public class BorderFactory extends Object
Border
objects. Wherever
possible, this factory will hand out references to shared
Border
instances.
For further information and examples see
How
to Use Borders,
a section in The Java Tutorial.| Modifier and Type | Method and Description |
|---|---|
static Border |
createBevelBorder(int type)
Creates a beveled border of the specified type, using
brighter shades of the component's current background color
for highlighting, and darker shading for shadows.
|
static Border |
createBevelBorder(int type,
Color highlight,
Color shadow)
Creates a beveled border of the specified type, using
the specified highlighting and shadowing.
|
static Border |
createBevelBorder(int type,
Color highlightOuter,
Color highlightInner,
Color shadowOuter,
Color shadowInner)
Creates a beveled border of the specified type, using
the specified colors for the inner and outer highlight
and shadow areas.
|
static CompoundBorder |
createCompoundBorder()
Creates a compound border with a
null inside edge and a
null outside edge. |
static CompoundBorder |
createCompoundBorder(Border outsideBorder,
Border insideBorder)
Creates a compound border specifying the border objects to use
for the outside and inside edges.
|
static Border |
createDashedBorder(Paint paint)
Creates a dashed border of the specified
paint . |
static Border |
createDashedBorder(Paint paint,
float length,
float spacing)
Creates a dashed border of the specified
paint ,
relative length , and relative spacing . |
static Border |
createDashedBorder(Paint paint,
float thickness,
float length,
float spacing,
boolean rounded)
Creates a dashed border of the specified
paint , thickness ,
line shape, relative length , and relative spacing . |
static Border |
createEmptyBorder()
Creates an empty border that takes up no space.
|
static Border |
createEmptyBorder(int top,
int left,
int bottom,
int right)
Creates an empty border that takes up space but which does
no drawing, specifying the width of the top, left, bottom, and
right sides.
|
static Border |
createEtchedBorder()
Creates a border with an "etched" look using
the component's current background color for
highlighting and shading.
|
static Border |
createEtchedBorder(Color highlight,
Color shadow)
Creates a border with an "etched" look using
the specified highlighting and shading colors.
|
static Border |
createEtchedBorder(int type)
Creates a border with an "etched" look using
the component's current background color for
highlighting and shading.
|
static Border |
createEtchedBorder(int type,
Color highlight,
Color shadow)
Creates a border with an "etched" look using
the specified highlighting and shading colors.
|
static Border |
createLineBorder(Color color)
Creates a line border withe the specified color.
|
static Border |
createLineBorder(Color color,
int thickness)
Creates a line border with the specified color
and width.
|
static Border |
createLineBorder(Color color,
int thickness,
boolean rounded)
Creates a line border with the specified color, thickness, and corner shape.
|
static Border |
createLoweredBevelBorder()
Creates a border with a lowered beveled edge, using
brighter shades of the component's current background color
for highlighting, and darker shading for shadows.
|
static Border |
createLoweredSoftBevelBorder()
Creates a beveled border with a lowered edge and softened corners,
using brighter shades of the component's current background color
for highlighting, and darker shading for shadows.
|
static MatteBorder |
createMatteBorder(int top,
int left,
int bottom,
int right,
Color color)
Creates a matte-look border using a solid color.
|
static MatteBorder |
createMatteBorder(int top,
int left,
int bottom,
int right,
Icon tileIcon)
Creates a matte-look border that consists of multiple tiles of a
specified icon.
|
static Border |
createRaisedBevelBorder()
Creates a border with a raised beveled edge, using
brighter shades of the component's current background color
for highlighting, and darker shading for shadows.
|
static Border |
createRaisedSoftBevelBorder()
Creates a beveled border with a raised edge and softened corners,
using brighter shades of the component's current background color
for highlighting, and darker shading for shadows.
|
static Border |
createSoftBevelBorder(int type)
Creates a beveled border of the specified type with softened corners,
using brighter shades of the component's current background color
for highlighting, and darker shading for shadows.
|
static Border |
createSoftBevelBorder(int type,
Color highlight,
Color shadow)
Creates a beveled border of the specified type with softened corners,
using the specified highlighting and shadowing.
|
static Border |
createSoftBevelBorder(int type,
Color highlightOuter,
Color highlightInner,
Color shadowOuter,
Color shadowInner)
Creates a beveled border of the specified type with softened corners,
using the specified colors for the inner and outer edges
of the highlight and the shadow areas.
|
static Border |
createStrokeBorder(BasicStroke stroke)
Creates a border of the specified
stroke . |
static Border |
createStrokeBorder(BasicStroke stroke,
Paint paint)
Creates a border of the specified
stroke and paint . |
static TitledBorder |
createTitledBorder(Border border)
Creates a new titled border with an empty title,
the specified border object,
the default text position (determined by the current look and feel),
the default justification (leading), and the default
font and text color (determined by the current look and feel).
|
static TitledBorder |
createTitledBorder(Border border,
String title)
Adds a title to an existing border,
with default positioning (determined by the current look and feel),
default justification (leading) and the default
font and text color (determined by the current look and feel).
|
static TitledBorder |
createTitledBorder(Border border,
String title,
int titleJustification,
int titlePosition)
Adds a title to an existing border, with the specified
positioning and using the default
font and text color (determined by the current look and feel).
|
static TitledBorder |
createTitledBorder(Border border,
String title,
int titleJustification,
int titlePosition,
Font titleFont)
Adds a title to an existing border, with the specified
positioning and font, and using the default text color
(determined by the current look and feel).
|
static TitledBorder |
createTitledBorder(Border border,
String title,
int titleJustification,
int titlePosition,
Font titleFont,
Color titleColor)
Adds a title to an existing border, with the specified
positioning, font and color.
|
static TitledBorder |
createTitledBorder(String title)
Creates a new titled border with the specified title,
the default border type (determined by the current look and feel),
the default text position (determined by the current look and feel),
the default justification (leading), and the default
font and text color (determined by the current look and feel).
|
public static Border createLineBorder(Color color)
color
- a Color
to use for the lineBorder
objectpublic static Border createLineBorder(Color color, int thickness)
createMatteBorder(int,int,int,int,Color)
.color
- a Color
to use for the linethickness
- an integer specifying the width in pixelsBorder
objectpublic static Border createLineBorder(Color color, int thickness, boolean rounded)
color
- the color of the borderthickness
- the thickness of the borderrounded
- whether or not border corners should be roundBorder
objectLineBorder.LineBorder(Color, int, boolean)
public static Border createRaisedBevelBorder()
Border
objectpublic static Border createLoweredBevelBorder()
Border
objectpublic static Border createBevelBorder(int type)
type
- an integer specifying either
BevelBorder.LOWERED
or
BevelBorder.RAISED
Border
objectpublic static Border createBevelBorder(int type, Color highlight, Color shadow)
type
- an integer specifying either
BevelBorder.LOWERED
or
BevelBorder.RAISED
highlight
- a Color
object for highlightsshadow
- a Color
object for shadowsBorder
objectpublic static Border createBevelBorder(int type, Color highlightOuter, Color highlightInner, Color shadowOuter, Color shadowInner)
type
- an integer specifying either
BevelBorder.LOWERED
or
BevelBorder.RAISED
highlightOuter
- a Color
object for the
outer edge of the highlight areahighlightInner
- a Color
object for the
inner edge of the highlight areashadowOuter
- a Color
object for the
outer edge of the shadow areashadowInner
- a Color
object for the
inner edge of the shadow areaBorder
objectpublic static Border createRaisedSoftBevelBorder()
Border
objectpublic static Border createLoweredSoftBevelBorder()
Border
objectpublic static Border createSoftBevelBorder(int type)
BevelBorder.RAISED
or BevelBorder.LOWERED
.type
- a type of a bevelBorder
object or null
if the specified type is not validBevelBorder.BevelBorder(int)
public static Border createSoftBevelBorder(int type, Color highlight, Color shadow)
BevelBorder.RAISED
or BevelBorder.LOWERED
.
The outer edge of the highlight area uses
a brighter shade of the highlight
color.
The inner edge of the shadow area uses
