GetGadgetItemData()
Syntax
Result = GetGadgetItemData(#Gadget, Item)Description
Returns the value that was previously stored with this gadget item with the SetGadgetItemData() function. This allows to associate a custom value with the items of a gadget.
Parameters
#Gadget The gadget to use. Item The item from which the data should be read. The first item in the gadget has index 0.
Return value
Returns the stored data. If no value has been stored with the item yet, the return-value will be 0.
Remarks
This function works with the following gadgets:
- ComboBoxGadget()
- ListIconGadget()
- ListViewGadget()
- PanelGadget()
- TreeGadget()
See SetGadgetItemData() for an example.
See Also
SetGadgetItemData(), GetGadgetData(), SetGadgetData()
Supported OS
All


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My name is Niolas Hurtado, I am 23 years old and I am currently a student at Temple University's Fox School of Business majoring in Economics and Management Information Systems; and I am also enrolled in Epicodus' coding bootcam where I am looking to enhance my technical skills. I was introduced to programing in one of my classes at Fox and I found that I really liked it and that I had a talent for it which is what led me to enrolling in a course to learn much more abut it. I really like how powerful this is, and it is a very valuable skill in the current job market so I am hoping to get a lot out of this. I would like to work for a start-up company after finishing my course where I could learn much more about programming and put my new skills to test in a more practical form.


This example shows how to output the elements of each JDK sequenced collection supported by Dynamator (array, Vector, Enumeration, Iterator). In this example, the list is a shopping list.
To cause the content of the 'li' element to be subject to change at run-time, add a 'class' attribute to the 'li' element.
When page displays a list of items, more than one item is usually required to present sample data. The HTML page can be used to display sample data that is not intended for inclusion in the production system by marking each HTML element to be excluded with the attribute 'class="Discard"'. When Dynamator detects a element with a class or id with the identifier "Discard", it does not copy the element or its children to the JSP page. The effect is the same as using the element <discard/> in the Dynamator file. The difference is in who can specify the attribute.
To make it easier to see what's going on, we've defined the Java elements in the Dynamator file.
To indicate that an HTML element should be repeated for each element of a collection, add a 'foreach' element to the 'class' element in the Dynamator file.
The Dynamator file contains a 'foreach' element for each repeating element in the HTML file. In each case, the 'foreach' element contains an attribute to specify the type of the collection, and an attribute to specify the name of the Java variable that references the current element of the iteration. So, for example, the first 'foreach' iterates over the array of Strings referred to by variable 'dinner_', giving the variable name 'menuItem' to the current element.
The content of the 'foreach' element is a Java expression that evaluates to a collection of the same type as specified in the 'type' attribute. In the example, the first two foreach elements reference static collection variables, the third case references a local method call that returns a collection, and the last case references a method call on a local variable.
As elsewhere, the 'content' element within the 'class' element causes the content of the referenced element to be replaced with the content of the 'content' element. In each of these cases, the content uses the variable referring to the current element of the collection. To show that it doesn't have to be just the element, the reminder content element adds something extra.
The format of the 'type' attribute is consistent across all collection types except for arrays:
After processing with Dynamator, the resulting JSP file looks like this:
Dynamator generates variable names to support iteration. Generated names are based on the foreach expression. You can specify your own variable names; see the documentation for <foreach>. The dollar signs are valid Java, but are usually only used by code generators, which is what Dynamator is. They are used to ensure that generated names are readable but don't conflict with any programmer-defined names.
If you are viewing this page in a servlet engine, you can see the generated page in action.


@Any, wend no meh bootsrap components mit javascript bruchsch, muess ich diä zerst im organisms/BodyBootstrap.js intialisieren.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.


Select a stock to display its market price.
Do you want to display change and percent change?
What other information do you want to display? Pick up to 9 fields.


JSDraw - HELM RNA
Copyright 2016, Scilligence (
http://www.scilligence.com/
)
License:
Free for education (
Download
)
Please
contact us
for commercial licenses


- idle() - Method in class com.sun.mail.imap.IMAPFolder
-
Use the IMAP IDLE command (see
RFC 2177),
if supported by the server, to enter idle mode so that the server
can send unsolicited notifications of new messages arriving, etc.
- idle(boolean) - Method in class com.sun.mail.imap.IMAPFolder
-
Like
IMAPFolder.idle()
, but if
once
is true, abort the
IDLE command after the first notification, to allow the caller
to process any notification synchronously.
- idle() - Method in class com.sun.mail.imap.IMAPStore
-
Use the IMAP IDLE command (see
RFC 2177),
if supported by the server, to enter idle mode so that the server
can send unsolicited notifications
without the need for the client to constantly poll the server.
- ignoreBodyStructureSize() - Method in class com.sun.mail.imap.IMAPMessage
-
- ignoreCase - Variable in class javax.mail.search.StringTerm
-
Ignore case when comparing?
- IllegalWriteException - Exception in javax.mail
-
The exception thrown when a write is attempted on a read-only attribute
of any Messaging object.
- IllegalWriteException() - Constructor for exception javax.mail.IllegalWriteException
-
Constructs a IllegalWriteException with no detail message.
- IllegalWriteException(String) - Constructor for exception javax.mail.IllegalWriteException
-
Constructs a IllegalWriteException with the specified detail message.
- IMAPFolder - Class in com.sun.mail.imap
-
This class implements an IMAP folder.
- IMAPFolder(String, char, IMAPStore, Boolean) - Constructor for class com.sun.mail.imap.IMAPFolder
-
Constructor used to create a possibly non-existent folder.
- IMAPFolder(ListInfo, IMAPStore) - Constructor for class com.sun.mail.imap.IMAPFolder
-
Constructor used to create an existing folder.
- IMAPFolder.FetchProfileItem - Class in com.sun.mail.imap
-
A fetch profile item for fetching headers.
- IMAPFolder.FetchProfileItem(String) - Constructor for class com.sun.mail.imap.IMAPFolder.FetchProfileItem
-
- IMAPFolder.ProtocolCommand - Interface in com.sun.mail.imap
-
A simple interface for user-defined IMAP protocol commands.
- IMAPMessage - Class in com.sun.mail.imap
-
This class implements an IMAPMessage object.
- IMAPMessage(IMAPFolder, int) - Constructor for class com.sun.mail.imap.IMAPMessage
-
Constructor.
- IMAPMessage(Session) - Constructor for class com.sun.mail.imap.IMAPMessage
-
Constructor, for use by IMAPNestedMessage.
- IMAPMessage.FetchProfileCondition - Class in com.sun.mail.imap
-
This class implements the test to be done on each
message in the folder.
- IMAPMessage.FetchProfileCondition(FetchProfile, FetchItem[]) - Constructor for class com.sun.mail.imap.IMAPMessage.FetchProfileCondition
-
Create a FetchProfileCondition to determine if we need to fetch
any of the information specified in the FetchProfile.
- IMAPSSLStore - Class in com.sun.mail.imap
-
This class provides access to an IMAP message store over SSL.
- IMAPSSLStore(Session, URLName) - Constructor for class com.sun.mail.imap.IMAPSSLStore
-
Constructor that takes a Session object and a URLName that
represents a specific IMAP server.
- IMAPStore - Class in com.sun.mail.imap
-
This class provides access to an IMAP message store.
- IMAPStore(Session, URLName) - Constructor for class com.sun.mail.imap.IMAPStore
-
Constructor that takes a Session object and a URLName that
represents a specific IMAP server.
- IMAPStore(Session, URLName, String, boolean) - Constructor for class com.sun.mail.imap.IMAPStore
-
Constructor used by this class and by IMAPSSLStore subclass.
- in - Variable in class javax.mail.util.SharedFileInputStream
-
The file containing the data.
- INLINE - Static variable in interface javax.mail.Part
-
This part should be presented inline.
- INSERT - Static variable in class com.sun.mail.imap.Rights.Right
-
Insert - perform APPEND, COPY into mailbox.
- IntegerComparisonTerm - Class in javax.mail.search
-
This class implements comparisons for integers.
- IntegerComparisonTerm(int, int) - Constructor for class javax.mail.search.IntegerComparisonTerm
-
- InternetAddress - Class in javax.mail.internet
-
This class represents an Internet email address using the syntax
of
RFC822.
- InternetAddress() - Constructor for class javax.mail.internet.InternetAddress
-
Default constructor.
- InternetAddress(String) - Constructor for class javax.mail.internet.InternetAddress
-
Constructor.
- InternetAddress(String, boolean) - Constructor for class javax.mail.internet.InternetAddress
-
Parse the given string and create an InternetAddress.
- InternetAddress(String, String) - Constructor for class javax.mail.internet.InternetAddress
-
Construct an InternetAddress given the address and personal name.
- InternetAddress(String, String, String) - Constructor for class javax.mail.internet.InternetAddress
-
Construct an InternetAddress given the address and personal name.
- InternetHeaders - Class in javax.mail.internet
-
InternetHeaders is a utility class that manages RFC822 style
headers.
- InternetHeaders() - Constructor for class javax.mail.internet.InternetHeaders
-
Create an empty InternetHeaders object.
- InternetHeaders(InputStream) - Constructor for class javax.mail.internet.InternetHeaders
-
Read and parse the given RFC822 message stream till the
blank line separating the header from the body.
- InternetHeaders.InternetHeader - Class in javax.mail.internet
-
An individual internet header.
- InternetHeaders.InternetHeader(String) - Constructor for class javax.mail.internet.InternetHeaders.InternetHeader
-
Constructor that takes a line and splits out
the header name.
- InternetHeaders.InternetHeader(String, String) - Constructor for class javax.mail.internet.InternetHeaders.InternetHeader
-
Constructor that takes a header name and value.
- invalid - Variable in class javax.mail.event.TransportEvent
-
- invalid - Variable in exception javax.mail.SendFailedException
-
- invalidate(boolean) - Method in class com.sun.mail.pop3.POP3Message
-
Invalidate the cache of content for this message object, causing
it to be fetched again from the server the next time it is needed.
- invalidateHeaders() - Method in class com.sun.mail.imap.IMAPMessage
-
Invalidate cached header and envelope information for this
message.
- isComplete() - Method in class javax.mail.internet.MimeMultipart
-
Return true if the final boundary line for this
multipart was seen.
- isConnected() - Method in class com.sun.mail.imap.IMAPStore
-
Check whether this store is connected.
- isConnected() - Method in class com.sun.mail.pop3.POP3Store
-
Check whether this store is connected.
- isConnected() - Method in class com.sun.mail.smtp.SMTPTransport
-
Check whether the transport is connected.
- isConnected() - Method in class javax.mail.Service
-
Is this service currently connected?
- isExpunged() - Method in class javax.mail.Message
-
Checks whether this message is expunged.
- isGroup() - Method in class javax.mail.internet.InternetAddress
-
Indicates whether this address is an RFC 822 group address.
- isLoggable(LogRecord) - Method in class com.sun.mail.util.logging.MailHandler
-
Check if this Handler would actually log a given
LogRecord into its internal buffer.
- isMimeType(String) - Method in class javax.mail.internet.MimeBodyPart
-
Is this Part of the specified MIME type? This method
compares only the primaryType
and
subType
.
- isMimeType(String) - Method in class javax.mail.internet.MimeMessage
-
Is this Part of the specified MIME type? This method
compares only the primaryType
and
subType
.
- isMimeType(String) - Method in interface javax.mail.Part
-
Is this Part of the specified MIME type? This method
compares only the primaryType
and
subType
.
- isNamespace - Variable in class com.sun.mail.imap.IMAPFolder
-
- isOpen() - Method in class com.sun.mail.imap.IMAPFolder
-
Check whether this connection is really open.
- isOpen() - Method in class com.sun.mail.pop3.POP3Folder
-
- isOpen() - Method in class javax.mail.Folder
-
Indicates whether this Folder is in the 'open' state.
- isRemoved() - Method in class javax.mail.event.MessageCountEvent
-
Indicates whether this event is the result of an explicit
expunge by this client, or due to an expunge from external
sources.
- isREV1() - Method in class com.sun.mail.imap.IMAPMessage
-
- isServerTrusted(String, SSLSocket) - Method in class com.sun.mail.util.MailSSLSocketFactory
-
After a successful conection to the server, this method is
called to ensure that the server should be trusted.
- isSet(Flags.Flag) - Method in class com.sun.mail.imap.IMAPMessage
-
Test if the given Flags are set in this message.
- isSet(Flags.Flag) - Method in class javax.mail.internet.MimeMessage
-
Check whether the flag specified in the flag
argument is set in this message.
- isSet(Flags.Flag) - Method in class javax.mail.Message
-
Check whether the flag specified in the flag
argument is set in this message.
- isSSL - Variable in class com.sun.mail.imap.IMAPStore
-
- isSSL() - Method in class com.sun.mail.imap.IMAPStore
-
Does this IMAPStore use SSL when connecting to the server?
- isSSL() - Method in class com.sun.mail.pop3.POP3Store
-
Is this POP3Store using SSL to connect to the server?
- isSSL() - Method in class com.sun.mail.smtp.SMTPTransport
-
Is this Transport using SSL to connect to the server?
- isSubscribed() - Method in class com.sun.mail.imap.IMAPFolder
-
Check whether this folder is subscribed.
- isSubscribed() - Method in class javax.mail.Folder
-
Returns true if this Folder is subscribed.
- issueCommand(String, int) - Method in class com.sun.mail.smtp.SMTPTransport
-
Send the command to the server.
- isTrustAllHosts() - Method in class com.sun.mail.util.MailSSLSocketFactory
-
- items - Variable in class com.sun.mail.imap.IMAPMessage
-
A map of the extension FETCH items.


