Vehicle Detection and Tracking
Udacity Self Driving Car Nanodegree Project #5
Knowing where other cars are while driving is obviously extremely important. Modern multi-sensored self driving cars use their sensors largely to make predictions about the cars surrounding them. In this project, we will only focus on one type of sensor: a camera.
Histogram of Oriented Gradients (HOG)
Explain how (and identify where in your code) you extracted HOG features from the training images. Explain how you settled on your final choice of HOG parameters.
(Code for this section can be found in the “vehicle_detection_functions.py” file in lines 15-110, as well as the “Feature Extraction” Section section in the Jupyter notebook)
To extract Historgram of Oriented Gradient features (or HOG), I used the scikit-image function, hog(). The hog() function takes in a number of parameters, most of which I had to tune. Orientation was set to 9, which happens to be sufficient for the resolution of images we were dealing with. A higher bin size would have been better if we dealt with higher resolution images. I also had to specify the pixels per cell, as well as the cells per block. I specified 8 pixels per cell, and 2 cells per block. I selected these for the same reason I selected 9 bins, I felt it was sufficient for the resolution of images in our training data.
The color space used for this project is the YCrCb color space. The YCrCb color channel was chosen since it is a close alternative to RGB, and it gave better performance training my SVM (as well as having fewer false positives in my experiments). I decided to use all color channels of the YCrCb color space to get HOG features. This means that for each of the 3 color channels (Y, Cr, and Cb), I extracted separate HOG features and concatenated the results into a single vector.
The final feature vector included not only the HOG features of the 3 channels, but also the YCrCb color features (with 16 bins), and the spatial features of resized YCrCb images of size 32x32.
Concatenating all 3 of these feature representations results in a very long vector for each data image.
Describe how (and identify where in your code) you trained a classifier using your selected HOG features (and color features if you used them).
(Code for this section can be found in the “Data Prep” and “Linear SVC” Section section in the Jupyter notebook)
Our data was set up to be stored in a single numpy tensor, and then split up with the help of SciKit learns train_test_split function. Before using our image data, I first needed to convert every image in the data set into a new feature representation as described in the previous section. Using our new stretched out 8412 dimensional vector representation of our data, as well as their binary labels, we are almost ready to train a classifier.
Before using our data, it is important to scale the features so that they are around the same scale, so that no specific feature (since we stacked 3 different representations together) will overpower the others. To alleviate this problem, I normalized all of the features using SciKit Learns preprocess tool called StandardScaler(). This ensures that we have zero mean and unit variance for the data in our training set. I fit the StandardScaler() to the training data, and transformed the training and test sets to the statistics of just the training data.
:
As you can see, there is a huge range of values that the combined 3 features can take (since the features themselves come from different types of representations). After scaling, that range is shrunk significantly, as well as now having zero mean and unit variance for cleaner training.
I decided to use a Linear SVM, as suggested in the lessons. This process was made simpler with the help of SciKit learns svm API. Specifically, I used the LinearSVC estimator.
My trained SVM was able to get 0.9901 accuracy on the test split.
Sliding Window Search
Describe how (and identify where in your code) you implemented a sliding window search.
(Code for this section can be found in the “vehicle_detection_functions.py” file in lines 236-313, as well as the “Test on road image” Section section in the Jupyter notebook)
Rather than the conventional version of splitting our image into small windows, and having to do a full hog feature transformation on each window, I followed the advice in the lesson and did a HOG transform for the entire image (specifically, the portion of interest of the image). This cut down on compute time, and significantly sped up my pipeline.
After transforming an entire portion of an image to its hog representation, I then split that section up into chunks where we can just slice into (rather than HOG transform separately) the HOG features. I also do the cheap color and space feature transforms on those chunks. At each chunk, we stack the features up in the same order we stacked them prior to training, as well as preprocessing that new feature vector (with SciKit Learns StandardScalar() ) with the statistics we fit from the training data. We then make a prediction on whether or not there is a car in that chunk, and store that information into a “heat map”. Overlapping windows in which cars are predicted to be found, will have more “heat” associated with that specific area.
Instead of having different window sizes to search with, I scaled the entire image instead of the window. Keeping the window size the same, and adjusting the scale of the entire image, are very similar operations, and should result in similar (not exactly the same) results. That being said, I only used one scaling value (of 1.2) in my entire pipeline, which was sufficient to get reasonable results. I also specified a step size of 1 cell at a time for the overlap, which is equivalent to a 0.875 overlap with the window scheme (quite alot, but gave me reasonable results).
As you can see from the example above, there are a few false positives. It also picks up cars from the other side of the freeway (which is not necessarily useful information).
Show some examples of test images to demonstrate how your pipeline is working. How did you optimize the performance of your classifier?
(Code for this section can be found in the “vehicle_detection_functions.py” file in lines 166-222, as well as the “Test on road image” Section section in the Jupyter notebook)
(Examples of test images can be seen in the previous section)
In order to select my classifier, I tried out different combinations of color spaces and HOG parameters. The two parameters I found improved my classifier were the number of HOG channels, as well as the color channel used. I ended up choosing to use all HOG channels, as well as the YCbCr color space (over RGB).
To improve on the reliability of the classifier, I leveraged the temporal structure provided by video (not present in single frames). By leveraging temporal information, we can aggregate predictions over a series of frames, and get a stronger classifier with less false positives.
To do so, I created a class to keep track of the heatmaps. This class is a very basic class which just holds a specified length (the length of time to recall) numpy array for a series of heatmaps. It also has functionality to aggregate the heat over N frames. Through trial and error, I found 20 frames of recall was sufficient enough to get me reasonable results.
Video Implementation
Provide a link to your final video output. Your pipeline should perform reasonably well on the entire project video (somewhat wobbly or unstable bounding boxes are ok as long as you are identifying the vehicles most of the time with minimal false positives.)
Describe how (and identify where in your code) you implemented some kind of filter for false positives and some method for combining overlapping bounding boxes.
(Code for this section can be found in the “vehicle_detection_functions.py” file in lines 178-198, as well as the “Test on road image” Section section in the Jupyter notebook)
(Much of this question is addressed in the 2nd part of the Sliding Window Search section of this writeup, but I will continue the discussion here)
With a large number of frames to aggregate, we are now more flexible with the heat threshold value we tune. I selected a very high heat threshold, 50 (through trial and error), since we had 20 frames of video to filter from. This significantly reduced the number of false positives, as well as reducing the number of cars detected on the other side of the freeway, since those cars are traveling in the opposite direction, they quickly leave the area of interest and are thus not providing consistent enough “heat” over the 20 frame period.
Here is the basic class used to keep track of the 20 heatmaps:
class Car_Heatmap():
def __init__(self, heatmap_frame, frames_to_recall):
# Heatmap tensor from past N frames
self.n_heatmaps = np.zeros((frames_to_recall,
heatmap_frame.shape[0],
heatmap_frame.shape[1]))
# Heatmap combined from past N frames
self.heatmap = np.sum(self.n_heatmaps, axis=0)
def update_heatmap(self):
self.heatmap = np.sum(self.n_heatmaps, axis=0)
Discussion
Briefly discuss any problems / issues you faced in your implementation of this project. Where will your pipeline likely fail? What could you do to make it more robust?
At first, I struggled alot with the false positives popping up (briefly) at random times. So I decided to create the mentioned Car_Heatmap class to solve the problem.
My current implementation doesn’t pick up on cars close to the horizon, which might provide useful information. One way to fix this problem would be to add more scaled searches (equivalent to adding more windows of varying sizes), in order to pick up on the visually smaller cars in a camera view.
Some problems might occur when the car is moving at much higher speeds. If the driver is moving very fast compared to others on the road, the 20 frame period I hand selected may be too much time to aggregate results. That is, The “heat” of cars might not be sufficient enough to pass the high threshold (purposefully selected to be high due to the large number of frames to recall). Likewise, if other cars are moving very fast, the same problem might occur (similar to the cars on the other side of the lane). One way to solve this problem would be to further tune the number of frames to recall as well as the heat threshold.