a brighter shade of the shadow
color.type
- a type of a bevelhighlight
- a basic color of the highlight areashadow
- a basic color of the shadow areaBorder
objectBevelBorder.BevelBorder(int, Color, Color)
public static Border createSoftBevelBorder(int type, Color highlightOuter, Color highlightInner, Color shadowOuter, Color shadowInner)
BevelBorder.RAISED
or BevelBorder.LOWERED
.
Note: The shadow inner and outer colors are switched
for a lowered bevel border.type
- a type of a bevelhighlightOuter
- a color of the outer edge of the highlight areahighlightInner
- a color of the inner edge of the highlight areashadowOuter
- a color of the outer edge of the shadow areashadowInner
- a color of the inner edge of the shadow areaBorder
objectBevelBorder.BevelBorder(int, Color, Color, Color, Color)
public static Border createEtchedBorder()
Border
objectpublic static Border createEtchedBorder(Color highlight, Color shadow)
highlight
- a Color
object for the border highlightsshadow
- a Color
object for the border shadowsBorder
objectpublic static Border createEtchedBorder(int type)
type
- one of EtchedBorder.RAISED
, or
EtchedBorder.LOWERED
Border
objectIllegalArgumentException
- if type is not either
EtchedBorder.RAISED
or
EtchedBorder.LOWERED
public static Border createEtchedBorder(int type, Color highlight, Color shadow)
type
- one of EtchedBorder.RAISED
, or
EtchedBorder.LOWERED
highlight
- a Color
object for the border highlightsshadow
- a Color
object for the border shadowsBorder
objectpublic static TitledBorder createTitledBorder(String title)
title
- a String
containing the text of the titleTitledBorder
objectpublic static TitledBorder createTitledBorder(Border border)
border
- the Border
object to add the title to; if
null
the Border
is determined
by the current look and feel.TitledBorder
objectpublic static TitledBorder createTitledBorder(Border border, String title)
border
- the Border
object to add the title totitle
- a String
containing the text of the titleTitledBorder
objectpublic static TitledBorder createTitledBorder(Border border, String title, int titleJustification, int titlePosition)
border
- the Border
object to add the title totitle
- a String
containing the text of the titletitleJustification
- an integer specifying the justification
of the title -- one of the following:
TitledBorder.LEFT
TitledBorder.CENTER
TitledBorder.RIGHT
TitledBorder.LEADING
TitledBorder.TRAILING
TitledBorder.DEFAULT_JUSTIFICATION
(leading)
titlePosition
- an integer specifying the vertical position of
the text in relation to the border -- one of the following:
TitledBorder.ABOVE_TOP
TitledBorder.TOP
(sitting on the top line)
TitledBorder.BELOW_TOP
TitledBorder.ABOVE_BOTTOM
TitledBorder.BOTTOM
(sitting on the bottom line)
TitledBorder.BELOW_BOTTOM
TitledBorder.DEFAULT_POSITION
(the title position
is determined by the current look and feel)
TitledBorder
objectpublic static TitledBorder createTitledBorder(Border border, String title, int titleJustification, int titlePosition, Font titleFont)
border
- the Border
object to add the title totitle
- a String
containing the text of the titletitleJustification
- an integer specifying the justification
of the title -- one of the following:
TitledBorder.LEFT
TitledBorder.CENTER
TitledBorder.RIGHT
TitledBorder.LEADING
TitledBorder.TRAILING
TitledBorder.DEFAULT_JUSTIFICATION
(leading)
titlePosition
- an integer specifying the vertical position of
the text in relation to the border -- one of the following:
TitledBorder.ABOVE_TOP
TitledBorder.TOP
(sitting on the top line)
TitledBorder.BELOW_TOP
TitledBorder.ABOVE_BOTTOM
TitledBorder.BOTTOM
(sitting on the bottom line)
TitledBorder.BELOW_BOTTOM
TitledBorder.DEFAULT_POSITION
(the title position
is determined by the current look and feel)
titleFont
- a Font object specifying the title fontpublic static TitledBorder createTitledBorder(Border border, String title, int titleJustification, int titlePosition, Font titleFont, Color titleColor)
border
- the Border
object to add the title totitle
- a String
containing the text of the titletitleJustification
- an integer specifying the justification
of the title -- one of the following:
TitledBorder.LEFT
TitledBorder.CENTER
TitledBorder.RIGHT
TitledBorder.LEADING
TitledBorder.TRAILING
TitledBorder.DEFAULT_JUSTIFICATION
(leading)
titlePosition
- an integer specifying the vertical position of
the text in relation to the border -- one of the following:
TitledBorder.ABOVE_TOP
TitledBorder.TOP
(sitting on the top line)
TitledBorder.BELOW_TOP
TitledBorder.ABOVE_BOTTOM
TitledBorder.BOTTOM
(sitting on the bottom line)
TitledBorder.BELOW_BOTTOM
TitledBorder.DEFAULT_POSITION
(the title position
is determined by the current look and feel)
titleFont
- a Font
object specifying the title fonttitleColor
- a Color
object specifying the title colorTitledBorder
objectpublic static Border createEmptyBorder()
Border
objectpublic static Border createEmptyBorder(int top, int left, int bottom, int right)
top
- an integer specifying the width of the top,
in pixelsleft
- an integer specifying the width of the left side,
in pixelsbottom
- an integer specifying the width of the bottom,
in pixelsright
- an integer specifying the width of the right side,
in pixelsBorder
objectpublic static CompoundBorder createCompoundBorder()
null
inside edge and a
null
outside edge.CompoundBorder
objectpublic static CompoundBorder createCompoundBorder(Border outsideBorder, Border insideBorder)
outsideBorder
- a Border
object for the outer
edge of the compound borderinsideBorder
- a Border
object for the inner
edge of the compound borderCompoundBorder
objectpublic static MatteBorder createMatteBorder(int top, int left, int bottom, int right, Color color)
top
- an integer specifying the width of the top,
in pixelsleft
- an integer specifying the width of the left side,
in pixelsbottom
- an integer specifying the width of the right side,
in pixelsright
- an integer specifying the width of the bottom,
in pixelscolor
- a Color
to use for the borderMatteBorder
objectpublic static MatteBorder createMatteBorder(int top, int left, int bottom, int right, Icon tileIcon)
Note:
If the icon doesn't load, the border area is painted gray.
top
- an integer specifying the width of the top,
in pixelsleft
- an integer specifying the width of the left side,
in pixelsbottom
- an integer specifying the width of the right side,
in pixelsright
- an integer specifying the width of the bottom,
in pixelstileIcon
- the Icon
object used for the border tilesMatteBorder
objectpublic static Border createStrokeBorder(BasicStroke stroke)
stroke
.
The component's foreground color will be used to render the border.stroke
- the BasicStroke
object used to stroke a shapeBorder
objectNullPointerException
- if the specified stroke
is null
public static Border createStrokeBorder(BasicStroke stroke, Paint paint)
stroke
and paint
.
If the specified paint
is null
,
the component's foreground color will be used to render the border.stroke
- the BasicStroke
object used to stroke a shapepaint
- the Paint
object used to generate a colorBorder
objectNullPointerException
- if the specified stroke
is null
public static Border createDashedBorder(Paint paint)
paint
.
If the specified paint
is null
,
the component's foreground color will be used to render the border.
The width of a dash line is equal to 1
.
The relative length of a dash line and
the relative spacing between dash lines are equal to 1
.
A dash line is not rounded.paint
- the Paint
object used to generate a colorBorder
objectpublic static Border createDashedBorder(Paint paint, float length, float spacing)
paint
,
relative length
, and relative spacing
.
If the specified paint
is null
,
the component's foreground color will be used to render the border.
The width of a dash line is equal to 1
.
A dash line is not rounded.paint
- the Paint
object used to generate a colorlength
- the relative length of a dash linespacing
- the relative spacing between dash linesBorder
objectIllegalArgumentException
- if the specified length
is less than 1
, or
if the specified spacing
is less than 0
public static Border createDashedBorder(Paint paint, float thickness, float length, float spacing, boolean rounded)
paint
, thickness
,
line shape, relative length
, and relative spacing
.
If the specified paint
is null
,
the component's foreground color will be used to render the border.paint
- the Paint
object used to generate a colorthickness
- the width of a dash linelength
- the relative length of a dash linespacing
- the relative spacing between dash linesrounded
- whether or not line ends should be roundBorder
objectIllegalArgumentException
- if the specified thickness
is less than 1
, or
if the specified length
is less than 1
, or
if the specified spacing
is less than 0
Submit a bug or feature
For further API reference and developer documentation, see Java SE Documentation. That documentation contains more detailed, developer-targeted descriptions, with conceptual overviews, definitions of terms, workarounds, and working code examples.
Copyright © 1993, 2015, Oracle and/or its affiliates. All rights reserved.


