Interface for accessing AmazonSQS.
More...
Inherits AmazonWebServiceClient.
List of all members.
Detailed Description
Interface for accessing AmazonSQS.
Amazon SQS
Member Function Documentation
The AddPermission
action adds a permission to a queue for a specific principal. This allows for sharing access to the queue.
When you create a queue, you have full control access rights for the queue. Only you (as owner of the queue) can grant or deny permissions to the queue. For more information about these permissions, see Shared Queues in the Amazon SQS Developer Guide.
- Parameters:
-
| addPermissionRequest | Container for the necessary parameters to execute the AddPermission service method on AmazonSQS. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonSQS indicating either a problem with the data in the request, or a server side issue. |
The ChangeMessageVisibility
action changes the visibility timeout of a specified message in a queue to a new value. The maximum allowed timeout value you can set the value to is 12 hours. This means you can't extend the timeout of a message in an existing queue to more than a total visibility timeout of 12 hours. (For more information visibility timeout, see Visibility Timeout in the Amazon SQS Developer Guide.)
For example, let's say you have a message and its default message visibility timeout is 30 minutes. You could call ChangeMessageVisiblity
with a value of two hours and the effective timeout would be two hours and 30 minutes. When that time comes near you could again extend the time out by calling ChangeMessageVisiblity, but this time the maximum allowed timeout would be 9 hours and 30 minutes.
IMPORTANT: If you attempt to set the VisibilityTimeout to an amount more than the maximum time left, Amazon SQS returns an error. It will not automatically recalculate and increase the timeout to the maximum time remaining.
IMPORTANT: Unlike with a queue, when you change the visibility timeout for a specific message, that timeout value is applied immediately but is not saved in memory for that message. If you don't delete a message after it is received, the visibility timeout for the message the next time it is received reverts to the original timeout value, not the value you set with the ChangeMessageVisibility action.
- Parameters:
-
| changeMessageVisibilityRequest | Container for the necessary parameters to execute the ChangeMessageVisibility service method on AmazonSQS. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonSQS indicating either a problem with the data in the request, or a server side issue. |
The CreateQueue
action creates a new queue, or returns the URL of an existing one. When you request CreateQueue
, you provide a name for the queue. To successfully create a new queue, you must provide a name that is unique within the scope of your own queues. If you provide the name of an existing queue, a new queue isn't created and an error isn't returned. Instead, the request succeeds and the queue URL for the existing queue is returned.
IMPORTANT: If you provide a value for DefaultVisibilityTimeout that is different from the value for the existing queue, you receive an error.
- Parameters:
-
| createQueueRequest | Container for the necessary parameters to execute the CreateQueue service method on AmazonSQS. |
- Returns:
- The response from the CreateQueue service method, as returned by AmazonSQS.
- Exceptions:
-
The DeleteMessage
action unconditionally removes the specified message from the specified queue. Even if the message is locked by another reader due to the visibility timeout setting, it is still deleted from the queue.
- Parameters:
-
| deleteMessageRequest | Container for the necessary parameters to execute the DeleteMessage service method on AmazonSQS. |
- Exceptions:
-
This action unconditionally deletes the queue specified by the queue URL. Use this operation WITH CARE! The queue is deleted even if it is NOT empty.
- Parameters:
-
| deleteQueueRequest | Container for the necessary parameters to execute the DeleteQueue service method on AmazonSQS. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonSQS indicating either a problem with the data in the request, or a server side issue. |
Gets one or all attributes of a queue. Queues currently have two attributes you can get: ApproximateNumberOfMessages
and VisibilityTimeout
.
- Parameters:
-
| getQueueAttributesRequest | Container for the necessary parameters to execute the GetQueueAttributes service method on AmazonSQS. |
- Returns:
- The response from the GetQueueAttributes service method, as returned by AmazonSQS.
- Exceptions:
-
| SQSInvalidAttributeNameException | |
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonSQS indicating either a problem with the data in the request, or a server side issue. |
The ListQueues
action returns a list of your queues.
- Parameters:
-
| listQueuesRequest | Container for the necessary parameters to execute the ListQueues service method on AmazonSQS. |
- Returns:
- The response from the ListQueues service method, as returned by AmazonSQS.
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonSQS indicating either a problem with the data in the request, or a server side issue. |
Retrieves one or more messages from the specified queue, including the message body and message ID of each message. Messages returned by this action stay in the queue until you delete them. However, once a message is returned to a ReceiveMessage
request, it is not returned on subsequent ReceiveMessage
requests for the duration of the VisibilityTimeout
. If you do not specify a VisibilityTimeout
in the request, the overall visibility timeout for the queue is used for the returned messages.
- Parameters:
-
| receiveMessageRequest | Container for the necessary parameters to execute the ReceiveMessage service method on AmazonSQS. |
- Returns:
- The response from the ReceiveMessage service method, as returned by AmazonSQS.
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonSQS indicating either a problem with the data in the request, or a server side issue. |
The RemovePermission
action revokes any permissions in the queue policy that matches the specified Label
parameter. Only the owner of the queue can remove permissions.
- Parameters:
-
| removePermissionRequest | Container for the necessary parameters to execute the RemovePermission service method on AmazonSQS. |
- Exceptions:
-
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonSQS indicating either a problem with the data in the request, or a server side issue. |
The SendMessage
action delivers a message to the specified queue.
- Parameters:
-
| sendMessageRequest | Container for the necessary parameters to execute the SendMessage service method on AmazonSQS. |
- Returns:
- The response from the SendMessage service method, as returned by AmazonSQS.
- Exceptions:
-
Sets an attribute of a queue. Currently, you can set only the VisibilityTimeout
attribute for a queue.
- Parameters:
-
| setQueueAttributesRequest | Container for the necessary parameters to execute the SetQueueAttributes service method on AmazonSQS. |
- Exceptions:
-
| SQSInvalidAttributeNameException | |
| AmazonClientException | If any internal errors are encountered inside the client while attempting to make the request or handle the response. For example if a network connection is not available. |
| AmazonServiceException | If an error response is returned by AmazonSQS indicating either a problem with the data in the request, or a server side issue. |


Techfesia2019
> Official Website
Official Website is yet to be deployed.
> Proposed API Docs
The following releases are available.
| Version | Date Released | Docs Status | API Status |
|---|---|---|---|
| 0.2.0 | 16-06-2019 | Released | Not Started |
| 0.1.1 | 11-06-2019 | Released | Not Started |
| 0.1.0 | 2-06-2019 | Released | Not Started |
> Deployed Swagger API Docs
No release available yet
> Source
Source code is available here: https://github.com/adwait-thattey/Techfesia2019
> Contributors
Names will be added soon.


Thermal Capacity
Path: CARNOT/Basic/Thermal_Models
Purpose
One node model of a
thermal capacity with entering massflow and losses to ambien. This block breaks the direct feedthrough of any incoming temperature vector.
Description
The block breaks the incoming temperature vector and integrates it according
to:
mcap
* dT/dt = UA_loss * (Tamb
- Tnode)
+ mdot * cp
* (Tin- Tnode)
where
Characteristics
Direct Feedthrough
No
Sample time Inherited from driving block
Vectorized No
Parameters and Dialog Box


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Notebooks
Story: The old woman and the hyena (||kabbo's version)
The old woman and the hyena (||kabbo's version)
Title
The old woman and the hyena (||kabbo's version)
Collection
Summary
An old woman who was too weak to walk was left behind by her people when they, to escape starvation, travelled to another part of the country. She was afterwards picked up by a hyena, and carried off to the mountain on its back. But she contrived to kill it, and finding an old pot, she cooked and ate some of its flesh. From this she became so strong that she followed her people and arrived in good condition among them, while they were still starving. This popular story, which might perhaps be more properly put among fables, was related by /a!kúnta, as he heard it from his mother Ttoaken-an (L I.-2. 151-158), and also by //kábbo (L II.-2. 369-382).
Comments
1) p.371v: the old woman's children have taken the good cooking pot with them, leaving her the old one, 2) See also The old woman's song and The old woman and the hyena (/a!kunta's version which he heard from his mother) and Old woman's song (2nd version), 3) This story is found in Book II-2
Contributors
Date
7 August 1871 (started on or about)
Categories
Keywords
geriatricide (in the story of the old woman and hyena) , woman (the old woman and hyena) , hyena (and the old woman) , hyena (killed by the old woman) , old woman (and the hyena)
Story Pages
369-382


The Udacity tutorials described HTML in terms of house plans; html would include things such as a floorplan, the amount of rooms in a home and additionally what those rooms are designated to be e.g, lounge, kitchen, bathroom, etc. CSS then acts as the interior design features e.g, kitchen and bathroom both may have an x type of tiling, and the lounge could be painted in e.g, Half Spanish White or #E6DBC7. This is a pretty good analogy. Otherwise you could described HTML as a soon-to-be-fired ceramic bowl, and CSS as the kind of glaze you give it before firing. The DOM, in basic terms, is the convention which browsers use to interpret given html—I'm not sure if I understand this completely or how to fit this into my analogy.
Boxifying design refers to dividing elements of a webpage's content into a box—headers, paragraphs, images, and hyperlinks are all boxes within the larger box of a webpage.
The box model consists of 4 elements which make up the total area of an individual box. These are: Content — the content of the box itself/the element which the box exists for, Padding — the space which separates the content from the border, Border — the border around the box, and Margin — the space separating the border from other elements/boxes on the webpage.


