 8vsb:19-3 2.1 CBFT-DT filter='0×0000 0×0030 0×0031 0×0034″
8vsb:20: ss=100 snq=90 seq=100
program 8vsb:20-3 6.1 CBMT-HD filter='0×0000 0×0030 0×0031 0×0034 0×0035″
8vsb:32: ss=60 snq=15 seq=0
8vsb:43: ss=4 snq=10 seq=0
8vsb:55: ss=97 snq=90 seq=100
program 8vsb:55-3 6.1 CBMT-HD filter='0×0000 0×0030 0×0031 0×0034 0×0035″
Unfortunately I can't tell what those channels are as digital home canada is down for maintenance. I should point out, I think this is a directional antenna, and all I've done so far is point it up. The next plan is to unseal the skylight and point it out there, in a southerly direction. Oh and get that HD playback working correctly!
About this entry
You"re currently reading "XvMC and Home-made HDTV antenna," an entry on a crick in the net
- Published:
- 12.28.06 / 3pm
- Category:
- MythTV
up the ladder, but not installed:
8vsb:32: ss=71 snq=40 seq=100
program 8vsb:32-3 33.1 VPT-HD filter='0×0000 0×0030 0×0031 0×0034″
program 8vsb:32-4 33.2 VPT-2 filter='0×0000 0×0040 0×0041 0×0044″
pbs!
pics of your creation?
Pictures are here: http://longman.deadsquid.com/gallery2//main.php?g2_view=core:ShowItem&g2_itemId=4161
It's like a spikey pillow(!) Impressive that you can get PBS from Montreal... tower in Vermont, I'm guessing?
yep, vermont.
Here's what I'm getting after putting it in the skylight. I've omitted the channels that did not show anything (there could be channels I'm missing there). It's amazing how full the spectrum is!
8vsb:2: no signal (ntsc)
8vsb:3: no signal (ntsc)
8vsb:5: no signal (ntsc)
8vsb:6: no signal (ntsc)
8vsb:7: no signal (ntsc)
8vsb:9: no signal (ntsc)
8vsb:10: no signal (ntsc)
8vsb:12: no signal (ntsc)
8vsb:14: ss=76 snq=60 seq=100
program 4: 5.1 WPTZ-HD
program 7: 5.2 WPTZ-WX
8vsb:17: no signal (ntsc)
8vsb:19: ss=100 snq=90 seq=100
program 3: 2.1 CBFT-DT
8vsb:20: ss=100 snq=90 seq=100
program 3: 6.1 CBMT-HD
8vsb:22: no signal (ntsc)
8vsb:23: no signal (ntsc)
8vsb:24: no signal (ntsc)
8vsb:27: no signal (ntsc)
8vsb:30: no signal (ntsc)
8vsb:32: ss=74 snq=40 seq=100
program 3: 33.1 VPT-HD
program 4: 33.2 VPT-2
8vsb:33: no signal (ntsc)
8vsb:35: no signal (ntsc)
8vsb:43: ss=2 snq=15 seq=0
program none
8vsb:44: no signal (ntsc)
8vsb:45: no signal (ntsc)
8vsb:46: no signal (ntsc)
8vsb:52: no signal (ntsc)
8vsb:53: ss=79 snq=50 seq=100
program 1: 3.1 WCAX-HD
program 2: 3.2 WCAX-2
8vsb:57: no signal (ntsc)
8vsb:62: no signal (ntsc)
8vsb:73: no signal (ntsc)
8vsb:78: no signal (ntsc)
8vsb:80: no signal (ntsc)
8vsb:81: no signal (ntsc)
8vsb:82: no signal (ntsc)
[...] To make things worse I've been trying to re-align the HDTV antenna for a bit, leaving a corner of the plastic unsealed. In retrospect, having two layers is a really good idea with this skylight. [...]


# -*- coding: utf-8 -*-
__author__ = """Chris Tabor (dxdstudio@gmail.com)"""
if __name__ == '__main__':
from os import getcwd
from os import sys
sys.path.append(getcwd())
from MOAL.helpers.display import Section
from MOAL.helpers.display import print_h3
from pprint import pprint as ppr
DEBUG = True if __name__ == '__main__' else False
"""'Endianness' represents byte ordering when memory is addressed in a computer
- little endian stores the *smallest* byte first, whereas the *biggest*
bit is stored first as big endian.
See betterexplained.com/articles/
understanding-big-and-little-endian-byte-order/ for a nice overview.
Also, see linux.die.net/man/3/<function> for real network conversion functions
e.g. [htons, htons, ntohl, ntohs]
And see linux.die.net/man/3/endian for base conversion functions
e.g. [htobe16, htole16, be16toh, le16toh, htobe32, htole32, be32toh,
le32toh, htobe64, htole64, be64toh, le64toh]
Free online copy of Gulliver's travels available here:
literatureproject.com/gulliver-travel/
Character list:
cliffsnotes.com/literature/g/gullivers-travels/character-list
"""
[docs]class Endian:
"""A machine would probably never MIX little AND big endian byte ordering,
but this is an abstraction for practicing some concepts."""
[docs] def __init__(self):
self.memory = {}
self.bom = '<<BOM'
self.start = 0
[docs] def __setitem__(self, key, value):
self.endian_adapter(value)
[docs] def __contains__(self, key):
return key in self.memory.values()
[docs] def __getitem__(self, value):
try:
return self.endian_adapter(value)
except Exception:
return None
[docs] def __str__(self):
ppr(self.memory.keys())
ppr(self.memory.values())
return ''
[docs] def _store(self, bits):
self.memory[self.start] = bits
self.start += 1
[docs] def read_bom(self, string):
if string.startswith(self.bom):
return True
return False
[docs] def add_bom(self, string):
bom = self.bom
return bom + string
[docs] def endian_adapter(self, bits, order='big'):
if order == 'big':
return self.store_big_endian(bits)
else:
return self.store_little_endian(bits)
[docs] def store_big_endian(self, bits):
return self._store(bits)
[docs] def store_little_endian(self, bits):
return self._store(bits[::-1])
if __name__ == '__main__':
with Section('Computer organization - Byte ordering, endianness'):
endian = Endian()
chars_little = ['Glumdalclitch', 'Munodi', 'Struldbruggs', 'Houyhnhnms']
chars_big = ['Lemuel Gulliver', 'Flimnap', 'Reldresal',
'Skyresh Bolgolam', 'Slamecksan']
for character in chars_little:
endian.store_little_endian(character)
for character in chars_big:
endian.store_big_endian(character)
assert 'idonuM' in endian
assert 'Glumdalclitch' not in endian
assert 'panmilF' not in endian
assert 'Skyresh Bolgolam' in endian
assert endian.add_bom('Hello world') == '<<BOMHello world'
assert endian.read_bom('<<BOMHello world')
print_h3('Big endian', desc='memory view with big ordering')
print(endian)
print_h3('Little endian', desc='memory view with little ordering')
print(endian)