A DI-Box (Direct Input Box) allows you to connect high-impedance sources to a mixer without adverse effects. The most common uses are for connecting electric keyboards, guitars, bass guitars and most forms of instrument pickups directly to the mixer without using an amp or microphone.
If an amp is involved, but they still want a direct line as well, you’ll have to split the source. Usually the musician will have his/her instrument plugged directly into the amp, so you’ll have to break this chain.


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java.lang.Object org.openjena.atlas.lib.ColumnMap
public class ColumnMap
General descriptor of a reordering (mapping) of columns in tuples to columns in indexes, for example, from triples to triple index order. Naming: map is convert to the reordered form, fetch is get back.
| Constructor Summary | |
|---|---|
ColumnMap(java.lang.String label,
int... elements)
Construct a column map - the elements are the mappings of a tuple originally in the order 0,1,2,... |
|
ColumnMap(java.lang.String label,
java.util.List<T> input,
java.util.List<T> output)
|
|
ColumnMap(java.lang.String input,
java.lang.String output)
Construct a column mapping that maps the input (one col, one char) to the output |
|
ColumnMap(java.lang.String label,
T[] input,
T[] output)
|
| Method Summary | ||
|---|---|---|
|
fetchSlot(int idx,
T[] tuple)
Apply to an unmapped tuple to get the i'th slot after mapping : SPO->POS : 0'th slot is P from SPO |
|
|
fetchSlot(int idx,
Tuple<T> tuple)
Apply to an unmapped tuple to get the i'th slot after mapping : SPO->POS : 0'th slot is P from SPO |
|
int |
fetchSlotIdx(int idx)
Get the index of the i'th slot as it appears from a mapping : for SPO->POS : 0'th slot is P so 1->0 |
|
java.lang.String |
getLabel()
|
|
int |
length()
Length of mapping |
|
|
map(Tuple<T> src)
Apply to an unmapped tuple to get a tuple with the column mapping applied |
|
java.lang.String |
mapName(java.lang.String word)
Reorder the letters of a string by the same rules as this column map (forward, map direction) |
|
|
mapSlot(int idx,
T[] tuple)
Apply to a mapped tuple to get the i'th slot as it appears after mapping : SPO->POS : 0'th slot is S from POS |
|
|
mapSlot(int idx,
Tuple<T> tuple)
Apply to a mapped tuple to get the i'th slot as it appears after mapping : SPO->POS : 0'th slot is S from POS |
|
int |
mapSlotIdx(int idx)
Get the index of the i'th slot as it appears after mapping : SPO->POS : 0'th slot is S from POS so 2->0 |
|
java.lang.String |
toString()
|
|
|
unmap(Tuple<T> src)
Apply to a mapped tuple to get a tuple with the column mapping reverse-applied |
|
java.lang.String |
unmapName(java.lang.String word)
Reorder the letters of a string by the same rules as this column map (backward, fetch direction) |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
|---|
public ColumnMap(java.lang.String input, java.lang.String output)
public ColumnMap(java.lang.String label, java.util.List<T> input, java.util.List<T> output)
public ColumnMap(java.lang.String label, T[] input, T[] output)
public ColumnMap(java.lang.String label, int... elements)
| Method Detail |
|---|
public int length()
public <T> T fetchSlot(int idx, Tuple<T> tuple)
public <T> T fetchSlot(int idx, T[] tuple)
public <T> T mapSlot(int idx, Tuple<T> tuple)
public <T> T mapSlot(int idx, T[] tuple)
public int mapSlotIdx(int idx)
public int fetchSlotIdx(int idx)
public <T> Tuple<T> map(Tuple<T> src)
public <T> Tuple<T> unmap(Tuple<T> src)
public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getLabel()
public java.lang.String mapName(java.lang.String word)
public java.lang.String unmapName(java.lang.String word)
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| Packages that use Discrete2DSpace | |
|---|---|
| uchicago.src.repastdemos.heatBugs | |
| uchicago.src.repastdemos.hexabugs | |
| uchicago.src.sim.gui | |
| uchicago.src.sim.space |
| Uses of Discrete2DSpace in uchicago.src.repastdemos.heatBugs |
|---|
| Classes in uchicago.src.repastdemos.heatBugs that implement Discrete2DSpace | |
|---|---|
class |
HeatSpace
The environment that the heat bugs inhabit. |
| Uses of Discrete2DSpace in uchicago.src.repastdemos.hexabugs |
|---|
| Classes in uchicago.src.repastdemos.hexabugs that implement Discrete2DSpace | |
|---|---|
class |
HexaSpace
The environment that the heat bugs inhabit. |
| Uses of Discrete2DSpace in uchicago.src.sim.gui |
|---|
| Fields in uchicago.src.sim.gui declared as Discrete2DSpace | |
|---|---|
protected Discrete2DSpace |
Display2D.grid
|
| Methods in uchicago.src.sim.gui that return Discrete2DSpace | |
|---|---|
Discrete2DSpace |
Display2D.getGrid()
Gets the Discrete2DSpace that is being displayed |
| Constructors in uchicago.src.sim.gui with parameters of type Discrete2DSpace | |
|---|---|
Display2D(Discrete2DSpace grid)
Constructs a Display2D with specified Discrete2DSpace to display |
|
HexaDisplay2D(Discrete2DSpace hexagrid)
Creates a HexaDisplay2D for the specifed Discrete2DSpace |
|
Network2DGridDisplay(Discrete2DSpace grid)
Creates a new Network2DGridDisplay for the specified grid. |
|
Object2DDisplay(Discrete2DSpace grid)
Creates an Object2DDisplay for displaying the specified Discrete2DSpace. |
|
Object2DHexaDisplay(Discrete2DSpace hexagrid)
Creates an Object2DHexaDisplay for displaying the specified Discrete2DSpace. |
|
ProbeableNumber(int x,
int y,
Discrete2DSpace grid,
java.lang.Object o)
Creates a Probeable number with the specified coordinates, in the specified grid, out of the specified object. |
|
Value2DDisplay(Discrete2DSpace grid,
ColorMap map)
Creates a Value2DDisplay to display the specified Discrete2DSpace using the specified ColorMap. |
|
Value2DHexaDisplay(Discrete2DSpace grid,
ColorMap map)
Creates a Value2DDisplay to display the specified Discrete2DSpace using the specified ColorMap. |
| Uses of Discrete2DSpace in uchicago.src.sim.space |
|---|
| Subinterfaces of Discrete2DSpace in uchicago.src.sim.space | |
|---|---|
interface |
IMulti2DGrid
Interface for grids and tori whose cells can hold more than one object. |
| Classes in uchicago.src.sim.space that implement Discrete2DSpace | |
|---|---|
class |
AbsMulti2DGrid
Base class for grids whose cells can hold more that one occupant. |
class |
AbsMulti2DHexagonalGrid
Base class for hexagonal grids whose cells can hold more that one occupant. |
class |
AbsMulti2DHexagonalTorus
Base class for hexagonal torii whose cells can hold more that one occupant. |
class |
AbsMulti2DTorus
Base class for tori whose cells can hold more that one occupant. |
class |
Diffuse2D
Discrete 2nd order approximation of 2d diffusion with evaporation. |
class |
Diffuse2DHexagonal
Discrete 2nd order approximation of 2d diffusion with evaporation on a hexagonal grid. |
class |
Multi2DGrid
A grid object that can hold more than one object in its cells. |
class |
Multi2DHexagonalGrid
A hexagonal grid object that can hold more than one object in its cells. |
class |
Multi2DHexagonalTorus
A torus object that can hold more than one object in its cells. |
class |
Multi2DTorus
A torus object that can hold more than one object in its cells. |
class |
Object2DGrid
A discrete 2 dimensional grid of objects, accessed by x and y coordinates. |
class |
Object2DHexagonalGrid
A discrete hexagonal grid of objects accessed by x and y coordinates. |
class |
Object2DHexagonalTorus
A discrete 2 dimensional hexagonal torus of objects, accessed by x and y coordinates. |
class |
Object2DTorus
A discrete 2 dimensional torus of objects, accessed by x and y coordinates. |
class |
OrderedMulti2DGrid
A grid object that can hold more than one object in its cells. |
class |
OrderedMulti2DHexagonalGrid
A hexagonal grid object that can hold more than one object in its cells. |
class |
OrderedMulti2DHexagonalTorus
A torus that can hold more than one object in its hexagonal cells. |
class |
OrderedMulti2DTorus
A torus that can hold more than one object in its cells. |
class |
RasterSpace
A class that represents a raster image as a space. |
| Fields in uchicago.src.sim.space declared as Discrete2DSpace | |
|---|---|
protected Discrete2DSpace |
AbstractNeighborhooder.space
|
| Constructors in uchicago.src.sim.space with parameters of type Discrete2DSpace | |
|---|---|
AbstractNeighborhooder(Discrete2DSpace space)
|
|
HexNeighborhooder(Discrete2DSpace space)
|
|
MooreNeighborhooder(Discrete2DSpace space)
|
|
VNNeighborhooder(Discrete2DSpace space)
|
|
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Tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.0.2) |
| Published: | 2016-07-18 |
| Author: | Derek Qiu [aut, cre] |
| Maintainer: | Derek Qiu <qiu.derek.d at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | BoardGames results |
| Reference manual: | BoardGames.pdf |
| Package source: | BoardGames_1.0.0.tar.gz |
| Windows binaries: | r-devel: BoardGames_1.0.0.zip, r-release: BoardGames_1.0.0.zip, r-oldrel: BoardGames_1.0.0.zip |
| OS X binaries: | r-release: BoardGames_1.0.0.tgz, r-oldrel: BoardGames_1.0.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=BoardGames to link to this page.