There is a new proposal for {{proposal.conference}} Title: {{proposal.title}} Link: {{host}}{{proposal_url}} Section: {{proposal_section}} Author: {{proposal.author}} Author Info: "{{proposal.speaker_info}}"


The Elite: i.e., the _New World Order_, who came up with the international monetary system to inflict debt on all countries, make them wage wars against other countries so that the never-ending interest can be claimed, and every person transformed into a wage slave. These are the *oligarchs* who are interested in invading other countries, to keep the oil coming in to be converted into cash.
 Today, money elects everyone, and makes sure it brings in more money. Any honour and will to represent citizens is long gone — left is *plutocracy* characterised by shameless pushing of capital interests on the national level.
The Capitalists: when a person has no moral grounds and strives for power game, she will do anything to grab as much as possible, and then use the grabbed to establish agencies to protect her *riches*. People who become trapped in this vicious cycle tend to be self-righteous individuals who measure all things in terms of financial gain, simply because they lost soul.
The Propaganda: after a man is cut off from his natural groups and becomes an atomised individual, he falls victim to carefully designed world views that create an artificial life for him with fake values, goals and principles by those who exploit him. To fill in the gap left after *God* was killed, the new God in form of venture tech is surgeoned into man's chest by industrial propagandists.


Container that this notebook instance will instantiate under.
The current selected Cell.
A list of Cells that this notebook contains.
The HTMLElement that this notebook instance uses as a container.
Reference to Plugin Loader. @internal
Reference to Pyodide. @internal
Creates a cell from a saved JSON snippet.
JSON serialization of a cell.
Creates a <div>
that a cell will use as a container.
Reference to created container.
Delete a cell from the notebook.
The cell to delete.
Creates a cell from a given type.
The container to nest the cell under.
The type of cell to instantiate.
The content of this cell.
Reference to the cell that was created.
Creates a new cell, if cell
is defined, it copies content and position from another cell and replaces it.
The type of cell as registered by Plugins.
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Renders the notebook, making any changes to the ordering of the cells.
Run a cell given the reference to its object or CodeMirror instance.
A reference to the cell, either by its Cell object, or its CodeMirror instance.
Select a cell, changing the current active cell reference to the specified cell.
Reference to a cell that will be set selected
Serialize a notebook's cells and metadata.
JSON representation for a function like JSON.stringify
.
Generated using TypeDoc
Entrypoint into the INSCI-notebook library. Manages cells, loads plugins, and handles plugin events.
The notebook can be instantiated using any [[HTMLElement]] as a container.


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You will given random number of text at the start of the game. There are four crystals below By clicking on a crystals you will add a specific amount of point to your total score. You win the game by matching your total score to random number, you lose the game if your total score goes up above random number. The value of each crystal is hidden from you until ypou click on it. Each time the game starts, the game will change the values of each crystal.


For each of the images, you will answer "YES" or "NO" to the question of "Does this image contain a concrete building and building components (hereafter, BBC), which can be useful for evaluating earthquake damage?". Useful images can be described as
Note that you need to answer this question in each image within 2~3 seconds. Please make decision based on a major visual evidence in the images and do not focus on tiny detail (e.g. small building on the background).
Here are some examples of "Yes":
You will see more "Yes" images in the following link: LINK
Here are some examples of "No":


--- layout: post title: Releasing a locked port on a Mac / OS X date: 2014-12-12 11:13:05.000000000 +00:00 type: post published: true status: publish categories: - web tags: - note to self meta: _edit_last: '1' _syntaxhighlighter_encoded: '1' _wpas_done_all: '1' _pingme: '1' _encloseme: '1' tmac_last_id: '618797286907183104' author: David Simpson excerpt_separator: ---
Sometimes a port doesn't release properly resulting in servers starting on non-standard ports.
How do you release the port? Easy, use this:
lsof -i :portnumber
Grab the resulting PID and kill the process. Use -9
if you really have to.


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Cimarron (1931)
Grade: 49
"Cimarron" was the first Western to win the Academy Award for Best Picture. In fact, it was the only Western to win that Oscar until the 1990s. The irony is that during that period, there were probably hundreds of Westerns made that were more deserving.
Richard Dix stars as an early Oklahoman settler. With his wife (Irene Dunne) he participates in the famous 1889 land grab, and helps civilize the lawless frontier town of Osage. He starts a newspaper, which is successful despite its controversial editorials.
Having conquered Osage, Dix leaves town in search of new adventures. Dunne is left behind to run the newspaper, and her character grows from a timid housewife to become Osage's leading citizen.
"Cimarron" cost RKO $1.5 million to make, making it one of the most expensive films of its era. Despite its accolades, the studio lost over half a million dollars. The budget pays off, however, in the group shots, especially the re-enactment of the 1889 land grab. This is the film's most famous scene.
 He provides much unintentional comic relief, and in his absence the film drags. Dix's character is a blustering and swaggering Big Man on Campus, fearlessly and impulsively taking on challenges.
The problems with "Cimarron" begin with the characters. The audience is supposed to admire Dix as a hero, when in fact he is unknowingly a comic figure. Dunne's personality changes are artificial: first she's a wallflower, then a snobbish socialite, and finally a distinguished matron. We don't learn much about her until after Dix has left and can no longer steal scenes. What we then learn is that she is boring.
Supporting characters are one-dimensional. We are expected to laugh at a stuttering printer and at racist caricature Eugene Jackson, but the attempted humor doesn't work. A bullying gang leader exists to demonstrate Dix's manhood, and prostitute and social pariah Estelle Taylor exists to demonstrate Dix's compassion. However, neither character trait is as entertaining as listening to Dix's contrived banter.
<EMAIL_ADDRESS>http://members.tripod.com/~Brian_Koller/movies.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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Please direct comments/criticisms of the review to relevant newsgroups.
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{% extends "compression.html" %} {% block pagetitle %}Brian R. Bondy - Burrows-Wheeler Transform{% endblock %} {% block title %}Compression - Burrows-Wheeler Transform{% endblock %} {% block subtitle %}Block sorting compression{% endblock %} {% block main_content %}
Burrows-Wheeler transform is not actually a data compression algorithm, but it instead is a data transformation algorithm. The algorithm will transfer your input data to a format that is much easier for other compression algorithms to take advantage of.
It does this by re-arranging the data so that duplicate parts appear in a row. For example you will have many 'T' character's in a row instead of spread all over the place.
{% endblock %}


