Markov switching dynamic regression models¶
This notebook provides an example of the use of Markov switching models in statsmodels to estimate dynamic regression models with changes in regime. It follows the examples in the Stata Markov switching documentation, which can be found at http://www.stata.com/manuals14/tsmswitch.pdf.
[1]:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
# NBER recessions
from pandas_datareader.data import DataReader
from datetime import datetime
usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), end=datetime(2013, 4, 1))
Federal funds rate with switching intercept¶
The first example models the federal funds rate as noise around a constant intercept, but where the intercept changes during different regimes. The model is simply:
where \(S_t \in \{0, 1\}\), and the regime transitions according to
We will estimate the parameters of this model by maximum likelihood: \(p_{00}, p_{10}, \mu_0, \mu_1, \sigma^2\).
The data used in this example can be found at https://www.stata-press.com/data/r14/usmacro.
[2]:
# Get the federal funds rate data
from statsmodels.tsa.regime_switching.tests.test_markov_regression import fedfunds
dta_fedfunds = pd.Series(fedfunds, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))
# Plot the data
dta_fedfunds.plot(title='Federal funds rate', figsize=(12,3))
# Fit the model
# (a switching mean is the default of the MarkovRegession model)
mod_fedfunds = sm.tsa.MarkovRegression(dta_fedfunds, k_regimes=2)
res_fedfunds = mod_fedfunds.fit()
[3]:
res_fedfunds.summary()
[3]:
| Dep. Variable: | y | No. Observations: | 226 |
|---|---|---|---|
| Model: | MarkovRegression | Log Likelihood | -508.636 |
| Date: | Tue, 02 Feb 2021 | AIC | 1027.272 |
| Time: | 06:54:13 | BIC | 1044.375 |
| Sample: | 07-01-1954 | HQIC | 1034.174 |
| - 10-01-2010 | |||
| Covariance Type: | approx |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 3.7088 | 0.177 | 20.988 | 0.000 | 3.362 | 4.055 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 9.5568 | 0.300 | 31.857 | 0.000 | 8.969 | 10.145 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| sigma2 | 4.4418 | 0.425 | 10.447 | 0.000 | 3.608 | 5.275 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| p[0->0] | 0.9821 | 0.010 | 94.443 | 0.000 | 0.962 | 1.002 |
| p[1->0] | 0.0504 | 0.027 | 1.876 | 0.061 | -0.002 | 0.103 |
Warnings:
[1] Covariance matrix calculated using numerical (complex-step) differentiation.
From the summary output, the mean federal funds rate in the first regime (the “low regime”) is estimated to be \(3.7\) whereas in the “high regime” it is \(9.6\). Below we plot the smoothed probabilities of being in the high regime. The model suggests that the 1980’s was a time-period in which a high federal funds rate existed.
[4]:
res_fedfunds.smoothed_marginal_probabilities[1].plot(
title='Probability of being in the high regime', figsize=(12,3));
[4]:
<AxesSubplot:title={'center':'Probability of being in the high regime'}>
From the estimated transition matrix we can calculate the expected duration of a low regime versus a high regime.
[5]:
print(res_fedfunds.expected_durations)
[55.85400626 19.85506546]
A low regime is expected to persist for about fourteen years, whereas the high regime is expected to persist for only about five years.
Federal funds rate with switching intercept and lagged dependent variable¶
The second example augments the previous model to include the lagged value of the federal funds rate.
where \(S_t \in \{0, 1\}\), and the regime transitions according to
We will estimate the parameters of this model by maximum likelihood: \(p_{00}, p_{10}, \mu_0, \mu_1, \beta_0, \beta_1, \sigma^2\).
[6]:
# Fit the model
mod_fedfunds2 = sm.tsa.MarkovRegression(
dta_fedfunds.iloc[1:], k_regimes=2, exog=dta_fedfunds.iloc[:-1])
res_fedfunds2 = mod_fedfunds2.fit()
[7]:
res_fedfunds2.summary()
[7]:
| Dep. Variable: | y | No. Observations: | 225 |
|---|---|---|---|
| Model: | MarkovRegression | Log Likelihood | -264.711 |
| Date: | Tue, 02 Feb 2021 | AIC | 543.421 |
| Time: | 06:54:13 | BIC | 567.334 |
| Sample: | 10-01-1954 | HQIC | 553.073 |
| - 10-01-2010 | |||
| Covariance Type: | approx |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 0.7245 | 0.289 | 2.510 | 0.012 | 0.159 | 1.290 |
| x1 | 0.7631 | 0.034 | 22.629 | 0.000 | 0.697 | 0.829 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | -0.0989 | 0.118 | -0.835 | 0.404 | -0.331 | 0.133 |
| x1 | 1.0612 | 0.019 | 57.351 | 0.000 | 1.025 | 1.097 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| sigma2 | 0.4783 | 0.050 | 9.642 | 0.000 | 0.381 | 0.576 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| p[0->0] | 0.6378 | 0.120 | 5.304 | 0.000 | 0.402 | 0.874 |
| p[1->0] | 0.1306 | 0.050 | 2.634 | 0.008 | 0.033 | 0.228 |
Warnings:
[1] Covariance matrix calculated using numerical (complex-step) differentiation.
There are several things to notice from the summary output:
The information criteria have decreased substantially, indicating that this model has a better fit than the previous model.
The interpretation of the regimes, in terms of the intercept, have switched. Now the first regime has the higher intercept and the second regime has a lower intercept.
Examining the smoothed probabilities of the high regime state, we now see quite a bit more variability.
[8]:
res_fedfunds2.smoothed_marginal_probabilities[0].plot(
title='Probability of being in the high regime', figsize=(12,3));
[8]:
<AxesSubplot:title={'center':'Probability of being in the high regime'}>
Finally, the expected durations of each regime have decreased quite a bit.
[9]:
print(res_fedfunds2.expected_durations)
[2.76105188 7.65529154]
Taylor rule with 2 or 3 regimes¶
We now include two additional exogenous variables - a measure of the output gap and a measure of inflation - to estimate a switching Taylor-type rule with both 2 and 3 regimes to see which fits the data better.
Because the models can be often difficult to estimate, for the 3-regime model we employ a search over starting parameters to improve results, specifying 20 random search repetitions.
[10]:
# Get the additional data
from statsmodels.tsa.regime_switching.tests.test_markov_regression import ogap, inf
dta_ogap = pd.Series(ogap, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))
dta_inf = pd.Series(inf, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))
exog = pd.concat((dta_fedfunds.shift(), dta_ogap, dta_inf), axis=1).iloc[4:]
