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destroy()
- Method in class org.bbqjs.spring.servlet.
JsonHttpRequestFilter
doFilter(ServletRequest, ServletResponse, FilterChain)
- Method in class org.bbqjs.spring.servlet.
JsonHttpRequestFilter
G
getParameter(String)
- Method in class org.bbqjs.spring.servlet.
JsonHttpServletRequest
getParameterMap()
- Method in class org.bbqjs.spring.servlet.
JsonHttpServletRequest
getParameterNames()
- Method in class org.bbqjs.spring.servlet.
JsonHttpServletRequest
getParameterValues(String)
- Method in class org.bbqjs.spring.servlet.
JsonHttpServletRequest
H
handle(HttpServletRequest, HttpServletResponse, AccessDeniedException)
- Method in class org.bbqjs.spring.security.web.access.
HeaderBasedAccessDeniedHandler
handle(HttpServletRequest, HttpServletResponse, AccessDeniedException)
- Method in class org.bbqjs.spring.security.web.access.
StatusCodeBasedAccessDeniedHandler
HeaderBasedAccessDeniedHandler
- Class in
org.bbqjs.spring.security.web.access
Sends a X-BBQ-ResponseType header with the value 0 which you should extract from the XMLHttpRequest object.
HeaderBasedAccessDeniedHandler()
- Constructor for class org.bbqjs.spring.security.web.access.
HeaderBasedAccessDeniedHandler
HeaderBasedAuthenticationFailureHandler
- Class in
org.bbqjs.spring.security.web.authentication
Sends a X-BBQ-ResponseType header with the value 0 and writes {result: false} into the output stream.
HeaderBasedAuthenticationFailureHandler()
- Constructor for class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationFailureHandler
HeaderBasedAuthenticationSuccessHandler
- Class in
org.bbqjs.spring.security.web.authentication
Sends a X-BBQ-ResponseType header with the value 1 and writes {result: true} into the output stream.
HeaderBasedAuthenticationSuccessHandler()
- Constructor for class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationSuccessHandler
I
init(FilterConfig)
- Method in class org.bbqjs.spring.servlet.
JsonHttpRequestFilter
J
JsonHttpRequestFilter
- Class in
org.bbqjs.spring.servlet
Wraps a HttpServletRequest in a proxy object which converts the message body into JSON so we can use HttpServletRequest#getParameter in methods that can't take advantage of Jackson message conversion.
JsonHttpRequestFilter()
- Constructor for class org.bbqjs.spring.servlet.
JsonHttpRequestFilter
JsonHttpServletRequest
- Class in
org.bbqjs.spring.servlet
Wraps a HttpServletRequest that has had it's request body turned into a JSON object.
JsonHttpServletRequest(HttpServletRequest)
- Constructor for class org.bbqjs.spring.servlet.
JsonHttpServletRequest
O
onAuthenticationFailure(HttpServletRequest, HttpServletResponse, AuthenticationException)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationFailureHandler
onAuthenticationFailure(HttpServletRequest, HttpServletResponse, AuthenticationException)
- Method in class org.bbqjs.spring.security.web.authentication.
StatusCodeBasedAuthenticationFailureHandler
onAuthenticationSuccess(HttpServletRequest, HttpServletResponse, Authentication)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationSuccessHandler
onAuthenticationSuccess(HttpServletRequest, HttpServletResponse, Authentication)
- Method in class org.bbqjs.spring.security.web.authentication.
StatusCodeBasedAuthenticationSuccessHandler
org.bbqjs.spring.security.web.access
- package org.bbqjs.spring.security.web.access
org.bbqjs.spring.security.web.authentication
- package org.bbqjs.spring.security.web.authentication
org.bbqjs.spring.servlet
- package org.bbqjs.spring.servlet
S
setLoginFormUrl(String)
- Method in class org.bbqjs.spring.security.web.access.
HeaderBasedAccessDeniedHandler
If set, a 307 redirect will be issued to bounce the user to the log in form.
setResponseCode(int)
- Method in class org.bbqjs.spring.security.web.access.
HeaderBasedAccessDeniedHandler
setResponseCode(int)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationFailureHandler
setResponseCode(int)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationSuccessHandler
setResponseJSON(String)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationFailureHandler
setResponseJSON(String)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationSuccessHandler
setResponseMessage(String)
- Method in class org.bbqjs.spring.security.web.access.
HeaderBasedAccessDeniedHandler
setResponseMessage(String)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationFailureHandler
setResponseMessage(String)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationSuccessHandler
setResponseMessageHeader(String)
- Method in class org.bbqjs.spring.security.web.access.
HeaderBasedAccessDeniedHandler
setResponseMessageHeader(String)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationFailureHandler
setResponseMessageHeader(String)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationSuccessHandler
setResponseTypeHeader(String)
- Method in class org.bbqjs.spring.security.web.access.
HeaderBasedAccessDeniedHandler
setResponseTypeHeader(String)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationFailureHandler
setResponseTypeHeader(String)
- Method in class org.bbqjs.spring.security.web.authentication.
HeaderBasedAuthenticationSuccessHandler
setStatus(int)
- Method in class org.bbqjs.spring.security.web.access.
StatusCodeBasedAccessDeniedHandler
Override the HTTP status code that is sent access to a resource is denied.
setStatus(int)
- Method in class org.bbqjs.spring.security.web.authentication.
StatusCodeBasedAuthenticationFailureHandler
setStatus(int)
- Method in class org.bbqjs.spring.security.web.authentication.
StatusCodeBasedAuthenticationSuccessHandler
StatusCodeBasedAccessDeniedHandler
- Class in
org.bbqjs.spring.security.web.access
Sends an HTTP status code when an attempt to access a secured resource fails - defaults to 401 UNAUTHORIZED
StatusCodeBasedAccessDeniedHandler()
- Constructor for class org.bbqjs.spring.security.web.access.
StatusCodeBasedAccessDeniedHandler
StatusCodeBasedAuthenticationFailureHandler
- Class in
org.bbqjs.spring.security.web.authentication
Sends an HTTP status code when authentication fails - defaults to 401 UNAUTHORIZED
StatusCodeBasedAuthenticationFailureHandler()
- Constructor for class org.bbqjs.spring.security.web.authentication.
StatusCodeBasedAuthenticationFailureHandler
StatusCodeBasedAuthenticationSuccessHandler
- Class in
org.bbqjs.spring.security.web.authentication
Sends an HTTP status code when authentication fails - defaults to 200 OK
StatusCodeBasedAuthenticationSuccessHandler()
- Constructor for class org.bbqjs.spring.security.web.authentication.
StatusCodeBasedAuthenticationSuccessHandler
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Copyright © 2012. All Rights Reserved.


{% extends 'base.html' %} {% block content %}
We are Jin and Billy and this is our podcast where we talk about random tech related topics from different ends of the world.
Jin is based out of Seoul, South Korea and Billy is in Auckland, New Zealand currently.
{% endblock %}


Speed Tests
Specific Model
Generate Homepage GIF
Web Workers test
Exploring TFJS Ops
Exploring Layers and Graph Models
Generate Pixel Upsampler Model
Experiment with Public Models


211. Strange Counter
time limit per test: 2 sec.
memory limit per test: 65536
KB
input: standard
output: standard
The new secret counter invented in one theoretical computer
science lab is the great breakthrough in the computer microschemes design. This
counter consists of N registers numbered from 0 to N-1, each of which contains
0, 1 or 2. If the number in the i-th register is Ai, the number
stored in the counter is
AN-1 * 2N-1 + AN-2 *
2N-2 + ... + A1 * 2 + A0
One can see that the same number can be stored in the
counter in several different ways. For example, the number 5 can be stored in a
3-register counter as (1, 0, 1) or as (0, 2, 1).
The main feature of the
counter is that it can add numbers that are powers of two to the number stored
in the counter, only changing the value of a small number of registers. Namely,
the scientists of the lab developed the scheme that allowed adding such number
changing no more than four registers!
Unfortunately after the recent
experiments in the neighbouring physics lab, involving the creation of the
artificial black hole, the theoretical computer science laboratory was
accidently destroyed. However, the supercomputer project that the counter was
designed for is still on, so you were asked to reinvent the counter.
Input
The first line of the input file contains N — the number of
registers in the counter (1 ≤ N ≤ 1 000). Initially all registers contain
zeroes. The second line contains M — the number of additions you have to make (1
≤ M ≤ 10 000). The third line contains M integer numbers ranging from 0 to N-1.
Number i means that you must add 2i to the number in the counter.
There sum of all numbers added to the counter does not exceed 2N - 1.
Output
Output file must contain M lines. Each line must contain the
changes made adding the corresponding number to the counter.
The first
number in each line must be K — the number of registers changed (1 ≤ K ≤ 4). K
pairs must follow — for each changed register first output its number and then
the new value.
Sample test(s)
Input
5
6
0 0 0 1 0 0
Output
1 0 1
1 0 2
2 0 1 1 1
1 1 2
1 0 2
3 0 1 1 1
2 1
| Author: |
Andrew Stankevich |
| Resource: |
Petrozavodsk Summer Trainings 2003 |
| Date: |
2003-08-30
|
| Server time: 2004-01-14 13:41:43 |
Online Contester Team © 2002 - 2003. All
rights
reserved. | | |


Allison is a first generation SFSU graduate who double majored in Computer Science and Race and Resistance Studies, as well as minored in Mathematics. She enjoys merging her passion for technology and visual design through front-end development. She aspires contribute and create meaningful technology that impacts the lives of individuals at a micro and macro level. She enjoys film photography, darkroom printing and iPhone photography.


Home
Home
Contact
RPP is a graduate class taughtat the Federal University of Espírito Santo, São Mateus - Brazil.
Contact
Daniela Cassol
is currenty the main instructor.


Choose the Level. Enter your map's URL and click Load Map or pick one from the menu. When you see the map, click in rooms to place room numbers and drag to reposition. Contents for rooms will randomly generate as you place them. The node editor will let you edit or reroll contents.
Re-skin creatures or replace other repeating text in room descriptions and notes - there is no Undo so be careful.
Replace with in Notes and all Location Descriptions.DunMap saves to your browser's "LocalStorage" not to a server. If you need to clear all cached data in your browser, please export a backup file first, and import it when you need it again. Clearing all LocalStorage will wipe out any saved and edited dungeons.
Split out the notes field to separate fields, as a step towards better UI layout and data update.
I will be rescanning some of the maps I linked in the menu to make them a better fit here.
EA: Ed Allen
BL: British Library Flickr of free images from old books
Comments welcome at the Ruminations blog linked above. If you like hacking, take a look at the dungen_stock.js file first. It's pretty easy to customize content there. DunGen is now up on Github.