Black Pool Ferris wheel from Barrow in Furness¶
back to Examples
In this example we try to model an observation done by Ranty Flat Earth
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | from refraction_render.renderers import Scene,Renderer_35mm
from refraction_render.calcs import CurveCalc,FlatCalc
from refraction_render.misc import mi_to_m,ft_to_m
from pyproj import Geod
import numpy as np
import matplotlib.pyplot as plt
import os
def smooth_f(x,a=1):
return np.abs((x+a*np.logaddexp(x/a,-x/a))/2.0)
def T_prof(h):
e=np.nan_to_num(np.exp(-smooth_f(h/4)**0.5))
return -0.3*e
# Temperature data at time of observation
calc = CurveCalc(T0=16,P0=102000,T_prof=T_prof)
#plotting temperature profile
h = np.linspace(0,50)
plt.plot(calc.atm_model.T(h),h)
plt.xlabel("Temperature (Celsius)")
plt.ylabel("Height (meters)")
plt.savefig("T_prof.png",bbox_inches="tight",dpi=500)
plt.clf()
plt.plot(calc.atm_model.rho(h),h)
plt.xlabel("Density (kg/m$^3$)")
plt.ylabel("Height (meters)")
plt.savefig("rho_prof.png",bbox_inches="tight",dpi=500)
plt.clf()
h_obs, lat_obs, lon_obs = ft_to_m(10) ,54.045708, -3.201814
lat_fw, lon_fw = 53.810927, -3.057650
bg_color = np.array([0,0,0],dtype=np.uint8)
sf_color = np.array([20, 5, 55],dtype=np.uint8)
renderer = Renderer_35mm(calc,h_obs,lat_obs,lon_obs,(lat_fw,lon_fw),
mi_to_m(30),vert_res=1080,focal_length=2000)
s = Scene()
s.add_image("ferriswheel_lights.png",(ft_to_m(33),lat_fw,lon_fw),dimensions=(-1,ft_to_m(108)))
renderer.render_scene(s,"ferris_wheel.png".format(h_obs),
background_color=bg_color,surface_color=sf_color)
|
Just like in the example: Laser Tests Over Cool Lake, The model for the atmosphere uses this temperature profile which has cool air near the water’s surface. This is because when there is no direct sun on the surface of the water, the air temperature near the surface is primarily driven by the temperature of the water. If the water temperature is lower than the air temperature the air near the surface will be cooler than the air above. For this model we use the following temperature profile:
That can be used to calculate the air density as a function of height
This model leads to the following result for a render of the Ferris wheel:
Compare this to a still image grabbed from the video of the observation.
Notice there that there is a bit of compression in the image. This is because the temperature gradient at the low level for the atmosphere. The conclusion from this simulation is that the conditions for Ranty’s observations are not impossible on a globe and therefore the observation is not impossible on the globe.
back to Examples