# Fit the 2-regime model
mod_fedfunds3 = sm.tsa.MarkovRegression(
dta_fedfunds.iloc[4:], k_regimes=2, exog=exog)
res_fedfunds3 = mod_fedfunds3.fit()
# Fit the 3-regime model
np.random.seed(12345)
mod_fedfunds4 = sm.tsa.MarkovRegression(
dta_fedfunds.iloc[4:], k_regimes=3, exog=exog)
res_fedfunds4 = mod_fedfunds4.fit(search_reps=20)
[11]:
res_fedfunds3.summary()
[11]:
| Dep. Variable: | y | No. Observations: | 222 |
|---|---|---|---|
| Model: | MarkovRegression | Log Likelihood | -229.256 |
| Date: | Tue, 02 Feb 2021 | AIC | 480.512 |
| Time: | 06:54:19 | BIC | 517.942 |
| Sample: | 07-01-1955 | HQIC | 495.624 |
| - 10-01-2010 | |||
| Covariance Type: | approx |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 0.6555 | 0.137 | 4.771 | 0.000 | 0.386 | 0.925 |
| x1 | 0.8314 | 0.033 | 24.951 | 0.000 | 0.766 | 0.897 |
| x2 | 0.1355 | 0.029 | 4.609 | 0.000 | 0.078 | 0.193 |
| x3 | -0.0274 | 0.041 | -0.671 | 0.502 | -0.107 | 0.053 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | -0.0945 | 0.128 | -0.739 | 0.460 | -0.345 | 0.156 |
| x1 | 0.9293 | 0.027 | 34.309 | 0.000 | 0.876 | 0.982 |
| x2 | 0.0343 | 0.024 | 1.429 | 0.153 | -0.013 | 0.081 |
| x3 | 0.2125 | 0.030 | 7.147 | 0.000 | 0.154 | 0.271 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| sigma2 | 0.3323 | 0.035 | 9.526 | 0.000 | 0.264 | 0.401 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| p[0->0] | 0.7279 | 0.093 | 7.828 | 0.000 | 0.546 | 0.910 |
| p[1->0] | 0.2115 | 0.064 | 3.298 | 0.001 | 0.086 | 0.337 |
Warnings:
[1] Covariance matrix calculated using numerical (complex-step) differentiation.
[12]:
res_fedfunds4.summary()
[12]:
| Dep. Variable: | y | No. Observations: | 222 |
|---|---|---|---|
| Model: | MarkovRegression | Log Likelihood | -180.806 |
| Date: | Tue, 02 Feb 2021 | AIC | 399.611 |
| Time: | 06:54:19 | BIC | 464.262 |
| Sample: | 07-01-1955 | HQIC | 425.713 |
| - 10-01-2010 | |||
| Covariance Type: | approx |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | -1.0250 | 0.290 | -3.531 | 0.000 | -1.594 | -0.456 |
| x1 | 0.3277 | 0.086 | 3.812 | 0.000 | 0.159 | 0.496 |
| x2 | 0.2036 | 0.049 | 4.152 | 0.000 | 0.107 | 0.300 |
| x3 | 1.1381 | 0.081 | 13.977 | 0.000 | 0.978 | 1.298 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | -0.0259 | 0.087 | -0.298 | 0.765 | -0.196 | 0.144 |
| x1 | 0.9737 | 0.019 | 50.265 | 0.000 | 0.936 | 1.012 |
| x2 | 0.0341 | 0.017 | 2.030 | 0.042 | 0.001 | 0.067 |
| x3 | 0.1215 | 0.022 | 5.606 | 0.000 | 0.079 | 0.164 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 0.7346 | 0.130 | 5.632 | 0.000 | 0.479 | 0.990 |
| x1 | 0.8436 | 0.024 | 35.198 | 0.000 | 0.797 | 0.891 |
| x2 | 0.1633 | 0.025 | 6.515 | 0.000 | 0.114 | 0.212 |
| x3 | -0.0499 | 0.027 | -1.835 | 0.067 | -0.103 | 0.003 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| sigma2 | 0.1660 | 0.018 | 9.240 | 0.000 | 0.131 | 0.201 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| p[0->0] | 0.7214 | 0.117 | 6.177 | 0.000 | 0.493 | 0.950 |
| p[1->0] | 4.001e-08 | nan | nan | nan | nan | nan |
| p[2->0] | 0.0783 | 0.038 | 2.079 | 0.038 | 0.004 | 0.152 |
| p[0->1] | 0.1044 | 0.095 | 1.103 | 0.270 | -0.081 | 0.290 |
| p[1->1] | 0.8259 | 0.054 | 15.208 | 0.000 | 0.719 | 0.932 |
| p[2->1] | 0.2288 | 0.073 | 3.150 | 0.002 | 0.086 | 0.371 |
Warnings:
[1] Covariance matrix calculated using numerical (complex-step) differentiation.
Due to lower information criteria, we might prefer the 3-state model, with an interpretation of low-, medium-, and high-interest rate regimes. The smoothed probabilities of each regime are plotted below.
[13]:
fig, axes = plt.subplots(3, figsize=(10,7))
ax = axes[0]
ax.plot(res_fedfunds4.smoothed_marginal_probabilities[0])
ax.set(title='Smoothed probability of a low-interest rate regime')
ax = axes[1]
ax.plot(res_fedfunds4.smoothed_marginal_probabilities[1])
ax.set(title='Smoothed probability of a medium-interest rate regime')
ax = axes[2]
ax.plot(res_fedfunds4.smoothed_marginal_probabilities[2])
ax.set(title='Smoothed probability of a high-interest rate regime')
fig.tight_layout()
Switching variances¶
We can also accommodate switching variances. In particular, we consider the model
We use maximum likelihood to estimate the parameters of this model: \(p_{00}, p_{10}, \mu_0, \mu_1, \beta_0, \beta_1, \sigma_0^2, \sigma_1^2\).
The application is to absolute returns on stocks, where the data can be found at https://www.stata-press.com/data/r14/snp500.
[14]:
# Get the federal funds rate data
from statsmodels.tsa.regime_switching.tests.test_markov_regression import areturns
dta_areturns = pd.Series(areturns, index=pd.date_range('2004-05-04', '2014-5-03', freq='W'))
# Plot the data
dta_areturns.plot(title='Absolute returns, S&P500', figsize=(12,3))
# Fit the model
mod_areturns = sm.tsa.MarkovRegression(
dta_areturns.iloc[1:], k_regimes=2, exog=dta_areturns.iloc[:-1], switching_variance=True)
res_areturns = mod_areturns.fit()
[15]:
res_areturns.summary()
[15]:
| Dep. Variable: | y | No. Observations: | 520 |
|---|---|---|---|
| Model: | MarkovRegression | Log Likelihood | -745.798 |
| Date: | Tue, 02 Feb 2021 | AIC | 1507.595 |
| Time: | 06:54:20 | BIC | 1541.626 |
| Sample: | 05-16-2004 | HQIC | 1520.926 |
| - 04-27-2014 | |||
| Covariance Type: | approx |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 0.7641 | 0.078 | 9.761 | 0.000 | 0.611 | 0.918 |
| x1 | 0.0791 | 0.030 | 2.620 | 0.009 | 0.020 | 0.138 |
| sigma2 | 0.3476 | 0.061 | 5.694 | 0.000 | 0.228 | 0.467 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| const | 1.9728 | 0.278 | 7.086 | 0.000 | 1.427 | 2.518 |
| x1 | 0.5280 | 0.086 | 6.155 | 0.000 | 0.360 | 0.696 |
| sigma2 | 2.5771 | 0.405 | 6.357 | 0.000 | 1.783 | 3.372 |
| coef | std err | z | P>|z| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| p[0->0] | 0.7531 | 0.063 | 11.871 | 0.000 | 0.629 | 0.877 |
| p[1->0] | 0.6825 | 0.066 | 10.301 | 0.000 | 0.553 | 0.812 |
Warnings:
[1] Covariance matrix calculated using numerical (complex-step) differentiation.
The first regime is a low-variance regime and the second regime is a high-variance regime. Below we plot the probabilities of being in the low-variance regime. Between 2008 and 2012 there does not appear to be a clear indication of one regime guiding the economy.
[16]:
res_areturns.smoothed_marginal_probabilities[0].plot(
title='Probability of being in a low-variance regime', figsize=(12,3));
[16]:
<AxesSubplot:title={'center':'Probability of being in a low-variance regime'}>