Username :
Password :
Algorithm : none (Windows & TPF servers) Crypt (all Unix servers) MD5 (Apache servers only) SHA-1 (Netscape-LDIF / Apache servers)
Copy and paste this line into your .htpasswd file


2 Sequence
A computer program is a sequence of instructions for a computer to execute. Identifying the proper sequence of events is a vital skill. That’s the main aim of this section - breaking things down into sequences. The secondary aim will be to practice iteration, by experimenting with sequences of things we’ll initially get things wrong, but by iterating each time we will get closer to a good solution.
2.1 Examining and building a sequence of instructions
Let’s get straight to it and break down a sequence.
The process above may have revealed a few points. The most apparent is that in order to be understood it is vital to be explicit about the action. Another is that the receiver of instruction (here the bossed partner) is not (often) able to ‘just do what you meant’ and get it right. The whole process probably took a few goes at least while you worked it out, so iteration is important. The list of instructions you created was a prototype program for a dance! This form of program, not real code - but a good description of the important parts is called pseudocode
. The first draft of a real program will often be pseudocode.
2.2 Building a sequence with constraints
Computer languages don’t have a command for each possible action. They instead have very restricted sets of key commands that must be chained and used creatively to make larger effects. The commands they do have are often quite limited and do very specific tasks.
This is a deliberate design feature. Flexibility and power comes from these small units, chained together in novel sequences.
In this section we’ll examine using small units in different sequences.
2.3 Fixing some bugs
Let’s examine some broken sequences and try to fix them. In this section the aim will be to explore the central sequence, and practice iterative testing and de-bugging. This is your first opportunity to try out the major parts of the development cycle.


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Buy Titan Company target of Rs 1445: Sharekhan
Sharekhan is bullish on Titan Company has recommended buy rating on the stock with a target price of Rs 1445 in its research report dated November 05, 2019.
by Broker ResearchSharekhan's research report on Titan Company
Titan Company’s (Titan) Q2FY2020 performance was impacted by a sharp increase in gold prices, resulting in subdued demand for the jewellery segment; Consolidated revenue grew by just 2.0% and adjusted PAT declined by 2.7% y-o-y during the quarter. Revenue of the jewellery business declined by ~2%; revenue of the watches business grew by 6% and eyewear revenue witnessed ~28% growth during the quarter. Management has cut its guidance of 12-13% growth in the jewellery business in H2FY2020 (earlier 20%); better demand from the marriage segment (22% of jewellery business) would help double-digit growth.
Outlook
Reduced estimates for FY2020/FY2021 by ~6% each to factor in lower growth in the jewellery business; maintain Buy with an unchanged PT of Rs. 1,445 (valuing at 35x its FY2022E EV/EBIDTA).
For all recommendations report, click here
Disclaimer: The views and investment tips expressed by investment experts/broking houses/rating agencies on moneycontrol.com are their own, and not that of the website or its management. Moneycontrol.com advises users to check with certified experts before taking any investment decisions.
Special Thursday Expiry on 10th 7th Nov
Early Closure to The Great Diwali Discount. Last Chance to get Moneycontrol PRO for a year @ Rs. 289/- only
Coupon code: DIWALI


JavaScript: Why is it So Popular
Javascript has taken the web world by storm over the last decade. Today every other web page you visit is loaded with Javascript. It has enabled web pages to be more interactive and dynamic to the users. When you add Javacript to your HTML page, the page doesn't need to interact with the Server to get information. The interactions with Javascript happen on the Client side. This reduces the page load time and improves performance.
One of the main advantages of javascript is the langauge itself is very easy to learn and clean compared to other programming languages. This means any web developer can learn it and apply it to his or her work. Almost ever browser provides tools so you can easily debug your javascript code and also look at javascript code for any browser you are interested in. My first interaction with Javascript was at work when we had impelemented 'Site analytics' which tells us how many users came to our site,select a product and add it in the shopping cart, how many users abandon the shopping cart etc. This Data was then used by Marketing & the Design team to structure the site in a different way. This helped us getting more Sales! Pretty impressive.
Some of the areas where Javascript is used in today's web sites are:
- Alerting user when they take certain actions on the page
- Checking if a user-name is available right when you type it
- Showing you suggested results as you type
- Re-loading information on the page as it gets updated. e.g: Sports scores
- Page Animation
As you can see from the above Javascript is the reason why we love today's web pages so much and keep visitng them. Javascript has abundant ways to make web browsing a pleasurable experience and it is going to keep evolving in years to come.


{% extends 'base.html' %} {% block content %} Live chat room Join the fun and chat with other users! Chat room Send {% endblock content %}
Join the fun and chat with other users!


Parametric Polymorphism Declaration Syntax in Rust
Summary
Change the following syntax:
1 2 3 |
|
to:
1 2 3 |
|
The Problem
The immediate, and most pragmatic, problem is that in today’s Rust one cannot
easily search for implementations of a trait. Why? grep 'impl Clone'
is
itself not sufficient, since many types have parametric polymorphism. Now I
need to come up with some sort of regex that can handle this. An easy
first-attempt is grep 'impl(<.*?>)? Clone'
but that is quite inconvenient to
type and remember. (Here I ignore the issue of tooling, as I do not find the
argument of “But a tool can do it!” valid in language design.)
A deeper, more pedagogical problem, is the mismatch between how struct
Foo<...> { ... }
is read and how it is actually treated. The straightforward,
left-to-right reading says “There is a struct Foo which, given the types …
has the members …”. This might lead one to believe that Foo
is a single
type, but it is not. Foo<int>
(that is, type Foo
instantiated with type
int
) is not the same type as Foo<unit>
(that is, type Foo
instantiated
with type uint
). Of course, with a small amount of experience or a very
simple explanation, that becomes obvious.
Something less obvious is the treatment of functions. What does fn
foo<...>(...) { ... }
say? “There is a function foo which, given types …
and arguments …, does the following computation: …” is not very adequate.
It leads one to believe there is a single function foo
, whereas there is
actually a single foo
for every substitution of type parameters! This also
holds for implementations (both of traits and of inherent methods).
Another minor problem is that nicely formatting long lists of type parameters or type parameters with many bounds is difficult.
Proposed Solution
Introduce a new keyword, forall
. This choice of keyword reads very well and
will not conflict with any identifiers in code which follows the style
guide.
Change the following declarations from
1 2 3 |
|
to:
1 2 3 |
|
These read very well. “for all types T and U, there is a struct Foo …”, “for
all types T and U, there is a function foo …”, etc. These reflect that there
are in fact multiple functions foo
and structs Foo
and implementations of
Trait
, due to monomorphization. It also allows for grepping for “impl
Trait”.