|
Biomedical Image Analysis Library
The Biomedical Image Analysis Library is a poweful tool for developers, physicians, researchers, engineers, and so on.
|
#include <ConnPathFunction.hpp>
Public Member Functions | |
| ConnPathFunction (const C< D > &handicap, const C< D > &component) | |
| Basic constructor. More... | |
| ConnPathFunction (const ConnPathFunction< C, D > &pf) | |
| Copy constructor. More... | |
| ~ConnPathFunction () | |
| Destructor. More... | |
| ConnPathFunction< C, D > | operator= (const ConnPathFunction< C, D > &pf) |
| Assignment operator. More... | |
| void | Initialize (C< D > &init_value, C< int > *init_label, C< int > *init_predecessor, bool sequential_label) |
| Initializes object attributes. More... | |
| bool | RemoveSimple (size_t index, BucketState state) |
| Sets initial value for root pixel of index 'index'. More... | |
| bool | RemoveLabel (size_t index, BucketState state) |
| Sets initial value for root pixel of index 'index'. Also sets its label value. More... | |
| bool | Capable (int index, int adj_index, BucketState adj_state) |
| Checks if index can offer better value to adj_index, prior to computing it by Propagate function. This function makes IFT faster. More... | |
| bool | Propagate (int index, int adj_index) |
| Updates adjacent pixel values and returns true if path_function is propagated. More... | |
| bool | Increasing () |
| Returns whether this path function uses increasing or decreasing values. More... | |
| Public Member Functions inherited from Bial::PathFunction< C, D > | |
| PathFunction () | |
| Basic constructor. More... | |
| PathFunction (const PathFunction< C, D > &pf) | |
| Copy constructor. More... | |
| virtual | ~PathFunction () |
| Virtual destructor required for virtual classes. More... | |
| void | UpdateCompleteData (size_t index, size_t adj_index) |
| Updates value, label, and predecessor images of index adj_index according to 'index' and value. One of these functions will be used depending on the chosen resultant images. More... | |
| void | UpdatePredecessorData (size_t index, size_t adj_index) |
| void | UpdateLabelData (size_t index, size_t adj_index) |
| void | UpdateSimpleData (size_t index, size_t adj_index) |
| PathFunction< C, D >::RemoveFn | RemoveFunction () |
| Returns a root initialization function that will be used depending on used maps. More... | |
| PathFunction< C, D >::UpdateFn | UpdateFunction () |
| Returns an update function that will be used depending on used maps. More... | |
Protected Attributes | |
| C< D > | handicap |
| Handicap container (Vector, Matrix, Image, etc). More... | |
| C< D > | component |
| Pointer to component container (Vector, Matrix, Image, etc). More... | |
| Protected Attributes inherited from Bial::PathFunction< C, D > | |
| C< D > * | value |
| Pointer to value container (Vector, Matrix, Image, etc). More... | |
| C< int > * | label |
| Pointer to label container (Vector, Matrix, Image, etc). More... | |
| C< int > * | predecessor |
| Pointer to predecessor container (Vector, Matrix, Image, etc). More... | |
| int | next_label |
Additional Inherited Members | |
| Public Types inherited from Bial::PathFunction< C, D > | |
| typedef bool(PathFunction< C, D >::* | RemoveFn) (size_t index, BucketState state) |
| Initialization functions of IFT. It will operate on valid maps among value, label, predecessor, and. More... | |
| typedef void(PathFunction< C, D >::* | UpdateFn) (size_t index, size_t adj_index) |
| Update functions of IFT. It will operate on valid maps among value, label, and predecessor. More... | |
Definition at line 22 of file ConnPathFunction.hpp.
| Bial::ConnPathFunction< C, D >::ConnPathFunction | ( | const C< D > & | handicap, |
| const C< D > & | component | ||
| ) |
| Bial::ConnPathFunction< C, D >::ConnPathFunction | ( | const ConnPathFunction< C, D > & | pf | ) |
Copy constructor.
| pf | path-function to be copied. |
| Bial::ConnPathFunction< C, D >::~ConnPathFunction | ( | ) |
Destructor.
| none. |
|
virtual |
Checks if index can offer better value to adj_index, prior to computing it by Propagate function. This function makes IFT faster.
| source | Source pixel index. |
| target | Adjacent pixel index. |
Implements Bial::PathFunction< C, D >.
|
virtual |
Returns whether this path function uses increasing or decreasing values.
| none. |
Implements Bial::PathFunction< C, D >.
|
virtual |
Initializes object attributes.
| init_value | Reference for initial value container. |
| init_label | Reference for initial label container. |
| init_predecessor | Reference for predecessor container. |
| sequential_label | Sets labeling sequentially. |
Reimplemented from Bial::PathFunction< C, D >.
| ConnPathFunction< C, D > Bial::ConnPathFunction< C, D >::operator= | ( | const ConnPathFunction< C, D > & | pf | ) |
Assignment operator.
| pf | path-function to be assigned. |
|
virtual |
Updates adjacent pixel values and returns true if path_function is propagated.
| source | Source pixel index. |
| target | Adjacent pixel index. |
Implements Bial::PathFunction< C, D >.
|
virtual |
Sets initial value for root pixel of index 'index'. Also sets its label value.
| index | The index of the pixel to be initalized. |
Implements Bial::PathFunction< C, D >.
|
virtual |
Sets initial value for root pixel of index 'index'.
| index | The index of the pixel to be initalized. |
Implements Bial::PathFunction< C, D >.
|
protected |
Pointer to component container (Vector, Matrix, Image, etc).
Definition at line 29 of file ConnPathFunction.hpp.
|
protected |
Handicap container (Vector, Matrix, Image, etc).
Definition at line 27 of file ConnPathFunction.hpp.


This block continuously calculates the mean value of its input signal. It uses the function:
integral( u over time) y = ---------------------- time - startTime
This can be used to determine the empirical expectation value of a random signal, such as generated by the Noise blocks.
The parameter t_eps is used to guard against division by zero (the mean value computation starts at <simulation start time> + t_eps and before that time instant y = u).
See also the Mean block for a sampled implementation.
This block is demonstrated in the examples UniformNoiseProperties and NormalNoiseProperties.
| Date | Description | ||
|---|---|---|---|
| June 22, 2015 |
|