Introduction to SLogo.
SLogo is a programming language environment designed to teach elementary programmers basic
fundamentals about variables, commands, for loops, if-else statements, logic and more!
The following guide will help show you how to use the SLogo GUI to write commands to move the
turtle on screen. As you go from more simple commands to complex ones, you can create more interesting
results.
The environment.
Commands are entered in the bar at the bottom, and then the turtle will move within the display
window accordingly. The three gray panels on the right respectively show:
- Command history: the list of commands that were just run, also re-executable with a click.
- User-defined commands: the list of commands that you defined as a user. Commands defined by the user
can often represent a process you want to write by shorthand, i.e. a chain of base commands.
- Existing variables: variables can be saved to store data that must be used in the future by other
commands.
To run the commands typed, simply press the Run button.
On the top, there are a bunch of drop-down boxes with expandable options including changing the background color, pen color,
the image displayed, and the language in which commands are typed.
There is also a multiline input where multiple commands can be entered before execution, and an option to create a new SLogo
instance, as well as additional options.
Links to more help.
SLogo Project
Example Code
SLOGO Team 9: Addison Howenstine, Phil Foo, Noah Over, Harshil Garg


| simulate {pomp} | R Documentation |
simulate
generates simulations of the state and measurement
processes.
## S4 method for signature 'missing'
simulate(
nsim = 1,
seed = NULL,
times,
t0,
params,
rinit,
rprocess,
rmeasure,
format = c("pomps", "arrays", "data.frame"),
include.data = FALSE,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'data.frame'
simulate(
object,
nsim = 1,
seed = NULL,
times,
t0,
params,
rinit,
rprocess,
rmeasure,
format = c("pomps", "arrays", "data.frame"),
include.data = FALSE,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'pomp'
simulate(
object,
nsim = 1,
seed = NULL,
format = c("pomps", "arrays", "data.frame"),
include.data = FALSE,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'objfun'
simulate(object, nsim = 1, seed = NULL, ...)
nsim |
The number of simulations to perform.
Note that the number of replicates will be |
seed |
optional;
if set, the pseudorandom number generator (RNG) will be initialized with |
times |
the sequence of observation times.
|
t0 |
The zero-time, i.e., the time of the initial state.
This must be no later than the time of the first observation, i.e., |
params |
a named numeric vector or a matrix with rownames containing the parameters at which the simulations are to be performed. |
rinit |
simulator of the initial-state distribution.
This can be furnished either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library.
Setting |
rprocess |
simulator of the latent state process, specified using one of the rprocess plugins.
Setting |
rmeasure |
simulator of the measurement model, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library.
Setting |
format |
the format in which to return the results.
|
include.data |
if |
... |
additional arguments supply new or modify existing model characteristics or components.
See When named arguments not recognized by |
verbose |
logical; if |
object |
optional; if present, it should be a data frame or a ‘pomp’ object. |
A single “pomp” object,
a “pompList” object,
a named list of two arrays,
or a data frame, according to the format
option.
If params
is a matrix, each column is treated as a distinct parameter set.
In this case, if nsim=1
,
then simulate
will return one simulation for each parameter set.
If nsim>1
,
then simulate
will yield nsim
simulations for each parameter set.
These will be ordered such that
the first ncol(params)
simulations represent one simulation
from each of the distinct parameter sets,
the second ncol(params)
simulations represent a second simulation from each,
and so on.
Adding column names to params
can be helpful.
Some Windows users report problems when using C snippets in parallel computations.
These appear to arise when the temporary files created during the C snippet compilation process are not handled properly by the operating system.
To circumvent this problem, use the cdir
and cfile
options to cause the C snippets to be written to a file of your choice, thus avoiding the use of temporary files altogether.
Aaron A. King
More on pomp elementary algorithms:
elementary_algorithms
,
kalman
,
pfilter()
,
pomp-package
,
probe()
,
spect()
,
trajectory()
,
wpfilter()