--- layout: post title: iPhone 3.0 Software Updates date: 2009-03-17 13:05:42.000000000 -04:00 categories: - Technology tags: [] status: publish type: post published: true meta: _edit_last: '7106425' author: login: samarthbhaskar email<EMAIL_ADDRESS>display_name: samarthbhaskar first_name: '' last_name: '' ---
Apple recently released the previews for the 3.0 iPhone software update. A lot of interesting updates were announced, at a pretty crucial time (read: Palm Pre's recent release). Although the updates won't be available for users for a few months, developers will be able to start developing new apps before the mass-release. Below are some basic pro's and con's for me -
PROS: 1. New batch of apps from app developers
2. copy + paste: Finally, this has been a long time coming. I have tried some of the third-party apps but they have been nothing much to speak of.
3. MMS: this will be a nice addition, although I don't find myself sending too many media messages via text. I've been happy using the email functionality or posting media on the web.
4. SMS forwarding: again, nice addition, but haven't really found a need for it in the past. Maybe if it's available I'll make use of it.
5. Multiple photos in 1 E-mail message: this is something that I have found the need for in the past. Looking forward to having this feature to use.
6. Spotlight feature on home page: I have about 5 pages of apps, so this will come in handy, although like most iPhone users there are a handful that I use most often.
CONS: 1. The most glaring con is definitely that there is still no-flash available for the iPhone. Hope this get's changed soon.
2. I can see a hardware change coming soon, which means I will probably want to buy the new device. Which means I will need money.
Looking forward to upgrading and playing around with the new software.


Comet - A meta machine learning platform¶
Comet is a meta machine learning platform designed to help AI practitioners and teams build reliable machine learning models for real-world applications by streamlining the machine learning model lifecycle. By leveraging Comet, users can track, compare, explain and reproduce their machine learning experiments.
Comet can easily integrated into the Spark NLP workflow with the a dedicated
logging class CometLogger
, to log training and evaluation metrics,
pipeline parameters and NER visualization made with sparknlp-display.
To log a Spark NLP annotator, it will need an “outputLogPath” parameter, as the CometLogger reads the log file generated during the training process.
For more examples see the Spark NLP Workshop
Installation¶
To use the comet logger, you need to set up an account and install comet.
See Quick Start - Comet.ml for more information.
Using Comet with Spark NLP¶
Comet can be used to monitor the training process of an annotator. Loss, training accuracy, validation accuracy etc. can be submitted to Comet and will be available for inspection on the web interface.
The following example shows how to integrate it while training a
MultiClassifierDLApproach
.
First we need to do the necessary imports for the pipeline and Comet.
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from sparknlp.logging.comet import CometLogger
>>> spark = sparknlp.start()
The Comet experiment can then be initialized. For this step you will need an API key. See the Comet quick-start section on how to get one.
To run an online experiment, the logger is defined like so.
>>> OUTPUT_LOG_PATH = "./run"
>>> logger = CometLogger()
We continue by defining the training pipeline.
>>> document = DocumentAssembler() \
... .setInputCol("text") \
... .setOutputCol("document")
>>> embds = UniversalSentenceEncoder.pretrained() \
... .setInputCols("document") \
... .setOutputCol("sentence_embeddings")
>>> multiClassifier = MultiClassifierDLApproach() \
... .setInputCols("sentence_embeddings") \
... .setOutputCol("category") \
... .setLabelColumn("labels") \
... .setBatchSize(128) \
... .setLr(1e-3) \
... .setThreshold(0.5) \
... .setShufflePerEpoch(False) \
... .setEnableOutputLogs(True) \
... .setOutputLogsPath(OUTPUT_LOG_PATH) \
... .setMaxEpochs(1)
Comet monitors the output logs of the annotators during the training process. Before starting the training, the path to the output logs need to be monitored by the logger.
>>> logger.monitor(logdir=OUTPUT_LOG_PATH, model=multiClassifier)
>>> trainDataset = spark.createDataFrame(
... [("Nice.", ["positive"]), ("That's bad.", ["negative"])],
... schema=["text", "labels"],
... )
>>> pipeline = Pipeline(stages=[document, embds, multiClassifier])
>>> pipeline.fit(trainDataset)
>>> logger.end()
If you are using a jupyter notebook, it is possible to display the live web interface with
>>> logger.experiment.display(tab='charts')
Logging Pipeline Parameters¶
The pipeline model contains the annotators of Spark NLP, that were fitted to a dataframe.
>>> logger.log_pipeline_parameters(pipeline_model)
Logging Evaluation Metrics¶
In this example, sklearn is used to retrieve the metrics.
>>> from sklearn.preprocessing import MultiLabelBinarizer
>>> from sklearn.metrics import classification_report
>>> prediction = model.transform(testDataset)
>>> preds_df = prediction.select('labels', 'category.result').toPandas()
>>> mlb = MultiLabelBinarizer()
>>> y_true = mlb.fit_transform(preds_df['labels'])
>>> y_pred = mlb.fit_transform(preds_df['result'])
>>> report = classification_report(y_true, y_pred, output_dict=True)
Iterate over the report and log the metrics:
>>> for key, value in report.items():
... logger.log_metrics(value, prefix=key)
>>> logger.end()
If you are using Spark NLP in a notebook, then you can display the metrics directly with
>>> logger.experiment.display(tab='metrics')
Logging Visualizations¶
Visualizations from Spark NLP Display can also be submitted to comet.
This example has NER chunks (NER extracted by e.g. NerDLModel
and converted by a NerConverter
) extracted in the colum
“ner_chunk”.
>>> from sparknlp_display import NerVisualizer
>>> logger = CometLogger()
>>> for idx, result in enumerate(results.collect()):
... viz = NerVisualizer().display(
... result=result,
... label_col='ner_chunk',
... document_col='document',
... return_html=True
... )
... logger.log_visualization(viz, name=f'viz-{idx}.html')
The visualizations will then be available in comet.
Running An Offline Experiment¶
Experiments can also be defined to be run offline:
>>> import comet_ml
>>> OUTPUT_LOG_PATH = "./run"
>>> comet_ml.init(project_name="sparknlp_experiment", offline_directory="/tmp")
>>> logger = CometLogger(comet_mode="offline", offline_directory=OUTPUT_LOG_PATH)
Logs will be saved to the output directory and can be later submitted to Comet.


Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Makes 1 large loaf cake
Dietary
Madeira cake is more robust than a normal sponge cake - perfect for soaking with a citrusy syrup and a drizzle of thin icing.
Preheat the oven to 180C/165C Fan/Gas 4. Grease a 900g/2lb loaf tin with butter and line with baking parchment.
In a bowl, cream the sugar and butter until pale and fluffy. Add eggs one at a time, beating between each addition. Fold in the flour, a pinch of salt and the citrus zests.
Pour the batter into the prepared tin, smooth the surface and bake for 1 hour. Check the cake after 40 minutes, if it is starting to brown too much cover with aluminium foil for the remaining cooking time. The cake is cooked when a skewer inserted into the centre comes out clean.
Leave the cake to cool in the tin while you make the sugar syrup.
To decorate, cut the lemon and orange rind into very thin strips. Add 100ml/3½fl oz water and the caster sugar to a saucepan over a low heat, stir gently until the sugar is dissolved. Bring to the boil then add the rind to the pan, reserve a few strips of rind for decoration. Cook for about 5 minutes until a syrup forms and the rind is translucent.
Brush the syrup over the still-warm cake and set aside to cool completely. When the cake is completely cold, remove it from the tin.
To make the icing, mix the citrus juices. Mix the icing sugar with enough juice to make a thin icing. Drizzle the icing over the cooled cake and finish with a few thin strips of citrus rind.


You're not ready to make an application
Your ecologist must first visit the place the bats live before you make an application to move or disturb them.
{% endif %} {% if data.alternative=="yes" %}You're not eligible for this licence
If there is a alternative to moving or disturbing bats you cannot have a licence to do this.
{% endif %} {% if data.impact_on_bats=="yes" %}You're not eligible for this licence
You can't move or disturb bats if there will be a harmful impact on the species.
{% endif %}


Check out some front end work by Joseph Poschel and explore the responsive goodness.
Rosemary Tea & Co. Home Page
Barebones, A Static Front End Template
Branding and Design Portfolio


class: center, middle, inverse, title-slide # Joint Probability Distributions ### Sebastian Hoyos-Torres ### 2018-11-01 --- # Joint Probability Distributions - So far, we've covered how to find the probability for a single discrete or continuous variable. - But what if we wanted to find the pmf of two or more random variables? --- # Two Discrete Random Variables - For Discrete random variables, the joint probability mass function is defined by each pair of numbers (x,y) by `$$p(x,y) = P(X=x\text{and} Y = y)$$` `$$\Sigma_{(x,y)\in{E}}p(x,y)$$` - The marginal probability mass functions for X and y are `$$p_x(x) = \Sigma_yp(x,y)$$` .center[**and**] `$$p_y(y) = \Sigma_xp(x,y)$$` - In sum the marginal probability mass functions are just the functions for X considered alone and Y considered alone. --- # Two Continuous Random Variables - If X and Y are two continuous random variables defined on 2-dimensional sample space, space S and if for any event A in S we have `$$\iint{f(x,y)dxdy}$$` - For function f(x,y), then f(x,y) is the joint probability density function for X and Y. - If p(x,y) is the joint probability density function for X and Y, then the marginal probability density functions for X and Y are `$$\int_{-\infty}^{\infty}f(x,y)dy$$` `$$\int_{-\infty}^{\infty}f(x,y)dx$$` - As in the discrete case, the marginal probability density functions are just the probability density functions for x considered alone as well as y --- # Joint Distributions - Since the joint probability mass function for continuous and discrete distributions are direct extensions of the single variable discete and continuous distributions and have some of the same properties. - The joint probability mass and denstiy functions are non-negative everywhere. - The sum of p(x,y) over the sample space = 1 - The integral of f(x,y) over the sample space = 1 --- # Example - An insurance company sells both homeowners policies and auto policies. The deductibles on the homeowner's policy is variable Y and X for auto. ``` ## # A tibble: 2 x 4 ## x y0 y100 y200 ## <dbl> <dbl> <dbl> <dbl> ## 1 100 0.2 0.1 0.2 ## 2 250 0.05 0.15 0.3 ``` - The joint would give the probability for each x,y pair so we would have p(100,200) = .2 - The marginal probability mass function of X would be `\(p_x(100) = .2+.1 + .2 = .5\)` --- # Fubini's Theorem If f is continuous on the rectangle R = {(x,y)}|$a\leq{x}\leq{b},c\leq{y}\leq{d}$ then `$$\iint{f(x,y)dA}=\int_{c}^{d}(\int_{a}^{b}f(x,y)dy)dx = \int_{a}^{b}(\int_{c}^{d}f(x,y)dy)dx$$` Assuming the iterated integrals exist and R is the region over which we wish to integrate. To take the double integral, we could take one integral and take the result of that integral to integrate again. --- # Fubini's theorem example: **Example:** Suppose `\(1\leq{x}\leq{2}\)` and `\(0\leq{y}\leq{3}\)`. Show that `\(\frac{2}{21}*x^2*y\)` is a density function. -- `$$\int_{0}^{3}\int_{1}^{2}\frac{2}{21}x^2y dxdy = \int_{0}^{3}\frac{2}{9}ydy = 1$$` --- # Independent Random Variables - Remember: we talked about independence for either 1 discrete or continuous variable thus far. however, what happens when there are 2? - If two discrete random variables are jointly distributed, they are independent when: `$$p(x,y) = p_x(x)*p_y(y)$$` - If two continuous random variables are jointly distributed, they are independent when: `$$p(x,y) = f_x(x)*f_y(y)$$` **If these conditions do not hold, they are dependent** --- # Example of an Independent Random variable (and R code) - Suppose `\(X_1\)` and `\(X_2\)` represent the lifetimes of two components independent of one another. `\(X_1\)` is exponential with parameter `\(\lambda_1\)` and `\(\lambda_2\)`. Then the joint pdf is given by: `$$f(x_1,x_2) = \lambda_1e^{-\lambda_1x_1}\lambda_2e^{-\lambda_2x_2} = \lambda_1\lambda e^{-\lambda_1x_1-\lambda_2x_2}$$` for `\(x_1,x_2\gt{0}\)` Suppose `\(\lambda_1 =1/1000\)` and `\(\lambda_2 = 1/1200\)` then the probability that both lifetimes are at least 1500 hours equals: `$$e^{\frac{1500}{1000}}e^{\frac{1500}{1200}} = .2231*.2865 = .0639$$` In R ```r (1- pexp(1500,1/1000))*(1 - pexp(1500, 1/1200)) ``` ``` ## [1] 0.06392786 ``` **However, this only makes sense if they're independent. If not, we use the conditional distributions.** --- # Independent Random Variables- More than Two - If `\(X_1,X_2,...,X_n\)` are all random variables, the joint pmf of the variables is the function: `$$p(x_1,x_2,...,x_n) = P(X_1 = x_1, X_2 = x_2,..., X_n = x_n)$$` - If they are continuous, then `$$P(a_1\leq{x_1}\leq{b_1},...,a_n\leq{x_n}\leq{b_n}) = \int_{a_1}^{b_1}...\int_{a_n}^{b_n}f(x_1,...,x_n)dx_n...dx_1$$` This is similar to the independence of more than two events --- # Conditional Distributions - For continuous random variables X and Y with joint pdf `\(f(x,y)\)` and marginal pdfs f_y(y), the conditional probability density of Y given X=x is: `$$f_{Y|X}(y|x) = \frac{f(x,y)}{f_x(x)} -\infty\leq{y}\leq{\infty}$$` provided that `\(f_x(x)\gt{0}\)` - For discrete random variables X and Y with joint pmf p(x,y) and marginal pmfs `\(p_x(x)\)` and `\(p_y(y)\)` the conditional pmf of Y given X = x is `$$P_{Y|X}(y|x) = \frac{p(x,y)}{p_x(x)}$$` provided that `\(f_x(x)\gt{0}\)` --- # Conditional Distribution - Discrete - Deductible Example An insurance company sells both homeowners policies and auto policies. The deductibles on the homeowner's policy is variable Y, and X for auto represented below