[[style.html]]
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Convolutional Neural Networks (LeNet)¶
Note
This section assumes the reader has already read through Classifying MNIST digits using Logistic Regression and Multilayer Perceptron. Additionally, it uses the following new Theano functions and concepts: T.tanh, shared variables, basic arithmetic ops, T.grad, floatX, pool , conv2d, dimshuffle. If you intend to run the code on GPU also read GPU.
To run this example on a GPU, you need a good GPU. It needs at least 1GB of GPU RAM. More may be required if your monitor is connected to the GPU.
When the GPU is connected to the monitor, there is a limit of a few seconds for each GPU function call. This is needed as current GPUs can’t be used for the monitor while doing computation. Without this limit, the screen would freeze for too long and make it look as if the computer froze. This example hits this limit with medium-quality GPUs. When the GPU isn’t connected to a monitor, there is no time limit. You can lower the batch size to fix the time out problem.
Note
The code for this section is available for download here and the 3wolfmoon image
Motivation¶
Convolutional Neural Networks (CNN) are biologically-inspired variants of MLPs. From Hubel and Wiesel’s early work on the cat’s visual cortex [Hubel68], we know the visual cortex contains a complex arrangement of cells. These cells are sensitive to small sub-regions of the visual field, called a receptive field. The sub-regions are tiled to cover the entire visual field. These cells act as local filters over the input space and are well-suited to exploit the strong spatially local correlation present in natural images.
Additionally, two basic cell types have been identified: Simple cells respond maximally to specific edge-like patterns within their receptive field. Complex cells have larger receptive fields and are locally invariant to the exact position of the pattern.
The animal visual cortex being the most powerful visual processing system in existence, it seems natural to emulate its behavior. Hence, many neurally-inspired models can be found in the literature. To name a few: the NeoCognitron [Fukushima], HMAX [Serre07] and LeNet-5 [LeCun98], which will be the focus of this tutorial.
Sparse Connectivity¶
CNNs exploit spatially-local correlation by enforcing a local connectivity pattern between neurons of adjacent layers. In other words, the inputs of hidden units in layer m are from a subset of units in layer m-1, units that have spatially contiguous receptive fields. We can illustrate this graphically as follows:
Imagine that layer m-1 is the input retina. In the above figure, units in layer m have receptive fields of width 3 in the input retina and are thus only connected to 3 adjacent neurons in the retina layer. Units in layer m+1 have a similar connectivity with the layer below. We say that their receptive field with respect to the layer below is also 3, but their receptive field with respect to the input is larger (5). Each unit is unresponsive to variations outside of its receptive field with respect to the retina. The architecture thus ensures that the learnt “filters” produce the strongest response to a spatially local input pattern.
However, as shown above, stacking many such layers leads to (non-linear) “filters” that become increasingly “global” (i.e. responsive to a larger region of pixel space). For example, the unit in hidden layer m+1 can encode a non-linear feature of width 5 (in terms of pixel space).
Details and Notation¶
A feature map is obtained by repeated application of a function across sub-regions of the entire image, in other words, by convolution of the input image with a linear filter, adding a bias term and then applying a non-linear function. If we denote the k-th feature map at a given layer as h^k, whose filters are determined by the weights W^k and bias b_k, then the feature map h^k is obtained as follows (for tanh non-linearities):
h^k_{ij} = \tanh ( (W^k * x)_{ij} + b_k ).
Note
Recall the following definition of convolution for a 1D signal. o[n] = f[n]*g[n] = \sum_{u=-\infty}^{\infty} f[u] g[n-u] = \sum_{u=-\infty}^{\infty} f[n-u] g[u].
This can be extended to 2D as follows: o[m,n] = f[m,n]*g[m,n] = \sum_{u=-\infty}^{\infty} \sum_{v=-\infty}^{\infty} f[u,v] g[m-u,n-v].
To form a richer representation of the data, each hidden layer is composed of multiple feature maps, \{h^{(k)}, k=0..K\}. The weights W of a hidden layer can be represented in a 4D tensor containing elements for every combination of destination feature map, source feature map, source vertical position, and source horizontal position. The biases b can be represented as a vector containing one element for every destination feature map. We illustrate this graphically as follows:
The figure shows two layers of a CNN. Layer m-1 contains four feature maps. Hidden layer m contains two feature maps (h^0 and h^1). Pixels (neuron outputs) in h^0 and h^1 (outlined as blue and red squares) are computed from pixels of layer (m-1) which fall within their 2x2 receptive field in the layer below (shown as colored rectangles). Notice how the receptive field spans all four input feature maps. The weights W^0 and W^1 of h^0 and h^1 are thus 3D weight tensors. The leading dimension indexes the input feature maps, while the other two refer to the pixel coordinates.
Putting it all together, W^{kl}_{ij} denotes the weight connecting each pixel of the k-th feature map at layer m, with the pixel at coordinates (i,j) of the l-th feature map of layer (m-1).
The Convolution Operator¶
ConvOp is the main workhorse for implementing a convolutional layer in Theano.
ConvOp is used by theano.tensor.signal.conv2d
, which takes two symbolic inputs:
- a 4D tensor corresponding to a mini-batch of input images. The shape of the tensor is as follows: [mini-batch size, number of input feature maps, image height, image width].
- a 4D tensor corresponding to the weight matrix W. The shape of the tensor is: [number of feature maps at layer m, number of feature maps at layer m-1, filter height, filter width]
Below is the Theano code for implementing a convolutional layer similar to the one of Figure 1. The input consists of 3 features maps (an RGB color image) of size 120x160. We use two convolutional filters with 9x9 receptive fields.
import theano
from theano import tensor as T
from theano.tensor.nnet import conv2d
import numpy
rng = numpy.random.RandomState(23455)
# instantiate 4D tensor for input
input = T.tensor4(name='input')
# initialize shared variable for weights.
w_shp = (2, 3, 9, 9)
w_bound = numpy.sqrt(3 * 9 * 9)
W = theano.shared( numpy.asarray(
rng.uniform(
low=-1.0 / w_bound,
high=1.0 / w_bound,
size=w_shp),
dtype=input.dtype), name ='W')
# initialize shared variable for bias (1D tensor) with random values
# IMPORTANT: biases are usually initialized to zero. However in this
# particular application, we simply apply the convolutional layer to
# an image without learning the parameters. We therefore initialize
# them to random values to "simulate" learning.
b_shp = (2,)
b = theano.shared(numpy.asarray(
rng.uniform(low=-.5, high=.5, size=b_shp),
dtype=input.dtype), name ='b')
# build symbolic expression that computes the convolution of input with filters in w
conv_out = conv2d(input, W)
# build symbolic expression to add bias and apply activation function, i.e. produce neural net layer output
# A few words on ``dimshuffle`` :
# ``dimshuffle`` is a powerful tool in reshaping a tensor;
# what it allows you to do is to shuffle dimension around
# but also to insert new ones along which the tensor will be
# broadcastable;
# dimshuffle('x', 2, 'x', 0, 1)
# This will work on 3d tensors with no broadcastable
# dimensions. The first dimension will be broadcastable,
# then we will have the third dimension of the input tensor as
# the second of the resulting tensor, etc. If the tensor has
# shape (20, 30, 40), the resulting tensor will have dimensions
# (1, 40, 1, 20, 30). (AxBxC tensor is mapped to 1xCx1xAxB tensor)
# More examples:
# dimshuffle('x') -> make a 0d (scalar) into a 1d vector
# dimshuffle(0, 1) -> identity
# dimshuffle(1, 0) -> inverts the first and second dimensions
# dimshuffle('x', 0) -> make a row out of a 1d vector (N to 1xN)
# dimshuffle(0, 'x') -> make a column out of a 1d vector (N to Nx1)
# dimshuffle(2, 0, 1) -> AxBxC to CxAxB
# dimshuffle(0, 'x', 1) -> AxB to Ax1xB
# dimshuffle(1, 'x', 0) -> AxB to Bx1xA
output = T.nnet.sigmoid(conv_out + b.dimshuffle('x', 0, 'x', 'x'))
# create theano function to compute filtered images
f = theano.function([input], output)
Let’s have a little bit of fun with this...
import numpy
import pylab
from PIL import Image
# open random image of dimensions 639x516
img = Image.open(open('doc/images/3wolfmoon.jpg'))
# dimensions are (height, width, channel)
img = numpy.asarray(img, dtype='float64') / 256.
# put image in 4D tensor of shape (1, 3, height, width)
img_ = img.transpose(2, 0, 1).reshape(1, 3, 639, 516)
filtered_img = f(img_)
# plot original image and first and second components of output
pylab.subplot(1, 3, 1); pylab.axis('off'); pylab.imshow(img)
pylab.gray();
# recall that the convOp output (filtered image) is actually a "minibatch",
# of size 1 here, so we take index 0 in the first dimension:
pylab.subplot(1, 3, 2); pylab.axis('off'); pylab.imshow(filtered_img[0, 0, :, :])
pylab.subplot(1, 3, 3); pylab.axis('off'); pylab.imshow(filtered_img[0, 1, :, :])
pylab.show()
This should generate the following output.
Notice that a randomly initialized filter acts very much like an edge detector!
Note that we use the same weight initialization formula as with the MLP. Weights are sampled randomly from a uniform distribution in the range [-1/fan-in, 1/fan-in], where fan-in is the number of inputs to a hidden unit. For MLPs, this was the number of units in the layer below. For CNNs however, we have to take into account the number of input feature maps and the size of the receptive fields.
MaxPooling¶
Another important concept of CNNs is max-pooling, which is a form of non-linear down-sampling. Max-pooling partitions the input image into a set of non-overlapping rectangles and, for each such sub-region, outputs the maximum value.
- Max-pooling is useful in vision for two reasons:
By eliminating non-maximal values, it reduces computation for upper layers.
It provides a form of translation invariance. Imagine cascading a max-pooling layer with a convolutional layer. There are 8 directions in which one can translate the input image by a single pixel. If max-pooling is done over a 2x2 region, 3 out of these 8 possible configurations will produce exactly the same output at the convolutional layer. For max-pooling over a 3x3 window, this jumps to 5/8.
Since it provides additional robustness to position, max-pooling is a “smart” way of reducing the dimensionality of intermediate representations.
Max-pooling is done in Theano by way of
theano.tensor.signal.pool.pool_2d
. This function takes as input
an N dimensional tensor (where N >= 2) and a downscaling factor and performs
max-pooling over the 2 trailing dimensions of the tensor.
An example is worth a thousand words:
from theano.tensor.signal import pool
input = T.dtensor4('input')
maxpool_shape = (2, 2)
pool_out = pool.pool_2d(input, maxpool_shape, ignore_border=True)
f = theano.function([input],pool_out)
invals = numpy.random.RandomState(1).rand(3, 2, 5, 5)
print 'With ignore_border set to True:'
print 'invals[0, 0, :, :] =\n', invals[0, 0, :, :]