---
title: Response Actions
layout: page
pageOrder: 4.07
section: 'Mock Server'
subsection: false
---
Response actions contain:
- status code i.e. 200, 302, 404, etc
- body - a UTF-8 encoded sequence of bytes containing any content
- headers - each with a name and one or more values
- cookies - each with a name and with one or more values, more complex cookies can be modelled by using the a Set-Cookie header
Responses can be further controlled using:
- a delay before the response is sent
- the number of times the response is sent (including unlimited)
- a time to live the response will be continued to be returned (including unlimited)
Java
{% include_subpage creating_expectations_java_response_actions.html %}
JavaScript
{% include_subpage creating_expectations_javascript_response_actions.html %}


--- layout: post title: "morning lyrics" comments: true ---
Sometimes I get to feelin'
I was back in the old days - long ago
When we were kids when we were young
Thing seemed so perfect - you know
The days were endless we were crazy we were young
The sun was always shinin' - we just lived for fun
Sometimes it seems like lately - I just don't know
The rest of my life's been just a show
Those were the days of our lives
The bad things in life were so few
Those days are all gone now but one thing is true
When I look and I find I still love you


Login Module
This module will be used by all users to login to the system with proper privileges,
and to modify their personal information and/or the password.
Logging in:
- Enter your username
- Enter your password
- Press "Submit" to login
Modify user information:
- Select “Manage account settings” in home menu
- Redirected to a form filled out with current account settings
- Change the desired fields
- Enter a new password and re-enter to confirm password
- Press “Submit” to modify account settings


During the Runtime Lab we should have discovered:
This efficiency can be quantified using something called Big Oh
notation (not on AP exam). Big Oh describes how the runtime of an algorithm will increase as the array size (n) increases.
| Efficiency | Meaning |
|---|---|
| O(1) | Takes a constant amount of time regardless of the size of n. |
| O(log(n)) | Time increases with n, however more slowly than linear (e.g. Binary Search) |
| O(n) | Increases linearly (e.g. Linear Search) |
| O(n*log(n)) | Increases with n, faster than linear (e.g. Arrays.sort() ) |
| O(n^2) | Increases to the square of n. (e.g. Insertion Sort, Selection Sort) |
| O(2^n) | Increases exponentially |
O(n^2)
) - TodayO(n^2)
) - TodayO(n*log(n))
) - Chapter 10 (Recursion)note: the n^2
runtime comes from the fact that selection/insertion rely on double loops, resulting in having to iterate through n things n times.
1 for each position in the array
2 find the minimum value from the current position to the end of the array.
3 if the minimum is not at the current position
4 swap the current and the min
5 move to the next position in the array.
Simulation
http://www.cs.armstrong.edu/liang/animation/web/SelectionSort.html
1 for each position in the array (start @ index 1)
2 insert the current value into order relative to the sorted values on its left.
3 move to the next position in the array.
Simulation
https://www.hackerearth.com/practice/algorithms/sorting/insertion-sort/visualize/
Implement these to algorithms with the following static void
methods
selectionSort()
: takes an ArrayList of names and puts them into alphabetical order using the selection sort algorithm. (see compareTo()
if stuck on how to order)insertionSort()
: takes an ArrayList of names and puts them into alphabetical order using the insertion sort algorithm.shuffle()
: takes an ArrayList of names and puts them into a random order. (see Knuth shuffle
if you get stuck)Notes:
compareTo()
method to review how to put Strings in order. When your code is working you should get something like this:
| Table of Contents | t |
|---|---|
| Exposé | ESC |
| Full screen slides | e |
| Presenter View | p |
| Source Files | s |
| Slide Numbers | n |
| Toggle screen blanking | b |
| Show/hide slide context | c |
| Notes | 2 |
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This HTML page shows how one can embed videos made with the pdf2video tool in web pages. WebVTT subtitles are supported by the tool.


HAUNTING, THE (director: Jan de Bont; screenwriter: David Self, based on the novel The Haunting of Hill House by Shirley Jackson; cinematographer: Caleb Deschanel; editor: Michael Kahn; cast: Lili Taylor (Nell), Liam Neeson (Dr. David Marrow), Catherine Zeta Jones (Theo), Owen Wilson (Luke), Alix Koromzay (Mary), Marian Seldes (Mrs. Dudley), Bruce Dern (Mr. Dudley); Runtime:112; 1999)
Reviewed by Dennis Schwartz
This one's a bomb. It's a total special effect film, sapping away what is human about the characters, while unsuccessfully putting the human characteristics into the haunted house. The house is the star of the film and the actors are the props. This insipid horror story has little to recommend it; it wasn't scary, or campy, or well acted, it was just unimaginative. The few thrills it offers, are all a result of special effects. This is a date film more than anything else.
The real horror it provided, was zapping Shirley Jackson's very literate psychological thriller. A far better film version, one that was actually thrilling, is the one done in 1963 by Robert Wise. The director of this film, Jan de Bont (Speed/Speed 2/ Twister), is not up to dealing with a psychological story and directing characters in a persuasive way, he is more into making silly theme movies that rely solely on special effects. The man just can't make a literate film.
A research test for volunteer insomniacs is advertised in the paper, as a psychologist, Dr, Marrow (Liam), is using that as a cover for his study of group fear.  She seems relieved to get away from her unpleasant situation and do the experiment, and welcomes the chance to be living with other volunteers, even if they will live in the scary Hill House.
Theo (Zeta) is an extrovert, an attractive NYC artist, an insomniac who is bi-sexual, favoring lesbian relationships, causing her to have problems with her boyfriend and wanting to change her scene for awhile. Luke (Owen) is an insomniac, with a hyper personality and manic energy, who will comically walk around the schlock house among the griffins and huge hallways in his pajama bottoms and a baseball glove. Soon they will all experience strange phenomena, such as Nell's name written on the wall welcoming her home and telling her to leave or else, a moving flume, house breathing sounds, animated ghosts of murdered children and the ghost of the ogre who built the house, and one dramatic shot of the ogre's wife who committed suicide, as she is seen suspended from the ceiling by a rope around her neck. This house is haunted Hollywood style, as the Gothic house, which Theo describes as a cross between something Charles Foster Kane would build and the Munsters' house, becomes the central focus of the film and the insomniacs stay away trying to look afraid of what they are seeing.
Since this thriller was never thrilling, it just went on to show off the stage set designs illustriously created by Eugenio Zanetti, all one could do was go from room to room and stare at the cherubs, the leaping skeletons, the elaborately built stairway collapsing, an animated hand of a ghost coming out of the fountain, and at the futile effort to make a Disney fantasy picture. The film concludes on a bland note, not even bothering to go after what the Liam character was supposed to be about, whether he was an egotistical scientist out for his own ends or a good guy trying to help science with useful knowledge. After the film exhausts itself with its fake horror show and we see the ghost of Hill house chasing after Nell, the film gets a few laughs it never intended to get, as it concludes as a show-off will, when he is so proud of his wealthy house, that he thinks he has really impressed you, and continues to show-and-tell long after one has lost interest. The laughs that were gleaned, are of the kind that do not bode well for the film, they are the laughs that come from those who know they have just seen a bad film.
REVIEWED ON 8/21/2000 GRADE: D
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