{% load static %}
Vizard
{% include "util/header.html" %}
{% include "util/nav.html" %}
Perform an analysis
Look at previous analysis
Create reports from existing data
Interact with a chatbot to create a report


Strategy Prototype
–
This was a personal project prototype I made in the spring of 2015 for hex-grid base building, and some unit production & commanding.
- Simple economy using mines to produce gold, and gold to make units and buildings.
- Instanced hex-grid map, using an image as a source (Alpha channel as height, color as tile type)
- Limit buildings to specific tiles (e.g. mines can only be build on gold tiles)
- Playable over LAN using UE4’s replication system
I stopped working on this a while ago when I would’ve needed to take the time to delve into UE4’s AI systems in order to make substantial progress, and at the time I had too much actual coursework to do to deep delve into both the network replication stuff I was figuring out and AI as well. (Since then I have worked with Unreal’s AI systems in Adventures of Rimori)
Screenshots & Clips:


|
Copyright 2013-01-06 Werner Randelshofer | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object javax.imageio.stream.ImageInputStreamImpl javax.imageio.stream.ImageOutputStreamImpl org.monte.media.io.ByteArrayImageOutputStream
public class ByteArrayImageOutputStream
This class implements an image output stream in which the data is
written into a byte array. The buffer automatically grows as data
is written to it.
The data can be retrieved using toByteArray()
, toImageOutputStream()
and toOutputStream()
.
Closing a ByteArrayImageOutputStream
has no effect. The methods in
this class can be called after the stream has been closed without
generating an IOException
.
| Field Summary | |
|---|---|
protected byte[] |
buf
An array of bytes that was provided by the creator of the stream. |
protected int |
count
The index one greater than the last valid character in the input stream buffer. |
| Fields inherited from class javax.imageio.stream.ImageInputStreamImpl |
|---|
bitOffset, byteOrder, flushedPos, streamPos |
| Constructor Summary | |
|---|---|
ByteArrayImageOutputStream()
|
|
ByteArrayImageOutputStream(byte[] buf)
|
|
ByteArrayImageOutputStream(byte[] buf,
java.nio.ByteOrder byteOrder)
|
|
ByteArrayImageOutputStream(byte[] buf,
int offset,
int length,
java.nio.ByteOrder byteOrder)
|
|
ByteArrayImageOutputStream(java.nio.ByteOrder byteOrder)
|
|
ByteArrayImageOutputStream(int initialCapacity)
|
| Method Summary | |
|---|---|
int |
available()
Returns the number of remaining bytes that can be read (or skipped over) from this input stream. |
void |
clear()
Resets the count field of this byte array output
stream to zero, so that all currently accumulated output in the
output stream is discarded. |
void |
close()
Closing a ByteArrayInputStream has no effect. |
byte[] |
getBuffer()
Returns the internally used byte buffer. |
long |
getStreamPosition()
|
long |
length()
|
int |
read()
Reads the next byte of data from this input stream. |
int |
read(byte[] b,
int off,
int len)
Reads up to len bytes of data into an array of bytes
from this input stream. |
void |
seek(long pos)
|
long |
skip(long n)
Skips n bytes of input from this input stream. |
byte[] |
toByteArray()
Creates a newly allocated byte array. |
void |
toImageOutputStream(javax.imageio.stream.ImageOutputStream out)
Writes the contents of the byte array into the specified image output stream. |
void |
toOutputStream(java.io.OutputStream out)
Writes the contents of the byte array into the specified output stream. |
void |
write(byte[] b)
Writes the specified byte array to this output stream. |
void |
write(byte[] b,
int off,
int len)
Writes len bytes from the specified byte array
starting at offset off to this output stream. |
void |
write(int b)
Writes the specified byte to this output stream. |
| Methods inherited from class javax.imageio.stream.ImageOutputStreamImpl |
|---|
flushBits, writeBit, writeBits, writeBoolean, writeByte, writeBytes, writeChar, writeChars, writeChars, writeDouble, writeDoubles, writeFloat, writeFloats, writeInt, writeInts, writeLong, writeLongs, writeShort, writeShorts, writeUTF |
| Methods inherited from class javax.imageio.stream.ImageInputStreamImpl |
|---|
checkClosed, finalize, flush, flushBefore, getBitOffset, getByteOrder, getFlushedPosition, isCached, isCachedFile, isCachedMemory, mark, read, readBit, readBits, readBoolean, readByte, readBytes, readChar, readDouble, readFloat, readFully, readFully, readFully, readFully, readFully, readFully, readFully, readFully, readInt, readLine, readLong, readShort, readUnsignedByte, readUnsignedInt, readUnsignedShort, readUTF, reset, setBitOffset, setByteOrder, skipBytes, skipBytes |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Methods inherited from interface javax.imageio.stream.ImageOutputStream |
|---|
flushBefore |
| Methods inherited from interface javax.imageio.stream.ImageInputStream |
|---|
flush, getBitOffset, getByteOrder, getFlushedPosition, isCached, isCachedFile, isCachedMemory, mark, read, readBit, readBits, readBoolean, readByte, readBytes, readChar, readDouble, readFloat, readFully, readFully, readFully, readFully, readFully, readFully, readFully, readFully, readInt, readLine, readLong, readShort, readUnsignedByte, readUnsignedInt, readUnsignedShort, readUTF, reset, setBitOffset, setByteOrder, skipBytes, skipBytes |
| Field Detail |
|---|
protected byte[] buf
buf[0]
through buf[count-1]
are the
only bytes that can ever be read from the
stream; element buf[streamPos]
is
the next byte to be read.
protected int count
buf
.
It is one greater than the position of
the last byte within buf
that
can ever be read from the input stream buffer.
| Constructor Detail |
|---|
public ByteArrayImageOutputStream()
public ByteArrayImageOutputStream(int initialCapacity)
public ByteArrayImageOutputStream(byte[] buf)
public ByteArrayImageOutputStream(byte[] buf, java.nio.ByteOrder byteOrder)
public ByteArrayImageOutputStream(byte[] buf, int offset, int length, java.nio.ByteOrder byteOrder)
public ByteArrayImageOutputStream(java.nio.ByteOrder byteOrder)
| Method Detail |
|---|
public int read() throws java.io.IOException
int
in the range
0
to 255
. If no byte is available
because the end of the stream has been reached, the value
-1
is returned.
This read
method
cannot block.
read
in interface javax.imageio.stream.ImageInputStream
read
in class javax.imageio.stream.ImageInputStreamImpl
-1
if the end of the
stream has been reached.
java.io.IOException
public int read(byte[] b, int off, int len) throws java.io.IOException
len
bytes of data into an array of bytes
from this input stream.
If streamPos
equals count
,
then -1
is returned to indicate
end of file. Otherwise, the number k
of bytes read is equal to the smaller of
len
and count-streamPos
.
If k
is positive, then bytes
buf[streamPos]
through buf[streamPos+k-1]
are copied into b[off]
through
b[off+k-1]
in the manner performed
by System.arraycopy
. The
value k
is added into streamPos
and k
is returned.
This read
method cannot block.
read
in interface javax.imageio.stream.ImageInputStream
read
in class javax.imageio.stream.ImageInputStreamImpl
b
- the buffer into which the data is read.off
- the start offset in the destination array b
len
- the maximum number of bytes read.
-1
if there is no more data because the end of
the stream has been reached.
java.lang.NullPointerException
- If b
is null
.
java.lang.IndexOutOfBoundsException
- If off
is negative,
len
is negative, or len
is greater than
b.length - off
java.io.IOException
public long skip(long n)
n
bytes of input from this input stream. Fewer
bytes might be skipped if the end of the input stream is reached.
The actual number k
of bytes to be skipped is equal to the smaller
of n
and count-streamPos
.
The value k
is added into streamPos
and k
is returned.
n
- the number of bytes to be skipped.
public int available()
The value returned is count - streamPos
,
which is the number of bytes remaining to be read from the input buffer.
public void close()
close
in interface javax.imageio.stream.ImageInputStream
close
in class javax.imageio.stream.ImageInputStreamImpl
public long getStreamPosition() throws java.io.IOException
getStreamPosition
in interface javax.imageio.stream.ImageInputStream
getStreamPosition
in class javax.imageio.stream.ImageInputStreamImpl
java.io.IOException
public void seek(long pos) throws java.io.IOException
seek
in interface javax.imageio.stream.ImageInputStream
seek
in class javax.imageio.stream.ImageInputStreamImpl
java.io.IOException
public void write(int b) throws java.io.IOException
write
in interface java.io.DataOutput
write
in interface javax.imageio.stream.ImageOutputStream
write
in class javax.imageio.stream.ImageOutputStreamImpl
b
- the byte to be written.
java.io.IOException
public void write(byte[] b) throws java.io.IOException
write
in interface java.io.DataOutput
write
in interface javax.imageio.stream.ImageOutputStream
write
in class javax.imageio.stream.ImageOutputStreamImpl
b
- the data.
java.io.IOException
public void write(byte[] b, int off, int len) throws java.io.IOException
len
bytes from the specified byte array
starting at offset off
to this output stream.
write
in interface java.io.DataOutput
write
in interface javax.imageio.stream.ImageOutputStream
write
in class javax.imageio.stream.ImageOutputStreamImpl
b
- the data.off
- the start offset in the data.len
- the number of bytes to write.
java.io.IOException
public void toOutputStream(java.io.OutputStream out) throws java.io.IOException
out
-
java.io.IOException
public void toImageOutputStream(javax.imageio.stream.ImageOutputStream out) throws java.io.IOException
out
-
java.io.IOException
public byte[] toByteArray()
ByteArrayOutputStream.size()
public byte[] getBuffer()
public long length()
length
in interface javax.imageio.stream.ImageInputStream
length
in class javax.imageio.stream.ImageInputStreamImpl
public void clear()
count
field of this byte array output
stream to zero, so that all currently accumulated output in the
output stream is discarded. The output stream can be used again,
reusing the already allocated buffer space.
ByteArrayInputStream.count
|
Copyright 2013-01-06 Werner Randelshofer | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Lean Mean Web App Machine!
Have fun exploring from here onwards :-)
Feel free to get in touch with me to let me know what you think of the book.
Enjoy,
Dan Miller.
Once you're familiar with the foundation technology, you might like to head on over and have a look at the repository from which these code examples were based. Check out MEAN.JS:


Most people believe that you need hours to get fit and in shape, when in reality, it's just not true. Eating the right foods and spending around an hour a day working out, you can achieve the lifestyle and body that you want.
There is not a magic formula to macros, protiens, and fats. Everyone is different and what your body needs is going to be different than everyone else. Depending on what you're looking for and what your goals and desires are, your formula will be different. Take a look at the guide above to get a general idea and feel free to contact us to further help you get on a formula that works best for you!
Ectomorph: If you’re an ectomorph, you’re naturally thin with skinny limbs and a high tolerance for carbohydrates. Usually, your metabolic rate is fast. A good starting macronutrient ratio for you would be something like 25% protein, 55% carbs and 20% fat.
Mesomorph: Mesomorphs are naturally muscular and athletic. They have a moderate carbohydrate tolerance and a moderate metabolic rate. Mesomorphs can usually start at a 30% protein, 40% carb, 30% fat macronutrient ratio.
Endomorph: If you’re naturally broad and thick, you’re probably an endomorph. Endomorphs have a low carbohydrate tolerance and a slow metabolic rate. If you’re an endomorph, try a ratio of 35% protein, 25% carbs and 40% fat.


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WebAudioModules API
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description
is
Instrument
keywords
name
sdk
Version
thumbnail
vendor
version
website
Methods
has
Audio
Input
has
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Output
has
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Input
has
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Input
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Input
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Input
has
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Output
Properties
description
description
:
string
is
Instrument
is
Instrument
:
boolean
keywords
keywords
:
string
[]
name
name
:
string
sdk
Version
sdk
Version
:
string
thumbnail
thumbnail
:
string
vendor
vendor
:
string
version
version
:
string
website
website
:
string
Methods
has
Audio
Input
has
Audio
Input
:
has
Audio
Output
has
Audio
Output
:
has
Automation
Input
has
Automation
Input
:
has
Automation
Output
has
Automation
Output
:
has
Midi
Input
has
Midi
Input
:
has
Midi
Output
has
Midi
Output
:
has
Mpe
Input
has
Mpe
Input
:
has
Mpe
Output
has
Mpe
Output
:
has
Osc
Input
has
Osc
Input
:
has
Osc
Output
has
Osc
Output
:
has
Sysex
Input
has
Sysex
Input
:
has
Sysex
Output
has
Sysex
Output
:
Exports
Wam
Descriptor
description
is
Instrument
keywords
name
sdk
Version
thumbnail
vendor
version
website
has
Audio
Input
has
Audio
Output
has
Automation
Input
has
Automation
Output
has
Midi
Input
has
Midi
Output
has
Mpe
Input
has
Mpe
Output
has
Osc
Input
has
Osc
Output
has
Sysex
Input
has
Sysex
Output
Generated using
TypeDoc


Before becoming the go-to destination in the Federation, Risa was a desolate and inhospitable planet. With the help of weather control systems Risa was transformed into paradise. Tourism became (and remains) the number one source of activity for the planet’s inhabitants.
Risa became synonymous with pleasure. Whether to relax and swim in the crystal clear waters or partake in the jamaharon rite, creatures from across the quadrants flock to Risa.
Risa maintains a climate described as "the equivalent of a balmy eighty-three degrees Fahrenheit", and that "a warm breeze prevents the heat from becoming oppressive".
Risa is dedicated to pleasure. This is why no weapons are permitted on Risa. Guests on Risa feel secure knowing their safety is a priority.
Though the origin of Jamaharon is unknown, it has become well known far and wide. Those seeking to participate in the rite of Jamaharon must obtain a Horga’hn. Once the Horga’hn is displayed Risan’s will approach the willing participant and ask about their intentions.


CharacterItem |
Model view to display on the screen CharactersListFragment. data class CharacterItem |
CharacterItemMapper |
Helper class to transforms network response to visual model, catching the necessary data. open class CharacterItemMapper : Mapper<BaseResponse<CharacterResponse>, List<CharacterItem>> |


I had thoroughly researched this chemical quite heavily before using it and it still defied everything I expected. I am writing this almost a year to date after having taken it so I apologize for the lack of a timeline. However I have done considerably more research on this chem than most and im here to say its not for junkies and its not for people looking to get high. It is a door-opener it is 'the path', its primary function is mental and emotional rather than visual.
When I took it the first time I expected a larger change in visual perception and was disappointed to see rather little. I lay back and attempt to relax ( caused mad leg cramping) I felt restless, turned on some trance music and laid on the floor.  This was like lsd for in my head! I soon found I had complete control of the experience and could visualize anything almost before I finished the thought. I found I could leap back into dreams long gone and walk around in them! Touch them even! It was as if I had the key to the vast stores of my mind! Truly enlightening experience. Saw things that do not have words or even a true existence. This is a trance drug. A “look inside” chemical. Drugs for your imagination.
I felt a lil fried the day after mostly I think from a lack of sleep (was up till 6am) but had muscle pain from severe leg cramping leading me to believe that it causes mild to moderate CNS damage.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Want a chance to get away from it all? Do you prefer to see nature at your own pace? Respond to the call of the great outdoors with our Backpack Cal tour packages. Whether it's hiking beneath the towering Redwoods, walking the ridges of the beautiful Channel Islands, or testing yourself against the harshest environments nature has to offer, we have a tour for your abilities and interests.
If you're looking to just take in the incredible natural beauty of California at your own pace, try our Big Sur Retreat, a stress-free retreat into one of America's most beautiful forest regions.
Our Backpack Cal Tours offer a range of physical activity, from easy to difficult. Please check the difficulty rating before booking a tour to ensure that your experience is the best possible for your current fitness level. Tours with a rating of Moderate or higher require a signed waiver before booking.
Big Sur is big country. The Big Sur Retreat takes you to the most majestic part of the Pacific Coast and show you secret trails and spectacular scenery. This is one of our most flexible tours, with enough options to satisfy the casual hiker to the hard core experience junkie.
Optional 4 day tour available | Rating: Medium
The chain known as the Channel Islands offer some of the most diverse and unique landscape on the Pacific coast. No motor vehicles are allowed on the islands, which makes this relaxing day trip hiking package the best and most interesting way to visit.
Rating: Easy
Hot stuff? Need more of a challenge? Take this tour to the hottest place in North America: Death Valley. Due to extreme temperatures (120 degrees and higher) in the summer months, this tour is only offered November through April. Are you up to the challenge?
Rating: Difficult
Follow in the steps on John Muir, famous naturalist and founder of the Sierra Club, and walk the same trails he helped blaze in and around Yosemite National Park.
Rating: Difficult
Climb to the sky! The Mt. Whitney Climbers Tour takes you to the top of this 14,000 ft. of mountain in 4 days- our longest and most strenuous backpacking tour. Explore California will set you up with a trail permit, a two night camping pass, and an expert guide.
Rating: Difficult