  x 	  y0 	  y100 	  y200

  100 	  0.20 	  0.10 	  0.2

  250 	  0.05 	  0.15 	  0.3

 in our case, suppose x = 100, then `\(p_x(100) = .5\)` and the conditional probability mass function is `\(p_{y|x}(y|100)\)` is: `$$\frac{.2}{.5}:\frac{.1}{.5}:\frac{.2}{.5}$$` --- # A baby donkey in a hammock  --- # More on independent random variables! - If X and Y are independent random variables then the conditional distribution of Y given X does not depend upon X and the conditional distribution of X given Y does not depend upon Y. Thus: `$$f_{y|x}(y|t) = f_y(y), f_{x|y}(x|y) = f_x(x)$$` --- # Independent Random variables (Continuous) If X and Y are independent random variables then any probability of the form `\(P(X\leq{x} and Y\leq{b})\)` will equal the product of `\(P(X\leq{a})*P(Y\leq{b})\)`. In the continuous case, it looks as follows: `$$\int_{-\infty}^{b}\int_{-\infty}^{a}f(x,y)dxdy = \int_{-\infty}^{b}\int_{-\infty}^{a}f_x(x)*f_y(y)dxdy = \int_{-\infty}^{b}f_y(y)(\int_{-\infty}^{a}f_x(x)dx)dy = \int_{-\infty}^{b}f_y(y)dy(\int_{-\infty}^{a}f_x(x)dx)$$` Given this, `\(P(Y\leq{b})*P(X\leq{a})\)` --- # Example: Independent Random variables continuous Suppose `\(X_i, i = 1.5\)` is the amount of Nitrous Oxide emissions from a randomly and independently chosen Edsel Engine and each `\(X_i\)` has a weibull distribution with shape parameter a = 2 and scale parameter b = 10. What is the probability that the maximum of the 5 emissions is `\(\leq{12}\)` NOTE: Suppose Y is the maximum. Then `\(Y\leq{12}\)` if and only if each `\(X_i\leq{12}\)`. By independence, we can just figure these out in R ```r pweibull(12,shape = 2,scale = 10) ``` ``` ## [1] 0.7630722 ``` ```r pweibull(12,2,10)^5 ``` ``` ## [1] 0.258719 ``` --- # When Independence is Violated - If independence is violated, then we call the variables dependent. If this is the case, we usually want to examine how closely related they are. This leads us to covariance and correlation. --- # Expected Values - The expected value of a function, h(x,y),denoted E[h(x,y)] is defined as for discrete values `$$E[h(x,y)] = \Sigma_x\Sigma_yh(x,y) * p(x,y)$$` for continuous variables `$$E[h(x,y)] = \int_{-\infty}^{\infty}\int_{-\infty}^{\infty}h(x,y) * f(x,y)dxdy$$` --- # Covariance - The Covariance is defined as: for discrete random variables `$$Cov(x,y) = \Sigma_x\Sigma_y(x-\mu_x)(y - \mu_y)p(x,y)$$` for continuous random variables `$$Cov(x,y) = \int_{-\infty}^{\infty}\int_{-\infty}^{\infty}(x-\mu_x)(y - \mu_y)f(x,y)dxdy$$` --- # Correlations - The correlation coefficient of X and Y, denoted Corr(X,Y), or `\(\rho_{x,y}\)` or simply `\(\rho\)` is defined as: `$$\rho = \frac{Cov(x,y)}{\sigma_{X}*\sigma{Y}}$$` - For any two rv's X and Y, `\(-1 \leq{\rho}\leq{1}\)` - If a and c are either both positive or negative then, `$$Corr(aX+b,cY+d) = Corr(X,Y)$$` - If X and Y are independent, then `\(\rho = 0\)`. However,$\rho = 0$ does not imply that X and Y are independent. - `\(\rho = -1\)` or `\(\rho = 1\)` if and only if `\(Y = aX+b\)` for some numbers a and b --- # Discrete Example - An insurance company sells both homeowners policies and auto policies. The deductible on the homeowner's policies is variable Y, and X for auto. ``` ## # A tibble: 2 x 4 ## x y0 y100 y200 ## <dbl> <dbl> <dbl> <dbl> ## 1 100 0.2 0.1 0.2 ## 2 250 0.05 0.15 0.3 ``` - The joint pmf would be found by finding the correct x and y pairs in the table. Thus, p(100,100) = 0.1, p(250,0) = 0.05, etc... - The marginal pmf for `\(p_x(100) = \Sigma(px,y) = 0.2 + 0.1 + 0.2 = 0.5\)` --- # The good stuff in R - Although the joint pmf is pretty straightforward, the joint pmf can be easily calculated in R as long as you're careful. Let's store the data we were given into a tibble. ```r testdata <- tibble(x = c(100,250),y0 = c(0.2,0.05),y100 = c(0.1,0.15), y200 = c(0.2,0.3)) ``` This command stores the tibble. You can make this into a dataframe too. ```r sum(testdata[testdata$x == 100,2:4]) #since we are keeping the pmfs in cols 2-4, we need to index them. ``` ``` ## [1] 0.5 ``` ```r testdata %>% filter(x == 100) %>% dplyr::select(starts_with("y")) %>% summarise(joint_pmf = sum(y0,y100,y200)) #this is longer but more explicit. ``` ``` ## # A tibble: 1 x 1 ## joint_pmf ## <dbl> ## 1 0.5 ``` --- # Expected values and variances in R We can also find the expected values and variances through R. Let's create two different vectors as follows: ```r x <- testdata$x px <- c(.5,.5) (ex <- sum(x*px))#expected value of X ``` ``` ## [1] 175 ``` ```r ex2 <- sum(x^2*px) #E(X^2) (vx <- ex2-ex^2) ``` ``` ## [1] 5625 ``` ```r (sd <- sqrt(vx)) ``` ``` ## [1] 75 ``` --- # Can you find the expected value and Variance for Y?  --- # Solution for expected value and variance of Y - We would just need to find the marginal pmf of Y ```r y <- c(0,100,200) py <- c(.25,.25,.5) #look at the marginal pmf of Y (ey <- sum(y*py)) ``` ``` ## [1] 125 ``` ```r (ey2 <- sum(y^2*py)) ``` ``` ## [1] 22500 ``` ```r (vy <- ey2 - ey^2) ``` ``` ## [1] 6875 ``` ```r (sdy <- sqrt(vy)) ``` ``` ## [1] 82.91562 ``` --- --- # Covariance and Correlation in R - We can also compute the covariance and correlation in R. ```r exy <- sum(100*0*.2,100*100*.1,100*200*.2) exy <- sum(exy, 250*0*.05,250*100*.15,250*200*.3) exy ``` ``` ## [1] 23750 ``` ```r covxy <- exy-ex*ey covxy ``` ``` ## [1] 1875 ``` ```r corr <- covxy/(sd*sdy) corr ``` ``` ## [1] 0.3015113 ``` --- # The Big Question: So What is a statistic? - So far, we have been talking about *probability* and ideal distribution types (whether continuous or discrete). - A statistic is any value that can be calculated from sample data (these values typically inform you about the sample). - Since a statistic is calculated from sample data, which are the values of random variables; a statistic is a random variable. - Prior to collecting the data, there is uncertainty about the value of the statistic. Thus it has a distribution. - Once we collect the data, we evaluate the observed value of the statistic. - To evaluate the distribution of the statistic, we must consider not only the sample we observed but the possibility of other samples that we could have observed. --- # Application Let's say that there are two traffic lights on a commuter's route to and from work. Let `\(X_1\)` be the number of lights at which the commuter must stop on his way to work, and `\(X_2\)` be the number of lights at which he must stop when returning from work. Suppose that these two variables are independent, each with the probability mass function given by: ``` ## [,1] [,2] [,3] ## x 0.0 1.0 2.0 ## px 0.3 0.1 0.6 ``` a) Determine the pmf of `\(T_0 = X_1+X_2\)` b) Calculate the mean of `\(X_1\)` c) Calculate the mean of `\(T_0\)` d) Calculate the variance of `\(X_1\)` e) Calculate the variance of `\(T_0\)` f) How are the various means and variances related? --- # Application continued - Let's note that `\(T_0\)` can take on the values 0,1,2,3,4 (note the mins and maxs of `\(X_1\)` and `\(X_2\)`). From this, assuming independence, we can just use the properties of independence to figure things out. ```r trafficpmf <- tibble( X1_and_X2 = c("0,0","0,1","0,2","1,0","1,1","1,2","2,0","2,1","2,2"), px1_x2 = c(.3*.3,.3*.1,.3*.6,.1*.3,.1*.1,.1*.6,.6*.3,.6*.1,.6*.6), sumx1_x2 = c(0,1,2,1,2,3,2,3,4)) trafficpmf ``` ``` ## # A tibble: 9 x 3 ## X1_and_X2 px1_x2 sumx1_x2 ## <chr> <dbl> <dbl> ## 1 0,0 0.09 0 ## 2 0,1 0.03 1 ## 3 0,2 0.18 2 ## 4 1,0 0.03 1 ## 5 1,1 0.01 2 ## 6 1,2 0.06 3 ## 7 2,0 0.18 2 ## 8 2,1 0.06 3 ## 9 2,2 0.36 4 ``` --- # Application cont. - All we need to do now is add the corresponding probabilities we generated together. ```r t0pmf <- trafficpmf %>% group_by(sumx1_x2) %>% summarise(pt0 = sum(px1_x2)) %>% ungroup() t0pmf ``` ``` ## # A tibble: 5 x 2 ## sumx1_x2 pt0 ## <dbl> <dbl> ## 1 0 0.09 ## 2 1 0.06 ## 3 2 0.37 ## 4 3 0.12 ## 5 4 0.36 ``` --- # Application continued - Let's now calculate the mean and variance of `\(X_1\)`. Just use the formula for the expected value of a discrete random variable. ```r (ex <- sum(traffic[1,]*traffic[2,])) ``` ``` ## [1] 1.3 ``` ```r (ex2 <- sum(traffic[1,]^2*traffic[2,])) ``` ``` ## [1] 2.5 ``` ```r (vx <- ex2-ex^2) ``` ``` ## [1] 0.81 ``` --- # Application continued - The question asked us to do the same for `\(t_0\)` so lets do that quickly ```r (t0ex <- sum(t0pmf$sumx1_x2*t0pmf$pt0)) ``` ``` ## [1] 2.6 ``` ```r (t0ex2 <- sum(t0pmf$sumx1_x2^2*t0pmf$pt0)) ``` ``` ## [1] 8.38 ``` ```r (t0vx <- t0ex2-t0ex^2) ``` ``` ## [1] 1.62 ``` **Do you notice anything about these results?