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Vintage car driver, 80, killed in M23 crash named as millionaire businessman
Ron Carey, from Canada, who had a £3.8million classic car collection, made his fortune in the oil industry
by Louie Smith, https://www.facebook.com/dailymirror/A 80-year-old vintage car enthusiast killed in a smash during the London to Brighton rally was a Canadian oil millionaire, it has emerged.
Ron Carey ended up on the M23 in his 35mph 1903 Knox Runabout “Old Porcupine” after taking a wrong turn off Sunday’s official A23 route.
Witnesses said he and wife Billi were thrown into the air “like rag dolls” when they collided with a heavy goods vehicle.
Ron died at the scene while Billi was flown to hospital with serious head injuries.
Her condition was stable today.
Paying tribute, friend Rick Pikulski said: “It’s tragic, gut-wrenching news. I’ve known Ron almost 40 years. He was an avid collector. He would go on these vintage car rallies all the time.”
Ron, of Calgary, Alberta, worked his way up in the oil industry and founded J&L Supply in 1973. He had a £3.8million classic car collection and was a contributor to two vintage car museums.
He is thought to have completed the London to Brighton run in 2011, 2012 and 2013.
More than 400 enthusiasts took part in the 60-mile event this year, which only allows cars built before 1905. One local who saw the smash accused organisers of poor signage and a lack of stewards.
Jocelyn Gregory said: “If you are not familiar with the area, it is an easy mistake. When the lorry hit, they flew into the air like rag dolls. It was awful.”
A spokesman for the Veteran Car Run said the car had left the route, adding: “We are doing all we can to support the family and are working with the police.”
Ron’s death comes two years after David Corry, 68, died in the rally when his 1902 Benz was in a four-car crash.
A second man, believed to be the lorry driver, was treated at the scene for minor injuries. No arrests have been made.


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Fresh, local, and organic ingredients!
We serve our soups fresh at four Seattle Farmer's Markets weekly.
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--- layout: post title: Swimming sets date: 2012-11-17 12:10:17.000000000 +00:00 type: post parent_id: '0' published: true password: '' status: publish categories: - swimming tags: [] meta: _edit_last: '1' author: Noel permalink: "/swimming-sets/" ---
Some sets I have and plan to follow, adapted from 220 Triathlon
1) 50-50s: 50 x 50m reps getting you to 2.5km. Do 10 at either side for a warm-up and down. Mix up the strokes and drills in the middle eight sets.
2) Broken 1,500M: 15 x 100m.
3) 2 x 1km pyramids: After a warm-up, do a continuous swim of 25m hard, 25m easy, 50m hard, 50m easy, 75m hard, 75m easy and 100m hard, 100m easy. Rest. Repeat the 100m reps and go back down through 75s, 50s and 25s. A total of 1km. Repeat the set with a pull buoy.
4) Descending 50s - negative splits - starting slower, building up. Aim to swim 20 x 50m. For each set, include a rest time. The total time must decrease as the sets go on.


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Container is a placeholder for text, images, videos or other widgets. It defines the font styles and background color. It can be applied to body or div HTML-elements. The container widget defines the styles for HTML-headings (h1 to h6), paragraphs (p), labels (label) and links (a).
Container is should be used as a base class for higher-level placeholder widgets like the page or popup widgets.

Container example

Container widget is not really a widget but just a definition of a BODY or DIV to have a theme based font and
background colors and padding. It also includes definition for headings (H1 to H6), paragraphs (P), labels (LABEL) and links (A).
Another important feature of container is that the cursor is set to default, selection is disabled within its elements
except inputs (INPUT and TEXTAREA). Browser enhancements for the elements within a container are also disabled.

All widgets should be placed within a container but this is NOT a must - widgets outside a container
must have set EXPLICITLY the styles disabling the selection and browser enhancements.

All options are accessible throught the getOption and setOptions method of the widget [topmostMarkupElement.container] object. After setting any of the options for the container widget, it will be unenhanced and enhanced to apply the newly set options.
Please use the theme method of the widget to read the current theme.
HTML-Markup notation: data-theme
JavaScript notation: theme
The theme name as string to be used for the widget. Possible values 'a' or 'b'. By default, 'a' stays for dark theme, while 'b' for the bright theme. If omitted, the widget will inherit the theme from its parent.
HTML-Markup notation: data-selection
JavaScript notation: selection
If set to "yes" the selection will be enabled.
Default is: no.
Please click on the links bellow to see the different live examples. All links will open in a new window. Once a link has opened, you can view the source code of the example.


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Over a million designers use Sketch to transform their ideas into incredible products, every day. Would you like to join us and help take this leading design tool to the next level?
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BTS’s Jungkook’s ever popular BT21 character Cooky is a struggle to get despite tons of restocking
by dose-nelJungkook has been names as a sold out king by media and fans alike, likewise character Cooky is no stranger to being sold out too! Fans have been eagerly waiting to get the cooky for slippers but it's a struggle to get since despite being restocked several times, it's still sold out. Fans everywhere are in awe of Jungkook’s insane selling power.