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"You do not have to explain every single drop of water contained in a rain barrel. You have to explain one drop only. The reader will get it."
-George Singleton
Every day, hundreds of millions of
Internet users type search terms
into the address bar of their browsers
and come face to face with meaningful
information. While this everyday task
may seem simple and unremarkable, it
would be impossible without the innovative
thinking of one Internet company: Google.
Known around the world as a leader in online
search, Google has grown into one of the biggest
companies on the Internet.
Walmart is a multinational retailer that
has its roots in the United States of America.
It runs department stores mostly aimed at middle
class and lower-middle class people. Today, Walmart
is the second largest public corporation in the world
according to Fortune Global 500. It employees more
than 2 million employees and is one of the most
valuable companies in the world. It is headquartered in
Bentonville, Arkansas.
At first Amazon was started in the
name Cadabra in 1994 by Jeff Bezos.
It went online as amazon.com in 1995.
When started it was a small online store
and today Amazon is the largest online
retailer in the world. The company’s name
amazon has become as big as the amazon river.
It is the inspiration for other several online
businesses and Amazon.com is the world’s third
-largest information technology company by revenue.
Samsung comprises of numerous affiliated
businesses with shareholders holding a large
proportion of shares. This prestigious company
is the global leader and an award-winning innovator
producing smarter gadgets every day. The multinational
company is the largest in South Korea holding multiple
subsidiaries like Samsung Electronics, Samsung Heavy
Industries, Samsung Life Insurance, Samsung Electro-
mechanics and more. Have a captivating experience
through our infographic, and quickly grasp essential
insights & data that Samsung holds.
It's more rare for a company to dominate multiple
industries for years at once while continuing
to innovative in many others. For Steve Jobs,
Apple cofounder and former CEO, the goal of Apple
wasnt simply to create great products, but to create
a lasting company that will influence the world for
generations. While Apples current strength might
give younger readers the impression that its
always been a leader in technology, its far from the
truth. Just forty-five days from bankruptcy at its
worst point, Apples transformation has been a remarkable
event.
An e-commerce company from China, Alibaba
is currently the biggest IPO in the world.
The Group provides business to customer and
customer to customer sales and services through
the web portals. Alibaba’s other services include
electronic payments, cloud computing services that
are data centric and search engines for shopping.
The founders of the company are Jack Ma and Penh Lei.
An English teacher by profession, Jack Ma lacked in
computer knowledge. He made up for this weakness with
his vision. Ma and 17 of his friends launched Alibaba
in 1999.
When it comes to 21st century online success
stories, few compare to the incredible growth
and worldwide change that Facebook has achieved.
Founded in 2003 by an assortment of Harvard Univer-
sity students, the website has gone on to become
one of the Internet’s top businesses,boasting
over one billion members as of 2012.Founded in a
Harvard dorm room by Mark Zuckerberg, Eduardo Saverin,
Chris Hughes, Dustin Moskovitz, and Andrew McCollum,
the early Facebook is almost nothing like the
social network’s current website.
The story of Sony has a very interesting start.
Sony found its beginning in the wake of World
War II in the war plagued Japan. In 1946, Masaru
Ibuka started a radio repair shop electronics shop
in a bomb-damaged department store building in Tokyo
with a total of eight employees. The next year, he
was joined by his colleague, Akio Morita, and they
founded a company called Tokyo Tsushin Kogyo (Tokyo
Telecommunications Engineering Corporation). From
early attempts at creating products like the rice-cooker
to the later success of creating Japan’s first magnetic recorder,
the innovative company went on to create other hit products
which won the company widespread recognition and international
acclaim as a truly global company known for its quality
and innovative products
The U.S. electric car market includes the
established automakers such as Ford (F) and
Chevrolet (GM). But one company stands out
from the car maker mix, and that is Tesla
Motors (TSLA). Tesla entered the media spotlight
in 2013 along with its CEO Elon Musk when it
unleashed its flagship car the Model S.Tesla’s
release has been a hit with not only car buyers,
but also catapulted the company into the spotlight
as one of the few successful independent automakers
along with being a pioneer when it comes to the
electric car market.


Barren Bogs [am20], swamp, in Mandoria, wilderness
Last updated Turn #128
Barren Bogs [am20], swamp, in Mandoria, wilderness
------------------------------------------------------------------------
28: Stack of savages [cr2c], savages, number: 19, arrived from the north.
30: Wild men [hc0b], savages, number: 32, arrived from the west.
Routes leaving Barren Bogs:
North, swamp, to Barren Bogs [al20], 14 days
East, swamp, to Barren Bogs [am21], 14 days
South, swamp, to Barren Bogs [an20], 14 days
West, swamp, to Barren Bogs [am19], 14 days
Inner locations:
Graveyard [ac8p], graveyard, 1 day, owner:
Undead [hk4z], undead, number: 15
It is raining.
Seen here:
Stack of cyclops [gj7j], cyclops, number: 14
Stack of savages [cr2c], savages, number: 19
Wild men [hc0b], savages, number: 31


-
What is Programming?
You probably interact with computers on a daily basis, but let's define concretely what we mean when we talk about computers in relation to programming. A computer is a machine that processes and stores information. Programming is telling the computer how to ingest, process, and then store that data. When someone writes a program, that person is giving the computer a set of commands that it must follow.
When we write programs, we are writing instructions that the computer will follow. Computers are very literal, they will take our instructions and follow them exactly, but we must lay them out in a very detailed way so that they understand us. Programming, at its core, is taking a big problem and breaking it into smaller and smaller problems until they are small enough that we can tell the computer to solve that problem.
-
Why Programming?
There are many answers to this question, at first programming can pay really well. The average income in the USA is somewhere along $50K per year per household. Second is that you are never bored. There’s always something new. Third is that programming is very suitable for the remote lifestyle. Many more answers make programming special.
-
Super Computer to Start?
To start programming you don't need a super computer. In fact, a computer with any specifications is more than enough. Programming just need a code editor and browser to get the job done. After reaching to high levels in coding and start building big projects, then a computer with medium specifications is needed.
-
Frontend and Backend Development
The frontend of a website is the part that users interact with. Everything that you see when you’re navigating around the Internet, from fonts and colors to dropdown menus and sliders, is a combo of HTML, CSS, and JavaScript being controlled by your computer’s browser.
So what makes the front end of a website possible? Where is all that data stored? This is where the backend comes in. The back end of a website consists of a server, an application, and a database. A back-end developer builds and maintains the technology that powers those components which, together, enable the user-facing side of the website to even exist in the first place. Back-end developers use server-side languages like PHP, Ruby, Python, Java, and .Net to build an application, and tools like MySQL, Oracle, and SQL Server to find, save, or change data and serve it back to the user in front-end code.
Here are some free books that give you a SPARK to start your journey with coding
including just the web development languages part, frontend & backend:


--- layout: post title: Birthday Card date: '2004-08-11T17:32:00-04:00' tags: - '2004' tumblr_url: http://poetry.liabogoev.com/post/938790773/birthday-card ---
As the days fly high,
Time does not die
Another year gone by
Growing wiser, older
Stronger, bolder
Reaching years that are “golder”.
As we have grown,
Time has flown
New seeds have been sown.


Generate Files and Folders¶
The next step after creating a scene is to generate the relevant files and folders required to create the simulations
Generating yaml files¶
To generate this scene, click on the “Generate yaml for scene” button. An environment.yaml yaml file will be created in the src/scenes/<scene_name> folder
This file is an easy to read textual representation of the scene. This file is important and required to properly generate the workcell. This is also important for reloading a scene into the GUI for editing. It is possible to do these changes via the YAML file directly rather than the GUI. However, do note that errors may arise if you do not follow the proper YAML format.
Once succesful, the Generate Files from yaml will be available
Other Files (General)¶
Click the Generate Files from yaml button to generate the actual required components to build the simulation
The following components will be generated through this button:
Environment Object Packages¶
The environment objects that were created in the GUI will now have its URDF Xacros and folders generated in the assets/environment_objects/<object_name>_description
folder. This folder will be referenced in the environment urdf
environment.urdf.xacro¶
This file is located in the scenes/<scene_name>/urdf/ folder. This is the main urdf file that combines all the different elements of the workcell and will be the main file that launch files will reference to when launching files.
arm_hand.srdf.xacro¶
This file is located in the scenes/<scene_name>/urdf/
folder. This is the srdf file that combines the robot arm and end effector and contains information such as ignoring link collisions , and will be referenced in launch files.
Other Files (ROS1)¶
<will be edited later>
The launch file for simulation
The move_group.launch file
The planning_context.launch
Other Files (ROS2)¶
demo.launch.py¶
This file is located in the scenes/<scene_name>/launch/ folder. This file serves as a demo launch file that launches an example RVIZ simulation of the workspace. Note that this launch file does not incorporate Moveit2 Components. To find out how to do so, check out Grasp Execution Demo
Once done, you will see the following screen. You can then exit the GUI
Next Steps: Run workcell demo


You must unassign all applications from this assessor before you can change their role status.
Role profile
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Date status change
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Change made by
Steve, Admin
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+++
title = "ConsenSource"
+++
{{< blocks/cover title="ConsenSource" image_anchor="top" height="full">}}
{{< /blocks/cover >}}
{{% blocks/lead color="white" %}}
What is ConsenSource?
ConsenSource is an application designed to bring transparency and trust to the sourcing industry -
making it easier to find and verify the certifications and audit histories of a factory.
{{% /blocks/lead %}}
{{< blocks/section color="dark">}}
{{< /blocks/section >}}


Information Sciences & Technology
Rochester Institute of Technology
The version of browser that you use is old.
Click
here
to download the new version of Chrome.


| | |
- Category
class Category |
| |
The Category class represents a category within the 3taps client APIs.
A Category object has the following attributes:
code
A brief string uniquely identifying this category.
group
A string identifying the group this Category belongs to.
name
A string containing the name of this category.
annotations
A list of Annotation objects for the annotations associated
with this category.
You can retrieve and change these attributes directly as required. |
| |
Methods defined here:
- __init__(self, **kwargs)
- Standard initializer.
The initial attributes for the Category object can be passed as
keyword arguments if desired.
| |


Back to templates
Add Conditions
Your password isn't manly enough.
Your password isn't manly enough.
Your password isn't manly enough.