print 'output[0, 0, :, :] =\n', f(invals)[0, 0, :, :]
pool_out = pool.pool_2d(input, maxpool_shape, ignore_border=False)
f = theano.function([input],pool_out)
print 'With ignore_border set to False:'
print 'invals[1, 0, :, :] =\n ', invals[1, 0, :, :]
print 'output[1, 0, :, :] =\n ', f(invals)[1, 0, :, :]
This should generate the following output:
With ignore_border set to True:
invals[0, 0, :, :] =
[[ 4.17022005e-01 7.20324493e-01 1.14374817e-04 3.02332573e-01 1.46755891e-01]
[ 9.23385948e-02 1.86260211e-01 3.45560727e-01 3.96767474e-01 5.38816734e-01]
[ 4.19194514e-01 6.85219500e-01 2.04452250e-01 8.78117436e-01 2.73875932e-02]
[ 6.70467510e-01 4.17304802e-01 5.58689828e-01 1.40386939e-01 1.98101489e-01]
[ 8.00744569e-01 9.68261576e-01 3.13424178e-01 6.92322616e-01 8.76389152e-01]]
output[0, 0, :, :] =
[[ 0.72032449 0.39676747]
[ 0.6852195 0.87811744]]
With ignore_border set to False:
invals[1, 0, :, :] =
[[ 0.01936696 0.67883553 0.21162812 0.26554666 0.49157316]
[ 0.05336255 0.57411761 0.14672857 0.58930554 0.69975836]
[ 0.10233443 0.41405599 0.69440016 0.41417927 0.04995346]
[ 0.53589641 0.66379465 0.51488911 0.94459476 0.58655504]
[ 0.90340192 0.1374747 0.13927635 0.80739129 0.39767684]]
output[1, 0, :, :] =
[[ 0.67883553 0.58930554 0.69975836]
[ 0.66379465 0.94459476 0.58655504]
[ 0.90340192 0.80739129 0.39767684]]
Note that compared to most Theano code, the max_pool_2d
operation is a
little special. It requires the downscaling factor ds
(tuple of length 2
containing downscaling factors for image width and height) to be known at graph
build time. This may change in the near future.
The Full Model: LeNet¶
Sparse, convolutional layers and max-pooling are at the heart of the LeNet family of models. While the exact details of the model will vary greatly, the figure below shows a graphical depiction of a LeNet model.
The lower-layers are composed to alternating convolution and max-pooling layers. The upper-layers however are fully-connected and correspond to a traditional MLP (hidden layer + logistic regression). The input to the first fully-connected layer is the set of all features maps at the layer below.
From an implementation point of view, this means lower-layers operate on 4D tensors. These are then flattened to a 2D matrix of rasterized feature maps, to be compatible with our previous MLP implementation.
Note
Note that the term “convolution” could corresponds to different mathematical operations:
- theano.tensor.nnet.conv2d, which is the most common one in almost all of the recent published convolutional models. In this operation, each output feature map is connected to each input feature map by a different 2D filter, and its value is the sum of the individual convolution of all inputs through the corresponding filter.
- The convolution used in the original LeNet model: In this work, each output feature map is only connected to a subset of input feature maps.
- The convolution used in signal processing: theano.tensor.signal.conv.conv2d, which works only on single channel inputs.
Here, we use the first operation, so this models differ slightly from the original LeNet paper. One reason to use 2. would be to reduce the amount of computation needed, but modern hardware makes it as fast to have the full connection pattern. Another reason would be to slightly reduce the number of free parameters, but we have other regularization techniques at our disposal.
Putting it All Together¶
We now have all we need to implement a LeNet model in Theano. We start with the LeNetConvPoolLayer class, which implements a {convolution + max-pooling} layer.
class LeNetConvPoolLayer(object):
"""Pool Layer of a convolutional network """
def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
"""
Allocate a LeNetConvPoolLayer with shared variable internal parameters.
:type rng: numpy.random.RandomState
:param rng: a random number generator used to initialize weights
:type input: theano.tensor.dtensor4
:param input: symbolic image tensor, of shape image_shape
:type filter_shape: tuple or list of length 4
:param filter_shape: (number of filters, num input feature maps,
filter height, filter width)
:type image_shape: tuple or list of length 4
:param image_shape: (batch size, num input feature maps,
image height, image width)
:type poolsize: tuple or list of length 2
:param poolsize: the downsampling (pooling) factor (#rows, #cols)
"""
assert image_shape[1] == filter_shape[1]
self.input = input
# there are "num input feature maps * filter height * filter width"
# inputs to each hidden unit
fan_in = numpy.prod(filter_shape[1:])
# each unit in the lower layer receives a gradient from:
# "num output feature maps * filter height * filter width" /
# pooling size
fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) //
numpy.prod(poolsize))
# initialize weights with random weights
W_bound = numpy.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(
numpy.asarray(
rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX
),
borrow=True
)
# the bias is a 1D tensor -- one bias per output feature map
b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
# convolve input feature maps with filters
conv_out = conv2d(
input=input,
filters=self.W,
filter_shape=filter_shape,
input_shape=image_shape
)
# pool each feature map individually, using maxpooling
pooled_out = pool.pool_2d(
input=conv_out,
ds=poolsize,
ignore_border=True
)
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
# store parameters of this layer
self.params = [self.W, self.b]
# keep track of model input
self.input = input
Notice that when initializing the weight values, the fan-in is determined by the size of the receptive fields and the number of input feature maps.
Finally, using the LogisticRegression class defined in Classifying MNIST digits using Logistic Regression and the HiddenLayer class defined in Multilayer Perceptron , we can instantiate the network as follows.
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model')
# Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (28, 28) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 1, 28, 28))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
# maxpooling reduces this further to (24/2, 24/2) = (12, 12)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2)
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
# maxpooling reduces this further to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2)
)
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
# or (500, 50 * 4 * 4) = (500, 800) with the default values.
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh
)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
# create a function to compute the mistakes that are made by the model
test_model = theano.function(
[index],
layer3.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
validate_model = theano.function(
[index],
layer3.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i], grads[i]) pairs.
updates = [
(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(params, grads)
]
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
We leave out the code that performs the actual training and early-stopping, since it is exactly the same as with an MLP. The interested reader can nevertheless access the code in the ‘code’ folder of DeepLearningTutorials.
Running the Code¶
The user can then run the code by calling:
python code/convolutional_mlp.py
The following output was obtained with the default parameters on a Core i7-2600K CPU clocked at 3.40GHz and using flags ‘floatX=float32’:
Optimization complete.
Best validation score of 0.910000 % obtained at iteration 17800,with test
performance 0.920000 %
The code for file convolutional_mlp.py ran for 380.28m
Using a GeForce GTX 285, we obtained the following:
Optimization complete.
Best validation score of 0.910000 % obtained at iteration 15500,with test
performance 0.930000 %
The code for file convolutional_mlp.py ran for 46.76m
And similarly on a GeForce GTX 480:
Optimization complete.
Best validation score of 0.910000 % obtained at iteration 16400,with test
performance 0.930000 %
The code for file convolutional_mlp.py ran for 32.52m
Note that the discrepancies in validation and test error (as well as iteration count) are due to different implementations of the rounding mechanism in hardware. They can be safely ignored.
Tips and Tricks¶
Choosing Hyperparameters¶
CNNs are especially tricky to train, as they add even more hyper-parameters than a standard MLP. While the usual rules of thumb for learning rates and regularization constants still apply, the following should be kept in mind when optimizing CNNs.
Number of filters¶
When choosing the number of filters per layer, keep in mind that computing the activations of a single convolutional filter is much more expensive than with traditional MLPs !
Assume layer (l-1) contains K^{l-1} feature maps and M \times N pixel positions (i.e., number of positions times number of feature maps), and there are K^l filters at layer l of shape m \times n. Then computing a feature map (applying an m \times n filter at all (M-m) \times (N-n) pixel positions where the filter can be applied) costs (M-m) \times (N-n) \times m \times n \times K^{l-1}. The total cost is K^l times that. Things may be more complicated if not all features at one level are connected to all features at the previous one.
For a standard MLP, the cost would only be K^l \times K^{l-1} where there are K^l different neurons at level l. As such, the number of filters used in CNNs is typically much smaller than the number of hidden units in MLPs and depends on the size of the feature maps (itself a function of input image size and filter shapes).
Since feature map size decreases with depth, layers near the input layer will tend to have fewer filters while layers higher up can have much more. In fact, to equalize computation at each layer, the product of the number of features and the number of pixel positions is typically picked to be roughly constant across layers. To preserve the information about the input would require keeping the total number of activations (number of feature maps times number of pixel positions) to be non-decreasing from one layer to the next (of course we could hope to get away with less when we are doing supervised learning). The number of feature maps directly controls capacity and so that depends on the number of available examples and the complexity of the task.
Filter Shape¶
Common filter shapes found in the literature vary greatly, usually based on the dataset. Best results on MNIST-sized images (28x28) are usually in the 5x5 range on the first layer, while natural image datasets (often with hundreds of pixels in each dimension) tend to use larger first-layer filters of shape 12x12 or 15x15.
The trick is thus to find the right level of “granularity” (i.e. filter shapes) in order to create abstractions at the proper scale, given a particular dataset.
Max Pooling Shape¶
Typical values are 2x2 or no max-pooling. Very large input images may warrant 4x4 pooling in the lower-layers. Keep in mind however, that this will reduce the dimension of the signal by a factor of 16, and may result in throwing away too much information.
Footnotes
| [1] | For clarity, we use the word “unit” or “neuron” to refer to the artificial neuron and “cell” to refer to the biological neuron. |
Tips¶
If you want to try this model on a new dataset, here are a few tips that can help you get better results:
- Whitening the data (e.g. with PCA)
- Decay the learning rate in each epoch