We must understand and accept that children develop at their own
pace, so don't be alarmed if your child takes a slightly different
course. Every parent, however, has the right to find out if there is
something different.
The first five years of children’s life is most important as it during
this period when they develop most of the abilities. All children need
to be encouraged when they achieve these skills by us, their parents.
We need to give them love and affection, sing, talk, listen and play
with them
Developmental Delay provides an opportunity for provision of services
and programs based on a child’s strengths and needs, as measured by
the five developmental areas, when the assessment of a more specific
disability due to a child’s young age cannot be considered statistically
reliable or valid.
The five developmental domains assessed for Developmental Delay
are:
1. Physical/Motor coordination: Gross/fine motor skills, jumping,
hopping, throwing/catching, drawing, stacking
2. Cognitive: Thinking, reasoning, problem-solving, understanding
3. Communication: Smiling, listening, babbling, talking, pointing to
things
4. Social/Emotional: Initiating peer contact, group play, showing
affection, copying facial expressions
5. Adaptive: Dressing, eating, washing
Now when a child has delayed development in two or more of the
above domains, then the child may have “global developmental
delay” or GDD.
A child may just have delay in one specific domain this is not GDD and
may have a different diagnostic and treatment approach. So, it is
important that the type of delay is identified as early as possible so
support for your child can begin and it usually is tailored to your
child’s specific needs. The earlier support is put in place, the better
chance of improvement for the child.
Is your child showing signs of developmental delay?
More often than not, parents are often the first to realize that their
child is not developing in an expected way or at the same pace as
their peers.
A child’s development can be slower than others for various reasons,
including if they are born prematurely or become ill. If a child
becomes severely ill they may even appear to regress and no longer be
able to do things they could before the onset of the illness. For most
children, once they recover, their development will speed up and they
will catch up.
Family events such as the arrival of a new baby in a family can also
cause a child to slow down their progress for a short while. If a
child’s progress slows for a while, and there seems to be a reason for
this, such as one of the events above, this is not necessarily a cause
for concern. But if the delay is persistent, or happens for no obvious
reason, it is important that you seek advice so that any help can be
given as soon as possible.
Any accident or illness that affects the brain can also impact on a
child’s development. Some children will be slower in their progress
because they are born with a medical condition which restricts their
development. This condition might be diagnosed shortly after their
birth. However, a child being slow in their development can sometimes
be the first sign that the child has an underlying medical condition.
Important: If you are worried about your child’s progress you
should talk to your pediatrician. It is time to make a visit
“When I noticed Ishaani wasn’t progressing the way she should, the
doctor I took her to, didn’t know much about her medical condition,
she had never such a case but she did refer us to another doctor
from a bigger hospital”
Remember: It will take time for the doctors to make the diagnosis
Specialists will consider all the different areas of concern for your
child and think about the most likely reasons for their difficulties.
They might carry out tests to rule out possible causes or to confirm
what the reasons are. They may even find a name or ‘diagnosis’ for
the condition. However, different conditions can cause similar
symptoms and it is not always possible to test for all of these.
It might be necessary to wait to see if your child makes progress in
their development, or for other symptoms to appear that will help in
making a diagnosis.
If a child has a rare condition it can be even more difficult for
doctors to identify the reasons, as less is known about the condition
and the doctor might never have seen someone with that condition
before.
Advances in science and genetic testing means new conditions are
being identified all the time. It is not possible for doctors to know
about all of these conditions, but this should not prevent them from
being able to treat your child’s symptoms.
Children can sometimes wait for years for a diagnosis and some never
get a diagnosis, but support is still available for them even without a
diagnosis. Some parents can find this a worrying time. Not having a
diagnosis for a child’s condition can seem worse than knowing and
parents are often bewildered by what could be the cause.
Not all parents, however, will react in this way. Sometimes parents
prefer not to have a diagnosis as this helps them focus on the issues
that are happening rather than compare their child with others with
the same condition. Even if your child has been diagnosed with a
condition, it can still be difficult to know how this will affect them as
the same condition can affect children in different ways.
"In Ishaani’s case, the first few years was a struggle for me, I did not
know anyone who had a child like her or what her condition was called.
I constantly was under pressure to finding a name for our daughter’s
condition and kept researching on this wherever and whenever I
could. My daughter’s pediatrician listened to everything I said, but
was helpless as she explained that Ishaani had overlapping symptoms
and no single name could be given to her condition. Till the end
Ishaani was undiagnosed and there was no specific name for her
condition”
Source:
https://www.clevelandschools.org/cms/lib/TN01917036/Centricity/D
omain/277/Developmental%20Delay%20Resource%20Packet.pdf


--- layout: post title: no title date: '2016-09-25T00:30:40+02:00' categories: - Storyboards - Sketches picture_url: https://film-Storyboards.tumblr.com/post/150897513756 ---


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{% load i18n %} {% url 'grp_list' as url_list %} {% url 'grp_my_groups' as url_my_groups %} {% blocktrans %}
Group is a users unit that allowes sharing images between its members.
In Show groups section you'll find the list of all active groups. To join an existing group you must click send request at chosen group, which will notify the group leader. Once request is accepted, you will gain access to the group resources. In My groups section you can create new groups and manage ones that belong to you.
Be aware that any private keys and other data you left on the image you share become available for all group members.
It's strongly recommended to remove all unnecessary private keys and ensure security.
{% endblocktrans %}