This code takes two sorted arrays \(A\) and \(C\) and their lengths and creates a sorted array \(C\). Because the two arrays \(A\) and \(B\) are sorted, a sorted array \(C\) can be created in linear time. The algorithm considers each element from both arrays from the leftmost position and compares these elements to determine which element is to be placed next in the output array. Because both arrays are sorted, the creation of the output array can be done in linear time \(O(n)\).
Let \(M(n_1, n_2)\) be the number of comparisons that algorithm Merge does on line 6.
\(M(n_1,n_2) \leq n_1 + n_2 -1 \)
This code takes a unsorted array \(A\) and sorts it in ascending order into the array \(C\). This is done by recursively sorting the left half of the array \(A\) using \(Mergesort\), and recursively sorting the right half of the array using \(Mergesort\). With two sorted subarrays \(A\) and \(B\), the \(Merge\) code can be utilized to create sorted array \(C\). Because \(Mergesort\) is an effective use of a divide and conquer algorithm where each subarray is roughly half the size of its parent, the recursive calls to \(Mergesort\) are made in \(O(log(n))\) time. For each sorted subarray \(A\) and \(B\), the \(Merge\) algorithm is called and from previous analysis, it is known this algorithm takes \(O(n)\) time. Therefore, the \(Mergesort\) algorithm takes a worse case time of \(O(n(log(n)))\). Let's create and solve the recurrence relation to show this!
let \(C(n)\) be the number of comparisons that \(Mergesort\) does.
There are \(C(n_1)\) comparisons for the recursive call on line 11.
There are \(C(n_2)\) comparisons for the recursive call on line 12.
There are \(n_1 + n_2 - 1\) comparisons from the call to \(Merge\) on line 13.
Therefore the recurrence relation is as follows:
\(C(n) \leq C(n_1) + C(n_2) + n_1 + n_2 - 1 \)
\(C(n) \leq C(\frac{n}{2}) + C(\frac{n}{2}) + \frac{n}{2} + \frac{n}{2} - 1 \)
\(C(n) \leq C(\frac{n}{2}) + n - 1 \)
Now solve the recurrence relation.
\(C(n) \leq 2C(\frac{n}{2}) + n - 1 \)
\(2C(\frac{n}{2}) \leq 2(2C(\frac{n}{4}) + \frac{n}{2} - 1) = 2^2C(\frac{n}{4}) + n-2\)
\(2^2C(\frac{n}{4}) \leq 2^2(2C(\frac{n}{8}) + \frac{n}{4} - 1) = 2^3C(\frac{n}{8}) + n-4\)
. . .
\(\frac{n}{2}C(2) \leq \frac{n}{2}(2C(1)+(2-1)) = nC(1) + \frac{n}{2}\)
\(nC(1) = 0 \)
By adding and cancelling these terms, we get
\(C(n) \leq (n-1) + (n-2) + (n-4) + \dots + (n - \frac{n}{2})\)
\(C(n) \leq \displaystyle\sum_{i=0}^{k-1}n - \displaystyle\sum_{i=0}^{k-1} 2^i\)
\(C(n) \leq kn - (n-1)\)
By our assumption that \(n=2^k\) and \(k = \log_2n\)
\(C(n) \leq n\log_2n-n+1\)
Below is a representation of the steps the \(Mergesort\) algorithm takes. This is shown by ordering columns in ascending order. When the randomize button is invoked, a completely random ordering of columns is generated. These random columns can be sorted by pressing the Merge Sort button. This website shows the steps that the merge sort algorithm takes to sort objects. Enjoy viewing the algorithm and thank you for visiting my merge sort website! I have more interesting projects linked from by website.


Energy Disaggregation.
Improving on REDD benchmarks using variational autoencoders.
Introduction
Energy disaggregation (also referred to as non-intrusive load monitoring or NILM) involves taking the aggregate energy signal from a household (for example, data from a smart meter), and inferring which devices/appliances are being used and how much they are using at any given point in time.
Background
The Reference Energy Disaggregation Data Set (REDD) was released in 2011. It contains several weeks of power data for 6 different homes, along with data for individual appliance consumption for each house (typically around 15-20 devices).
In 2014, the Non Intrusive Load Monitoring Toolkit (NILMTK) was released. At the time, state of the art approaches relied on discrete methods, predominantly combinatorial optimization and factorial hidden Markov models.
In 2015, Jack Kelly released Neural NILM, applying neural networks to energy disaggregation, implementing autoencoders, LSTMs and a "rectangles" based approach (which I thought was a really novel and interesting one) on the UK DALE data set (another energy disaggregation data set). Broadly speaking, the autoencoders outperformed LSTMs. While the "rectangles" performance was comparable to autoencoders, it still enforced discreteness on the predictions and so we will ignore it for the purposes of our project.
Since Neural NILM, there have been a number of different deep learning efforts aiming to either improve on Jack Kelly's results, or implement his approaches on other data sets. In this project, I aim to improve on a traditional autoencoder approach by using variational autoencoders.
Why autoencoders don't really work
The general idea of an autoencoder is a neural network that takes some higher dimensional vector, encodes that vector to some lower dimensional space, decodes that encoded representation and tries to recover the same, original, high dimensional vector. In the energy disaggregation setting, instead of recovering the original energy signal, we try to recover an individual appliance signal.
While you usually see it as one network, there are really two parts here: an encoder and a decoder. People usually think about them in the context of compression and decompression, and this intuitively makes sense (i.e. reducing the dimensionality of your data).
The issue is in practice, they don't work particularly well for a lot of problems. The (very) general idea is that autoencoders tend to "cheat", memorizing vectors they are trained on by mapping them to a somewhat arbitrary point in the lower dimensional encoded space before decoding back to the original vector. While the weights will continue to adjust during training to minimize reconstruction error, the encoded representation itself isn't particularly meaningful. The consequences of this are:
- Similar input vectors do not necessarily map to similar encoded vectors
- Autoencoders are data-specific (i.e. they don't generalize well)
So how can we get more meaningful, generalizable latent representations of our input vectors, and will this also improve our reconstruction error?
Variational autoencoders
The simplest way to think about variational autoencoders is that we add Gaussian noise in the latent space to encourage efficient, meaningful representations of our data. With the addition of noise, our encoder is forced to map similar vectors to similar points in the latent space, so that our decoder can still get enough information from that latent representation to reconstruct a close approximation of those vectors.
We can't just add any old noise, however, otherwise our encoder could still map vectors arbitrarily far away so that the noise would have no bearing. In fact, we change the latent representation by encoding mean and standard deviation vectors and then sampling from it:
Furthermore, we add a penalty term to our loss function called the KL-divergence term. Without going into the derivation or math, this penalty term encourages our latent vector to approximate a unit Gaussian.
Now we can let the network decide the best trade-off between reconstruction error and ensuring that the latent representation is information-rich. The details of KL-divergence can be found elsewhere, but I couldn't find a visualization of the loss surface anywhere. So I made one quickly online below:
Above we can see that our KL-divergence term is at a minimum of 0 when the mean is 0 and standard deviation is 1 (i.e. the unit Gaussian). Furthermore, we see that our loss rapidly approaches infinity for small standard deviations.
Preliminary results
Long story short, variational autoencoders encourage more information-rich latent representations that generally outperform tradiational autoencoders. I've been able to use them for energy disaggregation, with improvements on REDD of around 20%:
In the coming weeks I'll be finishing the training on the top 5 appliances for each house to enable both better benchmark comparison, as well as some nicer visuals of disaggregated energy signals. Stay tuned!