In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.
Run the following cell to download the CIFAR-10 dataset for python.
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10_location = '/input/cifar-10/python.tar.gz'
if isfile(floyd_cifar10_location):
tar_gz_path = floyd_cifar10_location
else:
tar_gz_path = 'cifar-10-python.tar.gz'
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = block_num
if not isfile(tar_gz_path):
with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
urlretrieve(
'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
tar_gz_path,
pbar.hook)
if not isdir(cifar10_dataset_folder_path):
with tarfile.open(tar_gz_path) as tar:
tar.extractall()
tar.close()
tests.test_folder_path(cifar10_dataset_folder_path)
The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named data_batch_1
, data_batch_2
, etc.. Each batch contains the labels and images that are one of the following:
Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the batch_id
and sample_id
. The batch_id
is the id for a batch (1-5). The sample_id
is the id for a image and label pair in the batch.
Ask yourself "What are all possible labels?", "What is the range of values for the image data?", "Are the labels in order or random?". Answers to questions like these will help you preprocess the data and end up with better predictions.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import helper
import numpy as np
# Explore the dataset
batch_id = 1
sample_id = 1
helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)
def normalize(x):
"""
Normalize a list of sample image data in the range of 0 to 1
: x: List of image data. The image shape is (32, 32, 3)
: return: Numpy array of normalize data
"""
a=0
b=255
print(x)
return (x-a)/(b-a)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_normalize(normalize)
Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the one_hot_encode
function. The input, x
, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to one_hot_encode
. Make sure to save the map of encodings outside the function.
Hint: Don't reinvent the wheel.
def one_hot_encode(x):
"""
One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
: x: List of sample Labels
: return: Numpy array of one-hot encoded labels
"""
# TODO: Implement Function
y = np.zeros((len(x), 10))
for i in range(len(x)):
y[i,x[i]]=1
return y
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_one_hot_encode(one_hot_encode)
As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset.
Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)
This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle
import problem_unittests as tests
import helper
# Load the Preprocessed Validation data
valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))
For the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.
Note: If you're finding it hard to dedicate enough time for this course each week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages to build each layer, except the layers you build in the "Convolutional and Max Pooling Layer" section. TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.
However, if you would like to get the most out of this course, try to solve all the problems without using anything from the TF Layers packages. You can still use classes from other packages that happen to have the same name as ones you find in TF Layers! For example, instead of using the TF Layers version of the
conv2d
class, tf.layers.conv2d, you would want to use the TF Neural Network version ofconv2d
, tf.nn.conv2d.
Let's begin!
The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions
neural_net_image_input
image_shape
with batch size set to None
.name
parameter in the TF Placeholder.neural_net_label_input
n_classes
with batch size set to None
.name
parameter in the TF Placeholder.neural_net_keep_prob_input
name
parameter in the TF Placeholder.These names will be used at the end of the project to load your saved model.
Note: None
for shapes in TensorFlow allow for a dynamic size.
import tensorflow as tf
def neural_net_image_input(image_shape):
"""
Return a Tensor for a batch of image input
: image_shape: Shape of the images
: return: Tensor for image input.
"""
# TODO: Implement Function
return tf.placeholder(tf.float32, shape=((None,) + image_shape), name='x')
def neural_net_label_input(n_classes):
"""
Return a Tensor for a batch of label input
: n_classes: Number of classes
: return: Tensor for label input.
"""
# TODO: Implement Function
return tf.placeholder(tf.float32, shape=(None, n_classes), name='y')
def neural_net_keep_prob_input():
"""
Return a Tensor for keep probability
: return: Tensor for keep probability.
"""
# TODO: Implement Function
return tf.placeholder(tf.float32, None, "keep_prob")
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tf.reset_default_graph()
tests.test_nn_image_inputs(neural_net_image_input)
tests.test_nn_label_inputs(neural_net_label_input)
tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)
Convolution layers have a lot of success with images. For this code cell, you should implement the function conv2d_maxpool
to apply convolution then max pooling:
conv_ksize
, conv_num_outputs
and the shape of x_tensor
.x_tensor
using weight and conv_strides
.pool_ksize
and pool_strides
.Note: You can't use TensorFlow Layers or TensorFlow Layers (contrib) for this layer, but you can still use TensorFlow's Neural Network package. You may still use the shortcut option for all the other layers.
def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):
"""
Apply convolution then max pooling to x_tensor
:param x_tensor: TensorFlow Tensor
:param conv_num_outputs: Number of outputs for the convolutional layer
:param conv_ksize: kernal size 2-D Tuple for the convolutional layer
:param conv_strides: Stride 2-D Tuple for convolution
:param pool_ksize: kernal size 2-D Tuple for pool
:param pool_strides: Stride 2-D Tuple for pool
: return: A tensor that represents convolution and max pooling of x_tensor
"""
# TODO: Implement Function
weights_shape = list(conv_ksize) + [x_tensor.get_shape().as_list()[3], conv_num_outputs]
#weights = tf.Variable(tf.truncated_normal(weights_shape, stddev=5e-2))
weights = tf.Variable(tf.truncated_normal(weights_shape, stddev=5e-2))
bias = tf.Variable(tf.zeros(conv_num_outputs))
output = tf.nn.conv2d(x_tensor, weights,
strides=[1, conv_strides[0], conv_strides[1], 1],
padding='SAME')
output = tf.nn.bias_add(output, bias)
output = tf.nn.relu(output)
output = tf.nn.max_pool(output,
ksize=[1, pool_ksize[0], pool_ksize[1], 1],
strides=[1, pool_strides[0], pool_strides[1], 1],
padding='SAME')
return output
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_con_pool(conv2d_maxpool)
Implement the flatten
function to change the dimension of x_tensor
from a 4-D tensor to a 2-D tensor. The output should be the shape (Batch Size, Flattened Image Size). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
def flatten(x_tensor):
"""
Flatten x_tensor to (Batch Size, Flattened Image Size)
: x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
: return: A tensor of size (Batch Size, Flattened Image Size).
"""
tensor_shape = x_tensor.get_shape().as_list()
# Get the length of the flattened dimensions.
flattened_shape = np.array(tensor_shape[1:]).prod()
# Batch size is casted by tf.shape.
return tf.reshape(x_tensor, [tf.shape(x_tensor)[0], flattened_shape])
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_flatten(flatten)
Implement the fully_conn
function to apply a fully connected layer to x_tensor
with the shape (Batch Size, num_outputs). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
def fully_conn(x_tensor, num_outputs):
"""
Apply a fully connected layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
"""
flattened_shape = np.array(x_tensor.get_shape().as_list()[1:]).prod()
# Define trainable variables.
weights = tf.Variable(tf.truncated_normal([flattened_shape, num_outputs], stddev=0.04))
bias = tf.Variable(tf.zeros([num_outputs]))
# Fully convolution layer.
fc = tf.nn.relu(tf.add(tf.matmul(x_tensor, weights), bias))
return fc
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_fully_conn(fully_conn)
Implement the output
function to apply a fully connected layer to x_tensor
with the shape (Batch Size, num_outputs). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
Note: Activation, softmax, or cross entropy should not be applied to this.
def output(x_tensor, num_outputs):
"""
Apply a output layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
"""
flattened_shape = np.array(x_tensor.get_shape().as_list()[1:]).prod()
# Define trainable variables.
weights = tf.Variable(tf.truncated_normal([flattened_shape, num_outputs], stddev=0.04))
bias = tf.Variable(tf.zeros([num_outputs]))
# Output layer.
return tf.add(tf.matmul(x_tensor, weights), bias)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_output(output)
Implement the function conv_net
to create a convolutional neural network model. The function takes in a batch of images, x
, and outputs logits. Use the layers you created above to create this model:
keep_prob
. def conv_net(x, keep_prob, num_classes=10):
"""
Create a convolutional neural network model
: x: Placeholder tensor that holds image data.
: keep_prob: Placeholder tensor that hold dropout keep probability.
: return: Tensor that represents logits
"""
# Network architecture inspired from:
# https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10/cifar10.py
# 2 Convoultion and Max Pool Layers applied.
conv = conv2d_maxpool(x,
conv_num_outputs=64,
conv_ksize=[5,5],
conv_strides=[1,1],
pool_ksize=[3,3],
pool_strides=[2,2])
conv = conv2d_maxpool(conv,
conv_num_outputs=64,
conv_ksize=[5,5],
conv_strides=[1,1],
pool_ksize=[3,3],
pool_strides=[2,2])
# Apply a Flatten Layer
flattened_conv = flatten(conv)
# 2 Fully-Connected Layers.
fc = fully_conn(flattened_conv, 384)
fc = fully_conn(fc, 192)
# Dropout layer.
fc = tf.nn.dropout(fc, keep_prob)
# Output Layer.
return output(fc, num_classes)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
##############################
## Build the Neural Network ##
##############################
# Remove previous weights, bias, inputs, etc..
tf.reset_default_graph()
# Inputs
x = neural_net_image_input((32, 32, 3))
y = neural_net_label_input(10)
keep_prob = neural_net_keep_prob_input()
# Model
logits = conv_net(x, keep_prob)
# Name logits Tensor, so that is can be loaded from disk after training
logits = tf.identity(logits, name='logits')
# Loss and Optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
tests.test_conv_net(conv_net)
Implement the function train_neural_network
to do a single optimization. The optimization should use optimizer
to optimize in session
with a feed_dict
of the following:
x
for image inputy
for labelskeep_prob
for keep probability for dropoutThis function will be called for each batch, so tf.global_variables_initializer()
has already been called.
Note: Nothing needs to be returned. This function is only optimizing the neural network.
def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
"""
Optimize the session on a batch of images and labels
: session: Current TensorFlow session
: optimizer: TensorFlow optimizer function
: keep_probability: keep probability
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
"""
session.run(optimizer, feed_dict={x: feature_batch,
y: label_batch,
keep_prob: keep_probability})
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_train_nn(train_neural_network)
Implement the function print_stats
to print loss and validation accuracy. Use the global variables valid_features
and valid_labels
to calculate validation accuracy. Use a keep probability of 1.0
to calculate the loss and validation accuracy.
def print_stats(session, feature_batch, label_batch, cost, accuracy):
"""
Print information about loss and validation accuracy
: session: Current TensorFlow session
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
: cost: TensorFlow cost function
: accuracy: TensorFlow accuracy function
"""
# Calculate batch loss and accuracy
loss = sess.run(cost, feed_dict={x: feature_batch,
y: label_batch,
keep_prob: 1.})
valid_acc = sess.run(accuracy, feed_dict={x: valid_features,
y: valid_labels,
keep_prob: 1.})
print('Loss: {:>10.4f} Validation Accuracy: {:.6f}'.format(loss, valid_acc))
Tune the following parameters:
epochs
to the number of iterations until the network stops learning or start overfittingbatch_size
to the highest number that your machine has memory for. Most people set them to common sizes of memory:keep_probability
to the probability of keeping a node using dropout# TODO: Tune Parameters
epochs = 20
batch_size = 128
keep_probability = 1
Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
print('Checking the Training on a Single Batch...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
batch_i = 1
for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')
print_stats(sess, batch_features, batch_labels, cost, accuracy)
Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
save_model_path = './image_classification'
print('Training...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
# Loop over all batches
n_batches = 5
for batch_i in range(1, n_batches + 1):
for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')
print_stats(sess, batch_features, batch_labels, cost, accuracy)
# Save Model
saver = tf.train.Saver()
save_path = saver.save(sess, save_model_path)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import tensorflow as tf
import pickle
import helper
import random
# Set batch size if not already set
try:
if batch_size:
pass
except NameError:
batch_size = 64
save_model_path = './image_classification'
n_samples = 4
top_n_predictions = 3
def test_model():
"""
Test the saved model against the test dataset
"""
test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load model
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
# Get Tensors from loaded model
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
test_batch_acc_total += sess.run(
loaded_acc,
feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})
test_batch_count += 1
print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))
# Print Random Samples
random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
random_test_predictions = sess.run(
tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)
test_model()
You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN. Pure guessing would get you 10% accuracy. However, you might notice people are getting scores well above 80%. That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_image_classification.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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Create Your First Video Category.