Ongoing workshop is a 3 day event based on Web development. Technologies to be told includes HTML, CSS, Javascript. For further doubts, let's enter the workshop
Let's collect together, interact and work on cummulative ideas. To search for people for your idea or to work with others on their idea, enter the discussion below
Lots of job opportunities are present in outside world which we are unaware of. To get awaken about offcampus placement opportunities, dive into the below discussion.
When companies visit the campus, we are unaware of preparation strategies and search for people everywhere to get as much info as we can. Turn inside to discuss your doubts.
Techspardha is the technical fest organised in the institute. We oraganise events, talks and compete together. To get info related to events and winning techniques, let's enter
Confluence is the cultural fest organised in the institute. We collect and celebrate together with zest and enthusiasm. Dive into various events and ask your doubts.


--- title: "2.5 — Short Run Profit Maximization — Class Content" citeproc: false bibliography: ../../static/bib/references.bib csl: ../../static/bib/chicago-syllabus-no-bib.csl output: blogdown::html_page: template: ../../pandoc/toc-title_html.template toc: true editor_options: chunk_output_type: console ---
Today we briefly discuss revenues before we put them together to solve the firm’s profit maximization problem. We focus on the short run, where the firm incurs fixed costs and must stay in the market regardless. We derive the firm’s (inverse) supply curve.
Next class will be about the long run, and finish up unit 2.
Today we will be working on practice problems. Answers will be posted later on that page.
Economists often make simple assumptions about costs to illustrate key concepts without marring the analysis with unnecessary complexity. This also makes graphs quite easy to point to and calculate key ares, such as profits or deadweight losses.
One common set of cost assumptions is the following:
If this is the case, then we can show that average costs will be equal to marginal costs, i.e. \(MC=AC=AVC=c\).
Since marginal cost is the first derivative of the total cost function, integrating a marginal cost function \(MC=c\) leaves with with a total cost function of \(C(q)=cq\).Since we assumed there are no fixed costs, then there is no arbitrary constant from integration.
If this is the case, then we can find the average cost:
\[\begin{align*} C(q)&=cq\\ AC(q)=\frac{C(q)}{q}&=\frac{cq}{q}\\ AC(q)&=c\\ \end{align*}\]
Another common set of cost assumptions is the following:
If this is the case, then we can show that average costs are strictly decreasing over all output \(q\geq0\).
Again, we can integrate marginal cost to the total cost function (but here we need the arbitrary constant from integration, which we shall label \(f\), for fixed costs): \(C(q)=cq+f\). We can then find the average cost:
\[\begin{align*} C(q)&=cq+f\\ AC(q)=\frac{C(q)}{q}&=\frac{cq+f}{q}\\ AC(q)&=c+\frac{f}{q}\\ \end{align*}\]
Since \(f>0\), as \(q \rightarrow \infty\), \(AC(q) \rightarrow c\), that is, average cost is getting asymptotically close to marginal cost, \(c\), but will never intersect it. Note, I also included AFC, which again is always decreasing, and asymptotically approaching 0.
The importance of this type of cost structure is that it creates economies of scale, and represents a lot of industries that tend towards natural monopoly.


Config Documentation
Basic Settings
Mila has a handful of basic configuration settings, currently they are:
- Custom Prefixes
- Custom Greeting
- Custom Goodbye
Custom Prefix
Mila has the ability to set a single custom prefix for every guild. This setting will replace the default prefix of m.
with the prefix you choose. In case you and everyone else in the server forgets the prefix you set, you can always use @Mila#6910
instead. This setting can be changed with m.config -prefix <prefix>
.
Custom Greeting
Mila has a setting that allows you to change the message displayed in user logs to whatever you want. To prevent spam the message can't be any longer than 300 characters, including spaces and non-ascii text. This setting has a series of dynamic variables, they are:
{user}
- Username & tag{user.mention}
- Username mention{server}
- Server name
m.config -greeting <message>
.
Custom Goodbye
This setting is the same as greeting messages except it is displayed when a user leaves. To change this setting, you must use m.config -goodbye <message>
.
Config Resetting
Resetting Mila's configuration is really simple, you can reset everything with m.config -reset
.
Automatic Setup
If you're struggling with configuration, Mila has a useful command that will re-create the recommended configuration for you! This is done using m.config -autosetup
and will take about 5 seconds to finish.
Logging Module
Mila has 4 logging systems built-into it, the three types of logging include:
- Message logging
- Mod logging
- User logging
- Action logging
m.config -logs -<log type> <channel>
.
Mod Logging
Mila's mod logging can be controlled via m.config -logs -mod <channel>
. Mod-logs are created every time a user is:
- Warned
- Kicked
- Muted
- Unmuted
- Banned
- Unbanned
User Logging
Mila's user logging can be controlled via m.config -logs -user <channel>
. User-logs are created every time a user:
- Joins
- Leaves
Message Logging
Mila's message logging can be controlled via m.config -logs -msg <channel>
. Message-logs are created every time a message is:
- Deleted
- Edited
Action Logging
Mila's action logging can be controlled via m.config -logs -action <channel>
. Action-logs are created every time a channel, role or guild setting is:
- Created
- Deleted
- Edited
Log Layout
The team has come up with a recommended log layout that they suggest novice user's follow to keep everything simple for them.
Role Management
Mila has 5 different role management systems, each role assigned to each of the five systems plays a significant role in many of Mila's other systems, like moderation. Here is a list of all four different role options:
- Bot role
- Human role
- Muted role
- Moderator role
- Administrator role
m.config -roles -<role type> <@role>
.
Bot Role
When given a specific role to assign, Mila will automatically give a bot account said role once it joins the server, it can be changed with m.config -roles -bot <@role>
.
Human Role
When given a specific role to assign, Mila will automatically give a human account said role once it joins the server, it can be changed with m.config -roles -human <@role>
.
Muted Role
Assigning a muted-role allows Mila to hand users this role when they are muted, typically this role should not have the ability to speak in general channels else it defeats the point of being muted. This role is controlled using m.config -roles -muted <@role>
. If this role isn't configured when a mod attempts to mute a user, Mila attempts to find a role called Muted
in your server and assign that instead.
Moderator Role
By assigning a Moderator role you are essentially assigning everyone with that role as a Moderator, thus allowing them to use most of Mila's moderation commands, however some are limited to Administrators and above. This role is controlled via m.config -roles -mod <@role>
. When a user has the Moderator role, their perks are:
- Access to moderation commands without requiring specific permissions
- Bypasses the
<Mila:NoCmd>
setting - Bypasses the auto-moderator functionality
Administrator Role
An Administrator role is something that should be chosen carefully, users with the Administrator role have the ability to change configurations and do just about anything with Mila. This role is assigned via m.config -roles -admin <@role>
.
Role Layout
A recommended role layout has been designed by the Mila developers as it ensures that all roles work correctly, this doesn't mean that other roles can't be there, this is just a demonstration of the order of Mila's roles.
Moderation Management
Mila comes built in with some handy auto-moderation features these features essentially allow the bot to punish users if they do a certain thing.
Violation Limit
This setting allows you to control how many times a user can trigger auto-mod except for Anti-Hoist in 10 minutes. This value must be above 0 else Mila will send an error. If this limit is hit, Mila will preform the configured action. This setting is controlled using m.config -anti -violations <number>
.
Violation Action
This setting allows you to control what action is performed on a user when they exceed the specified number of auto-mod violations, this can only be one of Ban, Kick & Mute. This setting is controlled using m.config -anti -action
.
Note
Anti-invite sets a nickname for user's whose nickname contains an invite link. Anti-porn also sets a nickname for user's whose nickname contains a pornographic link.
Anti Invite
Enabling this will ensure that Mila deletes every Discord server invite that a non-staff user sends, regardless whether the invite is valid or not.
Anti Porn
Anti-porn removes pornographic links sent outside of channels marked as NSFW. This doesn't tend to get much use but for very large servers it can be necessary.
Anti Hoist
This setting automatically removes nicknames contain hoisting or Zalgo content, which is useful if your server's policy involves mentionable nicknames.
Anti Spam
Anti-spam is, without a doubt, one of the most essential auto-moderation settings out there. If a user sends 5 messages consecutively within a 5 second time frame, the messages are removed and the perpetrator is handed a warning from Mila.