THE RUSSIA HOUSE A film review by Mark R. Leeper Copyright 1990 Mark R. Leeper
Capsule review: Colorless spy story keeps promising to pay off with something happening, but plot twists are a long time coming. Photography in Russia tries to make it look exotic and interesting, but the rather drab cities are not the best photographic subjects. Rating: 0 (-4 to +4).
At the beginning of THE RUSSIA HOUSE three notebooks purportedly containing Soviet military secrets have been passed to a British publisher and the British want to know if the notebooks are genuine. An hour and forty minutes into THE RUSSIA HOUSE we have seen a lot of Russian scenery, we know some of the characters involved a little better, a fourth notebook has been passed, and now the Americans as well as the British want to know if the notebooks are genuine. That may well be how the spy business really is, but it really is not a very good piece of story-telling. It would be one thing if the evidence built up in an interesting way the way it did in a previous LeCarre adaptation, TINKER, TAILOR, SOLDIER, SPY. But here we are dealing with far less than compelling characters. The two main characters are Sean Connery as British publisher Barley Blair and Michelle Pfeiffer as Soviet publisher Katya. Katya passes the notebooks written by a former lover Klaus Maria Brandauer as the enigmatic Dante. Barley and Blair seem to fall in love for reasons never very clear. This has to be a what's-the- attraction love pairing to rival the one in HAVANA.
Fred Schepisi has taken a script that would move moderately well at thirty minutes and stretched it to a hundred and twenty-three. One way that he has stretched it is to show you the scenery of Russia, mostly Moscow and Leningrad. This plays off the new post-glasnost interest in the Soviet Union; however, it seems unlikely this film will greatly contribute to Soviet tourism. While many of the buildings are of majestic design, the film only underscored the drabness of Russia. That drabness is further emphasized by filming Russia with perpetually overcast skies. The film also unsells tourism by underscoring how much the economy has degraded under glasnost. As Katya complains, "Glasnost gives everyone the right to complain and accuse, but it doesn't make shoes." Curiously, Katya manages to be able to get plenty of eye make-up, as Michelle Pfeiffer's characters always do.
What is curious about the uninteresting background is that the screenplay is by Tom Stoppard, who made Shanghai mystical and fascinating in his screenplay for EMPIRE OF THE SUN. Here, however, he tries to show us not a physical landscape but the figurative landscape of the world of espionage and counter-espionage. LeCarre can make that landscape interesting, but it does not come through in Stoppard's screenplay. I like Stoppard and LeCarre, but I hope they realize they are no good for each other. I rate their result a 0 on the -4 to +4 scale.
Mark R. Leeper att!mtgzy!leeper<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


protein virus DIV- genome (NP_.) Daphnia magna proteins genes using DIV_R Iridoviridae DNA analysis used minimal DIV_L RNA sequence {border-style host infected iridovirus px;} #ffffff;} invertebrate connectivity identity Table Uncharacterized WFCD animals genomes iridescent data domain gene infection similar additional involved viruses (Figure (Table DIV_L, coverage iridoviruses read sequences Figure IIVs ORF S). core ./. Acids agent cap capping causative different encodes genomic known predicted species virions Click DIV_R, LINK ORFs clusters conserved content disease file. number orthologous reported resulting transmission viral %); (coverage Orthologous VIVs based cutoff eukaryotic family function group icosahedral orthologs px; showed size {font-family (DIV_L, Ebert HGT Iridoviridae, containing e-, e-value electron evolution font-size highly homolog host. hypothetical interactions mRNA maximum members new observed phylogenetic population reads reference sequencing solution total The (alg. Bergoin Decaestecker IVs PCR analyses assembly bacterial conn. crustacean did double; form gap genotypes groups hosts ident. iridoviral laboratory performed proposed represents search set shared software specific study uncharacterized viruses, %), (Decroly (Eaton ./nar/gku Cell DIV-, Disease D–D. Fat GAAP Iridoviridae. NC_ Res.(): Res.(D): Using Van Virol.(): Vávra White alignment belonging border-color border-width candidate caused cell cells cellular clone clones culture described fat final functions genes. genome. genomics h/i high large larger nucleotide position prediction present previously probably pulex remaining replication shows solid; structure subunit symptoms tissue tool transfer unique water work {font-style {font-weight (DIV_L) (Green (IIV-, -mL ./viro.. Ascoviridae Biol.(): Czech DNA, Ferron Finnish Furthermore, Genome IIV- Illumina Iridoviruses Material WFCD. acquired activity annotated assigned available binding bold capping. clade clustering collected contains courier; daphnids database day determined downloaded em; experiment fluorescence freshwater genus given homology host, independently infectious interaction isolate likely mapping molecular motif multiple natural novo order originated ortholog polymerase pt;} reading relationship represent representative research rock second sequenced settings signs single site stringent transcription treated type v.. version virus,


Fall 2017 Satisfaction Survey
created: 2017-09-02;
tagged:
These are the results of a survey of volunteers at the end of August 2017.
Results
Part I. Satisfaction Index
For each statement, please gauge your level of satisfaction in volunteering with the Family Support Center (where 1 = very dissatisfied; 7 = very satisfied).
| Avg | Stddev | Prompt |
| 5.92 | 1.44 | I experience personal growth as a volunteer. |
| 5.75 | 1.54 | I have autonomy to make decisions during my shift. |
| 5.75 | 1.48 | I have a deep feeling that I am contributing to my community. |
| 5.75 | 1.66 | I feel mutual respect in interactions I have with paid staff. |
| 5.67 | 1.56 | I communicate my needs and expectations with clients and staff. |
| 5.42 | 1.73 | I am fulfilled because I can bring about good changes to clients. |
| 5.42 | 1.73 | I gain satisfaction from relationships I have with my volunteering peers. |
| 4.73 | 2.05 | I derive satisfaction through educational and emotional resources provided by the Family Support Center. |
Part II. Why volunteer?
Please gauge each motivation’s importance to you (where 1 = not at all important/accurate and 7 = extremely important/accurate).
| Avg | Stddev | Prompt |
| 5.92 | 1.51 | To express significant values, e.g., altruism, collective responsibility. |
| 5.00 | 1.65 | To gain personal growth and development. |
| 4.92 | 1.51 | To gain new experiences. |
| 4.42 | 2.07 | To exercise unpracticed skills and abilities. |
| 3.50 | 2.43 | To obtain career-related experiences. |
We had 11 respondents between 8/29/17 and 9/2/17.