Driver and witnesses sought following e-scooter incident
by New Zealand Police"Driver and witnesses sought following e-scooter incident"
Please attribute to Detective Senior Sergeant Kathryn Bostock, Manager, Auckland City Crime Squad
Police have commenced an investigation after a man reportedly fell off an e-scooter before being struck by a vehicle in central Auckland last night.
Emergency services were alerted to the incident on Symonds Street at around 11.20pm.
The man is reported to have become unconscious as a result of the fall.
A vehicle travelling on Symonds Street was able to avoid the man however a second vehicle following behind struck him.
The second vehicle did not remain at the scene.
Police would like to speak to the driver of that vehicle and any witnesses to the incident.
The man remains in a critical condition in hospital.
Anyone with information relating to this incident can call 105 and quote file number 191117/1249.
ENDS
Issued b


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October 2016
| # | Topic | Newspaper | Date | Link |
|---|---|---|---|---|
| 1 | Scientists in Islam Exhibition | Al-Riyadh newspaper | 1st October 2016 | Click Here |
| Al-bilad Online Newspaper | 2nd October 2016 | Click Here | ||
| Boroz Electronic Newspaper | 2nd October 2016 | Click Here Click Here |
||
| Nas Electronic Newspaper | 2nd October 2016 | Click Here | ||
| Alwatan Online Newspaper | 2nd October 2016 | Click Here | ||
| Dar-Alakhbar Electronic Newspaper | 2nd October 2016 | Click Here | ||
| You Tube.com | 1st October 2016 | Click Here | ||
| Sjl- News Online Newspaper | 1st October 2016 | Click Here | ||
| Bal3raby.com | 1st October 2016 | Click Here | ||
| Eqtisadiya TV | 2nd October 2016 | Click Here | ||
| Aqtsad.com | 2nd October 2016 | Click Here | ||
| Saudi News Online Newspaper | 1st October 2016 | Click Here | ||
| Nas News Online Newspaper | 2nd October 2016 | Click Here | ||
| 2 | MOU agreement signed between Alfaisal University and ANB | Al-Riyadh newspaper | 10th October 2016 | Click Here |
| Elnadanews.com | 10th October 2016 | Click Here | ||
Dar-Alakhbar.com |
10th October 2016 | Click Here | ||
| Saudishare.com | 10th October 2016 | Click Here | ||
| Saudinow.net | 10th October 2016 | Click Here |
September 2016
| # | Topic | Newspaper | Date | Link |
|---|---|---|---|---|
| 1 | MOU agreement signed between Alfaisal University and Specialist Medical Center | Al-Jazira Newspaper | 20th September 2016 | Click Here |
| Alhayat newspaper | 19th September 2016 | Click Here | ||
| Arbynews Electronic Newspaper | 20th September 2016 | Click Here | ||
| SABQ Online Newspaper | 19th September 2016 | Click Here | ||
| 2 | Scientists in Islam Exhibition | Al-Riyadh newspaper | 28th September 2016 | Click Here |
| Al-Jazira Newspaper | 26th September 2016 | Click Here | ||
| Almowaten Electronic Newspaper | 26th September 2016 | Click Here | ||
| SABQ Online Newspaper | 25th September 2016 | Click Here | ||
| Abo-link Electronic Newspaper | 28th September 2016 | Click Here | ||
| Eye Of Riyadh.com | 28th September 2016 | Click Here | ||
| Oassf Electronic Newspaper | 26th September 2016 | Click Here | ||
| Msader Electronic Newspaper | 28th September 2016 | Click Here | ||
| Alyuwm.com | 28th September 2016 | Click Here | ||
| Saudi News Online Newspaper | 28th September 2016 | Click Here | ||
| The Foundation Of Abdulaziz Saud Al-babtain's Prize for Poetic Creativity (Website) | 28th September 2016 | Click Here | ||
| Arabs Free YouTube ( An interview ith two of the Team) | 27th September 2016 | Click Here | ||
| Demo. Femtolayer (Website) | 26th September 2016 | Click Here |
July 2016
| # | Topic | Newspaper | Date | Link |
|---|---|---|---|---|
| 1 | HE President Dr. Mohammed Alhayaza announces the U.S. News and World Report university rankings | Al-Jazira Newspaper | 31st July 2016 | Click Here |
| Riyadh Newspaper | 30th July 2016 | Click Here |
April 2016
| # | Topic | Newspaper | Date | Link |
|---|---|---|---|---|
| 1 | Alfaisal University fifth Graduation ceremony 2016 College of Medicine under the auspices of HRH Prince Turki Alfaisal. |
Riyadh Newspaper | 28th April 2016 | Click Here |
| Al-Madina Newspaper | 1st April 2016 | Click Here | ||
| Watny1 Newspaper | 28th April 2016 | Click Here | ||
| Saudi Press Agency (SPA) | 28th April 2016 | Click Here | ||
| Sabq Newspaper | 23rd April 2016 | Click Here | ||
Almowaten |
23rd April 2016 | Click Here | ||
| Alyaum | 23rd April 2016 | Click Here | ||
| Al-Madina Newspaper | 24th April 2016 | Click Here | ||
| 2 | SIMEC 2016 and ACP program | Alriyadh newspaper | April 6th 2016 | Click Here |
| Alriyadh newspaper | April 9th 2016 | Click Here | ||
| Aljazirah Newspaper | April 6th 2016 | Click Here | ||
| Alwatan Newspaper | April 6th 2016 | Click Here | ||
| Al- Madina Newspaper | April 6th 2016 | Click Here | ||
| Al- Madina Newspaper | April 15th 2016 | Click Here | ||
| Alhayat Newspaper | April 6th 2016 | Click Here | ||
| Saudi Press Agency | April 6th 2016 | Click Here | ||
| SABQ Online Newspaper | April 6th 2016 | Click Here | ||
| (Almnatiq) Electronic Newspaper | April 15th 2016 | Click Here | ||
| Bab Electronic Newspaper | April 6th 2016 | Click Here | ||
| 3 | HRH Prince Bader Bin Abdulmohsen cuts the ribbon to open the Artology Exhibition at Alfaisal University. |
Riyadh Newspaper | 1st April 2016 | Click Here |
| (Sabq) Electronic Newspaper | 1st April 2016 | Click Here | ||
| (Almnatiq) Electronic Newspaper | 1st April 2016 | Click Here | ||
| 4 | Dr Mohammed Alkhazem announces; the success of Saudi physicians in Canada | Riyadh Newspaper | 11th April 2016 | Click Here |
| Aleqtisadiah Newspaper | 11th April 2016 | Click Here | ||
| Alwatan Newspaper | 11th April 2016 | Click Here | ||
| 5 | Graduation Ceremony | Aljazirah Newspaper | 25th April 2016 | Click Here |
| Riyadh Newspaper | 27th April 2016 | Click Here | ||
| Sabq Newspaper | 23rd April 2016 | Click Here | ||
Almowaten Newspaper |
23rd April 2016 | Click Here | ||
| Alyaum Newspaper | 17th April 2016 | Click Here | ||
Al Madina Newspaper |
24th April 2016 | Click Here |
March 2016
| # | Topic | Newspaper | Date | Link |
|---|---|---|---|---|
| 1 | 18th Meeting of our Board of Trustees at Alfaisal University | Riyadh Newspaper | 29th March 2016 | Click Here |
| Aljazirah Newspaper | 29th March 2016 | Click Here | ||
| Alwatan Newspaper | 29th March 2016 | Click Here | ||
| Almasar Electronic Newspaper | 28th March 2016 | Click Here | ||
| Almnatiq Electronic Newspaper | 28th March 2016 | Click Here | ||
| Alwast Electronic Newspaper | 28th March 2016 | Click Here |
February 2016
| # | Topic | Newspaper | Date | Link |
|---|---|---|---|---|
| 1 | TEDx AlfaisalU 2016 at Alfaisal University. | Al-Riyadh Newspaper | 8th Feb 2016 | Click Here |
| Al-Jazirah Newspaper | 8th Feb 2016 | Click Here | ||
| Al-Hayat Newspaper | 8th Feb 2016 | Click Here | ||
| Asharq Al-Awsat Newspaper | 8th Feb 2016 | Click Here | ||
| Al-Madina Newspaper | 8th Feb 2016 | Click Here | ||
| Alwatan Newspaper | 8th Feb 2016 | Click Here | ||
| Aleqtisadiah Newspaper | 8th Feb 2016 | Click Here | ||
| Arriyadiyah Newspaper | 8th Feb 2016 | Click Here | ||
| (Sabq) Electronic Newspaper | 8th Feb 2016 | Click Here | ||
| Alweeam Electronic Newspaper | 8th Feb 2016 | Click Here | ||
| Almowaten Electronic Newspaper | 8th Feb 2016 | Click Here | ||
| (Almnatiq) Electronic Newspaper | 8th Feb 2016 | Click Here | ||
| 2 | HRH Prince Sultan Bin Salman's lecture- Topic "Management Innovation and the Economy of Tourism and Culture" | Al-Riyadh Newspaper | 12th Feb 2016 | Click Here |
| Aleqtisadiah Newspaper | 12th Feb 2016 | Click Here | ||
| Asharq Al-Awsat Newspaper | 12th Feb 2016 | Click Here | ||
| Sabq) Electronic Newspaper | 12th Feb 2016 | Click Here | ||
| Almowaten Electronic Newspaper | 12th Feb 2016 | Click Here | ||
| 3 | Alfaisal University prepares for the 5th Annual Career Expo | Al-Riyadh Newspaper | 19th Feb 2016 | Click Here |
| (Sabq) Electronic Newspaper | 19th Feb 2016 | Click Here | ||
| 4 | Alfaisal would like to congratulate Dr Maha Bint Mishari AlSaud ,Vice president for external relations , on her recent appointment as a member of the international council of the American college of physicians. | Alriyadh newspaper | 23rd Feb 2016 | Click Here |
| Almowaten Electronic Newspaper | 22nd Feb 2016 | Click Here | ||
| 5 | Alfaisal University prepares for the 5th Annual Career Expo. |
Alriyadh newspaper | 29th Feb 2016 | Click Here |
January 2016
| # | Topic | Newspaper | Date | Link |
|---|---|---|---|---|
| 1 | Boeing's continuous support to Alfaisal University |
Ar-Riyadh newspaper | 25Th Jan 2016 | Click Here |
| Aleqtisadiah newspaper | 24th Jan 2016 | Click Here | ||
| Dr. Bajis Dodin who speaks about the programs offered, our commitment to producing graduates that will meet the Saudi labor market requirements and of our international faculty and their expertise. | Ar-Riyadh newspaper | 11th Jan 2016 | Click Here | |
| 2 | Al-Madina Newspaper | 11th Jan 2016 | Click Here | |
| 3 | College of Engineering Latest Achievements | Ar-Riyadh newspaper | 31st Jan 2016 | Click Here |
| (Almnatiq) Electronic Newspaper | 30 th Jan 2016 | Click Here | ||
Almowaten Electronic Newspaper |
30 th Jan 2016 | Click Here | ||
(Sabq) Electronic Newspaper |
31st Jan 2016 | Click Here |