Torque 2D – Fish Demo Tutorial - Part 6
6. Come in at a Random Position
6.1
Adding a random Y position to our script
Now that we have our fish swimming back and forth properly, we can add a couple of things to make it a bit more interesting. One of these things is to place our fish in a random Y position when it flips and swims back across the screen after hitting a world limit. This will give the illusion of another fish swimming in, or at least as though our fish was moving vertically and not just horizontally. If you don't still have it open, open your game.cs script file again. Locate your onWorldLimit() function. It should look like this.
function FishClass::onWorldLimit(%this, %mode, %limit) { switch$ (%limit) { case "left": %this.setLinearVelocityX(20); %this.setFlipX(false); case "right": %this.setLinearVelocityX(-20); %this.setFlipX(true); } }
We need to add a call to both the “left” and “right” responses. This call will set the fish's Y position in a random number between the top of the level and just above the ground. Make your function look like this.
function FishClass::onWorldLimit(%this, %mode, %limit) { switch$ (%limit) { case "left": %this.setLinearVelocityX(20); %this.setFlipX(false); %this.setPositionY(getRandom(-35, 25)); case "right": %this.setLinearVelocityX(-20); %this.setFlipX(true); %this.setPositionY(getRandom(-35, 25)); } }
Code Sample 6.1.1
We get a random number between -35 and 25 to set our fish's Y position to. This way the fish's Y position will change when it hits the world limit. It will now swim in at a different position than where it left.
Click the Play Level button and you should see your fish swim out of the level and in at a random position (as shown in Figure 6.1.1). Or, just make sure you followed the steps exactly.
Figure 6.1.1


{%extends "confreg/confadmin_base.html"%} {%block title%}Purge personal data{%endblock%} {%block layoutblock%}
You are about to purge the following data:


libwifi is a fast, simple C shared library with a
permissive license for generating and parsing a wide variety of
802.11 wireless frames on Linux and macOS with a few lines of straight forward code.
It is written with a simple-to-use approach while also exposing features that allow more advanced use, with clean and readable code being a priority.
Other goals of the library include cross-architecture support, clean compilation without warnings and strict error checking.
libwifi exposes functions and structs to make parsing and generating WiFi frames very easy, and examples can be found in the source
examples directory.
When using libwifi, be sure to pass
-lwifi
to the linker, and make sure that the libwifi shared library is installed on the system.
Parsing
The generic flow of a program using libwifi to parse frames is a loop that reads captured packets as raw data, such as with
libpcap from a file or monitor interface, then parse the frame into a common datatype, then parse again to retrieve frame specific data.
static int got_radiotap = 0;
int main(int argc, const char *argv[]) {
pcap_t handle = {0};
char errbuf[PCAP_ERRBUF_SIZE] = {0};
if ((handle = pcap_create(argv[2], errbuf)) == NULL) {
exit(EXIT_FAILURE);
}
if (pcap_activate(handle) != 0) {
pcap_close(handle);
exit(EXIT_FAILURE);
}
int linktype = pcap_datalink(handle);
if (linktype == DLT_IEEE802_11_RADIO) {
got_radiotap = 1;
} else if (linktype == DLT_IEEE802_11) {
got_radiotap = 0;
} else {
pcap_close(handle);
exit(EXIT_FAILURE);
}
pd = pcap_dump_open(handle, PCAP_SAVEFILE);
pcap_loop(handle, -1 /*INFINITY*/, &parse_packet, (unsigned char *) pd);
}
The data from the libpcap loop is then given to
libwifi_get_frame()
which checks for frame validity and type/subtype, and stores the data in a
struct libwifi_frame
.
void parse_packet(unsigned char *args,
const struct pcap_pkthdr *header,
const unsigned char *packet) {
unsigned long data_len = header->caplen;
unsigned char *data = (unsigned char *) packet;
struct libwifi_frame frame = {0};
int ret = libwifi_get_wifi_frame(&frame, data, data_len, got_radiotap);
if (ret != 0) {
printf("[!] Error getting libwifi_frame: %d\n", ret);
return;
}
The
libwifi_frame
struct can then be given to one of the frame parser functions, such as
libwifi_parse_beacon()
. Since the header comment for
libwifi_parse_beacon()
indicates that the parsed data is stored in a
struct libwifi_bss
, we need to initalise one and pass it as a parameter.
We'll use the BSS struct to easily show the SSID and Channel from the sniffed beacon frame.
if (frame.frame_control.type == TYPE_MANAGEMENT &&
frame.frame_control.subtype == SUBTYPE_BEACON) {
struct libwifi_bss bss = {0};
int ret = libwifi_parse_beacon(&bss, &frame);
if (ret != 0) {
printf("Failed to parse beacon: %d\n", ret);
return;
}
printf("SSID: %s, Channel: %d\n", bss.ssid, bss.channel);
}
}
Generation
For frame generation, you only need to provide the required data to one of the frame generation functions. In this example,
libwifi_create_beacon()
.
int main(int argc, char **argv) {
struct libwifi_beacon beacon = {0};
static unsigned char bcast[] = "\xFF\xFF\xFF\xFF\xFF\xFF";
static unsigned char tx[] = "\x00\x20\x91\xAA\xBB\CC";
int ret = libwifi_create_beacon(&beacon, bcast, tx, tx, "wifi-beacon", 11);
if (ret != 0) {
return ret;
}
From here, we can use the dumper function for this frame subtype to write the beacon in raw byte format to a buffer. This can be useful for writing the
generated frame out to a pcap file using
pcap_dump()
or transmitting from a monitor mode interface.
unsigned char *buf = NULL;
size_t buf_sz = libwifi_get_beacon_length(&beacon);
buf = malloc(buf_sz);
if (buf == NULL) {
exit(EXIT_FAILURE);
}
ret = libwifi_dump_beacon(&beacon, buf, buf_sz);
if (ret < 0) {
return ret;
}
// Inject frame bytes or write bytes to file
libwifi_free_beacon(&beacon);
free(buf);
}


{% extends "portal/base.html" %} {% block content %}
Number of searches and average time
| # | Number of searches | Average search time | Average search time (high priority) |
|---|---|---|---|
| Last 24 hours | {{ context.searches_last_24_hours }} | {{ context.average_last_24_hours }} | {{ context.average_last_24_hours_high_priority }} |
| Last 7 days | {{ context.searches_last_week }} | {{ context.average_last_week }} | {{ context.average_last_week_high_priority }} |
Current month
Last month
Number of searches per month


--- layout: newsstory title: TWO ON THE TROT match: Yeltz 3 Atherstone 1 ---
Despite their ridiculously long injury list getting even longer, The Yeltz continued to improve with a 3-1 victory at home to Atherstone.
It was 0-0 at half time, but a penalty converted by Crispy after a handball put Halesowen into the lead. Delton Francis scored from an acute angle soon afterwards, and Mark Peters scored a third near the end, with Atherstone scoring from the spot in injury time.


That's unlucky, if you searched for a GUID or Job - it could not be found or you don't have permission to view the job. Better luck maybe with doing a search?


Alulu makes everything from scratch and sources locally for ingredients. Pulling from his interest in Eastern European cuisine and an eclectic mix of global dishes, Chef Jeff Hedin has created a seasonal menu to please everyone.


importcom.workday.warp.arbiters.ArbiterLikeimportcom.workday.warp.persistence.CorePersistenceAwareimportcom.workday.warp.persistence.TablesLike._importcom.workday.warp.persistence.Tables._classResponseTimeArbiterextendsArbiterLikewithCorePersistenceAware {
/**
* Checks that the measured test took less than 5 seconds.
*
* @param ballot box used to register vote result.
* @param testExecution [[TestExecutionRowLikeType]] we are voting on.
* @return a wrapped error with a useful message, or None if the measured test passed its requirement.
*/overridedef vote[T:TestExecutionRowLikeType](ballot:Ballot, testExecution:T):Option[Throwable] = {
val testId:String = this.persistenceUtils.getMethodSignature(testExecution)
if (testExecution.responseTime > 5) {
Option(newRequirementViolationException(s"$testId took longer than 5 seconds"))
}
else {
None
}
}
}
Some arbiters may want access to historical data in order to make an informed decision about the most recent test invocation.
To easily access historical data, mix in the trait CanReadHistory. For example, using this mechanism, we can
implement an arbiter that votes based on the z-score of the most recently test execution. Using such an arbiter,
for example, we can fail any test whose recorded measurement value is greater standard deviations away from the
historical mean.
Anomaly Detection
WARP includes an arbiter that uses machine learning to cast votes.
The RobustPcaArbiter votes based on the results of an anomaly detection algorithm,
Robust Principal Component Analysis (RPCA).
Internally, we use this arbiter to fail any tests that have
suspicious or anomalous measurements.
See the dedicated RPCA page here for a more detailed description of how
this algorithm works, including our novel algorithmic extension designed to improve strictness and avoid
false negatives over time.


Huge prediction made in Championship race with Leeds United set for promotion in boost for Nottingham Forest and Bristol City
The experts have updated their opinions
by David ByromBristol City's win over Fulham at the weekend has given both them - and Leeds United - a boost, according to a huge new Championship prediction.
Data website Five Thirty Eight has a model to predict the outcome of the Championship this season and Leeds United are predicted to come top.
In-form West Brom will run them close for the title - Slaven Bilic's side are predicted to finish just three points behind them in second.
Yet Leeds have a 54 per cent chance of winning the title, with West Brom being given a 32 per cent possibility.
Leeds have been boosted by the Robins' win at Craven Cottage, though - they now have a massive 84 per cent chance of getting promoted, and are predicted to finish 15 points clear of Fulham.
The Robins' victory also sees them strengthen their claim for the play-off places.
City are predicted to finish sixth, in the final play-off spot, two points ahead of Sheffield Wednesday.
The Robins have been given a 39 per cent chance of making the top six, and a 14 per cent chance of being promoted.
Also involved in the play-off places are Fulham, Brentford and Nottingham Forest.
If the website's prediction rings true, City will face Fulham in the play-off semi-finals.
At the other end of the table, Barnsley, Wigan Athletic and Luton Town are predicted to be relegated.
Get lots more news, opinion and transfer updates on our Bristol City FC page and add a bookmark to find it quickly next time.