|
UQTk: Uncertainty Quantification Toolkit 3.1.3
|
#include <XMLElement.h>
Public Member Functions | |
| XMLElement (const std::string &) | |
| Construct a new xml element object and give it a label. More... | |
| virtual | ~XMLElement () |
| Destructor. More... | |
| const std::string & | label () const |
| Get the label of this node. More... | |
| void | set_label (const std::string &) |
| Assign a new label to this node. More... | |
| int | count_attributes () const |
| RefPtr< XMLAttributeList > | attributes () |
| Get access to the attribute list. More... | |
| void | set_attributes (RefPtr< XMLAttributeList >) |
| Assign an attribute list to this element. More... | |
| int | count_children () const |
| Utility function to check how many children this element has. More... | |
| RefPtr< XMLElement > | get_child (int) |
| RefPtr< XMLElement > | get_child (const std::string &) |
| void | add_child (RefPtr< XMLElement >) |
| void | add_child_rpt (RefPtr< XMLElement >) |
| Same as add_child, but this allows for repeating children. More... | |
| void | clear_children () |
| Erase all child elements from this node. More... | |
| int | count_content () const |
| const std::string & | get_content_line (int) |
| void | add_content_line (const std::string &) |
| Add a line of content. More... | |
| void | clear_content () |
| Clear all text content. More... | |
| Public Member Functions inherited from Object | |
| Object () | |
| Construct a new reference counted object with a zero reference count. More... | |
| virtual | ~Object () |
| Destroy this object. More... | |
| long int | reference_count () const |
| Returns the number of references that are held to this object. More... | |
Private Member Functions | |
| XMLElement (const XMLElement &) | |
| XMLElement & | operator= (const XMLElement &) |
| void | recurse (RefPtr< XMLElement >, std::set< RefPtr< XMLElement > >) |
Private Attributes | |
| std::string | label_ |
| The iterator type returned for list of children. More... | |
| RefPtr< XMLAttributeList > | attributes_ |
| The list of attributes associated with this element. More... | |
| std::vector< RefPtr< XMLElement > > | children_ |
| The list of children associated with this element. More... | |
| std::vector< std::string > | content_ |
| The list of content associated with this element. More... | |
Friends | |
| template<class T > | |
| class | RefPtr |
| template<class T > | |
| class | ConstRefPtr |
Additional Inherited Members | |
| Protected Member Functions inherited from Object | |
| long int | reference_grab () const |
| long int | reference_release () const |
This is the implementation of a node in an XML parse tree. Each node contains the following three containers, any (or all) of which may be empty.
attributes (XMLAttributeList): Contains attributes (key/value pairs). children (Vector<XMLElement>): Contains children of this node. content (Vector<std::string>): Contains content (text) data.
The implementation is very limited. In particular, the following advanced features are missing:
encoding: Support for character types other than char/std::string. comments: Allowing comment blocks to accompany each element. other xml types (control statements, etc.).
This implementation is heavily based on Kevin Long's XMLObject.
| XMLElement::XMLElement | ( | const std::string & | lbl | ) |
Construct a new xml element object and give it a label.
|
private |
Blocked copy constructor.
| MyException. |
|
virtual |
Destructor.
| void XMLElement::add_child | ( | RefPtr< XMLElement > | kid | ) |
Add a child to the back of the list. Ignored if the child is already in the list.
| MyException | if adding the child would result in a cyclic relationship. |
| MyException | if the child holds a NULL pointer. |
| void XMLElement::add_child_rpt | ( | RefPtr< XMLElement > | kid | ) |
Same as add_child, but this allows for repeating children.
| void XMLElement::add_content_line | ( | const std::string & | text | ) |
Add a line of content.
| RefPtr< XMLAttributeList > XMLElement::attributes | ( | ) |
Get access to the attribute list.
| void XMLElement::clear_children | ( | ) |
Erase all child elements from this node.
| void XMLElement::clear_content | ( | ) |
Clear all text content.
| int XMLElement::count_attributes | ( | ) | const |
Utility function to check how many attributes this element has. This amounts to the same as calling '.attributes().size()'
| int XMLElement::count_children | ( | ) | const |
Utility function to check how many children this element has.
| int XMLElement::count_content | ( | ) | const |
Utility function to check how many lines of text content are associated with this element.
| RefPtr< XMLElement > XMLElement::get_child | ( | const std::string & | lbl | ) |
Find the first instance of a child with a given label and return a pointer to it.
| MyException | if the child label can not be found |
| RefPtr< XMLElement > XMLElement::get_child | ( | int | index | ) |
Get the child with the given index.
| MyException | if the index is invalid. |
| const std::string & XMLElement::get_content_line | ( | int | index | ) |
Get a line of content by index.
| MyException | if the index is out of range. |
| const std::string & XMLElement::label | ( | ) | const |
Get the label of this node.
|
private |
Blocked assignment operator.
| MyExcepiton. |
|
private |
A private routine called recursively to ensure that we don't have a cyclic relationship.
| void XMLElement::set_attributes | ( | RefPtr< XMLAttributeList > | att | ) |
Assign an attribute list to this element.
| void XMLElement::set_label | ( | const std::string & | lbl | ) |
Assign a new label to this node.
|
friend |
|
friend |
|
private |
The list of attributes associated with this element.
|
private |
The list of children associated with this element.
|
private |
The list of content associated with this element.
|
private |
The iterator type returned for list of children.
The label of this element.


LINE FAIL
Virgin Media broadband and website down leaving customers without access to internet
by Levi WinchesterVIRGIN Media broadband is down this morning with frustrated customers unable to get online.
Customers are also unable to check the My Virgin website for a service update due to "essential" maintenance work being done on the page.
At 10am, outage tracker Downdetector reported almost 300 complaints from customers about their Virgin broadband.
A map on the website appears to show reports of Virgin broadband being down in several areas of the UK.
We've asked Virgin if it's aware of the issue and how many customers are affected and we'll update this story when we get a response.
Irritated customers have been taking to Twitter to complain about their lack of service.