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I am front end web developer. I pride myself on being reliable and professional. My professional experience and a passion for web development help me to be a valuable team member. Some technologies that I have experience with: Express, Angular, Node, and Postgres. I am looking for new ways I can readily utilize my combination of previous experience, educational pursuits and strategic thinking abilities. I love learning new things and plan on continuing to learn and grow throughout my career. I have a unique skill set that can contribute to a team environment. I would like to help a quality organization reach their goals.
BS New Media
Oregon State University, Corvallis Oregon


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CaptnClumsy respects your privacy. We will not use your E-mail or other personal information apart from displaying it to you on this web site. This web site is for fun not profit.
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Any questions on this policy of the LuckyLister web site please contact<EMAIL_ADDRESS>The source code for LuckyLister is available under the open source MIT license.
You can access the code, contribute to it, review the open issues and raise new ones at:
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All images and names owned and trademarked by Nintendo, Niantic, The Pokémon Company, and GAMEFREAK are property of their respective owners.


Item Quantity Price
{{item.itemDescription}} {{item.promoCodeText ? item.promoCodeText : item.itemDescription}}
${{item.unitListPrice}}
Save ${{(item.unitListPrice - item.unitAmount).toFixed(2)}}
Do you have a Promotion Code?
1 SHIPPING ADDRESS Change
2 SHIPPING METHOD Change
3 PAYMENT METHODS Change
4 COMPLETE PURCHASE
YOUR TOTAL IS: ${{productsInCart.orderHeader.grandTotal.toFixed(2)}}
Place order below to complete your purchase


Service Controller: sc.exe config <service_name> binpath= <command>
Metasploit: exploit/windows/local/service_permissions
PowerSploit: Invoke-ServiceAbuse -Name <service_name> -Command <command>


Before Change
logger.info("n_features after fit_transform: %d" % int(self.n_features))
self.penalty = penalty
super(TensorflowMultitaskIRVClassifier, self).__init__(**kwargs)
self.build_graph()
def build_graph(self):
Constructs the graph architecture of IRV as described in:
After Change
self.K = K
self.n_features = 2 * self.K * self.n_tasks
self.penalty = penalty
mol_features = Input(shape=(self.n_features,))
predictions = IRVLayer(self.n_tasks, self.K, self.penalty)(mol_features)
logits = []
outputs = []
for task in range(self.n_tasks):
task_output = Slice(task, 1)(predictions)
sigmoid = Activation(tf.sigmoid)(task_output)
logits.append(task_output)
outputs.append(sigmoid)
outputs = layers.Stack(axis=1)(outputs)
outputs2 = Lambda(lambda x: 1 - x)(outputs)


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--- layout: post title: Extending AJS.params in Atlassian Confluence date: 2014-02-22 23:05:37.000000000 +00:00 type: post published: true status: publish categories: - Programming tags: - add-ons - atlassian - confluence - java - javascript meta: _edit_last: '1' _syntaxhighlighter_encoded: '1' tmac_last_id: '618797318553403392' _pingme: '1' _encloseme: '1' _wpas_skip_11278197: '1' _wpcom_is_markdown: '1' author: David Simpson excerpt_separator: ---
Have you seen all the good stuff there is in AJS.params in Atlassian Confluence?
OK, it's not that exciting, but if you're a Confluence add-on developer it can be a treasure trove for client-side DOM manipulation. As an add-on developer, you can add to AJS.params very easily. All you need is to create a Web Panel module which adds a meta tag.
Here's an example:
FulfillmentWebPanel.java:
package com.appfusions.confluence.plugins.fulfillment.webpanels;
import com.atlassian.plugin.web.model.WebPanel;
import java.io.IOException;
import java.io.Writer;
import java.util.Map;
public class FulfillmentWebPanel implements WebPanel
{
public String getHtml(Map<String, Object> stringObjectMap)
{
// AJS.params.workForAppFusions
return "<meta name=\"ajs-work-for-app-fusions\" content=\"Looking for something more fulfilling? Try AppFusions http://www.appfusions.com/display/Dashboard/Make+it+Happen+Team\" />";
}
@Override
public void writeHtml(Writer writer, Map<String, Object> context) throws IOException
{
writer.write(getHtml(context));
}
}
atlassian-plugin.xml:
<web-panel name="Fulfillment Web Panel"
key="fulfillment-web-panel"
location="atl.header"
weight="1000"
class="com.appfusions.confluence.plugins.fulfillment.webpanels.FulfillmentWebPanel">
<description key="fulfillment-web-panel.description">Looking for something more fulfilling?</description>
</web-panel>
The example WebPanel added in the location atl.header simply adds a single meta tag. That's all.
A meta tag with a name attribute of ajs-work-for-app-fusions will populate AJS.params.workForAppFusions. A "dash" separated lower-case name attribute with the prefix "ajs-" leads to a camelCase variable within AJS.params.
Now in your browser console, type:
AJS.params.workForAppFusions
Here's what you get:
If nothing else, it's a simple way of injecting Easter Eggs into your add-ons, which I guess could be kinda cute.
In addition, if you've read this far and you really are looking for something more fulfilling, consider working for AppFusions.
Update:
Alternatively and more easily, use ConfluenceWebResourceManager
in your code (hat tip @alainmoran):
confluenceWebResourceManager.putMetadata("remote-user", user != null ? user.getName() : "");
You'll need to inject ConfluenceWebResourceManager
first.