Web Scraping
Data scraping, also known as web scraping, is the process of importing information from a website into a spreadsheet or local file saved on your computer. It’s one of the most efficient ways to get data from the web, and in some cases to channel that data to another website.
Search any Quote you like:


nz.co.jedsimson.lgp.core.environment
/
ComponentProvider
/
component
component
abstract
val
component
:
TComponent
The component that this provider is responsible for.


This procedure is useful for allowing a quick halt to a program while indicating an error condition.
Error.Halt ("Execution terminated by the program.")
See example file for an example of how to use Error.Halt.
This means that you can only call the function by calling Error.Halt, not by calling Halt.


By: Christian Hughes
Instructions: Enter input in the Rules, Axiom, and Iterations fields. The Axiom should be a single string without spaces. Rules should follow the pattern before->after. Multiple rules should be seperated by commas, and spaces are not allowed (Ex. A->B,B->AB). Press generate to draw the L-System/create an equivalent SVG snippet. Fill is off by default, and stroke is set to black by default.
Gramatical Components:
#(Color) - Use this to set the fill color. Color input should be a hexadecmial value with the hashtag ommited (ex. #(00008B)). Once set, the subsequent path is filled with color, until the fill color is changed. Specifying #(none) will turn off the fill.
|(Color) - Use this to set the stroke color. Color input should be a hexadecmial value with the hashtag ommited (ex. |(00008B)). Once set, the subsequent path is stroked with color, until the stroke color is changed. Specifying |(none) will turn off the fill.
+(Degrees) - Turn right by the specified degrees.
-(Degrees) - Turn left by the specified degrees.
[ - Saves the state (x value, y value, angle, fill color and state, stroke color and state).
] - Restores the last saved state.


jhsearch - JavaHelp command line utility
f3 f3jhsearch f2database_directory
database_directory datadase directory is created by command jhindexer.
jhsearch command is a command-line program that can be used to query the JavaHelp system full-text search database created with the jhindexer command. The jhsearch command can be used to verify the validity of a search database without invoking the help viewer.
jhindexer (1)


Check the Report output: img2 and img3 must be loaded or completed, img4 must report error.
Click F5. Click "Test".
Click Ctrl+F5 (reload all). Click "Test".
Testing in progress (not completed, maybe because of an error).


NBA 2K Playgrounds Android beta is underway in select regions
Source: 2K / Google Play Store
What you need to know
- NBA 2K Playgrounds is a two-on-two arcade-style basketball game licensed by the NBA.
- A version for Android is currently being beta-tested in select regions.
- It will be a free-to-play title with a reliance on unlocking player packs and in-app purchases.
NBA 2K Playgrounds is a two-on-two arcade-style basketball game that first landed on consoles back in 2017. Now, it would seem 2K is keen on bringing the franchise to mobile as a free-to-play title, with beta testing currently underway in select regions.
2K Playgrounds features exaggerated character models and simplified gameplay that should translate well to mobile. I mean, this is a tried and tested format that we've seen before from NBA Jam. It's nice to see more sports arcade games hitting the Google Play Store, but I am discouraged by the free-to-play mechanics that 2K is building this game around.
Unlike NBA Jam, which gives you a full roster of NBA talent to choose from, NBA 2K Playgrounds has you unlocking and upgrading your favorite players using a random card pack system. That basically means you're going to need to grind away for hours or spend real money to buy card packs outright for a chance at unlocking your favorite NBA superstars.
The Google Play listing shows that the game is currently being beta-tested in select regions such as the Philippines. There's no word when the game will be ready for an official release.


§336-14 When this part not applicable. Where support or bond has already been pledged or is available, this part shall not be applicable. [L 1957, c 90, §4; Supp, §50A-4; HRS §336-14]


A ruby wrapper for uploadcare.com service.
Installation
Add this line to your application's Gemfile:
gem 'uploadcare-api'
And then execute:
$ bundle
Or install it yourself as:
$ gem install uploadcare-api
Usage
Testing
Make spec/config.yml
like this:
public_key: 'PUBLIC KEY'
private_key: 'PRIVATE KEY'
And run bundle exec rspec


block
back & forward: back forward
pushState & replaceState: push / push /one push /two replace /three
regular links: / /one


Air Bass
Air Drums
Air DJ Set 1
Air DJ Set2
Air Piano
Hit The Gong!!!
About the Team
Behind the Scenes
Trump Mode
Home


Retrieves data about a single link if the requesting advertiser/publisher has access to it. Advertisers can look up their own links. Publishers can look up visible links or links belonging to advertisers they are in a relationship with.
Inserts a new link.
Retrieves all links that match the query parameters.
list_next(previous_request, previous_response)
Retrieves the next page of results.
get(role, roleId, linkId)
Retrieves data about a single link if the requesting advertiser/publisher has access to it. Advertisers can look up their own links. Publishers can look up visible links or links belonging to advertisers they are in a relationship with. Args: role: string, The role of the requester. Valid values: 'advertisers' or 'publishers'. (required) Allowed values advertisers - The requester is requesting as an advertiser. publishers - The requester is requesting as a publisher. roleId: string, The ID of the requesting advertiser or publisher. (required) linkId: string, The ID of the link to look up. (required) Returns: An object of the form: { # A LinkResource. "isActive": True or False, # Flag for if this link is active. "linkType": "A String", # The link type. "kind": "gan#link", # The kind for one entity. "endDate": "A String", # Date that this link becomes inactive. "description": "A String", # Description. "name": "A String", # The logical name for this link. "startDate": "A String", # Date that this link becomes active. "createDate": "A String", # Date that this link was created. "imageAltText": "A String", # image alt text. "id": "A String", # The ID of this link. "advertiserId": "A String", # The advertiser id for the advertiser who owns this link. "impressionTrackingUrl": "A String", # Tracking url for impressions. "promotionType": "A String", # Promotion Type "duration": "A String", # Duration "authorship": "A String", # Authorship "availability": "A String", # Availability. "clickTrackingUrl": "A String", # Tracking url for clicks. "destinationUrl": "A String", # The destination URL for the link. }
insert(role, roleId, body)
Inserts a new link. Args: role: string, The role of the requester. Valid values: 'advertisers' or 'publishers'. (required) Allowed values advertisers - The requester is requesting as an advertiser. publishers - The requester is requesting as a publisher. roleId: string, The ID of the requesting advertiser or publisher. (required) body: object, The request body. (required) The object takes the form of: { # A LinkResource. "isActive": True or False, # Flag for if this link is active. "linkType": "A String", # The link type. "kind": "gan#link", # The kind for one entity. "endDate": "A String", # Date that this link becomes inactive. "description": "A String", # Description. "name": "A String", # The logical name for this link. "startDate": "A String", # Date that this link becomes active. "createDate": "A String", # Date that this link was created. "imageAltText": "A String", # image alt text. "id": "A String", # The ID of this link. "advertiserId": "A String", # The advertiser id for the advertiser who owns this link. "impressionTrackingUrl": "A String", # Tracking url for impressions. "promotionType": "A String", # Promotion Type "duration": "A String", # Duration "authorship": "A String", # Authorship "availability": "A String", # Availability. "clickTrackingUrl": "A String", # Tracking url for clicks. "destinationUrl": "A String", # The destination URL for the link. } Returns: An object of the form: { # A LinkResource. "isActive": True or False, # Flag for if this link is active. "linkType": "A String", # The link type. "kind": "gan#link", # The kind for one entity. "endDate": "A String", # Date that this link becomes inactive. "description": "A String", # Description. "name": "A String", # The logical name for this link. "startDate": "A String", # Date that this link becomes active. "createDate": "A String", # Date that this link was created. "imageAltText": "A String", # image alt text. "id": "A String", # The ID of this link. "advertiserId": "A String", # The advertiser id for the advertiser who owns this link. "impressionTrackingUrl": "A String", # Tracking url for impressions. "promotionType": "A String", # Promotion Type "duration": "A String", # Duration "authorship": "A String", # Authorship "availability": "A String", # Availability. "clickTrackingUrl": "A String", # Tracking url for clicks. "destinationUrl": "A String", # The destination URL for the link. }
list(role, roleId, linkType=None, promotionType=None, advertiserId=None, pageToken=None, relationshipStatus=None, maxResults=None, advertiserCategory=None, authorship=None, startDateMin=None, assetSize=None, startDateMax=None)
Retrieves all links that match the query parameters. Args: role: string, The role of the requester. Valid values: 'advertisers' or 'publishers'. (required) Allowed values advertisers - The requester is requesting as an advertiser. publishers - The requester is requesting as a publisher. roleId: string, The ID of the requesting advertiser or publisher. (required) linkType: string, The type of the link. Allowed values banner - text - promotionType: string, The promotion type. (repeated) Allowed values buy_get - coupon - free_gift - free_gift_wrap - free_shipping - none - ongoing - percent_off - price_cut - product_promotion - sale - sweepstakes - advertiserId: string, Limits the resulting links to the ones belonging to the listed advertisers. (repeated) pageToken: string, The value of 'nextPageToken' from the previous page. Optional. relationshipStatus: string, The status of the relationship. Allowed values approved - available - maxResults: integer, Max number of items to return in this page. Optional. Defaults to 20. advertiserCategory: string, The advertiser's primary vertical. (repeated) Allowed values apparel_accessories - appliances_electronics - auto_dealer - automotive - babies_kids - blogs_personal_sites - books_magazines - computers - dating - department_stores - education - employment - financial_credit_cards - financial_other - flowers_gifts - grocery - health_beauty - home_garden - hosting_domain - internet_providers - legal - media_entertainment - medical - movies_games - music - nonprofit - office_supplies - online_games - outdoor - pets - real_estate - restaurants - sport_fitness - telecom - ticketing - toys_hobbies - travel - utilities - wholesale_relationship - wine_spirits - authorship: string, The role of the author of the link. Allowed values advertiser - publisher - startDateMin: string, The beginning of the start date range. assetSize: string, The size of the given asset. (repeated) startDateMax: string, The end of the start date range. Returns: An object of the form: { "nextPageToken": "A String", # The next page token. "items": [ # The links. { # A LinkResource. "isActive": True or False, # Flag for if this link is active. "linkType": "A String", # The link type. "kind": "gan#link", # The kind for one entity. "endDate": "A String", # Date that this link becomes inactive. "description": "A String", # Description. "name": "A String", # The logical name for this link. "startDate": "A String", # Date that this link becomes active. "createDate": "A String", # Date that this link was created. "imageAltText": "A String", # image alt text. "id": "A String", # The ID of this link. "advertiserId": "A String", # The advertiser id for the advertiser who owns this link. "impressionTrackingUrl": "A String", # Tracking url for impressions. "promotionType": "A String", # Promotion Type "duration": "A String", # Duration "authorship": "A String", # Authorship "availability": "A String", # Availability. "clickTrackingUrl": "A String", # Tracking url for clicks. "destinationUrl": "A String", # The destination URL for the link. }, ], "kind": "gan#links", # The kind for a page of links. }
list_next(previous_request, previous_response)
Retrieves the next page of results. Args: previous_request: The request for the previous page. (required) previous_response: The response from the request for the previous page. (required) Returns: A request object that you can call 'execute()' on to request the next page. Returns None if there are no more items in the collection.