** --- # What we were supposed to notice - In the example, `\(X_1\)` and `\(X_2\)` were independent and `\(T_0 = X_1 + X_2\)` - Also, since `\(X_1\)` and `\(X_2\)` have identical distributions, they also have the same expected values and the same variances. - We also demonstrated the following: `\(E(X_1+X_2) = E(X_1)+E(X_2)\)` and `\(V(X1+X2) = V(X_1) + V(X_2)\)` - The first relationship holds whenever `\(E(X_1)\)` and `\(E(X_2)\)` exist - The second relationship holds when `\(X_1\)` and `\(X_2\)` are independent. --- # Random samples - Evaluating the distribution of a statistic from a sample with an arbitrary joint distribution is often difficult. - To counter this, we typically make the simplifying assumption that our data constitute a random sample `\(X_1,X_2,...X_n\)` from a distribution. This means that: - The `\(X_i\)`'s are independent. - all the `\(X_i\)`'s have the same probability distribution. --- # Simulations (Or what are those r + distribution name functions good for?) - You may have noticed along the course that we have been using a host of functions to generate the pmfs, cdfs and quantiles for a variety of probability distributions. - We left one function untouched however and those were the functions that began with r - R + distribution name functions such as rnorm, rpois,etc. generate a series of random variables from the theoretical distribution which we are interested in. So let's finally try some of the distribution functions out. - These also allow us to examine the distribution or forms of the statistic. --- # Trying out the r family of functions Our results will be different because we aren't setting our seed which would affect the operation of the r family of functions. Let's say we wanted to simulate a distribution with a sample size of 30 from a normal distribution such that X~N(65,3) ```r xsim <- rnorm(n = 30,mean = 65,sd = 3) head(xsim) ``` ``` ## [1] 65.00266 68.57784 61.08447 63.14884 65.98282 62.53087 ``` ```r mean(xsim) #note the mean of xsim seems pretty close to the mean of the distribution it was drawn from ``` ``` ## [1] 65.87247 ``` ```r sd(xsim) #and so is the standard deviation. ``` ``` ## [1] 3.106282 ``` **Try it on your own and see what you get.** --- --- # Simulation of the Normal Distribution - Remember: the statistic in and of itself is a random variable. So how do you think their means and standard deviations will behave if we sample a lot of them? - While we're at it, if we sample a lot of them, how will the distribution of the mean of each sample look like? - Let's bring out the R --- # The R way to simulate Let's say we wanted to calculate the sample mean of 1000 samples of size 30 with each sample being drawn from the normal distribution ```r emptlist <- c() meansimulation <- sapply(1:1000, function(k){ emptlist[k] <- mean(rnorm(30,65,3)) }) #using the apply family of functions in R # or using a for loop (up to you for the purposes of this class) meansimulationloop <- c() for (i in 1:1000) { meansimulationloop[i] <- mean(rnorm(30,65,3)) } head(meansimulation);head(meansimulationloop) ``` ``` ## [1] 64.05004 65.42210 64.56844 65.30873 65.90043 65.01402 ``` ``` ## [1] 64.93807 64.75069 64.89256 64.15106 65.60062 63.64884 ``` **Now that we have the simulation stored, let's do some exploring** --- # Exploration of our simulation - Lets examine the distribution of the means that we just generated. ```r mean(meansimulation) #approximately 65 ``` ``` ## [1] 65.03606 ``` ```r sd(meansimulation) #this changes significantly but we will explain this later. ``` ``` ## [1] 0.5520281 ``` ```r qplot(meansimulation,geom= "histogram") ```  --- # Some notes from our exploration - We saw that the simulated means of the means is almost 65 which is the true mean. - The histogram shows that the distribution of the means looks bell shaped and normal. - The simulated standard deviation **does not** equal to the population standard deviation of 3 but is approximately the population standard deviation divided by the square root of n. - We could continue this experiment with any of the distributions. Would you like to try a simulation? --- --- # Properties of the sample mean and sample sum - Let `\(X_1,X_2,X_3,...,X_n\)` be a random variable from a distribution with mean value `\(\mu\)` and standard deviation `\(\sigma\)`. Then - `\(E(X) = \mu_x = \mu\)` - `\(V(X) = \sigma^2/n\)` and `\(\sigma_x = \sigma/\sqrt{n}\)` (this is often known as the standard error of the mean) - Let `\(T_n = X_1 + X_2 + X_3 + ... +X_n\)` be the sample total. Then : - `\(E[T_n] = n\mu\)` - `\(V[T_n] = n\sigma^2\)` and `\(\sigma_T = \sqrt{n\sigma}\)` - If the distribution of the `\(X_i\)`'s is normal, then the distribution of `\(\bar{X}\)` and `\(T_n\)` is also normal. - In other words Averaging moves probability towards the center whereas totaling spreads probability out over a wider range of values. --- # Example In a notched tensile fatigue test on a titanium specimen, the expected number of cycles to first acoustic emission (used to indicate crack initiation) is `\(\mu = 28,000\)` and the standard deviation of the number of cycles is `\(\sigma = 5000\)` Let `\(X_1,...,X_{25}\)` be a random sample of size 5 where `\(X_i\)` is the number of cycles on a different randomly selected specimen. Find the standard error of the mean and the standard deviation of `\(t_0\)`. ```r mu <- 28000 sigmas <- 5000 n <- 25 sigmas/sqrt(n) #standard error of the sampling mean. ``` ``` ## [1] 1000 ``` ```r sqrt(n*sigmas) #standard deviation of T0 ``` ``` ## [1] 353.5534 ``` **Note: if the sample size increases,the mean will remain unchanged but the standard error will decrease. Why?** --- # The Central Limit Theorem - Let `\(X_1,X_2,X_n\)` be a random sample from a distribution with mean `\(\mu\)` and variance `\(\sigma^2\)`. Then for n sufficiently large, `\(\bar{X}\)` has approximately a normal distribution with mean = `\(\mu\)` and variance = `\(\sigma^2/n\)` Formally, the formula for the distribution is: $$\frac{x-\mu}{\sigma/\sqrt{n}} ~ N(0,1) $$ - The larger the value of n, the better the approximation. - For distributions that are continuous and reasonably close to being symmetric, the convergence to the normal distribution is good for even small values of n. --- # The example of the uniform distribution: Let's examine some code for simulations. In R, to generate random values from the normal distribution is through the runif.  As sample size gets larger, the sampling distribution of the means becomes more bell shaped and more concentrated** --- # Looking at the statistics - How about the statistics of these distributions? ```r data.frame(EX = unlist(lapply(meanslist, mean)),sd = unlist(lapply(meanslist,sd)), trsd = unlist(lapply(1:4,function(x)(sqrt(3)/3)/sqrt(x)))) ``` ``` ## EX sd trsd ## 1 0.0016859584 0.5718055 0.5773503 ## 2 -0.0049474026 0.4087197 0.4082483 ## 3 -0.0005440291 0.3320445 0.3333333 ## 4 0.0054696147 0.2856265 0.2886751 ``` --- # Why the normal distribution is the best distribution - Throughout the course, everyone may have noted my fascination with the normal distribution. Besides the fact that I just like it, it has the following really useful properties. Let `\(X_1,X_2,X_n\)` be a random sample from a normal distribution with a mean `\(\mu\)` and standard deviation `\(\sigma\)`. For any `\(n>0, \bar{X}\)` is normally distributed with mean `\(\mu\)` and standard deviation `\(\frac{\sigma}{\sqrt{n}}\)`. Additionally, `\(T_0 = X_1 + X_2 +...+X_n\)` has a normal distribution with mean `\(n\mu\)` and standard deviation `\(\sqrt{n\sigma}\)`. If we have a normal distribution and we know the mean and standard deviations, then we can calculate the probabilities (using pnorm or through standardization) and know everything about the distribution. --- # The Normal Distribution continued. - If n is large enough (n > 30) we don't need normality. By the central limit theorem, if n is large enough and `\(X_1,...,X_n\)` is a random sample with mean `\(\mu\)` and standard deviation `\(\sigma\)`, then `\(\bar{X}\)` is approximately normally distributed with mean `\(\mu\)` and sd `\(\frac{\sigma}{\sqrt{n}}\)` - `\(T_0 = X_1+X_2 + ...+ X_n\)` has approximately a normal distribution with mean `\(n\mu\)` and standard deviation `\(\sqrt{n\sigma}\)`. We can calculate the probabilities as if they were normal. --- # Example The time it takes a randomly selected rat to find its way through a maze is a normally distributed random variable with `\(\mu = 1.5\)` mins and `\(\sigma = .37\)`mins. Suppose 6 rats are selected. Let `\(X_1...X_6\)` denote their times in the maze. Assuming the `\(X_i's\)` to be a random sample from the normal distribution, what is the probability that the total time `\(T_0 = X_1+...+X_6\)` for the six is between 8 and 10 mins --- # Example worked out - To find the mean of `\(T_0\)` has a normal distribution with `\(\mu_{T_{0}} = n\mu\)` therefore: ```r 6*1.5 ``` ``` ## [1] 9 ``` - To find the variance use `\(n\sigma^2\)`: ```r 6*0.36^2 ``` ``` ## [1] 0.7776 ``` - To find the standard deviation, simply plug into the formula `\(\sqrt{n}*\sigma}\)` ```r sqrt(6)*0.36 ``` ``` ## [1] 0.8818163 ``` ```r sqrt(.7776) ``` ``` ## [1] 0.8818163 ``` - With these values, we are ready to start calculating the probability that the time is between 8 and 10 minutes. --- # Example worked out continued - All we have to do is use pnorm and treat this as a nonstandard normal distribution ```r pnorm(10,9,sqrt(.7776))- pnorm(8,9,sqrt(.7776)) ``` ``` ## [1] 0.7432151 ``` --- # Linear combinations and their means - Given a collection of random variables `\(X_1,X_2,...,X_n\)` andn numerical constants `\(a_1,a_2,...,a_n\)`, thus the random variable `$$Y = a_1X_1 + a_2X_2 +...+ a_nX_n$$` is a linear combination of `\(X_is\)` whether or not the observations are independent, thus: `$$E[a_1X_1+a_2X_2+,...,+a_nX_n] = a_1E(X_1)+a_2E(X_2)+... + a_nE(X_n)$$` --- # The Variance of linear combinations - If `\(X_1,X_2,...,X_n\)` are independent with variances `\(\sigma^2_1,\sigma^2_2\,...,\sigma^2_n\)`, then: `$$V(a_1X_1+a_2X_2 + ... +a_nX_n) = a^2_1\sigma^2_1+a^2_2\sigma^2_2+...+a^2_n\sigma^2_n$$` --- # The difference between random variables - A common special case of linear combinations is the difference of random variables `\(Y = X_1-X_2\)`. That is, `\(n = 2\)`, `\(a_1 = 1\)` and `\(a_2 = -1\)` - The mean of the difference of Y is `\(\mu_1-\mu_2\)`. In other words, the mean of the difference is the difference of the means. - If `\(X_1\)` and `\(X_2\)` are independent, then the variance of the difference is `$$V(Y) = V(X_1) + V(X_2)$$` This means that the variance of the difference is the sum of the variance. **IMPORTANT: This does not mean standard deviations add!**