RT @baruco: Ruby isn't perfect, do you want to know why? @itchyankles will tell you! A Dangerous Game: Safety in Ruby http://buff.ly/1sbzeCE
10/13/2014, 11:48:37 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
RT @baruco: Ruby isn't perfect, do you want to know why? @itchyankles will tell you! A Dangerous Game: Safety in Ruby http://buff.ly/1sbzeCE
10/13/2014, 11:48:37 PM
Favs: 0
Retweets: 0
link


AffinityNow
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This project was made during the 2020 pandemic
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Assign values to variables and add them together
var x = 5; // assign the value 5 to x
var y = 2; // assign the value 2 to y
var z = x + y; // assign the value 7 to z (x + y)
JavaScript Arithmetic Operators
| Operator | Description |
|---|---|
| + | Addition |
| - | Subtraction |
| * | Multiplication |
| ** | Exponentiation (ES2016) |
| % | Modulus (Remainder) |
| ++ | Increment |
| -- | Decrement |
Operators and Operands
The numbers (in an arithmetic operation) are called operands. The operation (to be performed between the two operands) is defined by an operator.
| Operand | Operator | Operand |
|---|---|---|
| 100 | + | 50 |
Multiplying
The multiplication operator (*) multiplies numbers.
var x = 5;
var y = 2;
var z = x * y;
Remainder
The modulus operator (%) returns the division remainder
var x = 5;
var y = 2;
var z = x % y;
Incrementing
The increment operator (++) increments numbers.
var x = 5;
x++;
var z = x;
Decrementing
The decrement operator (--) decrements numbers.
var x = 5;
x--;
var z = x;
Exponentiation
The exponentiation operator (**) raises the first operand to the power of the second operand.
var x = 5;
var z = x ** 2; // result is 25


class TMail::MhMailbox
The class to handle MH mailboxes.
new(dirname)
-> TMail::MhMailbox
dirname: String
creates new MhMailbox object. DIRNAME must be valid MH mailbox directory.
each_port {|port| .... }
each {|port| .... }
port: TMail::FilePort
iterates for each mail in the mailbox, in assendant order (older mail first).
reverse_each_port {|port| .... }
reverse_each {|port| .... }
port: TMail::FilePort
iterates for each mail in the mailbox, in descendant order (newer mail first).
last_atime
-> Time
last_atime=(time)
time: Time
The time which last each_new_port/each_port/reverse_each_port is called.
each_new_port(time = last_atime()) {|port| .... }
time: Time
port: TMail::FilePort
iterates for each mails in mailbox, which are newer than TIME.
new_port
-> TMail::FilePort
creates a new file in the mailbox and returns its port.
close
does nothing.
class TMail::UNIXMbox
The class to handle UNIX mbox. Current implementation creates temporary MH mbox.
new(filename)
-> TMail::UNIXMbox
filename: String
creates new TMail::UNIMbox object. FILENAME must be valid UNIX mbox file name.
each_port {|port| .... }
each {|port| .... }
port: TMail::FilePort
iterates for each mail in the mailbox, in assendant order (older mail first).
reverse_each_port {|port| ... }
reverse_each {|port| ... }
port: TMail::FilePort
iterates for each mail in the mailbox, in descendant order (newer mail first).
each_new_port(time = @last_loaded_time) {|port| .... }
time: Time
port: TMail::FilePort
iterates for each mails in mailbox, which are newer than TIME. @last_loaded_time is updated when each_new_port/each_port is called.
new_port
-> TMail::FilePort
creates a new file in the mailbox and returns its port.
close
forces an UNIXMbox to write back mails to real mbox file. Once this method is called, any method calls causes to raise IOError exception.
class TMail::Maildir
The class to handle "maildir" mailbox.
new(dirname)
-> TMail::Maildir
dirname: String
creates new TMail::Maildir object. DIRNAME must be valid maildir.
each_port {|port| .... }
each {|port| .... }
port: TMail::FilePort
iterates for each mail in the mailbox, in assendant order (older mail first).
reverse_each_port {|port| .... }
reverse_each {|port| .... }
port: TMail::FilePort
iterates for each mail in the mailbox, in descendant order (newer mail first).
each_new_port {|port| .... }
port: TMail::FilePort
iterates for each mails in MAILDIR/new.
new_port
-> TMail::FilePort
creates a new file in the mailbox and returns its port.