Propagation, Bubbling and Once
When you make an application, sometimes you nedde to get some input from the user. In Javascript, you most likely will use the addEventListener, and make a callback function fire when the user interacts.
What I didn't know (among a lot of things) was, that it is not only the element that have the eventListener added, that are affected byt the user action, but all of the parents to the element are registering the action.
It is not something you normally notice, and normally it is not a problem, but if you have some nested elements with an eventListener, you will see this happend.
I will try to make a small demostaration here. I hope it makes any sense.
If this have spiked your interest, you can find a great article here on Medium, where Vaibhav Sharma explain this in a really good way,


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
input_real = tf.placeholder(tf.float32, (None,image_width,image_height, image_channels),name='input_real')
z_input = tf.placeholder(tf.float32, (None, z_dim), name='z_input')
lr = tf.placeholder(tf.float32, None, name='learning_rate')
return (input_real, z_input, lr)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
keep_prob=0.5
alpha=0.2
width=int(images.get_shape()[1])
with tf.variable_scope('discriminator', reuse=reuse):
# Input layer is 28*28*3
x1 = tf.layers.conv2d(images, width*2, 5, strides=2, padding='same')
relu1 = tf.maximum(alpha * x1, x1)
# 14*14*56
x2 = tf.layers.conv2d(relu1, width*4, 5, strides=2, padding='same')
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
#7*7*128
x3 = tf.layers.conv2d(relu2, width*8, 5, strides=2, padding='same')
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
#4*4*256
dropped = tf.layers.dropout(relu3, rate=0.5)
# Flatten it
flat = tf.reshape(dropped, (-1, 4*4*8*width))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
keep_prob=0.7
alpha=0.2
with tf.variable_scope('generator', reuse=(not is_train)):
# First fully connected layer
x1 = tf.layers.dense(z, 2*2*512)
# Reshape it to start the convolutional stack
x1 = tf.reshape(x1, (-1, 2, 2, 512))
# x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha * x1, x1)
# 2*2*512 now
x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid')
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
# 4*4*256 now
x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(alpha * x3, x3)
# 4*4*512 now
x4 = tf.layers.conv2d_transpose(x3, 64, 5, strides=2, padding='same')
x4 = tf.layers.batch_normalization(x4, training=is_train)
x4 = tf.maximum(alpha * x4, x4)
# 4*4*512 now
dropped = tf.layers.dropout(x4, rate=0.2)
# Output layer
logits = tf.layers.conv2d_transpose(dropped, out_channel_dim, 5, strides=1, padding='same')
# 28*28*out_channel_dim now
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
steps = 0
print_every=100
sample_z = np.random.uniform(-1, 1, size=(72, z_dim))
real_dim = data_shape[1:]
samples, losses = [], []
figsize=(28,28)
input_real, z_input, lr = model_inputs(*real_dim,z_dim)
out_channel_dim = input_real.get_shape()[3]
d_loss, g_loss = model_loss(input_real, z_input, real_dim[2])
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
batch_images *=2
# TODO: Train Model
# Sample random noise for G
steps += 1
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, z_input: batch_z,lr:learning_rate})
_ = sess.run(g_train_opt, feed_dict={z_input: batch_z, input_real: batch_images})
if steps % print_every == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({z_input: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({z_input: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
# losses.append((train_loss_d, train_loss_g))
show_generator_output(sess, 25, z_input, out_channel_dim, data_image_mode)
return losses, samples
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 128
z_dim = 128
learning_rate = 0.001
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 256
z_dim = 128
learning_rate = 0.001
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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|
Analysis of serum cytokines in patients with severe acute respiratory syndrome. |
Zhang Y, Li J, Zhan Y, Wu L, Yu X, Zhang W, Ye L, Xu S, Sun R, Wang Y, Lou J |
China |
2004 |
Following the discovery of SARS-CoV, no specific or efficient clinical treatments were available since pathogenesis was not well understood.
Acute lung injury associated with SARS can be attributed to complex and multifactorial pathophysiological process involving cytokines. Demonstrated that IL-1 play key role in pathogenesis of acute lung injury. WHO inferred that severe immune response kills SARS patients and that some cytokines may play important role in the process.
IL-1 cytokine promotes inflammation by inducing cell injury.
Study measured levels of IL-1 in 4 patient groups: controls, patients with SARS, patients with severe SARS and convalescent SARS patients.
Study showed that levels of IL-1alpha did not differ between 3 SARS groups or controls.
Suggests that host immune response to novel coronavirus may be different from the immune reaction to other pathogens.
Overall results suggest that acute lung injury associated with SARS may not be induced by circulating cytokines. HOWEVER some patients had accepted corticosteroids and therefore results consistent with reports that only long term treatment (7-10 days) with a steroid can alter serum cytokine levels.
|
| [
34]
|
Plasma inflammatory cytokines and chemokines in severe acute respiratory syndrome. |
Wong CK, Lam CW, Wu AK, Ip WK, Lee NL, Chan IH, Lit LC, Hui DS, Chan MH, Chung SS, Sung JJ |
Hong Kong |
2004 |
Known that over-expression of IL-1beta hallmark of SARS infection probably through activation of transcription factor nuclear factor, activator protein AP1 and activating factor 2. Therefore lung damage associated with SARS postulated to occur through cytokine and chemokine dysregulation.
Study involved measurement of cytokines and chemokines including IL-1beta in 20 consecutive patients admitted to Hong Kong hospital.
IL-1beta was significantly elevated above normal range within first 12, 7 and 5 days after disease onset.
Those patients with more severe disease treated with pulsed methylprednisolone. IL-1beta levels higher in this patient group particularly 3-10 days after onset but returned to normal levels 1 week after disease onset in both sets of patients.
IL-1beta can act as early response cytokine to viral infection. IL-1 elevation in SARS can induce hyperinnate inflammatory response leading to recruitment and accumulation of alveolar macrophages and PMN.
Higher disease severity associated with more elevated plasma IL-1beta and addition of pulsed steroid controlled rapidly deteriorating clinical condition and attenuated otherwise exaggerated immunological response.
|


This page contains a element hierarchy that is removed and recreated continuously. The hierarchy has the ids: top, middle and bottom