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A quick note, if the videos do not launch, please hit refresh and if the video does show up but does not play, click the small cloud icon at the bottom right of the page.
I know this may be tedious and entirely not-UX friendly but hey, this was the most efficient way for me to showcasing these videos, unfortunately.
Yeah, well I decided to say a few words about these Videos as well.
First of all, I started off, being inspired by other equivalent music videos featured on youtube. So I decided to make my own. And considering I was big on gaming, in fact, just about to revitalise my hobby with a new gaming rig, I just did as much. Capturing some content in fraps, some in gamecam, looking for the right software. Then, editing it in windows movie maker (so easy and forthcoming, the first and old versions!) and combining it with the music I enjoyed listening to. They did not rather contain any cuts. And the framerate... Well, capturing anything did impact the game's performance, plus my hard drive couldn't take it (more Gigabytes) anymore.
Throughout the years, I started walking away from gaming. However, I still managed to employ more cuts and tried matching what was happening on the screen in better fashion, in conjunction with music. It was around 2012 that I shifted toward working with TV Shows, fascinated by the narrative aspects and how much content a show could deliver in so many angles.
The software changes as well. I rather used Sony Vegas and FRAPS as a standard setup. Once I departed to Sydney, I changed once again to Adobe Premiere, but still preferred Sony Vegas in certain cases. Yet, the most important aspect that has changed? I tried making my mixes so that they contained a certain atmosphere, that would run along well with the music introduced. Sometimes, I even introduced alternate stories, which didn't even have to involve dialogues. They were just self-explanatory. But what fascinated me the most, was how I was able to create certain mixes, that each person was able to deliver a different opinion, weighing in with a different clutch, pointing my attention to different aspects, some that I did not even notice.
But then, one thing did not change at all. Would you like to know what it was? I never spent more than, probably, 5 hours on one mix. I tried capturing them as fast as possible. And then editing them as fast as possible, so that my inspiration didn't go with the end(wind?). That said, you will always notice parts that could have been fixed in better fashion. That said, I did not care. I made these mashups just for fun and my own personal pleasure. Constructive criticism, or... you know what? Any would do. It is always vital. So throw as much of it as you may. Even if I won't seem greatful, rest assured, I will be.
Haibane Renmei // Thornhill - Leather Wings
Wonder Egg Priority // Tricot - Butter
Tenshi no Tamago // Woods of Ypres - December in Winsdor
Paranoia Agent // Skywalker - Charon's Song
Animatrix // Uneven Structure - Alkaline Throat & Brazen Tongue
Re:Zero 1st season // Good Tiger - Young Speak
Mix of sources // Tubylcy - Beverly Hills, 05806
My own assets // Grimes - Delete Forever
Code Geass: Lelouch of the Rebellion R2 // The Anix - Everlasting Love
Attack on Titan (Season 3 Part 2) // Karnivool - Sky Machine
Neon Genesis Evangelion // Tool - Culling Voices
Chernobyl // Northlane - 4D
Carole & Tuesday // Slowly Slowly - Ten Leaf Clover
Love Death and Robots // Idiot Pilot - Mammoth
Ślepnąc od Świateł // Aimer - Insane Dream
Terror in Resonance // Pymlico - NOL861613060
Attack on Titan // Triptykon - Aurorae
Made in Abyss // Babymetal - Starlight
Your Lie in April // Skyharbor - Ethos
Samurai Champloo // Hyper - Centre Attraction
The Expanse // Tesseract - King
Pitbull (Series) // Pro8l3m - Macki Meduzy
Neo Tokyo // Danger - 22:41
Ji 2049 - Blade Runner: 2049 // Periphery - Ji
Cowboy Butterfly - Cowboy Bebop: The Movie // Grimes - Butterfly
Vietnam Faith - The Vietnam War (Episode 2) // Nine Inch Nails & Atticus Ross - Before and After Faith
Burning Men - Mad Men (Episode 1) // Nine Inch Nails - Burning Bright (Field on Fire) (Live)
Ghost in the Elysium - Ghost in the Shell (1995) // Celldweller - New Elysium
Fargo on the Radio - Fargo (Season 3) // TV on the Radio - Happy Idiot
Misunderstood Brothers - Band of Brothers // Dream Theater - Misunderstood
Nothingworld - Westworld // The Dillinger Escape Plan - Nothing to Forget
Poltergoon - Black Lagoon // Deftones - Poltergeist
Mr. Yourself - Mr. Robot (Season 1) // SONOIO - See Yourself
In Viking Licht - Vikings (Season 3) // In Extremo - In Diesem Licht
UnderExposed - Six Feet Under (Season 5) // A Day to Remember - Exposed
Bad Fist - Breaking Bad (Season 4) // Health - Stonefist
Heavy Sopranos - The Sopranos (Season 6) // The Smashing Pumpkins - Heavy Metal
True Pacify - True Detective (Season 2) // FKA Twigs - Papi Pacify
Felint - Breaking Bad (Season 5) // Trent Reznor & Atticus Ross - What If We Could, Nine Inch Nails - 12 Ghosts II, Trent Reznor & Atticus Ross - Great Bird of Prey
Lost in Love - Lost (Season 3) // Crystal Castles - Not in Love ft. Robert Smith
Game of Varandra - Game of Thrones (Season 1) // Shining - Låt Oss Ta Allt Från Varandra
Spec Black Teaser - Spec Ops: The Line // Nine Inch Nails - Black Noise
Inertial - Hannibal (Season 2) // beats:metaphysical - Inertia Creeps Edit (Massive Attack cover)
Heathland - Homeland (Season 1) // Health - Tears
Celestica V - The Elder Scrolls V: Skyrim // Crystal Castles - Celestica
Blind Detective - True Detective (Season 1) // TV on the Radio - Blind
Nine Inch Liberty City - Grand Theft Auto IV: The Ballad of Gay Tony // Nine Inch Nails - Running
Breaking Negative - Breaking Bad (Seasons 1-5) // Type O Negative - Everyone I Love is Dead
Crystal Payne - Max Payne 3 // Crystal Castles - Kerosene
MW2 Gameplay - Call of Duty: Modern Warfare 2 // Drowning Pool - Killin' Me, Linkin Park - Forgotten
Crysis Tornado - Crysis // Nightwish - Master Passion Greed, Nightwish - Bye Bye Beautiful, System of a Down - Sugar
Call of Duty 4: Modern Warfare Gameplay // Linkin Park - Qwerty
I'll leave the rest (2008 vids) unknown for y'all ^^


07. Get Your Own Wallet
Bitcoin Wallet Overview
Wallets types and private keys are interesting, but so far it's all been theory.
Getting a wallet and working with it for yourself helps you see what’s really going on with these new concepts. It also gives you the opportunity to create your very own blockchain identity!
Choose a Wallet
First, we have to pick a wallet.
There’s a lot of wallets to choose from, and any of them might make sense for you depending on your needs. Feel free to choose any wallet you like, but do some research before installing one for yourself.
A good way to get started is just to google ‘bitcoin wallets’ and start looking around.
Electrum
For this course, we’re going to use a wallet called Electrum.
We’re using electrum for a few reasons. It works on almost any computer. It’s fast and lightweight. And it includes all the functionality we’ll need.
To use electrum, you’ll need to install it on your computer. Let’s work through the details of how to do that.
Getting Started with Electrum
For more information on how to get started with electrum check out the link below. This is an excellent overview of the basic features of this wallet.
Task Description:
Setup Your Wallet
Task Feedback:
Hope you were were able to get some funds!


Format: BackdropBlendAll,
Example: BackdropBlendAll,
Purpose: Use transparency by alpha channels. An image with alpha channel transparency but without this, will be displayed wrong. Allows also to see the stuff below the Frame
Used in: BACKDROP


The grid system used for the application uses a 12 column grid and is based on the CSS grid specification.
The grid system can be applied to custom styles by using the @mixin
capability of Sass. Using these mixins is the preferred method of applying grid positioning to the layout.
@include grid-container();
@include grid-row();
@include column($number-of-columns-to-span, $start-column-position);
//
// Default span and position values are provided so that only the necessary
// values need to be specified. column(1, auto);
//
@include column($number-of-columns-to-span);
@include column();
The grid container constrains the width of the content. Grid row
and col
classes should be children of an element with the container
class.
The grid row establishes the grid itself (columns, gutters, etc). The row should be a child of an element with the container
class.
The grid column defines a specific item within the grid. The col is naturally 1 column wide and will be positioned based upon the order of the markup.
Columns can be sized using utility classes span1
, span2
, span3
, etc.