Rest Services
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The partition map can be used to provide a visual representation of a hierarchy structure.
The partition map appearance and behavior can be customized using the following:
PartitionMapSegment
interface.
value
property must be provided.
Additionally the content of a segment can be customized by adding a child ng-template
identified as #partitionMapSegment
.
This custom template can be used to add functionality such as a tooltip or popover. The custom template can use the segmentFocus
and
segmentBlur
events to trigger events when the segment is focused or blurred.
The template context provides access to several variables that can be used to display information within the segment:
data
property.


Flexible floating form labels to make your boring placeholders super fly. Based on the FloatLabel pattern by Matt Smith. With flyLabel.js, it's easy to trigger custom behaviors on form labels. Check out the examples, and contribute your own.
.classic { opacity: 0; top: 2em; z-index: -1; } .classic.is-active { opacity: 1; top: 1em; z-index: 1; }
.drop { opacity: 0; text-shadow: 0 0 25px blue; transform: scale(11); z-index: -1; } .drop.is-active { opacity: 1; text-shadow: 0 0 0 blue; transform: scale(1); z-index: 1; }
.from-right { left: 5em; opacity: 0; } .from-right.is-active { left: 0%; opacity: 1; }
.jiggle.is-active { animation: JIGGLE .3s ease; animation-iteration-count: 3; }
.classic { opacity: 0; top: 2em; z-index: -1; } .classic.is-active { opacity: 1; top: 1em; z-index: 1; }


We previously have applied deep autoencoder (DAE) for noise reduction and speech enhancement. However, the DAE was trained using only clean speech. In this study, we further introduce an explicit denoising process in learning the DAE. In training the DAE, we still adopt greedy layer-wised pretraining plus fine tuning strategy. In pretraining, each layer is trained as a one hidden layer neural autoencoder (AE) using noisy-clean speech pairs as input and output (or transformed noisy-clean speech pairs by preceding AEs). Fine tuning was done by stacking all AEs with pretrained parameters for initialization. The trained DAE is used as a filter for speech estimation when noisy speech is given. Speech enhancement experiments were done to examine the performance of the trained denoising DAE. Noise reduction, speech distortion, and perceptual evaluation of speech quality (PESQ) criteria are used in the performance evaluations. Experimental results show that adding depth of the DAE consistently increase the performance when a large training data set is given. In addition, compared with a minimum mean square error based speech enhancement algorithm, our proposed denoising DAE provided superior performance on the three objective evaluations.


Before Change
self._crawler = self.crawler(config, conn_timeout=conn_timeout)
self._fetchers = self.fetchers(config, target)
self._platforms = get_platforms(platforms)
super(MultiResolver, self).__init__(cache=self._install_cache)
def make_installer(self, reqs, interpreter, platform):
assert len(reqs) == 1 and isinstance(reqs[0], PythonRequirement), "Got requirement list: %s" % (
repr(reqs))
After Change
class SilentResolver(Resolver):
@classmethod
def _log(cls, msg):
if "PANTS_VERBOSE" in os.environ:
print(" Resolver: %s" % msg)
class PythonResolver(object):
_VALID_DEPENDENCIES = {
PythonLibrary: "libraries",
PythonRequirement: "reqs",
PythonBinary: "binaries",


Allows you to use this job as a template.
Whenever this job is saved, all implementations will sync the changes except for any configured exemptions. See README for details.


penrose
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HOME
NEW COLLECTIONS
STORE
Women
Men
Kids
CONTACTS


Project
Create a movie/book/tv show/music database app
Home page / Library Page
Categories page
Navbar Component
Detail Page
Using Angular CLI, create a new project
Create new Repo in your github account
Push new local project to repo in github
Checkout to a new branch called homepage
Begin work on your home page
Send me a link to your PR for homepage


---
layout: page
title: "About"
description: ""
active: about
header-img: "img/about-bg.jpg"
---
I'm an anywhere, anytime, anyone kind of person.
I don't just do one specific genre of photos, I usually tend to branch out and do whatever I find inspiration in! I can see something in just about anything, and when I do I make sure to capture it! And that's what I'm all about, capturing the moments you never want to forget, big or small!
I was born in Missouri and I lived there most my life until about the fifth grade. Then my family and I moved to Oklahoma for school/job opportunities. It was a little rough at first. We all had to learn to adapt and change but over the years it got a lot easier and we began the new chapter of our lives. Oklahoma is the place in which I found my passion for photography and because of that, over the years I've learned that change isn't always bad. Sometimes it just leads to bigger and better things!


It’s a funky time to be alive. There would appear to be a civil war going on in the deep state. That deep state has infiltrated countries and political parties all throughout the world. It might be hard to believe, but remember these guys can print money without congressional approval. How big of an empire could you grow if you could print 16T or more in a single year, like they did in 2008, without any oversight?
Anyway, there are bombings with a gun control agenda, stopped mass shootouts without a gun control agenda, humanity being awesome, government being idiotic, and a purge in the USG positions… It’s a lot of news to read, which is why for people who are short on time I present to you: Meme the News.
Reminder about the MSM
Quick Breakdown- hint: Obama is a traitor
Gotta love tough guys
Not a bro kiss- that’s a pulitzer prize winning photo of one pole worker saving the life of a recently heart stopped and electricuted pole worker 50 ft off the ground
Victoria Secret Super Model Breastfeeding- It’s natural. Good to see people with high stakes and high publicity doing this.
Drugs are bad m’kay- I kinda wonder if he’s setting the precedent to kill the people that actually run the show and not just the guys on the street.
Never a camera around sometimes… well, good thing we spy all the time anyway…

Why isn’t she an American Household Name?
Modern cops make a select group of rich people safer. They are dangerous for the rest of society.
He’s not a Libertarian, but I like this photo. Why do you think it’s called a Bill…
It’s a handy reference to know how the MSM will talk about people
Clinton bodycount +1
McMaster Out John Bolton in
CDC False Advertising
Deep State Social Media companies having large change over in leadership
This page is synchronized from the post: ‘Meme the News- So much seems to be happening these days’


---
title: "PERENNIALS: SPRING & SUMMER BLOOMERS"
date: "2000-01-01 20:05:12 -06:00"
categories:
- adult
subtitle: ""
layout: "events"
event-image: "perennials.jpg"
event-dates: "TUESDAYS, MAY 12 - 26"
event-location: "Online via Zoom"
button-type: "register"
tickets-link: "https://continue.utah.edu/lifelong/class/llhg_617_perennials___spring_and_summer_bloomers"
registration-info: "Contact Info:<PHONE_NUMBER>
Lifelong Learning: LLHG 617"
instructor-info: "Kim Eden"
member-cost: "$81"
public-cost: "$89"
event-notes: "online"
tickets-button: ""
published: false
---
REGISTRATION REQUIRED
TUESDAYS, MAY 12 - 26 @ 6 - 8:30PM
Walk the aisles of any garden center in spring, and you'll find yourself in the midst of a cornucopia of scent and color. The sign says "perennials", and you know which ones appeal to you, but what else do you know about them? We can help! Whether you are starting a new garden or refreshing an existing one, you'll want to select plants suited to your individual landscape and to pick combinations that offer color and blooms from early spring until frost. Through slides, discussion, and garden walks, we will show you the characteristics of a diverse group of perennials suited to Utah gardens. This class will focus on perennials that bloom in spring and summer. For perennials that bloom late summer and fall, please take Perennials: Late Summer - Fall Bloomers. We'll also cover design, soil preparation, planting, and maintenance. Co-sponsored with Lifelong Learning.