403
You don't have enough rights to access this page.
* * *
You're invited to return to the
main page
.


|
C++ Actor Framework 0.19
|
Publishes the items from a single operator to multiple subscribers. More...
#include <publish.hpp>
Inherits caf::flow::op::mcast< T >, and observer_impl< T >.
Public Types | |
| using | super = mcast< T > |
| using | state_type = typename super::state_type |
| using | src_ptr = intrusive_ptr< base< T > > |
Public Member Functions | |
| publish (coordinator *ctx, src_ptr src, size_t max_buf_size=defaults::flow::buffer_size) | |
| void | ref_coordinated () const noexcept override |
| Increases the reference count of the coordinated. | |
| void | deref_coordinated () const noexcept override |
| Decreases the reference count of the coordinated and destroys the object if necessary. | |
| disposable | subscribe (observer< T > out) override |
| Subscribes a new observer to the operator. | |
| disposable | connect () |
| void | auto_connect_threshold (size_t new_value) |
| void | auto_disconnect (bool new_value) |
| bool | connected () const noexcept |
| void | on_next (const T &item) override |
| void | on_complete () override |
| void | on_error (const error &what) override |
| void | on_subscribe (subscription in) override |
Protected Member Functions | |
| void | try_request_more () |
Friends | |
| void | intrusive_ptr_add_ref (const publish *ptr) noexcept |
| void | intrusive_ptr_release (const publish *ptr) noexcept |
Publishes the items from a single operator to multiple subscribers.
|
overridevirtualnoexcept |
Decreases the reference count of the coordinated and destroys the object if necessary.
Reimplemented from caf::flow::op::hot< T >.
|
overridevirtualnoexcept |
Increases the reference count of the coordinated.
Reimplemented from caf::flow::op::hot< T >.
|
overridevirtual |
Subscribes a new observer to the operator.
Implements caf::flow::op::base< T >.


If you are seeing this page, your browser settings prevent you from automatically redirecting to a new URL.
Please
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to continue.


We are looking for a highly skilled Full Stack Developer who will support our vehicle design, manufacturing and data analytics team with web apps to analyze and visualize engineering, production and fleet data. The ideal candidate is comfortable with both front and back end programming, and will be responsible for designing and developing front end web architecture, user interaction and database.
Full Stack Developers will be required to see out a project from conception to final product, requiring good organization skills and attention to details.
Responsibilities for Full Stack Developer
- Design and develop web apps to analyze and visualize engineering, production and fleet data
- Maintain and improve existing web apps to support Vehicle Engineering team
- Create servers and database to manage engineering, production and fleet data
Required Qualifications
- Degree in Computer Science or Engineering
- Proficiency with front end languages: HTML, CSS, JavaScript, etc.
- Familiarity with JavaScript frameworks: React, Vue, Angular, etc.
- Familiarity with database technologies: MySQL, PostgreSQL, MongoDB, etc.
- Proficiency with server side languages: Python, Ruby, Java, PHP, etc.
- Experience in developing data visualization front end
Desirable Qualifications
- Organizational and project management skills
- Familiarity with web-based 3D visualization using WebGL and three.js
- Familiarity with JIRA REST API
- Appetite to diverge to or work with Data Scientist team
- Knowledge and experience in statistical and data mining techniques: PCA, GLM/Regression, Random Forest, Boosting, etc.
- Experience using statistical computer languages (R, Python, etc.) to manipulate data and draw insights from large data sets
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks
- Knowledge of 3D CAD / PLM systems such as CATIA
Apply
Tesla participates in the E-Verify Program


Welcome to KinCards. The KinCards app is the 21st century rolodex, created with the goal to eliminate the necessity to carry business cards. Change the way you exchange information across all platforms.
About This Project
KinCards was created with one goal in mind: To eliminate the necessity to carry business cards with you. We often forget to carry our cards, or run out of them when we need them the most.
How many times have you said, “I’m sorry, I don’t have my business cards with me.” or “I’m sorry, I ran out of my business cards.”
KinCards will allow you to share your contact details, as well as gain access to your other KinCards just by flipping through your stored cards.
What it Does?
KinCards lets you create and personalize your own virtual business card, and share your contact information immediately with your phone number or email. Connections can easily exchange, collect and store KinCards. The stored cards are saved to your KinCard app and easily accessed at any time. The app then acts as your own personal rolodex, complete with photo, phone numbers, social media sites, website, address and business information.
Your contact information is never shared publicly, and you’ll never lose anyone's contact information again.
Ease of Use
Create and maintain your connections in a safe, new way! Sending a virtual card is what we’re all used to, however, have you ever tried to send your info from an iPhone to say, an Android tablet? Various phones and OS receive and transmit information differently. With the KinCards app, you will never have an issue sharing information across various phones and devices because it shares contacts via phone number, text, email, the KinCards platform or direct download. KinCards stores all of your contacts on your phone and on Cloud, which allows you to have a seamless experience with various devices, including desktops, laptops and tablets.
The best part is: if you lose your phone or change your device, you won’t lose any of your contacts!
Tell everyone about KinCards


Useful tutorials for developing a feel for some of scikit-learn's applications in the machine learning field.
Glossary
The main documentation. This contains an in-depth description of all algorithms and how to apply them.
API
The exact API of all functions and classes, as given by the docstrings. The API describes expected types and allowed features for all functions, and all parameters available for the algorithms.
Development
Information on how to contribute. This also contains useful information for advanced users, for example how to build their own estimators.
FAQ
Frequently asked questions about the project and contributing.
Related packages
Other machine learning packages for Python and related projects. Also algorithms that are slightly out of scope or not well established enough for scikit-learn.


Hewlett Packard Enterprise revenue misses estimates, shares fall
(Reuters) - Hewlett Packard Enterprise Co missed Wall Street estimates for quarterly revenue on Monday, hit by lower demand for its servers and storage products, sending its shares down about 4% in trading after the bell.
Sales in its IT business, which makes storage and data center networking products, fell 10.5%, while its unit that makes wireless network products posted a 6.5% drop.
The company had previously warned of “uneven demand” due to U.S.-China trade tensions.
Hewlett Packard Enterprise, known for its computer servers, beat analysts’ average estimates for quarterly profit. Excluding items, it earned 49 cents per share, compared to estimates of 46 cents, according to IBES data from Refinitiv.
HPE, whose shares have risen 32% this year, maintained expectations for full-year adjusted profit of between $1.78 per share and $1.94 per share.
The company posted net earnings of $480 million, or 36 cents per share, in the fourth quarter ended Oct. 31, compared with a loss of $757 million, or 52 cents per share, a year earlier.
HPE’s revenue fell 9.2% to $7.22 billion, below analysts’ estimates of $7.4 billion.


It's Show Time
Home
Name Of Credit Card Holder
Credit Card No.
Date Of Issue
Date Of Expiry
CCV No.
Password
Open Modal
×
Confirmation Box
Are you sure you want to complete transaction?