Another customers tweeted: "This is ridiculous. Internet off. Service status page down.
"I try calling and apparently the service is actually down ON PURPOSE for fictional ‘improvements’?
"No service is NOT an improvement and we received no notice of this planned outage. Absolutely unacceptable."
Someone else said: "What's happening with your internet service?
"It's down more than it's working, are there problems in the neath area again?
Can I claim compensation if my network goes down?
YOU may be able to claim compensation if you’re not getting the level of service that you have been promised, according to regulator Ofcom.
You will need to give your provider a "reasonable opportunity" to fix the issue before escalating your concerns though.
Where repairs take much longer, for example it takes longer than usual to access a mast site, you may be entitled to a refund or compensation.
If your provider fails to repair fault by a promised time, or you’re unhappy with how long it is taking to fix it, you should follow their formal complaints procedure.
You will be able to find these on the provider's website or through their customer services team.
If your problem is still unresolved after eight weeks, you can escalate it to an independent Alternative Dispute Resolution (ADR) scheme.
To do that, you’ll need to ask your provider for a “deadlock” letter that you will have to show one of the two ADR schemes approved by the watchdog - CISAS and Ombudsman Services: Communications.
Your provider will be a member of at least one of them. You can find out which one by using the ADR checker.
"Very frustrating paying for a service that does not work for half of the month."
While another person asked: "Anyone having problems with Virgin Broadband in Basingstoke area this morning?"
Virgin isn't the only network to have experienced issues this week.
Yesterday saw Sky broadband leave hundreds of customers without access to internet after a broken network card disrupted the network.
Last week, TalkTalk customers were left without internet after a problem with Openreach.
Back in October thousands of Three customers were unable to make calls or go online for hours.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Makes 12-15
Dietary
Simple and easy small cakes perfect for a lunch box or tea-time.
Preheat the oven to 180C/350F/Gas 4.
Cream the butter and the sugar together until light and fluffy. Add your chosen flavoring of lemon zest cocoa or vanilla.
Add the whisked egg to the mixture. Slowly fold in the self-raising flour and spoon the mixture into paper cases sitting in a bun tin.
Cook for approximately 10 minutes until risen and bouncy to touch.
Decorate with icing in pale colours eg white, pink, blue and yellow.


--- layout: post title: "@Disclosedtech: Edsurge and OOHLALA" date: 2017-02-03 12:35:00 +0000 tags: image: https://s3.amazonaws.com/hackedu/2017-02-03-pigeon.jpg imagecredits: https://www.flickr.com/photos/stevenbarham/9696650718 ---
"Canadian Startup Raises $4 Million to Boost University Communications and Engagement," Edsurge wrote on January 27, 2017. This article fails to disclose that Edsurge and OOHLALA share an investor: University Ventures.
For more information about this disclosure alert, read this blog post.


So we don't exactly have a Hollywood promotional budget, either! We'd really appreciate it if you would help spread the word of Death Knight Love Story!
To make it as easy as possible for you to do that, here are a whole bunch of ready-prepared resources, including embeddable video, images from the film, images from behind the scenes, Tweets, and interesting facts about the film.
Feel free to share 'em, tweet 'em, blog 'em, pin 'em, or whatever else you want to do!
And please do let us know if you join in spreading the word - we'd love to say "thanks"!
Embeddable Video
Here's the Death Knight Love Story trailer - feel free to embed it wherever you like!
To share the video, just share this link: Facebook and Google Plus will automatically expand it and post the video.
Or to embed the video in a blog post or a web page, just copy this text and paste it into the HTML:
Pictures From The Film
Here are some pictures from DKLS for you to share! You can also share anything from the Gallery Page, but we've set these ones up to be easy to share!
To share this image, just share this link: Facebook and Google Plus will automatically find the image.
Or to embed the image in a blog post or a web page, just copy this text and paste it into the HTML:
To share this image, just share this link: Facebook and Google Plus will automatically find the image.
Or to embed the image in a blog post or a web page, just copy this text and paste it into the HTML:
To share this image, just share this link: Facebook and Google Plus will automatically find the image.
Or to embed the image in a blog post or a web page, just copy this text and paste it into the HTML:
Pictures From Behind The Scenes
And if you'd prefer to share some pictures of the sausage being made, here are some shots of our cast, crew and director in action.
There are lots more behind the scenes shots on the Gallery Page!
To share this image, just share this link: Facebook and Google Plus will automatically find the image.
Or to embed the image in a blog post or a web page, just copy this text and paste it into the HTML:
To share this image, just share this link: Facebook and Google Plus will automatically find the image.
Or to embed the image in a blog post or a web page, just copy this text and paste it into the HTML:
To share this image, just share this link: Facebook and Google Plus will automatically find the image.
Or to embed the image in a blog post or a web page, just copy this text and paste it into the HTML:
Fun Facts About Death Knight Love Story
- The production of Death Knight Love Story took 5 years all told. During that time, Hugh and the rest of the team threw away near-complete drafts of the film three times over.
- The motion capture didn't always use very appropriate props: for example, the cannon loading sequence was acted using a mop, a litter bin, and some waste plastic found in the warehouse we were using as a studio!
- At one point Hugh became so fed up with existing motion capture technology that he developed his own motion capture system, which had an entire wearable computer attached to the waistband.
- Three crew members have had children born during the course of Death Knight Love Story's production.
- Death Knight Love Story's crew come from three continents and even more countries, including Scotland, Iceland, India, and the USA.
- One of the less necessary delays to Death Knight Love Story's production happened when Hugh forgot to check that the 3D models for his lead characters could actually open their mouths!
- Death Knight Love Story is actually technically a "Machinima" animated film: it was entirely created in the real-time 3D rendering software "Mach Studio Pro", using the same techniques used by computer games like Far Cry 3.
Fun Facts About The Cast And Crew of Death Knight Love Story
- Hugh Hancock, the director, has been making films using computer games for over 15 years, and is the man responsible for the word "Machinima".
- Anna Chancellor, who plays Miria, has played an extremely eclectic series of film and television roles, from "Duckface" in the '90s UK hit "Four Weddings And A Funeral" to "Questular Rontok" in the film adaption of "Hitchhiker's Guide To the Galaxy".
- Brian Blessed, who plays Arthas, is equally well-known as one of the UK's great Shakespearean actors and as a larger-than-life TV and film personality. The website TV Tropes has an informal rule that Brian shall always be referred to in capital letters.
- Jack Davenport, who plays Zelieck, is best known for his role as Commander Norrington in "Pirates of the Carribean". He is currently in the news as the possible replacement for Daniel Craig in the role of James Bond!
- Joanna Lumley, who plays Lady Blaumeaux, is best known as Patsy in TV series "Absolutely Fabulous", as well as for her voice-acting work on Tim Burton's "The Corpse Bride". She's currently in the headlines for her role in Martin Scorsese's "The Wolf Of Wall Street".
- Ross Campbell, who composed the music for Death Knight Love Story, has moved over the years from house music with Scottish band Nightcrawlers to work with Scottish Opera to composing the music for dour detective series Taggart.
- The animators who worked on Death Knight Love Story come from very different backgrounds: lead animator Justin Hall worked on Oscar-nominated arthouse animation "The Illusionist", whilst facial animator Tom Bryant has worked with UK pop band Gorillaz.
- DKLS's motion capture artists learned their skills in many different ways: Ross Bambrey was a fire juggler and appeared in UK TV series Merlin, whilst Ian Mulliner learned his physical acting through Live-Action Roleplaying.
Tweets / Facebook updates / Google+ Updates
Stuck for inspiration? No problem - here are some Tweets you can share to help get the word out. You can also use these for Facebook, Google+ or other social media, of course!
- Machinima movie stars Anna Chancellor, Brian Blessed, Jack Davenport and Joanna Lumley! http://www.deathknightlovestory.com #deathknightlovestory
(Click to Tweet this!
- #Warcraft animated fan-film starring Brian Blessed, Jack Davenport and Joanna Lumley! http://ctt.ec/2bAmL+ #deathknightlovestory
(Click to Tweet this! )
- Death Knights fall in love - new animated film from founder of #Machinima! http://www.deathknightlovestory.com #deathknightlovestory
("Click to Tweet this!)
- Watchable now - new World of Warcraft fan-film with Hollywood stars! http://ctt.ec/8HzC6+ #deathknightlovestory Pls RT!
(Click to Tweet this!)
Click to share Death Knight Love Story on Facebook.
Click to share Death Knight Love Story on Google+
Interviews and Podcasts
If you're a journalist, a blogger or a podcaster and would like to interview Hugh Hancock, the director of Death Knight Love Story, please do let us know.
Time permitting, Hugh's very happy to talk to anyone about the film, motion capture, Machinima and so on.