pidfd_open - obtain a file descriptor that refers to a process
#include <sys/types.h>
int pidfd_open(pid_t pid, unsigned int flags);
The pidfd_open() system call creates a file descriptor that refers to the
process whose PID is specified in pid. The file descriptor is returned
as the function result; the close-on-exec flag is set on the file descriptor.
The flags argument is reserved for future use; currently,
this argument must be specified as 0.
On success, pidfd_open() returns a nonnegative file descriptor. On error,
-1 is returned and errno is set to indicate the cause of the error.
- EINVAL
- flags is not 0.
- EINVAL
- pid is not valid.
- EMFILE
- The per-process limit on the number of open file descriptors has been
reached (see the description of RLIMIT_NOFILE in
getrlimit(2)).
- ENFILE
- The system-wide limit on the total number of open files has been
reached.
- ENODEV
- The anonymous inode filesystem is not available in this kernel.
- ENOMEM
- Insufficient kernel memory was available.
- ESRCH
- The process specified by pid does not exist.
pidfd_open() first appeared in Linux 5.3.
pidfd_open() is Linux specific.
Currently, there is no glibc wrapper for this system call; call it using
syscall(2).
The following code sequence can be used to obtain a file
descriptor for the child of fork(2):
pid = fork();
if (pid > 0) { /* If parent */
pidfd = pidfd_open(pid, 0);
...
}
Even if the child has already terminated by the time of the
pidfd_open() call, its PID will not have been recycled and the
returned file descriptor will refer to the resulting zombie process. Note,
however, that this is guaranteed only if the following conditions hold
true:
- the disposition of SIGCHLD has not been explicitly set to
SIG_IGN (see sigaction(2));
- the SA_NOCLDWAIT flag was not specified while establishing a
handler for SIGCHLD or while setting the disposition of that signal
to SIG_DFL (see sigaction(2)); and
- the zombie process was not reaped elsewhere in the program (e.g., either
by an asynchronously executed signal handler or by wait(2) or
similar in another thread).
If any of these conditions does not hold, then the child process
(along with a PID file descriptor that refers to it) should instead be
created using clone(2) with the CLONE_PIDFD flag.
A PID file descriptor returned by pidfd_open() (of by clone(2)
with the CLONE_PID flag) can be used for the following purposes:
- The pidfd_send_signal(2) system call can be used to send a signal
to the process referred to by a PID file descriptor.
- A PID file descriptor can be monitored using poll(2),
select(2), and epoll(7). When the process that it refers to
terminates, these interfaces indicate the file descriptor as readable.
Note, however, that in the current implementation, nothing can be read
from the file descriptor (read(2) on the file descriptor fails with
the error EINVAL).
- If the PID file descriptor refers to a child of the calling process, then
it can be waited on using waitid(2).
The pidfd_open() system call is the preferred way of
obtaining a PID file descriptor for an already existing process. The
alternative is to obtain a file descriptor by opening a /proc/[pid]
directory. However, the latter technique is possible only if the
proc(5) filesystem is mounted; furthermore, the file descriptor
obtained in this way is not pollable and can't be waited on with
waitid(2).
The program below opens a PID file descriptor for the process whose PID is
specified as its command-line argument. It then uses poll(2) to monitor
the file descriptor for process exit, as indicated by an EPOLLIN event.
#define _GNU_SOURCE
#include <sys/types.h>
#include <sys/syscall.h>
#include <unistd.h>
#include <poll.h>
#include <stdlib.h>
#include <stdio.h>
#ifndef __NR_pidfd_open
#define __NR_pidfd_open 434 /* System call # on most architectures */
#endif
static int
pidfd_open(pid_t pid, unsigned int flags)
{
return syscall(__NR_pidfd_open, pid, flags);
}
int
main(int argc, char *argv[])
{
struct pollfd pollfd;
int pidfd, ready;
if (argc != 2) {
fprintf(stderr, "Usage: %s <pid>\n", argv[0]);
exit(EXIT_SUCCESS);
}
pidfd = pidfd_open(atoi(argv[1]), 0);
if (pidfd == -1) {
perror("pidfd_open");
exit(EXIT_FAILURE);
}
pollfd.fd = pidfd;
pollfd.events = POLLIN;
ready = poll(&pollfd, 1, -1);
if (ready == -1) {
perror("poll");
exit(EXIT_FAILURE);
}
printf("Events (0x%x): POLLIN is %sset\n", pollfd.revents,
(pollfd.revents & POLLIN) ? "" : "not ");
exit(EXIT_SUCCESS);
}
clone(2), kill(2), pidfd_send_signal(2), poll(2),
select(2), waitid(2), epoll(7)
This page is part of release 5.05 of the Linux man-pages project. A
description of the project, information about reporting bugs, and the latest
version of this page, can be found at https://www.kernel.org/doc/man-pages/.


{% if closed_detail.explanation %}
Explanation: {{ closed_detail.explanation }}
{% endif %}
{% if closed_detail.connection %}
Connection: {{ closed_detail.connection }}
{% endif %}


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by Jacob Cano
Posted on December 12th 2016
XCom 2 is the sequel game to XCOM: Enemy Unkown by Firaxis Games. It is a turn-based strategy RPG that revolves around resource and unit management while trying to retake the earth from its new alien overlords.
Read Moreby Jacob Cano
Posted on December 13, 2016 at 10:45 PM
Cars and soccer, two very different things combined into one action-packed, fun game. Rocket League is the newest, and fastest growing mmo-soccer game out there.
Read Moreby Jacob Cano
Posted on December 15, 2016 at 2:00 PM
Dark Souls, a game not for the faint-hearted. One of the most difficult RPG's in the genre, and maybe the most difficult, rage inducing game I have ever played. I haven't even beaten it yet! A classic in the RPG genre and a staple in the gaming community as a whole.
Read MoreSid Meier's Civilization VI is the newest big release. It came out a few weeks ago, and is the next game in the Civ series, an extremely popular series amoung gamers of all ages. It has many of the same mechanics as Civ V but updated graphics and new engine. I am excited to give it a try and spend hundreds of ours playing this with my friends!


Game News
Genshin
person
person
Update 2.1 Leak
Raiden Shogun Gameplay
send
person
Update 2.1 Leak
Shangonomiya Kokomi Gameplay
send
person
Update 2.1 Leak
Kujo Sara Gameplay
send


|
|
bool | m_initialized = false |
| | Variable to keep track if the window is initialized.
|
| |
|
bool | m_fullscreen = false |
| | Variable that keeps track if the window is full screen or not.
|
| |
|
bool | m_selfCreated = false |
| | Variable that is used to keep track if the window was created from already existing native one or not.
|
| |
|
bool | m_closed = false |
| | Variable that is used to keep track if the window was closed or not.
|
| |
|
bool | m_focused = true |
| | Variable that is used to keep track if the window is currently focused or not.
|
| |
|
std::function< void(std::vector< std::string >)> | m_dragDropCallback |
| | Variable that is used to keep track of the assigned callback for drag drop operations.
|
| |
|
IO::Keyboard * | m_keyboardState |
| | Variable used to track the keyboard state, all windows share a single keyboard state.
|
| |
|
IO::Mouse * | m_mouseState |
| | Variable used to track the mouse state, all windows share a single mouse state.
|
| |
◆ destroy()
| void Triton::Graphics::DXWindow::destroy |
( |
| ) |
|
|
overridevirtual |
◆ initFromCreated()
| bool Triton::Graphics::DXWindow::initFromCreated |
( |
std::vector< void * > |
params | ) |
|
|
overridevirtual |
Create a new window from an already existing native window
- Parameters
-
| params | The parameters needed to create the window (Windows: HWND) |
- Returns
- Did the window succeeded in creating and initializing
Implements Triton::Graphics::Window.
◆ initNew()
| bool Triton::Graphics::DXWindow::initNew |
( |
float |
startingWidth, |
|
|
float |
startingHeight |
|
) |
| |
|
overridevirtual |
Create a new window and initialize it
- Parameters
-
| startingWidth | The starting width of the window |
| startingHeight | The starting height of the window |
- Returns
- Did the window succeeded in creating and initializing
Implements Triton::Graphics::Window.
◆ makeCurrent()
| void Triton::Graphics::DXWindow::makeCurrent |
( |
| ) |
|
|
overridevirtual |
◆ nativeHandle()
| HWND Triton::Graphics::DXWindow::nativeHandle |
( |
| ) |
|
Get the native handle to the window
- Returns
- The HWND of the window
◆ setCursorPos()
| void Triton::Graphics::DXWindow::setCursorPos |
( |
double |
x, |
|
|
double |
y |
|
) |
| |
|
overridevirtual |
Set the mouse cursor position
- Parameters
-
| x | new X coordinate of the cursor |
| y | new Y coordinate of the cursor |
Implements Triton::Graphics::Window.
◆ showCursor()
| void Triton::Graphics::DXWindow::showCursor |
( |
bool |
value | ) |
|
|
overridevirtual |
Hide or show cursor
- Parameters
-
| value | To hide cursor false to make cursor visible true |
Implements Triton::Graphics::Window.
◆ size()
| std::tuple< int, int > Triton::Graphics::DXWindow::size |
( |
| ) |
|
|
overridevirtual |
Get the size of the window
- Returns
- tuple of the size first is width second is height
Implements Triton::Graphics::Window.
◆ update()
| void Triton::Graphics::DXWindow::update |
( |
| ) |
|
|
overridevirtual |
The documentation for this class was generated from the following files:
- C:/dev/Triton/TritonGraphics/src/TritonPlatform2/DirectX/Core/DXWindow.h
- C:/dev/Triton/TritonGraphics/src/TritonPlatform2/DirectX/Core/DXWindow.cpp


How It Works
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Most of the time, its always hard to find some services near you cause you dont know where you can get them. This services include plumbing, car repair, barber shop or hairdressing or even electronics repair e.t.c Most of the time, its always hard to find some services near you cause you dont know where you can get them. This services include plumbing, car repair, barber shop or hairdressing or even electronics repair e.t.c
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Smokeview Verification Tests
Animations
-
Stills
-
Manuals
Version info:
plume5c
temperature slice
vector temperature slice
temperature iso-surface
3d smoke/hrrpuv
temperature boundary
particles
thouse5
3d smoke/hrrpuv
temperature slice
WUI cases
hill_structure
BT10m_2x2km_LS
levelset
wind_test1
tree_test2


Tottenham identify Bundesliga manager as a potential replacement for Pochettino
by Neelabhra RoyTottenham Hotspur have reportedly identified Julian Nagelsmann as a potential replacement for Mauricio Pochettino.
Spurs have had a pretty shaky start to the season and are currently 14th in the Premier League table. According to the Telegraph, Pochettino has held talks with the club’s chairman Daniel Levy.
A report from the Times states that Spurs have identified Nagelsmann as a potential replacement if the Argentine departs.
The 32-year-old German has made quite a name for himself in the Bundesliga. He became the manager of Hoffenheim in 2016 and helped them to fourth place in the league in 2016/17. After leaving them, Nagelsmann joined RB Leipzig who have been doing pretty well under him. Die Roten Bullen are currently second in the Bundesliga table with 21 points from 11 matches so far. They are four points behind leaders Borussia Monchengladbach at the moment.
It looks quite unlikely that Tottenham will sack Pochettino since he still looks the right man to turn things around. However, if they were to, then Nagelsmann will be a suitable candidate. He has done quite a job with the German clubs and can do well in England.