Body of decomposed man clad in wetsuit, flippers found on Cape Town beach
by Jenna EtheridgeThe body of a man found on a Cape Town beach has yet to be identified, authorities said on Wednesday.
A member of the public made the gruesome discovery while walking dogs along the shore between Misty Cliffs, Scarborough, and Witsands Soetwater, Ocean View, on Tuesday morning, said Captain FC van Wyk.
"The body was dressed in a black wetsuit with flippers and was in an advanced state of decomposition," he said.
While the police know the body belonged to a man, his age and race were unknown.
Cape Town law enforcement spokesperson Wayne Dyson said the body had yet to be identified.
Police have opened an inquest.


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Take a look at some of these instead:
Or take a gander at the right side of the page!


The analysis plots relationships between income, obesity, poverty with demographic factors and health risks for 50 states and the District of Columbia in the US. Median age, household income, and poverty rates are plotted against the smoking, lack of healthcare, and obesity rates for each state or district.
Showing the relationship between health factors (x-axis) obesity, smoking, lack of health care with demographic factors (y-axis) age, household income, and poverty rate. Click on any of the factors from the horizontal (x-axis) or vertical panel (y-axis), when the font color changed to black it means it’s active in the chart. The data points for each state represented by the scatter plots in the active chart, hover over the bubble to see more info.
There seems to be a positive correlation between poverty rate (%) and the rate of risk factors. Lack of health care is also directly related to the poverty rate. On ther other hand, there is a negative correlation between household income (Median), and the rate of risk factors. When income increases the risk factors decrease.
The relationship between age and health risks from the data needs more color. However, the age demographic is most in the age range of 30-45.


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This page shows some neuro-evolution examples. Please note that not every example may always be successful. More may be added in the future!
This neural network gets taught to increase the input by 0.2 until 1.0 is reached, then it must decrease the input by 2.0.
This neural network gets taught to wait 9 inputs of 0, to output 1 at input number 10.
This neural network gets taught to classify if a letter of the alphabet is a vowel or not. The data is one-hot-encoded.


--- layout: post title: 'LIFT2010: The Old New Media' date: 2010-05-06 00:00:00 categories: digital-media interfaces-&-interaction social ---
Media, audiovisual content meets the digital universe: Catherine Lottier and Virginia Mouseler
Dividing lines in content are blurring: fiction/documentary, say. We now have hybrid genres: X meets Y (fiction with a bit of documentary benefit, documentary with a real-TV aspect to it, mix of real and virtual worlds.
On 9/11, CNN was a major news source for officials, never mind the public. Nowadays events come to our attention not via TV/radio, but social networks (think not just of the recent volcanic ash, but of the Mumbai attacks in 2008).
Visual codes of the digital universe (URLs, hashtags) are migrating into broadcast. Instead of story-telling, immersion. Comment culture: lots of programmes are now comment on other media, whether it be satire (The Daily Show).
How does this change things? See The Execution of Gary Glitter in the UJ, Mes Cheres Etudes in France, or The District in the US.
Also showed a clip of "Stand out from the crowd", a new Noel Edmonds property which looks like it was written for Charlie Brooker to take the piss out of. Edmonds' face bursts out of advertising hoardings to offer innocent members of the public a chance to WIN CASH PRIZES.
Mercedes Bunz
Algorithmically generated news in the US: "not to replace journalists, but to deliver material that journalists can build on".
"Newspapers produce masses that don't even need to gather"
Design principles for Mag+: Bjorn Jeffrey, Bonnier R&D


Three nabbed for gang-raping 14-year-old pupil
by Afeez HanafiAfeez Hanafi
Three persons have been arrested by the police in Lagos for allegedly defiling a 14-year-old girl at Eyita, Ikorodu area of the state.
The police are also on the trail of a fourth suspect, a secondary school pupil, said to be involved in the crime.
City Round learnt that the suspects reside in the neighbourhood and usually waylaid the victim when she was sent on errands and had sex with her.
It was gathered that relations of the victim, who lives with her grandmother, observed she had been wayward lately and started monitoring her closely.
A neighbour recently chanced on four boys reportedly molesting the girl at a dark spot close to her residence. They were said to have fled on sighting the man.
Our correspondent gathered that the neighbour notified the victim’s relations about the incident and she was queried.
It was learnt that she mentioned the suspects and the case was reported at the Sagamu Road Police Station.
An uncle of the victim, Tunde Animola, told City Round that the girl had been living with the grandmother since her childhood.
He said, “She has been wayward lately.  Recently a neighbour sent her on an errand and she didn’t come back in time. It was in the process of searching for her that the man saw her with some boys. The following Saturday, November 16, around 7.30pm, the neighbour caught the boys at a dark spot molesting her.
“We then reported the case to the police. Four persons were involved and one of them is also a secondary school pupil who is still at large. Her parents have separated and remarried. She lives with her grandmother. She is in SS3.”
Animola added that a medical test had been carried out on the girl and submitted to the police.
A rights activist, Okla Gogo, who is on the case, said an investigation police officer at the Sagamu Road Division, one Inspector Ariet, initially wanted to treat the incident as a case of indecent assault and not defilement.
He said, “The IPO said the survivor was not a virgin so she can only charge the perpetrators with indecent assault not defilement. She (the IPO) also threatened to check if the girl was acutely raped or not.”
“We appreciate the area commander and all parties that helped on the case. If not, the Sagamu Division IPO would have released the perpetrators,” Gogo added.
The spokesperson for the police in the state, DSP Bala Elkana, had yet to reply to a message sent to him, seeking an update on the case.
Copyright PUNCH.
All rights reserved. This material, and other digital content on this website, may not be reproduced, published, broadcast, rewritten or redistributed in whole or in part without prior express written permission from PUNCH.
Contact: theeditor@punchng.com


We already knew about Richard Milhous Nixon's love of mashed potatoes. Now, thanks to Salim Fadhley, we learn of his diametrically opposite view of soup. Mr Fadhley pointed me to this passage from President Nixon : Alone In The White House by Richard Reeves (2001):
. . .on March 24 [1969], the President hosted his first state dinner, for Prime Minister Pierre Trudeau of Canada. He complained to Haldeman about it the next morning : “We've got to speed up these dinners. They take forever. So why don't we just leave out the soup course?”
“Well. . .” Haldeman began.
Nixon cut him off : “Men don't really like soup.”
On a hunch, the chief of staff called the President's valet, Manolo Sanchez, and asked : “Was there anything wrong with the President's suit after that dinner last night?”
“Yes. He spilled soup down the vest.”
The action memo went out : No more soup, ever.


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Above & Beyond
Music for Soul
We Are All We Need
2015
Spotify
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Don't Forget Me
Acoustic
2014
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Don't Forget Me
Group Therapy
2011
Spotify
Apple Music
Don't Forget Me


Expertise in Web Development, Design, and Communications. Seeking an opportunity to apply this know-how in the professional environmentof a small or medium size private company or in the public sector. Creative, hard-working, disciplined, and people-oriented.
Classes: Financial Accounting II, Business Law, Business Statistics
Classes: Managing Operations, Managing People, Introduction to Organizational Structure, Financial Accounting
Classes: ASP.NET, COM+ Applications, XML Web Services, Windows Apps, VB.NET, ADO.NET, SQL Server 2000, Querying MS SQL Server 2000, Building XML-Based Web Applications.
Classes: Web II (HTML + CSS), Web III (Responsive Design), Programming in Javascript & JQuery, Project Management, , Visual Identity (Branding), Advanced Adobe Photoshop, Adobe Illustrator Workshop, Typography with Adobe InDesign, Studio Lighting & Photography
Study and specialization in graphic design, photography, fabrication & installation art
Studied French language and culture as well as the Zone System coined by Ansel Adams.
The Puppy Palace — Design and development of three responsive website templates for pet daycare establishment in the Lehigh Valley capable of being maintained by the client.
Cybel LLC — Updated website, produced marketing banners, stickers, product labels, and company logo for a small business reseller of high-tech lasers.
Fulfilled thermo-scientific study with an illustration about where best to store different kinds offoods in the refrigerator for an online food reviews and recipes magazine. See link: http://www.foodrepublic.com/2012/07/26/where-food-goes-fridge
Responsible for extensive collaboration with Marketing clients enabling them to track costs, update budgets, forecast spending, and communicate effectively over many channels with respective nationwide Sales Divisions. Also, learned new methodologies for problem solving and restructuring in a large organization. Contributed to the effective management of complex business processes and IT projects with multiple clients and sponsors. Adept at writing functional specifications, evaluating, contracting, and tracking vendors. Made skillful presentations. Performed corporate training on IT systems. .NET applications enabled the company to monitor costs, track performance, and distribute controlled product information.
Experienced in web systems, layout, and object-oriented computer programming, designed custom, end-to-end, database-driven web pages for clients ranging from standardized test maker The College Board, to Puma athletic shoe-wear website, and ‘The Port of Technology’ a University of Pennsylvania small company incubator. As part of a Venture Capital fundraising campaign, I also created logo, letterhead, sales presentations, and instruction manuals for a mobile CRM prototype on the RIM Blackberry for the Salesforce.
Server in several fine dining establishments in New York and Paris.
Seasonal tax preparation services. Three (3) month training on tax law and guided tax prep software applications.
Designed exhibition labels, display cases, silkscreen typography, info-graphics, and posters for a sequence of thematic exhibitions about Literature and Book Arts at the New York Public Library. Designed an 18-panel exhibition on the history of Heavy Die Cast Steel for Lehigh University.
France, Germany, Belgium. Improved my French language skills.
Visited the South Africa littoral and parks, also traveled within Botswana, Namibia, Angola, Ethiopia, and Egypt.
Over the duration of one year, traveled through Taiwan, China, Tibet, Vietnam, and Indonesia. Over the course of my trip, apprenticed with a Vietnamese tailor, studied Chinese silk painting, toured and interned in textile and dye companies. Discovered cities, traditional ways of life, and sites of ancient civilizations.


Hello,
This website is served by MicroHttpd, a minimal web server written in .NET Core.
Currently, it supports
The source code is made extensible and is available on GitHub.
https://github.com/nam178/MicroHttpdThis server supports byte-range request for video too!