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Thanks for trying our demo questions!Why not sign up to BMJ OnExamination to get access to quality questions and expert clinical advice.
Youscored<%= percentCorrect %>%
Questionsanswered<%= questionsAnswered %>
Questionscorrect<%= questionsCorrect %>


{% extends 'base.html' %} {% block head %} {% endblock head %} {% block title %}Home{% endblock title %} {% block doc %} {{ message }} {% endblock doc %} {% block log %} logged in as {{mail}} {% endblock log %} {% block body %}
Hope you find your health related problems solution.
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Cryptocurrencies are here. Built on a technology called the blockchain it has potential to change the informational systems of the world and how we interact with them. BfyB believes that everybody have the opportunity to learn about this technology in a simple and organized manner. The blockchain can be difficult to understand but dont worry this community is for absolute beginners.


Index
All Classes|All Packages
S
- saveCourses() - Static method in class Database
-
Serialize all courses.
- saveNews() - Static method in class Database
-
Serialize all news.
- saveUsers() - Static method in class Database
-
Serialize all users.
- seeNews() - Method in class Teacher
-
seeing news
- sendRequestToLibrary(Order, Book) - Method in class Student
- sentMessage() - Method in class Manager
- sentMessage() - Method in class Teacher
- setAcademicDegree(AcademicDegree) - Method in class Teacher
- setCourseMark(Course, Mark) - Method in class Student
-
Set mark for student for particular type(first attestation,second or final).
- setCourses(Course) - Method in class Student
-
Set course to courses.
- setCourses(Course) - Method in class Teacher
-
get courses of Teacher
- setCourses(ArrayList<Course>) - Method in class Manager
-
Set all courses.
- setCourses(ArrayList<Course>) - Method in class Student
- setCreditLimit(Integer) - Method in class Student
- setDegree(Degree) - Method in class Student
- setFaculty(Faculty) - Method in class Student
- setFaculty(Faculty) - Method in class Teacher
- setFinal(double) - Method in class Mark
-
Set points to final
- setFirstAttestation(double) - Method in class Mark
-
Set points to 1 attestation
- setGpa(double) - Method in class Mark
-
Set gpa
- setNews(ArrayList<News>) - Method in class Manager
-
Set all news.
- setNumberOfVotes(int) - Method in class Teacher
-
set number of votes
- setRate(double) - Method in class Teacher
-
set rate of teacher
- setSecondAttestation(double) - Method in class Mark
-
Set points to 2 attestation
- setStudent(Student) - Method in class Mark
-
Setting student
- setStudents(ArrayList<Student>) - Method in class Manager
-
Set all students.
- setTeachers(ArrayList<Teacher>) - Method in class Manager
-
Set all teachers.
- setTotal(double) - Method in class Mark
-
Set points to total
- setTotalCredits(Integer) - Method in class Student
-
Set new value for total credits.
- setYearOfStudy(Integer) - Method in class Student
- Student - Class in <Unnamed>
-
Represents Students information and account
- Student() - Constructor for class Student
- Student(String, String, String, Integer, Faculty, Degree) - Constructor for class Student
- Student(String, String, String, Integer, ArrayList<Course>, Degree, Integer, Integer, HashMap<Course, Mark>) - Constructor for class Student
All Classes|All Packages