Overview
ABOUT PROGRAM
Additional Resources
Overview
- What is Alzheimer's Disease?
-
Alzheimer's is the most common form of dementia in which generalized degeneration of the brain causes problems with memory, thinking and behavior. Symptoms usually develop slowly and get worse over time, becoming severe enough to interfere with daily tasks.
- How common is Alzheimer's Disease?
- An estimated 5.4 million Americans of all ages have Alzheimer's disease in 2016. Of the 5.4 million Americans with Alzheimer's, an estimated 5.2 million people are age 65 and older, and approximately 200,000 individuals are under age 65 (younger-onset Alzheimer's). This number is projected to triple by the year 2050.
- What is the MIND Diet?
- "The MIND diet, short for the Mediterranean-DASH Intervention for Neurodegenerative Delay diet, has been directly linked to a significant improvement in neurological function. What does this mean for you?
Through an extensive research trial, scientists at Rush University Medical Center found that following this diet can reduce your risk of Alzheimer's and dementia by up to 53%! Even moderately following the diet reduces your risk by over a third. The diet focuses on 10 brain-healthy foods including leafy greens, nuts and limits potentially damaging foods like red meat, pastries, and fried foods. In addition to reducing incidence of Alzheimer’s disease, following the MIND diet has also been linked to lower risk of cardiovascular conditions like hypertension, heart attack and stroke."
- Is there research to back the MIND Diet's claim on reducing risk of dementia and Alzheimer's?
- Yes. The MIND Trial was conducted by Dr. Morris and her team in which over 900 Chicago area seniors were followed over an average of 4.5 years. A comprehensive questionnaire was used to collect data on participant dietary patterns and yearly cognitive tests were performed. The study found the MIND diet lowered Alzheimer’s risk by about 35% amongst those who followed it moderately well and up to 53% for those who stricktly adhered to the diet. Other large studies have also found significant links between the consumption of specific MIND Diet foods like leafy greens and brain health.
ABOUT PROGRAM
- What is the Brainsalvation Dietary Program?
-
The Brainsalvation Dietary Program is the first of its kind, designed to help women reduce their risk of developing dementia and Alzheimer's disease using the scientifically proven principles of the MIND diet. With the guidance and support of Brainsalvation’s expert Nutrition Consultants and Health Coaches, program participants will implement dietary modifications focused on achieving optimal brain health. While our primary focus is to reduce the risk of Alzheimer’s disease, feedback and coaching will be tailored to meet individual needs and help users achieve their personal health and wellness goals.
- How much does the Brainsalvation Dietary Program cost?
-
The program enrollment fee is $149 which includes the initial consultation with a Brainsalvation Nutrition Consultant and 3 months of nutrition coaching. This also includes a MIND Diet recipe E-book, and a montly progress report. After the initial 3 month period the monthly rate is only $45.
- What qualifications do Brainsalvation's Nutrition Consultants have?
-
Nutrition Consultants are certified Registered Dietitian Nutritionists with a minimum of 4 years of experience in the field.
- What qualifications do Brainsalvation's Health Coaches have?
-
Health Coaches hold a minimum of a bachelor's degree in nutrition or 2 health related degrees plus 1 nutrition related coaching certification. All coaches have passed Pegara's rigorous selection process.
- How do I get started?
-
Please go to the top of the Brainsalvation diet page, and click "get started".
- How do I receive my consultation with a dietitian?
-
Consultations are conducted via video conferencing via Skype. If you prefer not to use Skype, we are glad to arrange a phone call instead.
- How do I receive advice from a Health Coach?
-
You can send meal photos as well as written messages to your Health Coach whenever you like. Coaches will respond to your texts at least 5 days each week.
Additional Resources
- Can I switch my Health Coach?
-
Yes, you can! If you would like to switch your Health Coach for any reason, please send an email request to<EMAIL_ADDRESS>We would also appreciate any feedback you might have regarding why you would like a different coach.
- Please tell me how to unsubscribe.
-
Please send an email to<EMAIL_ADDRESS>- When is the starting date of subscription? (the date of application, the date of appointment with healthcoach or the date of consultation with NC)
-
You can begin the 90-day program anytime you like. Within 24 hours of enrollment, you will 1.) receive email with instructions on how to complete a 3-day photographic food record and 2.) will be connected with a nutrition consultant. Your consultant will schedule a phone or Skype nutrition consultation with you at this time. We offer great flexibility with the scheduling of the consultation, as we know how busy our clients are. After your 3-day food record has been received, and you complete the phone or skype nutrition consultantion, the 90 day coaching program offically begins! Within one day of your nutrition consultation, you should receive a greeting text message from your health coach. You will communicate via text with your coach on a daily basis. Your coach will respond to you at least 5 days each week. The number of responses you receive each of those 5 days will vary based on a variety of factors.


Internal CSS
The internal style sheet is used to add a unique style for a single document. It is defined in <head> section of the HTML page inside the <style> tag.
Example:
The internal style sheet is used to add a unique style for a single document. It is defined in <head> section of the HTML page inside the <style> tag.
Example:


unique-selector
Given a DOM node, return a unique CSS selector matching only that element. This is particularly useful when tracking in custom variables in analytics:
document.addEventListener('click', function(event) {
var selector = unique(event.target);
_gaq.push(['_trackEvent', 'Engagement', 'Click', selector]);
}, false);
Installation
Options
e.g.1 DomElement =
import unique from 'unique-selector';
// Optional Options
options = {
// Array of selector types based on which the unique selector will generate
selectorTypes : [ 'ID', 'Class', 'Tag', 'NthChild' ]
}
unique( DomElement, options ); // #test
e.g.2 DomElement =
import unique from 'unique-selector';
// Optional Options
options = {
// Array of selector types based on which the unique selector will be generate
selectorTypes : [ 'Attributes' ]
}
unique( DomElement, options ); // [test="2"]
e.g.3 DomElement =

import unique from 'unique-selector';
// Optional Options
options = {
// Regular expression of ID and class names to ignore
excludeRegex : RegExp( 'xyz|abc' )
}
unique( DomElement, options ); // .test
Tests
$ npm run test
Contributing
Feel free to open issues, make suggestions or send PRs. This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code.
Contact
Avraam Mavridis : @avraamakis
Eric Clemmons : @ericclemmons
Releases
v0.1.0
- Big refactor/rewrite using es6
- More test + Change the test script
- Script to compile es6 to es5 using babel
- Ability to pass options for the selectorTypes based on which the unique selector will be generated, see the options
v0.0.4
- Handle extra whitespace in
className
(#9)
- Handle extra whitespace in
v0.0.3
- Add support for various
<form>
elements (#2)
- Add support for various
v0.0.2
- Fix bug with
nth-child
calculation
- Fix bug with
v0.0.1
- Initial release
License
MIT
Overview
All of the model building classes are structured similarly to ml.js models. Each model has three shared components regardless if it's a regression, classification or time series model.
This library was built to quickly implement Tensorflow and Keras models in JavaScript.
Configuration
All of the model configuration is managed during creating an instance of the class. All models have fit
and compile
properties that are assigned to the instance settings
property.
You can also pass the Tensorflow reference to be used, as a configuration property if you want to use the compiled C++ or GPU via CUDA versions on the tf
property;
this.settings.compile
is passed totensorflow.model.compile
and contains the configuration for optimizers (read more
).this.settings.fit
is passed totensorflow.model.fit
and contains the configuration for model fitting (read more
)
Model constructor example
import { MultipleLinearRegression, } from 'tensorscript';
import tf from '@tensorflow/tfjs-node-gpu';
const MLR = new MultipleLinearRegression({
fit:{
epochs:500,
batchSize:5,
},
compile:{
loss: 'meanSquaredError',
optimizer: 'adam',
},
},{
tf,
});
Each constructor takes two parameters settings
and properties
. As in the example above, settings are used to configure tensorflow objects and properties are used to configure tensorscript (like which version of tensorflow to use).
Training
All tensorflow models train asynchronously, therefore all tensorscript model train functions are all asynchronous. Always pass dependent and independent variables as matrix inputs.
Time series models can be trained with only one input matrix.
Training example
import { MultipleLinearRegression, } from 'tensorscript';
async function main(){
const MLR = new MultipleLinearRegression();
const x = [ [1], [2] ];
const y = [ [3], [4] ];
await MLR.train(x,y);
}
Predicting
All prediction inputs must be the same shape as training inputs. TensorScript provides an asynchronous predict method that converts tensor values to javascript objects. If you want the tensor from tensorflow, use the calculate method instead.
Predicting example
import { MultipleLinearRegression, } from 'tensorscript';
async function main(){
const MLR = new MultipleLinearRegression();
const x = [ [1], [2] ];
const y = [ [3], [4] ];
await MLR.train(x,y);
await MLR.predict([[3]]) //=> [[5]];
const tensorPrediction = await MLR.calculate([[3]]) //=> [[5]];
tensorPrediction.data()
.then(tensors=> /*Typed Array*/)
}
Examples
For more examples on how to do feature scaling and more, check out the Examples folder.
Frequently Asked Questions
Scaling
- How do I scale and descale my inputs?
- The library is only for model create, data engineering and modeling is typically handled elsewhere
- Check out the regression examples (hint: use ModelScript).
- How do I cross validate, test/train split, K-fold, etc?
- (See above) The library is only for model create, data engineering and modeling is typically handled elsewhere
- Check out the code examples (hint: use ModelScript).
Frequently Asked Questions
Scaling
- How do I scale and descale my inputs?
- The library is only for model create, data engineering and modeling is typically handled elsewhere
- Check out the regression examples (hint: use ModelScript).
- How do I cross validate, test/train split, K-fold, etc?
- (See above) The library is only for model create, data engineering and modeling is typically handled elsewhere
- Check out the code examples (hint: use ModelScript).