| my navigation bar | my navigation bar | my navigation bar | my navigation bar
Table Fixed Header
A title
A big title
Comment
Sub1
Sub2
Sub3
Val1
Val2
Val3
Val4
Val5
Bigval
Some1
Some1
Some1
This is awesome
Some2
Some2
Some2


| 1 sec | 5 sec | |
|---|---|---|
| Memory (MB Free) | ||
| Memory (MB Avail) | ||
| CPU (% User) | ||
| CPU (% System) | ||
| CPU (% Idle) | ||
| Network (Kbps) | ||
| CPU Temp (°C) |
Background Service (Automatic Restart)
Romi Status
| Value | |
|---|---|
| Romi Service Version | --- |
| Firmware Compatible | --- |
| Battery Voltage | --- |
Web Service Update
To perform an offline update of the Romi webservice, obtain an appropriate version from the GitHub release page, and upload the .tgz file here.
External IO Configuration
Each of the 5 external pins can be configured to perform one of three functions: DIO, Analog In or PWM (EXT 0 can only be set to DIO or PWM).
After saving the IO configuration, the Robot Port section will update with the appropriate channels to use in robot code.
| Romi Pin | EXT 0 | EXT 1 | EXT 2 | EXT 3 | EXT 4 |
| Setting | |||||
| Robot Port | VAL | VAL | VAL | VAL | VAL |
IMU Calibration
Most gyros will have some sort of zero offset. In order to get more accurate rate-of-turn readings, the gyro can be calibrated to calculate an appropriate zero offset.
To calibrate the gyro, place the Romi on a flat surface and click the "Calibrate Gyro" button. While the calibration is running, please do not touch the Romi.
Current Gyro Offsets
Firmware
To start firmware imaging, first connect the Romi's micro USB-B port to one of the Pi's USB-A ports. A USB data cable is required (many phone charging cables are power only). Connecting this cable can cause the Romi or Pi to reset, so it's recommended this be done when the Romi is powered off.
Do not cut power to the Romi while firmware update is in progress!


PAT
Pat doesn't want to show his face so this is the ony picture I have of him. Sorry.
THE STORY
Well, the way they make shows is, they make one show. That show's called a pilot. Then they show that show to the people who make shows, and on the strength of that one show they decide if they're going to make more shows. Some pilots get picked and become television programs. Some don't, become nothing. She starred in one of the ones that became nothing.
THE COOKIES
Do you see any Teletubbies in here? Do you see a slender plastic tag clipped to my shirt with my name printed on it? Do you see a little Asian child with a blank expression on his face sitting outside on a mechanical helicopter that shakes when you put quarters in it? No? Well, that's what you see at a toy store. And you must think you're in a toy store, because you're here shopping for an infant named Jeb.


jumbo ... yey!
Simple hero unit, a simple jumbotron-style component for calling extra attention to featured content or information like the following:
Docs
This is basically a setup of Bootstrap v4.0.0-alpha.5 with source scss files and
slightly modified grunt config. (Grundfile.js
) - please refer to the
Boostrap v4 alpha docs for almost all further information.
Icons
Choose one* from two included icon fonts:
- entypo+ by Daniel Bruce (generated by fontello.com open source project).
... - glyphicons pro by Jan Kovarik.
...
Icon-overviews are also included locally here overview entypo and here overview glyphicons.
To use: comment/uncomment in scss/custom.scss
this @import "custom/glyphiconspro";
or this @import "custom/entypo";
Boilerplates
empty.html
forms.html
register.html
login.html
question.html
(add own)
Raum für Notizen:
CARDS
Card Title
Card Text - Some quick example text to build on the card title and make up the bulk of the card's content.
Card Title
Card Text - Some quick example text to build on the card title and make up the bulk of the card's content.
Card Title
Card Text - Some quick example text to build on the card title and make up the bulk of the card's content.
CARD GROUP
Card Title
Card Text - Some quick example text to build on the card title and make up the bulk of the card's content.
Card Title
Card Text - Some quick example text to build on the card title and make up the bulk of the card's content. Card Text - Some quick example text to build on the card title and make up the bulk of the card's content. Card Text - Some quick example text to build on the card title and make up the bulk of the card's content.
Card Title
Card Text - Some quick example text to build on the card title and make up the bulk of the card's content.