|
||||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
| Class Summary | |
|---|---|
| BaseNonStringProperties | Test if non-string properties are handled correctly. |
| ConfigurationAssert | Assertions on configurations for the unit tests. |
| ConfigurationErrorListenerImpl | An implementation of the ConfigurationErrorListener interface
that can be used in unit tests. |
| DatabaseConfigurationTestHelper | A helper class for performing tests for DatabaseConfiguration . |
| FileURLStreamHandler | A custom URLStreamHandler to test loading and saving configurations to non standard URLs. |
| InterpolationTestHelper | A helper class that defines a bunch of tests related to variable interpolation. |
| Logging | Configures logging for tests. |
| MockInitialContextFactory | A mock implementation of the InitialContextFactory interface. |
| NonCloneableConfiguration | A specialized configuration implementation that does not support cloning. |
| NonStringTestHolder | Pulling out the calls to do the tests so both JUnit and Cactus tests can share. |
| TestAbstractConfiguration | Abstract TestCase for implementations of AbstractConfiguration . |
| TestAbstractConfigurationBasicFeatures | A test class for some of the basic functionality implemented by AbstractConfiguration. |
| TestBaseConfiguration | Tests some basic functions of the BaseConfiguration class. |
| TestBaseConfigurationXMLReader | Test class for BaseConfigurationXMLReader. |
| TestBaseNullConfiguration | Tests some basic functions of the BaseConfiguration class. |
| TestCatalogResolver | Test class for CatalogResolver. |
| TestCombinedConfiguration | Test class for CombinedConfiguration. |
| TestCompositeConfiguration | Test loading multiple configurations. |
| TestCompositeConfigurationNonStringProperties | Test if non-string properties are handled correctly. |
| TestConfigurationConverter | Tests the ConfigurationConverter class. |
| TestConfigurationFactory | Test the ConfigurationFactory. |
| TestConfigurationKey | Test class for ConfigurationKey. |
| TestConfigurationMap | |
| TestConfigurationSet | |
| TestConfigurationUtils | Tests the ConfigurationUtils class |
| TestDatabaseConfiguration | Test for database stored configurations. |
| TestDataConfiguration | |
| TestDefaultConfigurationBuilder | Test class for DefaultConfigurationBuilder. |
| TestDefaultConfigurationBuilder.ExtendedCombinedConfiguration | A specialized combined configuration implementation used for testing custom result classes. |
| TestDefaultConfigurationBuilder.ExtendedXMLConfiguration | |
| TestDefaultConfigurationBuilder.TestLookup | |
| TestDynamicCombinedConfiguration | |
| TestDynamicCombinedConfiguration.ThreadLookup | |
| TestEnvironmentConfiguration | Test class for EnvironmentConfiguration. |
| TestEqualBehaviour | Compare the behavior of various methods between CompositeConfiguration and normal (Properties) Configuration |
| TestEqualsProperty | test if properties that contain a "=" will be loaded correctly. |
| TestFileConfiguration | |
| TestHierarchicalConfiguration | Test class for HierarchicalConfiguration. |
| TestHierarchicalConfigurationXMLReader | Test class for HierarchicalConfigurationXMLReader. |
| TestHierarchicalINIConfiguration | Test class for HierarchicalINIConfiguration. |
| TestHierarchicalXMLConfiguration | Test class for XMLConfiguration. |
| TestINIConfiguration | Test class for INIConfiguration. |
| TestJNDIConfiguration | Test to see if the JNDIConfiguration works properly. |
| TestJNDIConfiguration.PotentialErrorJNDIConfiguration | A special JNDI configuration implementation that can be configured to throw an exception when accessing the base context. |
| TestJNDIEnvironmentValues | |
| TestMapConfiguration | Tests for MapConfiguration. |
| TestMultiFileHierarchicalConfiguration | Unit test for simple MultiConfigurationTest. |
| TestNonStringProperties | Test if non-string properties are handled correctly. |
| TestNullCompositeConfiguration | Test loading multiple configurations. |
| TestNullJNDIEnvironmentValues | |
| TestPatternSubtreeConfiguration | Unit test for simple MultiConfigurationTest. |
| TestPropertiesConfiguration | Test for loading and saving properties files. |
| TestPropertiesConfigurationLayout | Test class for PropertiesConfigurationLayout. |
| TestPropertiesSequence | Test that the configuration factory returns keys in the same sequence as the properties configurator |
| TestPropertyConverter | Test class for PropertyConverter. |
| TestStrictConfigurationComparator | Tests the StrintConfigurationComparator class |
| TestSubnodeConfiguration | Test case for SubnodeConfiguration. |
| TestSubsetConfiguration | Test case for the SubsetConfiguration class. |
| TestSystemConfiguration | Tests for SystemConfiguration . |
| TestThreesomeConfiguration | A base class for testing BasePropertiesConfiguration
extensions. |
| TestVFSConfigurationBuilder | Test class for VFSConfigurationBuilder. |
| TestWebdavConfigurationBuilder | Test class for DefaultConfigurationBuilder. |
| TestXMLConfiguration | test for loading and saving xml properties files |
| TestXMLPropertiesConfiguration |
|
||||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |


Projects
Project Details (Example)
Summer House
Simple elegance in clinker and wood.
Summer house with two bedrooms, two bathrooms, living room, dining room, kitchen, sauna and fireplace for up to 6 people. Underfloor heating/gas floor heating.
The spacious living area is equipped with noble real wood. On the ground floor there is a kitchen, a shower room with sauna and on the first floor two bedrooms with bath and toilet.
About
We provide consulting services in all aspects of architecture from conception
to implementation.
EXAMPLE Architects creates transformative cultural, corporate, residential
and other spaces that integrate seamlessly into their environment.
Architecture is our living passion and we are happy to pass it on to
our customers.
John Doe
CEO & Founder
John graduated with a Bachelor of Architecture. He loves to travel for new experiences and inspiration; architecture is his passion.
Jane Doe
Architect
Since joining in 2016, she has been working on feasibility studies, design and implementation plans and independently managing projects.
Mike Ross
Architect
Mike has received numerous national and international awards and is internationally known and enjoys great popularity.
Dan Star
Architect
Dan has extensive knowledge of Building Information Modeling (BIM), technical and sustainable design and building performance.
Team Details (Example)
John Doe
CEO & Founder
John is the founder and CEO of EXAMPLE Architects. Prior to founding the firm in 1996, John worked in Copenhagen and Berlin for well-known companies. "My goal as an architect is to support clients in realizing buildings that promote their ambitions and dreams. I believe that a building can and should reflect the tastes and values of its owners and users", a philosophy that permeates the entire company.
John graduated with a Bachelor of Architecture. He loves to travel for new experiences and inspiration; architecture is his passion.
Contact
Lets get in touch and talk about your next project.
Chicago, US
000 123 456 789
<EMAIL_ADDRESS>Office Hours
| Mon - Thu | 09:00 - 18:00 |
| Fri | 09:00 - 16:00 |
| Sat | 09:00 - 12:00 |
| Sun | closed |


RendererLayer Class
Represents a map tile layer to be rendered.
Constructor
RendererLayer
-
game
-
type
Parameters:
-
game
Game- Game instance this obj is attached to.
-
type
String- Type of
RendererLayer
. When created this object is merged with the value ofRendererLayer.Types[type]
.
- Type of
Item Index
Methods
getModifiedTileData
-
x
-
y
-
[prevTileData]
Get layer's TileData
for a given map tile coord.
Optionally modifying the prevTileData
object param if this.mergeWithPrevLayer = true
.
Parameters:
-
x
Number- Map tile x coord.
-
y
Object- Map tile y coord.
-
[prevTileData]
Object optionaltileData
object for the given map tile coord from previous layer.
Returns:
false if nothing to render
getTileData
-
x
-
y
-
[prevTileData]
Get layer's tileData
for a given map tile coord.
Parameters:
-
x
Number- Map tile x coord.
-
y
Object- Map tile y coord.
-
[prevTileData]
Object optionaltileData
object for the given map tile coord from previous layer.
Returns:
false if nothing to render
mergeTileData
-
tileData1
-
tileData2
Merges 2 tileData
objects.
Used to Merges layers of the same tile before drawing them.
Parameters:
-
tileData1
TileDatatileData
to merge to.
-
tileData2
TileDatatileData
to merge from, properties with values on tileData2 replace matching properties on tileData1
Returns:
A new tileData
object with merged values.
Properties
afterDraw
Function function(x, y, tileData, ctx)
After draw function. Ignored if this.draw = false
.
beforeDraw
Function function(x, y, tileData, ctx)
Before draw function. Ignored if this.draw = false
.
cancelTileDrawWhenNotFound
Boolean
If true, when this.getTileData(x, y)
returns a value that evaluates to false, all tile layers above this one are not drawn by the Renderer
for this map tile coord.
draw
Boolean
If true, tileData
is drawn to the canvas after processing this layer.
mergeWithPrevLayer
Boolean
If true, this.getTileData()
will merge the tileData
it would otherwise return with the prevTileData
param (if provided).
objects are from this layer should be merged with layers below before drawing.
type
String
The type of RendererLayer
this is.
When created this object is merged with the value of RendererLayer.Types[type]
. See constructor.