GET /setup/api/settings/authorized-keys
curl -L 'https://api_key:your-amazing-password@hostname:admin_port/setup/api/settings/authorized-keys'
Status: 200 OK
[
{
"key": "ssh-rsa AAAAB3NzaC1yc2EAAAAB...",
"pretty-print": "ssh-rsa 01:14:0f:f2:0f:e2:fe:e8:f4:72:62:af:75:f7:1a:88:3e:04:92:64"
},
{
"key": "ssh-rsa AAAAB3NzaC1yc2EAAAAB...",
"pretty-print": "ssh-rsa 01:14:0f:f2:0f:e2:fe:e8:f4:72:62:af:75:f7:1a:88:3e:04:92:64"
}
]


{{ partial "docstart.html" . }} {{ partial "header.html" . }} {{ partial "menu.html" . }} {{ partial "bodystart.html" . }}
← Back to Publications


gnome-control-center(1) gnome-control-center(1) NAME gnome-control-center — Desktop properties manager SYNOPSIS gnome-control-center [-?, --help] [--usage] [--use-shell] [--gdk- debug=FLAGS] [--gdk-no-debug=FLAGS] [--display=DISPLAY] [--screen=SCREEN] [--sync] [--name=NAME] [--class=CLASS] [--gxid- host=HOST] [--gxid-port=PORT] [--gtk-debug=FLAGS] [--gtk-no- debug=FLAGS] [--g-fatal-warnings] [--gtk-module=MODULE] [--oaf-ior- fd=FD] [--oaf-activate-iid=IID] [--oaf-private] [--disable-sound] [--enable-sound] [--espeaker=HOSTNAME:PORT] [--version] [--sm-client- id=ID] [--sm-config-prefix=PREFIX] [--sm-disable] [--disable-crash- dialog] DESCRIPTION This manual page documents briefly the gnome-control-center command. This manual page was written for the Debian distribution because the original program does not have a manual page. gnome-control-center is a front-end to configuration applets (capplets) that lets you configure your GNOME desktop. You may set things such as the background, the fonts, or the theme SEE ALSO gtk-options (7). OPTIONS These programs follow the usual GNU command line syntax, with long options starting with two dashes (`-'). A summary of options is included below. Theses options are mainly the standard command line options of gtk. -?, --help Show the help message. --usage Display brief usage message. --use-shell Prevent Gnome Control Center from using Nautilus --gdk-debug=FLAGS Enable Gdk debugging flags. --gdk-no-debug=FLAGS Disable Gdk debugging flags. --display=DISPLAY X display to use. --screen=SCREEN X screen to use. --sync Make X calls synchronous. --name=NAME Program name as used by the window manager. --class=CLASS Program class as used by the window manager --gtk-debug=FLAGS Enable Gtk debugging flags. --gtk-no-debug=FLAGS Disable Gtk debugging flags. --g-fatal-warnings Make all warnings fatal --gtk-module=MODULE Load an additional Gtk module. --oaf-ior-fd=FD File descriptor to print IOR on --oaf-activate-iid=IID IID to activate --oaf-private Prevent registering of server with OAF --disable-sound Disable sound server usage --enable-sound Enable sound server usage --espeaker=HOSTNAME:PORT Host:port on which the sound server to use is running --version Show version. --sm-client-id=ID Specify session management ID --sm-config-prefix=PREFIX Specify prefix of saved configuration --sm-disable Disable connection to session manager --disable-crash-dialog Don't show crash dialog box. AUTHOR This manual page was written by Arnaud Patard<EMAIL_ADDRESS>for the Debian system (but may be used by others). Permission is granted to copy, distribute and/or modify this document under the terms of the GNU General Public License, Version 2 any later version published by the Free Software Foundation. On Debian systems, the complete text of the GNU General Public License can be found in /usr/share/common-licenses/GPL. gnome-control-center(1)


Sarah is a designer interested in privacy, security and systems change. She creates interventions that show how technology can respect more of our rights. She founded
IF
.


Drawing letters to the screen in Processing uses a technology developed in the mid 1990s at the Visual Language Workshop at the MIT Media Laboratory. It is a closed system, but we have supplied a number of fonts located in the "font" directory in the main "processing" directory. We expect to change the Processing font technology in the future.
Original Processing.org Example: Letters
// All Examples Written by Casey Reas and Ben Fry // unless otherwise stated. size(200, 200); background(0); // Load the font. Fonts are located within the // main Processing directory/folder and they // must be placed within the data directory // of your sketch for them to load PFont fontA = loadFont("Courier New"); textFont(fontA, 36); textAlign(CENTER); // Set the gray value of the letters fill(255); // Set the left and top margin int margin = 6; int gap = 30; translate(margin*1.5, margin*2); // Create a matrix of letterforms int counter = 0; for(int i=0; i<margin; i++) { for(int j=0; j<margin; j++) { char letter; // Select the letter int count = 65+(i*margin)+j; if(count <= 90) { letter = char(65+counter); if(letter == 'A' || letter == 'E' || letter == 'I' || letter == 'O' || letter == 'U') { fill(204, 204, 0); } else { fill(255); } } else { fill(153); letter = char(48+counter); } // Draw the letter to the screen text(letter, 15+j*gap, 20+i*gap); // Increment the counter counter++; if(counter >= 26) { counter = 0; } } }


|
Wendy Hill, Maker
My fundamental identity is of someone who makes things. This goes back to my earliest memories of drawing with my father and crafting with my mother. My Home Economics teachers broke me of my bad self-taught habits while encouraging my creativity; I made a bread wrapper raincoat, a vinyl bikini bathing suit and a faux-fur coat with bound buttonholes while learning solid sewing skills. When I made my first quilt at age 19, I asked "how hard can it be?". It's not hard, but it takes a lifetime to find the quilt within. Someone Who Makes Things Paper crafts, drawing, painting, 3D pop-ups, cross-stitching, embroidery, weaving, sewing (clothing, stuffed animals, costumes), quilting, whatever-I follow my ideas, learning what I need to learn along the way. Encouraging My Creativity I credit Mrs. Birkenbuel, my high school Home Economics teacher, for everything I know about sewing. We were allowed a lot of freedom to choose our sewing projects, but we had to learn the skills to do it right (or as right as possible). Make a smocked dress? Sure, but the smocking will be up to par. Use horizontal stripes in a dress with a zipper?  It was the perfect combination of learning basic skills plus with the freedom to try anything. First Quilt At Age 19 I lied about being a quilt maker so I could get a booth at the Artifactory in Palo Alto, California. I really did ask myself 'how hard can it be to learn to quilt?'. I've been pursuing the answers ever since. I stocked my booth with some baby quilts and cute quilted accessories. One day, a Stanford University student walked in and ordered a custom quilt: a replica of the floor of the Taj Mahal, 120" by 130", in black and white corduroy. There was no one to tell me I couldn't or shouldn't do it. But I did have the voice of Mrs. Birkenbuel whispering you can do it in my mind. It Takes a Lifetime Athletes talk about The Zone but makers experience this too. When immersed in the creative process, deep in the optimal zone of concentration and discovery, time stops moving. We ask what if questions and handle the materials until something appears out of nothing. It's not hard to start making things, but for many of us, it's impossible to stop. We must keep searching for that quilt-or some other object-within. |