Alternian Solar Sweeps
Earth Human Years
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Made by MiffBased on Homestuck by Andrew Hussie


Notes from Lesson 1
The Web
HTML is Hyper Markup Language. It is a document that contains all sort of content
but is the basis for everything on the web.
Your browser makes request that servers reply to with content. HTTP is the protocol
that the browser uses to speak to servers.
HTML is made up of text content like what I am writing right now. It is also made up
of links to content and markup which is the way a site looks.
HTML
HTML markup is made up of tags. Which are made up of < Name > Contents Then
closing with < /name >
The B tag makes anything inside of the tag BOLD
The EM tag means emphasis aka ITALICS.
HTML Attributes
Anchor tag is a link tag that is GREAT for checking the NEWS
The Image tag is how you would link image content to the page!! Image tags are VOID tags so they
do not need a closing tag.
Whitespace - The br tag allows for a line break. It is also a void tag that does not need a closing tag.
In comparsion a P tag (paragrah) is not a void tag and requires a closing tag.
The br tag in considered an inline tage which just breaks the line in comparsion to
the P tag which is a block tag that has an invisible box.
Inline elements include B em br and even img tags
Span is an inline element that contains it content. It allows you to add style to the content through CSS.
Div is a block element that contains its content. The div element will create a separate line.
HTML Structure
An HTML document includes a head (meta and java etc.) with a title which would
be shown in the browser tab. Then it has the body content which would contain
content like the kind I am making right now.


{% extends 'base.html' %}
{% load static %}
{% block header %}
You have successfully revoked access of user {{locperm.user}} with email address {{locperm.user.email}} . The user cannot sign up for Movie Nights anymore and will not receive invitations for future Movie Nights. You can change the user's access rights anytime here. Back to Home


Usage
Using KubeVirt should be fairly natural if you are used to working with Kubernetes.
The primary way of using KubeVirt is by working with the KubeVirt kinds in the Kubernetes API:
$ kubectl create -f vmi.yaml
$ kubectl get vmis
$ kubectl delete vmis testvmi
The following pages describe how to use and discover the API, manage, and access virtual machines.
User Interface
KubeVirt does not come with a UI, it is only extending the Kuebrnetes API with virtualization functionality.


{% extends 'baseLayout.html' %} {% block content %}
View or modify the threshold of each sensor through GrowSmart's sensors page.


To use the Scala Plugin, switch to the Scala perspective (Window/Open Perspective/Other/Scala).
In this perspective, the following wizards are available in your File/New menu:
- Scala Project: creates an empty Scala project with a source directory called "src."
- Scala Package: creates an empty Scala package in the selected project's source directory.
- Scala Class/Object/Trait/Application: creates a top-level Scala class or object in a new source file of the selected package.
Note that the use of these wizards is optional and an entire Scala source
tree can be copied into the "src" directory of a project. If you do this, ensure
that the paths of the source files only include their packages relative from the "src" directory,
and remember to manually "refresh" (F5 or File/Refresh) the "src" directory after the copy is completed.
Building
Any Scala source file that exists in the source directory will be compiled when the project is built.
If "Project/Build Automatically" is enabled, Scala source files will be recompiled whenever they are saved.
Alternatively, otherwise, Scala source files are recompiled when "Project/Build Project" and "Project/Build All" are selected.
By default, class files are saved in the project's "bin" directory. "Project/Clean" will delete all classfiles in
the bin directory, as well as get rid of all project error markers. As in the Java development tool, compilation errors
will appear as markers and as rows in the "Problems" view.
Editing
The Scala plugin enhances the editing of Scala source files with compilation information.
This information is continuously updated during editng.
The following editing services are supported:
- Hover Help: when the mouse pointer hovers over an identifier, the Scala editor will display
the type of the identifier as well as any available Scala or Java documentation.
- Hyperlink Navigation: holding down the control/command key and clicking on an identifier will reveal
the identifier's definition in a Scala or Java editor. Goto declaration (usually F5) can be used to go to the identifier's definition from the keyboard.
- Content Assist: holding control/command and typing space will popup a list of identifiers that can be
used in the current editing context. Content assist resolves any identifier in an editor buffer, but does not resolve keywords.
For example, typing control-space after "to" in "list.to" will replace "to" with "toString",
which is the only list method that begins with "to."
Compilation information is also used to implement syntax coloring. The coloring scheme used to highlight a file
can be configured at "Window/Preferences/Scala/Editor."
Running
Given an object that extends "scala.Application", the object can be executed as an application inside Eclipse.
To do this, select the "Run/Run..." menu option, select the "Scala Application" option, enter the name of the
project that includes the executable object in the "Project" field, and type the package-qualified name of the
object in the "Scala application object" field.
Although the Scala application can be executed inside Eclipse's debugger, debugging of a Scala application
is not supported yet (i.e., there is no way to step through code in Scala files). Alternatively, you can
use an Interpreter shell to evaluate Scala expressions that can access Scala code in your project.
Debugging
You can also debug a running Scala application. To do this, select the "Run/Debug..." menu item and choose the
Scala application you want to debug. The following debugging services are supported:
- Breakpoints: double-click the left ruler on the line where you want to stop the program execution. Alternatively,
you can use the "Run/Toggle line breakpoint" menu item. When the program execution will be stopped at a breakpoint,
Eclipse will switch to the Debug perspective wich will allow you to step the execution, view call stack and variables
values.
- Hot Code Replace: while debugging a program you can modify it and the running program will be updated automatically.
If the modifications are too important, Eclipse will notify you. Welcome to powerful debugging!