Radar Chart
A reusable radar chart implementation in D3.js
Reusable D3 API
Simple single chart drawing
No lines, only ticks


Isar735.github.io
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| Date | Name | Description | Duration |
|---|
| ID | Name | Raised Date | Closed Date | Description | Resolution |
|---|---|---|---|---|---|
| 409 | even though a tips file is present in the application directory it does not get loaded | 15/04/2003 12:22:02 | 15/04/2003 | I created a tips file called tips.txt and put it in the application directory (with 1 tips in it) and the tips file was not loaded I still got the "did you know the tips file was not found" message  | User error, the file was named tips.txt.txt  |
| 410 | tips are not displayed in tips window | 15/04/2003 13:05:03 | 22/04/2003 22:35:00 | the tips.txt file has information in it, but the tips window displays text1 instead of the tip  | I was calling a function on the class of the form rather than the instance so a new instance was being created that wiped out all the loaded in tip text !  |
| 411 | changing the name of an entity attribute does not flag the entity as dirty | 15/04/2003 18:48:18 | 22/04/2003 20:48:18 | On entity edit pane changing the name of an entity attribute does not flag the entity as dirty so we do not get the opportunity to save the entity and so the attribute change does not happen WORKAROUND - edit the text in the description  |
The edit code didn't check the return values so didn't recognise a change, now it does  |
| ID | Name | Raised Date | Status | Criticality | Description |
|---|---|---|---|---|---|
| 378 | delete on treebrowser does not maintain expansion | 16/08/2002 09:30:03 | OPEN | LOW | delete in the treebrowser should try to maintain expansion at the moment it just contracts. It should just remove the deleted item from the tree, rather than updating the whole tree. This is a problem with both treeviews as the updating algorithms are simple and tend to redraw most of the tree, they certainly don't maintain expansion.  |
| 62 | icons on buttons are not transparent | 17/04/2002 | OPEN | LOW | need to turn the .ico into .bmp and setup the mask property  |
| 412 | hierarchy sibling created with elder sibling name | 15/04/2003 18:49:20 | OPEN | MEDIUM | In the hierarchy I created a sibling for an entity and in the tree it was named the same as the sibling but in the edit pane it was named Default9. This was the last branch in the tree. It was created by using the "New Sibling Under" button. This doesn't always happen so there is some complexity in the scenario that I am unaware of. - t - b d f g - dfdd sibling newone (named sibling)  |
| 408 | mdi form close button does not shut down the system | 15/04/2003 12:20:42 | OPEN | MEDIUM | The menu exit command works, but sometimes clicking on the x button has no effect  |
| 381 | filter text is editable | 16/08/2002 09:46:20 | OPEN | LOW | user can edit the text in the filter, this has no functional effect, but the user may expect that the filter can be controlled this way when it can't  |
| 358 | revert only reverts first node, not all | 12/08/2002 11:55:48 | OPEN | MEDIUM | if a number of items are changed and revert is pressed, only one of the nodes will be reverted  |
| 134 | the hierarchy browser hierarchy should maintain expansion | 25/04/2002 17:55:54 | OPEN | LOW | during restructuring, the hierarchy should leave expanded the items which are expanded  |
| 133 | The hierarchy browser tree refreshes too much on drag | 25/04/2002 17:54:37 | OPEN | LOW | this can be disconcerting when dragging, perhaps the timer is not long enough?  |
| ID | Name | Raised Date | Status | Raised By | Description |
|---|


Thank you!
We appreciate you taking the time to suggest an article, and we’ll get in touch if we have any questions.
{% else %}Suggest an article
We want to hear about what you’re working on! Tell us what you want to write, and we’ll get right back to you.
{% if form.errors %}
{% endif %}
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{% endif %} {% if form.non_field_errors() %}{{ form.non_field_errors() }}
{% endif %}


1. Go to Redfin.com.
2. Login to your account.
3. Scroll over your name in the top right corner.
4. Click on 'favorites'.
5. Click the 'Download' button.
6. Save your favorites to your computer.
Go to the recommender, and upload your favorites file.


Cyclist takes nine hours to create reindeer outline on fitness app
A keen cyclist has brought some festive cheer to the streets of north London by using his fitness app to map out a reindeer.
Cheltenham-based Anthony Hoyle creates art using Strava, a GPS tracking app which follows the performance of athletes before uploading it online.
Mr Hoyte pedalled more than 80 miles around the capital, starting in Hammersmith in west London, went north as far as Edgware before heading south again and east through Hampstead Heath and Wood Green.
Finally he returned south through Kilburn and Maida Vale before finishing along Euston Road in central London.
It took him more than nine hours to compete the cycle - and it wasn't the first time he completed such a feat.
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In previous years, the 51-year-old has etched out a Santa Claus figure, a snowman and a Yorkshire terrier around Leeds.
Speaking about his latest feat, he said: "I’m pretty happy with it.
"Until you get back and upload it, you’re not quite sure whether it’s going to work or not.
"I live in the countryside and it’s very different to coming into London," he said.
"Particularly around Neasden and Wembley, it’s not the most pleasant cycling.
"That’s about my maximum distance – I wouldn’t want to go too much above that.
"It’s a long day in the saddle."
He continued: "I just spend a lot of time looking at maps and it’s waiting for things to jump out at you.
"Normally I look at maps until I see something – ‘That looks a bit like a nose’ – so I think ‘Where can I find eyes near that?’
"It’s like looking at patterns in clouds and seeing pictures.
"With these ones it’s a bit more tricky because I’m trying to do something festive so it’s harder, but it’s the same process really."

But in the end, the reindeer turned out almost exactly as planned.
"There’s a little glitch in there near Shepherd’s Bush where I obviously lost the GPS signal and it creates a straight line that cuts through a couple of street blocks, but that’s minor," he said.
"I made a couple of mistakes but they’re so small that nobody’s going to notice really.
"I’m happy. It looks how I wanted it to look."