Dear Coleen: I was police-checked by my girlfriend and I don’t understand why
I found an old file of paperwork and was dumbfounded to discover she'd done a Clare’s Law check on me
by Irishmirror.ieDear Coleen
I’m in a fantastic relationship, or so I thought.
I’m 35, my girlfriend is 37, and has a seven-year-old daughter from a previous marriage. We have been together for three years and I love her daughter like my own.
However, while clearing a room prior to decorating, I found an old file of paperwork.
I asked my girlfriend if she needed it, and she told me to have a look through it.
I was dumbfounded when I found a Clare’s Law check [the right to ask police if a partner poses a risk] carried out on me at her request.
I asked her to explain it and she said it was “nothing” and tried to laugh it off.
That was a week ago and I’m struggling to understand it all. I’ve told her I feel offended and now I’m making excuses not to be near her. I’m even considering leaving the relationship.
When we talked about it she apologised, but I’m still seriously considering walking out. If she doesn’t trust me, why should I stay?
I’ve never been in trouble, I’m a serving soldier, and just cannot get over this.
 Am I reacting rationally?
Coleen says
I actually admire your girlfriend – she was going into a new relationship as a single parent with a young daughter. She didn’t know your past and, we have to accept that if someone does have a criminal record, they might not admit to it.
I don’t think it’s a case of her not trusting you, she was just making sure everything would be OK for her daughter – she was being a responsible parent.
Putting myself in your shoes, if a new partner asked me if it was OK to get me checked out, I’d say “absolutely” because I’d know they wouldn’t find anything.
So I think it would help you to get over this by putting yourself in her shoes and putting your pride or ego to one side.
Many women have got into relationships and only find out when it’s too late that their partner has a domestic violence conviction.
This law protects potentially vulnerable people, and I think it’s a great thing.
If you love her and her daughter as you say you do, then be honest about your feelings, but then move on. Throwing in the towel now wouldn’t make any sense.
I understand how you feel but, from her point of view, I also understand it’s quite hard to tell your partner upfront that you want to get them checked out.
Think about how devastated you’d be if you broke up and she went out with someone else she hadn’t checked out and something awful happened to her or her daughter.
Try not to take it personally because you shouldn’t.


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We are the Los Angeles chapter of Game Workers Unite, a horizontal organization of people dedicated to advocating for workers' rights in the games industry.
We're not a union, but we're working towards building one. Los Angeles is notable for the number of game companies it hosts – workers here are a powerful force. Together, we can achieve better working conditions, recognition, and mutual support.
The easiest way to join us is to get in touch via one of the following channels:
We can't wait to see you!


Highlight Features
Easy to maintain
Easy to upgrade
Easy to add feature
Easy to install
Easy to deploy
Easy to understand
Up Coming Feature
Localization
Special Thanks To
Spring Framework
Wicket
Reflections
Bootstrap
Openfire
FlywayDB
JsonDoc
Wicket Chart
iText PDF
PDF Box
Wicked Forms


SAT Vocabulary Flashcards
Using <dl>, <dt>, and <dd>
- Bolster
- to support, strengthen, or fortify
- Capitalize
- to use to your advantage
- Cultivate
- to foster the growth of
- Deficient
- not enough in degree or amount
- Eloquent
- having refined or expressive communication skills
- Ferocity
- viciousness, violence


BASICS OF THE WEB
Some of the most important concepts that we will find when talking about the web are:
WWW, which stands for World Wide Web and is formed by a collection of HTML documents
HTML, which stands for Hypertext Markup Language and represents the main document type of the web
URL's which stands for Uniform Resource Locator
HTTP which stands for Hypertext Transfer Protocol and represents web´s main protocol.
Web applications, the purpose of this course.
You can refer to the video clicking this link
MAYOR PIECES OF THE WEB
Computer--------HTTP----The Internet-----HTTP--------Servers
+ Browser
HTML
HTML is made up of text content (what you see), markup (what it looks like), references to other documents (eg images or videos) and links to other pages.
Markup
In order to give format to the text in HTML (Markup) we instruct computer using TAGS. Keep in mind that sometimes we refer to computers as "stupid", because they follow instructions exactly as we give them. This
gives us a great amount of power, while at the same time we have to be very careful with detail.
BASIC STRUCTURE
< "NAME" > CONTENT < /"NAME" >
OPENING TAG-----------CLOSING TAG
The basic structure formed by opening tag, content and closing tag is called element.
Since there are lots of different tags, it would be very difficult to memorize them all. It is always easier to google them
the moment you need it.
A good thing to remember though, is that there are some elements that do not require the use of closing tag, which are called void tags(eg. < br >, < img >)
Inline vs block tags
Just as the name implies, inline tags keep all the text in a single line, while block tags create kind of an invisible box around the text.
< br >, < b >, < em >, < span > and < img > tags are all examples of inline tags, while < p > and < div > tags are both block ones.


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“Mind what you have learned. Save you it can.”
compiled into specific versions of used arguments
arguments don’t have to be type names
template <int n>
struct factorial {
enum { value = n * factorial<n - 1>::value };
};
template <>
struct factorial<0> {
enum { value = 1 };
};
// Usage examples:
// factorial<0>::value would yield 1;
// factorial<4>::value would yield 24.
// The Curiously Recurring Template Pattern (CRTP)
template<class T>
class Base
{
// methods within Base can use template to access members of Derived
};
class Derived : public Base<Derived>
{
// ...
};
type erasure
ArrayList strs = new ArrayList();
strs.add("hello");
String entry = (String) strs.get(0);
strs.add(new Object());
ArrayList<String> strs = new ArrayList<String>();
strs.add("hello");
String entry = strs.get(0);
strs.add(new Object());
static typing => compile checks
IList<int> myList = new List<int>();
Apple apple = new Apple();
Fruit fruit = apple;
fruit = new Banana();
so...
Apple[] apples = new Apple[] { new Apple(), new Apple() };
Fruit[] fruits = apples;
IEnumerable<Manager> managers = GetManagers();
IEnumerable<Employee> employees = managers;
IComparer<Employee> empl = GetEmployeeComparer();
IComparer<Manager> mng = empl;
public class TestClass<in T, out W> where W: T {}
public interface ITestInterface<in T, out W> where W: T {}


--- layout: page title: "CoffeeScript (for jay muratore)" prev: f_0003.html next: f_0005.html book_path: books/coffeescript-accelerated-javascript-development-pragmatic--_oeb/ --- {% include JB/setup %} {% raw %}
CoffeeScript is a young language. But from the start, it’s drawn an exceptionally diverse and spirited crowd. That wonderful energy—on IRC, GitHub, Hacker News, blogs, Twitter, and elsewhere—is what inspired me to write this book. To everyone who greeted CoffeeScript with enthusiasm in its infancy, I thank you.
Thanks, of course, to Jeremy Ashkenas for creating the language and contributing a generous foreword to this book; CoffeeScript could not have asked for a better BDFL. Thanks also to CoffeeScript’s other contributors, who are too numerous to name here.[1]
Thanks to the technical reviewers—any remaining errors are completely and utterly “my bad.” I received helpful feedback from Javier Collado, Kevin Gisi, Darcy Laycock, Scott Leberknight, Sam Stephenson, Travis Swicegood, Federico Tomassetti, Stefan Turalski, and Dr. Nic Williams. Special shout-outs to Jeremy Ashkenas (again) and Michael Ficarra, core contributors to the CoffeeScript project who took time from their busy schedules to set me straight on many of the language’s finer points. Thanks also to Brendan Eich, the creator of JavaScript, who graciously clarified several points.
Thanks to the Pragmatic Bookshelf crowd. First and foremost to Michael Swaine, whom I’m proud to call my editor. Thanks also to managing editor Susannah Pfalzer and to bigwigs Dave Thomas and Andy Hunt for taking a chance on a book on a lesser-known language from an even less-known author.
Thanks, finally, to Scott and Teresa Burnham, more commonly referred to by me and at least two other people as “Dad” and “Mom.” Their support, and their example, has been valuable beyond measure.


Mwahahahaha maybe I'll start modding...maybe. I'm afraid I don't have a fancy header image yet.
Home
Fallout
"Survival Chems"
"Wandering Scavengers"
Skyrim
"Golem Companion"
This is where all the main stuff is going to be happening.


{% extends 'layout.html' %} {% block pageTitle %} Content page example - NHS.UK prototype kit {% endblock %} {# {% block beforeContent %} {{ breadcrumb({ href: "/", text: "Home" }) }} {% endblock %} #} {% block content %}
Your NHS login is<EMAIL_ADDRESS>To continue, you need to agree to share your NHS login information.
'myHealth' will use your:
Before you agree, you should read their terms of use and privacy policy to check how they will use your information.
{{ button({ "text": "Agree and continue", "href": "/login/login-triage" }) }}
Should this layout be more of a checkbox "I agree" and then a continue box that can be clicked upon agreement
Option to not agree?