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| GNG12 |
guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-12 |
−2.18897 |
D0-D7 |
| CACNA1B |
voltage-dependent calcium channel N type alpha-1B |
−1.8346 |
D0-D7 |
| KCNQ3 |
potassium voltage-gated channel KQT-like subfamily member 3 |
−2.96129 |
D0-D7 |
| KCNJ12 |
potassium inwardly-rectifying channel subfamily J member 12 |
3.350697 |
D0-D7 |
| CHRNA3 |
nicotinic acetylcholine receptor alpha-3 |
2.793838 |
D0-D7 |
| CHRNB4 |
nicotinic acetylcholine receptor beta-4 |
4.925251 |
D0-D7 |
| ITPR2 |
inositol 1,4,5-triphosphate receptor type 2 |
−3.69982 |
D0-D7 |
| ITPR3 |
inositol 1,4,5-triphosphate receptor type 3 |
−2.4065 |
D0-D7 |
| ADCY2 |
adenylate cyclase 2 |
−3.4602539 |
D0-D7 |
| ADCY6 |
adenylate cyclase 6 |
1.892735 |
D0-D7 |
| CREB5 |
cyclic AMP-responsive element-binding protein 5 |
2.033579 |
D0-D7 |
| FYN |
tyrosine-protein kinase Fyn |
1.772436 |
D0-D7 |
| CAMK4 |
calcium/calmodulin-dependent protein kinase IV |
1.419104 |
D7-D13 |
| ChAT |
choline O-acetyltransferase |
4.392642 |
D13-D18 |
| CACNA1B |
voltage-dependent calcium channel N type alpha-1B |
1.880481 |
D13-D18 |
| SLC5A7 |
solute carrier family 5 (high affinity choline transporter), member 7 |
4.94003258 |
D13-D18 |
| KCNQ2 |
potassium voltage-gated channel KQT-like subfamily member 2 |
3.013059 |
D13-D18 |
| CHRNA4 |
nicotinic acetylcholine receptor alpha-4 |
2.830329 |
D13-D18 |
| CHRM3 |
muscarinic acetylcholine receptor M3 |
3.97881916 |
D18-D28 |
| ChAT |
choline O-acetyltransferase |
1.799457 |
D18-D28 |
| ACHE |
Acetylcholinesterase |
2.982588 |
D18-D28 |
| CHRM2 |
muscarinic acetylcholine receptor M2 |
3.383752 |
D18-D28 |
| SLC5A7 |
solute carrier family 5 (high affinity choline transporter), member 7 |
2.363264 |
D18-D28 |
| KCNQ3 |
potassium voltage-gated channel KQT-like subfamily member 3 |
3.701006 |
D18-D28 |
| CHRNA3 |
nicotinic acetylcholine receptor alpha-3 |
−2.67044 |
D18-D28 |
| PRKCB |
classical protein kinase C beta type |
1.642454 |
D18-D28 |
| ADCY2 |
adenylate cyclase 2 |
3.240853 |
D18-D28 |


Gateway
BOT
15-Apr-19 08:51 PM
[dscrd-hush] <JWolf (WW Clan leader)> Zmini only take about 300w thats why I picked it well and because the price on ebay was way better than anything
[dscrd-hush] <JWolf (WW Clan leader)> and having the power company lock in my bill for a year helps...:thinkaboutit:
[dscrd-hush] <JWolf (WW Clan leader)> yeah cant do s*** RN
[dscrd-hush] <Inevitability> HUSH - dead
[dscrd-hush] <JWolf (WW Clan leader)> everything is waiting on an airdrop
[dscrd-hush] <ComputerGenie - ChickenPool> Zmini only take about 300w thats why I picked it well
yeah, wattage isn't much more than a TI and at base they are just under 14x the hashrate
[dscrd-hush] <Inevitability> Are you waiting for the coin to come alive?
[dscrd-hush] <ComputerGenie - ChickenPool> @Inevitability What magic did you imagine happened in the last 4 hrs?
[dscrd-hush] <penguin> Ah ok , depending on card on MB specific volting would need to be set - provided the MB + cpu supports it
[dscrd-hush] <Inevitability> @ComputerGenie - ChickenPool I understand that the coin is dead. What about you?
[dscrd-hush] <penguin> why would you say that .. lol
[dscrd-hush] <ComputerGenie - ChickenPool> I understand that your narrow mind has no concept of currency and all you care is if you can play "hot potato" with it
[dscrd-hush] <Inevitability> Because the coin is missing from all exchanges
[dscrd-hush] <JWolf (WW Clan leader)> lmao
[dscrd-hush] <ComputerGenie - ChickenPool> fek CEXs
[dscrd-hush] <penguin> it's hushushushushushush
[dscrd-hush] <JWolf (WW Clan leader)> and they said they are waiting for the airdrop!!!!
[dscrd-hush] <penguin> deadrop *
[dscrd-hush] <ComputerGenie - ChickenPool> @Inevitability CEX dependence is literally the anti-crypto
[dscrd-hush] <ComputerGenie - ChickenPool> Have you never read the Bitcoin whitepaper?
[dscrd-hush] <Inevitability> @ComputerGenie - ChickenPool I don't care about you! You continue to mine this coin, buy it somewhere , But this is a big stupidity.
[dscrd-hush] <ComputerGenie - ChickenPool> peer-to-peer isn't peer-to-bank-to-peer
[dscrd-hush] <ComputerGenie - ChickenPool> But this is a big stupidity.
Yes, yes you are
[dscrd-hush] <JWolf (WW Clan leader)> wow
[dscrd-hush] <Inevitability> Tell me what happens to the coin. Will she live again ? Back up your words.
[dscrd-hush] <ComputerGenie - ChickenPool> It's already alive; if you understood crypto, you would know that
CEXs are meaningless
[dscrd-hush] <Inevitability> @ComputerGenie - ChickenPool I mean you said
[dscrd-hush] <ComputerGenie - ChickenPool> CEXs are the worst thing that's ever happened to crypto (even worse than csw)
[dscrd-hush] <ComputerGenie - ChickenPool> You mean I said what?
[dscrd-hush] <Inevitability> @ComputerGenie - ChickenPool Then why can't I exchange it anywhere?
[dscrd-hush] <ComputerGenie - ChickenPool> You can exchange it with me
peer-to-peer
[dscrd-hush] <Inevitability> Simple question.  All clear.
[dscrd-hush] <Inevitability> Done.....
[dscrd-hush] <ComputerGenie - ChickenPool> I offered to buy yours, if you think it's dead then how do you turn down free$
[dscrd-hush] <ComputerGenie - ChickenPool> goes back to working on his wallet