---
layout: post
title: 2/6
date: '2013-02-06T23:37:00.001-07:00'
author: Jacob .
tags:
modified_time: '2013-02-06T23:37:50.042-07:00'
blogger_id: tag:blogger.com,1999:blog-6400085873105072120.post-793938273166907516
blogger_orig_url: http://www.svlark.com/2013/02/audrey-did-great-with-lesson-63-today.html
---
Audrey did great with lesson 63 today. That is all.
239.7


Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 40
You'll have to fry the schnitzels in batches so keep them warm in a large roasting pan in a low oven as they're cooked.
Place the flour in a large bowl and season generously with salt and freshly ground black pepper.
Place the breadcrumbs in a large flattish bowl and mix in the chopped thyme and sage.
Dredge each pork escalope lightly in the flour on both sides, shaking off any excess, then submerge in the beaten egg and press into the breadcrumb mixture, coating on both sides.
Heat a little of the the oil in a large frying pan over a medium heat and fry the schnitzels in batches for 2-3 minutes on each side, or until golden-brown and completely cooked through (there should be no trace of pink in the middle). Remove the schnitzels from the pan and set aside to drain on kitchen paper. Cook the remaining escalopes, adding more oil as necessary.
For the sauce, melt the butter in a large saucepan, then add the flour and cook for 2-3 minutes. Slowly add the milk to the pan, whisking continually, until you have a thick white sauce. Season with salt then add the parsley and cook for a further five minutes.
Serve the schnitzel with a spoonful of the sauce and vegetables of your choice.


Home > CSS Questions > Explain the concept of Tweening?
Tweening is the process in which we create intermediate frames between two images to get the appearance of the first image which develops into the second image.
It is mainly used for creating animation.
There are two types of tween's:
p {
animation-duration: 3s;
animation-name: slidein;
}
@keyframes slidein {
from {
margin-left: 100%;
width: 300%;
}
to {
margin-left: 0%;
width: 100%;
}
}


1. Server message updates available randomly between 50ms to 3 seconds.
2. Time delay refers to the lag in a readily-available message from the server in returning to a client's response.
3. A simulation of 20 update messages to be pushed from server to client.


Welcome to SETAphish
Landing page removal
{% if error_msg %}KO! We were unable to remove the landing page
Reason for that: {{ error_msg }}
{% elif landing_page_id %}Landing page {{ landing_page_id }} removed successfully!
{% endif %}{% load static %} {% include "SETA_gophish/base.html" %}
KO! We were unable to remove the landing page
Reason for that: {{ error_msg }}
{% elif landing_page_id %}Landing page {{ landing_page_id }} removed successfully!
{% endif %}


Korean Telephone Conversations Speech and Transcripts were produced by Linguistic Data Consortium (LDC). The corpora are indexed under LDC catalog numbers LDC2003S03 (speech) and LDC2003T08 (transcripts), ISBNs 1-58563-264-3 and 1-58563-263-5, respectively.
The telephone conversations were collected by LDC as part of the CALLFRIEND project in support of the Language Identification (LID) project sponsored by the U.S. Department of Defense. The telephone speech was later transcribed for use in other projects.
The recorded conversations are between native speakers of Korean and last up to 30 minutes. The transcriptions cover 15-18 minutes from each conversation. All speakers were aware that they were being recorded. They were given no guidelines concerning conversation topics.. Once a caller was recruited to participate, he/she was given a free choice of whom to call. Most participants called family members or close friends. All calls originated in either the United States or Canada.
Presented below are three two-minute audio samples and their associated transcripts. The samples are in MS Wave (.wav) format. The data in the full corpus is in NIST Sphere audio format. The transcripts use EUC-KR character encoding. To ensure compatibility, the transcripts are also presented as PDF files as well as raw text.
| Speech | Transcript | Caller Gender | Caller Age | Caller Education | Caller Region |
| ko_4012.wav | ko_4012(PDF|text) | Female | 26 | 16 | Kwangju |
| ko_4361.wav | ko_4361(PDF|text) | Male | 26 | 16 | Seoul |
| ko_5941.wav | ko_5941(PDF|text) | Male | 28 | 20 | Pusan |
Portions © 2003 Trustees of the University of Pennsylvania


Allow Log parser to extract the maximum number of successive failed build retries from the failure log message.
Clear to enforce the configuration value to be always used.
The Regular expression to search for will need to have a integer number in its first match group e.g.
ERROR: (?:NETWORK ERROR|ARTIFACTORY ERROR) RETRY: (\d)
and the log message
ERROR: NETWORK ERROR RETRY: 2
and
ERROR: ARTIFACTORY ERROR RETRY: 1
in the build console output would trigger 2 and 1 maximum reruns.