FILM TITLE: LITTLE TONY (Kleine Teun) DIRECTOR: ALEX VAN WARMERDAM COUNTRY: NETHERLANDS 1990 CINEMATOGRAPHY: Marc Felperlaan MUSIC: Alex Van Warderdam CAST: Alex van Warmerdan (Brand), Annet Malherbe (Keet), Ariane Schluter (Lena) SUPER FEATURES: Best directed play in a movie you will ever see.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
(Seen at the 22nd Portland International Film Festival, in Portland, Oregon)
If one takes aside the tragic ending, this is probably one of the best directed films in this festival. and this is so, probably because the director resolves with a film camera all the dilemmas that any actor would have on a stage, the subtleties that the audience can not possibly keep track of, and appreciate. In this alone, the film is magnificent. In acting, let's just say that it would make for a rather exciting evening, with some very funny moments in between, the doors in the film not withstanding.
Never in film has anyone done so much, and so well, with doors, that could easily make the early days of cinema look foolish and petty. The line delivery is excellent around these set pieces, and rarely does one feel that we are not on a stage, watching this drama unfold in its own eccentric, and rather odd way.
But as the film creates its dilemma, it also sets the stage for an ending that, like comedy, is hard to finish. How does one finish this whole thing, that started as a bit of a lark, and ends in a murder?
The story centers around Keet and Brand, who by all looks, are married. And have been for many years, which have become a strain on Keet. She has not been able to have a baby, it seems, either to Brand's lack of attention, or worse yet, they probably are not married and Brand is now showing little attraction for her. This, I don't think that is ever made really clear. But Keet badly wants a baby, and she is beginning to think that she can get it anyway she can.
As chance would have it, Brand is illiterate and has a hard time reading the sub titles in many movies he watches on tv. Keet is tired of translating things for him. She hires a tutor to help him learn to read and write. And later we discover what her real plan was all along. What she didn't bargain for, was that Brand and Lena would fall in love, and that Keet would be pushed aside and forced to do the things she detests, like eating meat she doesn't like, or think is not good food. Eventually, Brand and Lena have a baby, and now the triangle is in serious trouble and the strain is getting impossible. Keet has lost a husband she likes, and now has a baby that she can look at, but she can not adore it like she would like, or a mother would.
And eventually the situation explodes, and the result is sad and unforgiving, and basically ends the whole thing for all three of them. Situation aside, this is a film that is very enjoyable, as its comic situations and pacing is excellent and its camera design next to the actors' work is indeed well thought out, specially when it comes to doors and windows. One would think that a prop is not that important, but nothing speaks more in this film than the doors. They are the one thing that eventually define the end. But in between, one can not avoid laughing at the way these things are used, or abused.
A one man show, a director who is obviously well trained and particularly sensitive to the actor's work on a stage, he knows how and when to fill moments. One rarely gets the feeling that something is wasted, and this show must have been superbly rehearsed, or the magnitude of the actors is way above and beyond the usual that we find.
Good film, very enjoyable. Tragi-comedy at its best.
4 of 5 GIBLOONS
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[gecode-users] model with float variables sometimes hangs during search
Vincent Barichard
Vincent.Barichard at univ-angers.fr
Wed Apr 15 17:05:38 CEST 2015
Hi,
My first thought is that it is a slow convergency process. It often occurs
when linear constraints are involved with particular coefficients. A first
clue may be the number of propagations done in one call to Space::status().
For the cases where the search ends (with a big amount of time) you may
check the number of propagations done. If it is abnormally huge, it's
probably a slow convergence. Without digging in the model and trace the
search, I can't say much more. Sorry ....
Regards,
Vincent
2015-04-15 16:05 GMT+02:00 Christian Schulte <cschulte at kth.se>:
> Hi,
>
>
>
> This could be a feature and not a bug in that constraint propagation is
> just very slow to converge. For floating point numbers there is this risk.
>
>
>
> I would not know what to suggest without knowing how the model looks like
> but I am also not an expert in floating point. Vincent: do you have an idea?
>
>
>
> Cheers
>
> Christian
>
>
>
> --
>
> Christian Schulte, www.gecode.org/~schulte
>
> Professor of Computer Science, KTH, cschulte at kth.se
>
> Expert Researcher, SICS, cschulte at sics.se
>
>
>
> *From:* users-bounces at gecode.org [mailto:users-bounces at gecode.org] *On
> Behalf Of *Kurt Van Den Branden
> *Sent:* Wednesday, April 15, 2015 4:00 PM
> *To:* users at gecode.org
> *Subject:* [gecode-users] model with float variables sometimes hangs
> during search
>
>
>
> hi,
>
> I have a Gecode model that uses float variables.
> I use a BAB searchengine with a Search::TimeStop object to interrupt the
> search when it takes too long.
>
> I noticed that when the model is created with certain parameters, the
> search isn't interrupted at the requested timelimit.
> it either continues a lot longer than allowed, or it doesn't stop at all.
>
> in these cases, the debugger shows that it's the method Space::status that
> doesn't return sometimes.
> it keeps on propagating and never stops.
>
> I haven't been able to reproduce this with a limited test model yet.
>
> Does anybody have tips on what could cause this problem?
>
> Kind Regards,
>
> *Kurt Van Den Branden | Agfa Graphics*
>
--
Vincent Barichard Université d'Angers (LERIA)
Tel: 02 41 73 52 06 Département Informatique
Fax: 02 41 73 50 73 H203
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src.libs_copy.kivydatagridplugin package
Submodules
src.libs_copy.kivydatagridplugin.BorderBehavior module
-
class libs_copy.kivydatagridplugin.BorderBehavior.BorderBehavior(**kwargs)
Bases: kivy.uix.widget.Widget
-
CAP = 'square'
-
JOINT = 'none'
-
border_origin
Property that allows the creation of a tuple of other properties.
For example, if x and y are NumericPropertys, we can create a
ReferenceListProperty for the pos. If you change the value of
pos, it will automatically change the values of x and y accordingly.
If you read the value of pos, it will return a tuple with the values of
x and y.
For example:
class MyWidget(EventDispatcher):
x = NumericProperty(0)
y = NumericProperty(0)
pos = ReferenceListProperty(x, y)
-
border_origin_x
Property that represents a numeric value.
| Parameters: | defaultvalue – int or float, defaults to 0:
Specifies the default value of the property. |
>>> wid = Widget()
>>> wid.x = 42
>>> print(wid.x)
42
>>> wid.x = "plop"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "properties.pyx", line 93, in kivy.properties.Property.__set__
File "properties.pyx", line 111, in kivy.properties.Property.set
File "properties.pyx", line 159, in kivy.properties.NumericProperty.check
ValueError: NumericProperty accept only int/float
Changed in version 1.4.1: NumericProperty can now accept custom text and tuple value to indicate a
type, like “in”, “pt”, “px”, “cm”, “mm”, in the format: ‘10pt’ or (10,
‘pt’).
-
border_origin_y
Property that represents a numeric value.
| Parameters: | defaultvalue – int or float, defaults to 0:
Specifies the default value of the property. |
>>> wid = Widget()
>>> wid.x = 42
>>> print(wid.x)
42
>>> wid.x = "plop"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "properties.pyx", line 93, in kivy.properties.Property.__set__
File "properties.pyx", line 111, in kivy.properties.Property.set
File "properties.pyx", line 159, in kivy.properties.NumericProperty.check
ValueError: NumericProperty accept only int/float
Changed in version 1.4.1: NumericProperty can now accept custom text and tuple value to indicate a
type, like “in”, “pt”, “px”, “cm”, “mm”, in the format: ‘10pt’ or (10,
‘pt’).
-
borders
Property that represents a Python object.
| Parameters: |
- defaultvalue – object type:
Specifies the default value of the property.
- rebind –
bool, defaults to False:
Whether kv rules using this object as an intermediate attribute
in a kv rule, will update the bound property when this object
changes.
That is the standard behavior is that if there’s a kv rule
text: self.a.b.c.d, where a, b, and c are
properties with rebind False and d is a
StringProperty. Then when the rule is applied, text
becomes bound only to d. If a, b, or c change,
text still remains bound to d. Furthermore, if any of them
were None when the rule was initially evaluated, e.g. b was
None; then text is bound to b and will not become bound
to d even when b is changed to not be None.
By setting rebind to True, however, the rule will be
re-evaluated and all the properties rebound when that intermediate
property changes. E.g. in the example above, whenever b changes
or becomes not None if it was None before, text is
evaluated again and becomes rebound to d. The overall result is
that text is now bound to all the properties among a,
b, or c that have rebind set to True.
- **kwargs –
a list of keyword arguments:
baseclass
If kwargs includes a baseclass argument, this value will be
used for validation: isinstance(value, kwargs[‘baseclass’]).
|
Warning
To mark the property as changed, you must reassign a new python object.
Changed in version 1.9.0: rebind has been introduced.
Changed in version 1.7.0: baseclass parameter added.
-
bottom_border_points = []
-
dash_styles = {'solid': {'dash_length': 1, 'dash_offset': 0}, 'dashed': {'dash_length': 10, 'dash_offset': 5}, 'dotted': {'dash_length': 1, 'dash_offset': 1}}
-
draw_border()
-
left_border_points = []
-
on_border_origin(instance, value)
-
on_borders(instance, value)
-
on_pos(instance, value)
-
on_size(instance, value)
-
right_border_points = []
-
set_border_origin()
-
top_border_points = []
-
update_borders()
src.libs_copy.kivydatagridplugin.Datagrid module
-
class libs_copy.kivydatagridplugin.Datagrid.BorderButton
Bases: kivy.uix.button.Button, libs.kivydatagridplugin.BorderBehavior.BorderBehavior
-
class libs_copy.kivydatagridplugin.Datagrid.BorderLabel
Bases: kivy.uix.label.Label, libs.kivydatagridplugin.BorderBehavior.BorderBehavior
-
class libs_copy.kivydatagridplugin.Datagrid.DataGrid(**kwargs)
Bases: kivy.uix.boxlayout.BoxLayout
-
addRow(rowsData, **kwargs)
-
changeCellValueAtRow(rowIndex, cellIndex, cellValue)
-
changeRowColor(rowIndex, changedColor)
-
changeRowColorByID(widget_id, changedColor)
-
removeAllContent()
-
removeRowAtIndex(index)
-
removeRowById(widget_id)
-
setupGrid(headers, width, height)
-
class libs_copy.kivydatagridplugin.Datagrid.DataGridContent(**kwargs)
Bases: kivy.uix.scrollview.ScrollView
-
addRow(widget, index)
-
changeCellValueAtRow(rowIndex, cellIndex, cellValue)
-
insertRowAtIndex(row, index)
-
removeRowAtIndex(index)
-
removeRowById(widget_id)
-
class libs_copy.kivydatagridplugin.Datagrid.DataGridRow(data, **kwargs)
Bases: kivy.uix.gridlayout.GridLayout
-
bgColorProperty
Property that represents a Python object.
| Parameters: |
- defaultvalue – object type:
Specifies the default value of the property.
- rebind –
bool, defaults to False:
Whether kv rules using this object as an intermediate attribute
in a kv rule, will update the bound property when this object
changes.
That is the standard behavior is that if there’s a kv rule
text: self.a.b.c.d, where a, b, and c are
properties with rebind False and d is a
StringProperty. Then when the rule is applied, text
becomes bound only to d. If a, b, or c change,
text still remains bound to d. Furthermore, if any of them
were None when the rule was initially evaluated, e.g. b was
None; then text is bound to b and will not become bound
to d even when b is changed to not be None.
By setting rebind to True, however, the rule will be
re-evaluated and all the properties rebound when that intermediate
property changes. E.g. in the example above, whenever b changes
or becomes not None if it was None before, text is
evaluated again and becomes rebound to d. The overall result is
that text is now bound to all the properties among a,
b, or c that have rebind set to True.
- **kwargs –
a list of keyword arguments:
baseclass
If kwargs includes a baseclass argument, this value will be
used for validation: isinstance(value, kwargs[‘baseclass’]).
|
Warning
To mark the property as changed, you must reassign a new python object.
Changed in version 1.9.0: rebind has been introduced.
Changed in version 1.7.0: baseclass parameter added.
-
changeCellValueAtIndex(index, value)
-
changeRowColor(changedColor)
-
cleanCells()
-
updateRect(*args)
src.libs_copy.kivydatagridplugin.example module
Module contents


[1]
well after 2 mps into would love this product consider buy an extended warranty.
[2]
well after 2 mps into would i have preferred paying less for serious photographers a single photo-well what got is dead.


Relevant sections of the Google Maps API: Autocomplete
NOTE: In order to use this subcomponent, the places library must be loaded. The easiest way to accomplish this is by appending '&libraries=places' to the gMap key in the application's web.xml.


The world of databases is truly vast. But it is governed by a standard that respects the SQL language API. But there is another world; that of NOSQL databases.
A little tidy
There are four types of NOSQL databases:
- Key/Value database
- Column database
- Document database
- Graph database
These four types of databases, unfortunately, do not respond to any API and the various libraries, such as the python ones, have various objects and various methods for extracting and inserting data.
For this reason, I decided to create these APIs across the various database types, in order to have consistent names for each database type.
A simple interface
Python SQL libraries have a standard defined in PEP 249, which describes what method and object names a library must have. In this way, all methods in libraries that respect this API are called the same way. nosqlapi defines the interfaces to build a suitable and consistent library for all NOSQL database types listed above.
API
All four types of databases are different from each other but still have similarities. Anyone can enter data, make updates, delete data and search within. In short, they mirror all CRUD (Create Read Update Delete) operations.
Furthermore, all four can mirror two objects that host two layers important for these operations: the Connection and the Session.
Connection
The Connection focuses on how to connect to the database and how to interact where the data will be stored on the server. Operations such as connecting, disconnecting, creating databases and more.
# My database library built-in with nosqlapi package
from my_nosqlapi_module import Connection
conn = Connection(host='myserve.local', username='admin', password='passw00rd')
conn.databases() # list of all database
conn.create_database('new_db') # new database
conn.delete_database('new_db') # delete database
All possible operations in the Connection are performed in the context of the databases.
Session
Once a NOSQL server is connected, you need to create a session to work directly with the data. This is done via a Session object.
# My database library built-in with nosqlapi package
from my_nosqlapi_module import Connection
conn = Connection(host='myserve.local', username='admin', password='passw00rd', database='db')
sess = conn.connect() # Session object
# CRUD operation
C = sess.insert('key', 'value') # Create
R = sess.get('key') # Read
U = sess.update('key', 'new_value') # Update
D = sess.delete('key') # Delete
In the previous example, the database in question is a key-value database, like Redis, but the interface expects the same names for the other types as well.
Other objects
In the world of SQL databases, there are libraries that map python database objects as if they were database objects. These libraries are called Object-relational mapping (ORM). In the world of NOSQL, being non-relational, there is a different kind of approach. noslapi also offers ODM (Object-data mappings), which transport the objects of the various NOSQL databases into the python world with ease.
>>> import nosqlapi.common.odm
>>> [t for t in dir(nosqlapi.common.odm) if not t.startswith('__')]
['Array', 'Ascii', 'Blob', 'Boolean', 'Counter', 'Date', 'Dc', 'Decimal', 'Double', 'Duration', 'Float', 'Inet', 'Int',
'List', 'Map', 'Null', 'SmallInt', 'Text', 'Time', 'Timestamp', 'Uuid', 'Varchar']
Backword compatibility
nosqlapi also offers a decorator to bring existing libraries (which have all methods of doing the same operations but with different names) to the standard of names offered by the same library.
import nosqlapi
from pymongo import Connection
# This decorator allows you to map existing method names to API compliant methods.
@nosqlapi.api(database_names='databases', drop_database='delete_database', close_cursor='close')
class ApiConnection(Connection):
pass
conn = ApiConnection('localhost', 27017, 'test_database')
hasattr(conn, 'databases') # True
conn.databases() # (test_database, 'db1', 'db2')
Conclusion
Thanks to this library, it is possible to have python libraries (but not only) consistent with an API, which in turn allows to have consistency between the various libraries as if it were an SQL database. Of course, there is extensive documentation for building your own library and examples on pre-made libraries.