Next: random, Previous: random-state, Up: Numbers Dictionary
make-random-state
&optional state ⇒ new-state
state—a random state, or nil, or t. The default is nil.
new-state—a random state object.
Creates a fresh object of type random-state suitable for use as the value of *random-state*.
If state is a random state object, the new-state is a copy_5 of that object. If state is nil, the new-state is a copy_5 of the current random state. If state is t, the new-state is a fresh random state object that has been randomly initialized by some means.
(let* ((rs1 (make-random-state nil)) (rs2 (make-random-state t)) (rs3 (make-random-state rs2)) (rs4 nil)) (list (loop for i from 1 to 10 collect (random 100) when (= i 5) do (setq rs4 (make-random-state))) (loop for i from 1 to 10 collect (random 100 rs1)) (loop for i from 1 to 10 collect (random 100 rs2)) (loop for i from 1 to 10 collect (random 100 rs3)) (loop for i from 1 to 10 collect (random 100 rs4)))) ⇒ ((29 25 72 57 55 68 24 35 54 65) (29 25 72 57 55 68 24 35 54 65) (93 85 53 99 58 62 2 23 23 59) (93 85 53 99 58 62 2 23 23 59) (68 24 35 54 65 54 55 50 59 49))
Should signal an error of type type-error if state is not a random state, or nil, or t.
One important use of make-random-state is to allow the same series of pseudo-random numbers to be generated many times within a single program.
Next: random, Previous: random-state, Up: Numbers Dictionary


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h2. Bootstrap heading |
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h3. Bootstrap heading |
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h4. Bootstrap heading |
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h5. Bootstrap heading |
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h6. Bootstrap heading |
h1. Bootstrap heading
h2. Bootstrap heading
h3. Bootstrap heading
h4. Bootstrap heading
h5. Bootstrap heading
h6. Bootstrap heading
You can use the mark tag to highlight text.
This line of text is meant to be treated as deleted text.
This line of text is meant to be treated as no longer accurate.
This line of text is meant to be treated as an addition to the document.
This line of text will render as underlined
This line of text is meant to be treated as fine print.
This line rendered as bold text.
This line rendered as italicized text.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer posuere erat a ante.


So I've gained a pretty solid grasp on the differences between margins, borders and padding. All three of these changes affect an element in a different way, and also how the element relates to others around it. Without further ado....
Let's start with borders. Borders are probably the most simple to understand. These simply surround the element, and can be applied to a single side, such as the top or bottom to form a line break. That's what I did with my footer:
#footer { background-color: #878787; width:954px; height:125px; border-top: 2px dashed #dddddd; margin-left:10%; margin-top:-16px; color: #444444; }
Well then, I suppose the next item up would be margin, and I feel like it relates to the border of an element. I you were to put a border all the way around an element, you could then see how changing that element's margin will affect it. If you were to give it a top-margin: 50px, you would see the distance between that element and the next element it relates to increase by 50 pixels. This works on all sides of the element. You can also set negative values for the margin to pull elements closer together, or even overlap.
Finally, we have padding of elements. I like to think of this as actually "within" the element. You can look at this at work again by applying a border all the way around the element. If you want the content inside of an element to move away from the border, you would apply padding-left, padding-right, padding-top, or padding-bottom to move it right, left, down, or up respectively. The use of padding really allows you to get content centered and display in a manner that you want.
Thanks for trying to see it from my perspective.


1. Which disease is the biggest killer of children under five?
Pneumonia. Described as “the forgotten killer”, pneumonia is the largest infectious cause of death in children worldwide.  The good news is you can act now to stop future deaths. Pneumonia is easily treatable with inexpensive antibiotics.
Source: WHO