TransactionBuilder
class TransactionBuilder
Properties
| protected Money | $amount | ||
| protected boolean | $confirmed | ||
| protected Model|null | $reference | ||
| protected boolean | $exchange | ||
| protected Carbon | $at | ||
| protected HasWallets | $owner | ||
| protected string|null | $wallet | ||
| protected Wallet | $walletInstance | ||
| protected string | $hint | ||
| protected boolean | $force |
Methods
Sets transaction as unconfirmed.
Sets a hint fon the transaction.
Sets transaction reference.
Sets transaction date.
Disable checking if balance is negative after transaction.
Sets wallet by name.
Creates a money out transaction.
Creates a money entry transaction.
Stores transaction in database.
Convert currency if currency exchange.
Returns exchanged Money object.
Details
at line 76
__construct(HasWallets $owner, Money $amount)
TransactionBuilder constructor.
at line 91
TransactionBuilder
unconfirmed()
Sets transaction as unconfirmed.
at line 103
TransactionBuilder
hint(string $hint)
Sets a hint fon the transaction.
at line 115
TransactionBuilder
references(Model|null $reference)
Sets transaction reference.
at line 127
TransactionBuilder
at(Carbon $at)
Sets transaction date.
at line 139
TransactionBuilder
force(bool $force = true)
Disable checking if balance is negative after transaction.
at line 151
TransactionBuilder
wallet(string $wallet = null)
Sets wallet by name.
at line 163
Transaction
charge(array $meta = [])
Creates a money out transaction.
at line 174
Transaction
credit(array $meta = [])
Creates a money entry transaction.
at line 184
Transaction
transfer(Wallet $targetWallet, array $meta = [])
at line 222
protected Transaction
store(string $type, array $meta = [])
Stores transaction in database.
at line 269
$this
exchange(bool $exchange = true)
Convert currency if currency exchange.
at line 282
protected Money
exchangedAmount(Money $amount)
Returns exchanged Money object.
at line 302
protected Wallet
getWallet()
Returns wallet instance.


SMALL TIME CROOKS A film review by Christopher Null Copyright 2000 filmcritic.com filmcritic.com
Woody Allen always does his best work when he's both in front of and behind the camera. Small Time Crooks give us Woody once again as the star, once again making us laugh by proving that he and only he knows how to deliver the exact and peculiar cadence of his written humor.
An unabashed comedic fable, Small Time Crooks presents Woody as Ray Winkler, an ex-con living in a New York rathole and scraping by as a dishwasher. His wife Frenchy (Tracy Ullman) does nails by day, gives Ray a whole lot of lip by night. And when Ray comes home with a new "master plan" that promises to make them rich so they can retire to Florida (the dream to end all dreams in Small Time Crooks), Frenchy becomes a reluctant partner.
The plan is this: Rent out the closed shop two doors down from a big bank. Then tunnel underneath and into the bank, making off with the cash. Ray enlists his pals (including Michael Rapaport and Jon Lovitz) and the plan is launched. Frenchy fronts the shop with a cookie business, which the fellas dig their tunnel. It isn't giving too much away to tell you that the bank robbery plan goes bust, while the cookie business front becomes a smash success. And that's just the beginning of the fun.
Small Time Crooks marks a return to fine form for Allen, who hasn't really dazzled me in a long while. While it hardly carries the depth of, say, Annie Hall or Hannah and Her Sisters, Crooks is the flat-out, wry, New York, Woody Allen-neurosis humor that we remember so fondly.
As its star, it's oddly disconcerting for Allen to be playing such a schlub. Ray has no class whatsoever and is extremely stupid, despite his prison nickname of "the brain" -- which Lovitz's character goes to pains to point out was "sarcastic." Woody Allen is a real-life sophisticate, and though his character has all the obsessions you'd expect, the man is a bit too smart to pull off a character this dumb.
Ullman, on the other hand, really develops her role far beyond its opening volley as a mere Julie Kavner understudy. Taking the trampy, former-stripper ditz character and breathing in layers of nuance into the role is inspired acting. I've never been a huge fan of Ullman's, but this movie may make me a convert. Also of note, Hugh Grant has a memorable and dryly funny part as a "Dr. Dolittle" type who later takes Frenchy on as a student of the high life.
Acting aside, Crooks ultimately succeeds on the strength of a wickedly hilarious script that proves Woody still has his funny bone. This isn't some art-house comedy for high-society types, it's a genuine crowd-pleaser that you can't help but enjoy.
 --- Christopher Null -<EMAIL_ADDRESS>- http://www.filmcritic.com
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


template<typename T>
cudaUSMAllocator class
class to create a unified shared memory (USM) allocator
| Template parameters | |
|---|---|
| T | element type |
A cudaUSMAllocator enables using unified shared memory (USM) allocation for standard library containers. It is typically passed as template parameter when declaring standard library containers (e.g. std::
Public types
-
template<typename U>struct rebind
- its member type
U
is the equivalent allocator type to allocate elements of type U - using value_type = T
- element type
- using pointer = T*
- element pointer type
- using reference = T&
- element reference type
- using const_pointer = const T*
- const element pointer type
- using const_reference = const T&
- constant element reference type
-
using size_type = std::
size_t - size type
-
using difference_type = std::
ptrdiff_t - pointer difference type
Constructors, destructors, conversion operators
- cudaUSMAllocator() noexcept
- Constructs a device allocator object.
- cudaUSMAllocator(const cudaUSMAllocator&) noexcept
- Constructs a device allocator object from another device allocator object.
-
template<typename U>cudaUSMAllocator(const cudaUSMAllocator<U>&) noexcept
- Constructs a device allocator object from another device allocator object with a different element type.
- ~cudaUSMAllocator() noexcept
- Destructs the device allocator object.
Public functions
- auto address(reference x) -> pointer
- Returns the address of x.
-
auto address(const_
reference x) const -> const_ pointer - Returns the address of x.
-
auto allocate(size_
type n, std:: allocator<void>::const_ pointer = 0) -> pointer - allocates block of storage.
-
void deallocate(pointer ptr,
size_
type) - Releases a block of storage previously allocated with member allocate and not yet released.
-
auto max_size() const -> size_
type noexcept - returns the maximum number of elements that could potentially be allocated by this allocator
-
void construct(pointer ptr,
const_
reference val) - Constructs an element object on the location pointed by ptr.
- void destroy(pointer ptr)
- destroys in-place the object pointed by
ptr
-
template<typename U>auto operator==(const cudaUSMAllocator<U>&) const -> bool noexcept
- compares two allocator of different types using
==
-
template<typename U>auto operator!=(const cudaUSMAllocator<U>&) const -> bool noexcept
- compares two allocator of different types using
!=
Function documentation
template<typename T>
const_ pointer tf:: cudaUSMAllocator<T>:: address(const_ reference x) const
Returns the address of x.
| Parameters | |
|---|---|
| x | reference to an object |
| Returns | a pointer to the object |
This effectively means returning &x.
template<typename T>
pointer tf:: cudaUSMAllocator<T>:: allocate(size_ type n,
std:: allocator<void>::const_ pointer = 0)
allocates block of storage.
| Parameters | |
|---|---|
| n | number of elements (each of size sizeof(value_type)) to be allocated |
| Returns | a pointer to the initial element in the block of storage. |
Attempts to allocate a block of storage with a size large enough to contain n
elements of member type, value_type
, and returns a pointer to the first element.
The storage is aligned appropriately for object of type value_type
, but they are not constructed.
The block of storage is allocated using cudaMalloc and throws std::
template<typename T>
void tf:: cudaUSMAllocator<T>:: deallocate(pointer ptr,
size_ type)
Releases a block of storage previously allocated with member allocate and not yet released.
| Parameters | |
|---|---|
| ptr | pointer to a block of storage previously allocated with allocate |
The elements in the array are not destroyed by a call to this member function.
template<typename T>
size_ type tf:: cudaUSMAllocator<T>:: max_size() const noexcept
returns the maximum number of elements that could potentially be allocated by this allocator
| Returns | the nubmer of elements that might be allcoated as maximum by a call to member allocate |
|---|
A call to member allocate with the value returned by this function can still fail to allocate the requested storage.
template<typename T>
void tf:: cudaUSMAllocator<T>:: construct(pointer ptr,
const_ reference val)
Constructs an element object on the location pointed by ptr.
| Parameters | |
|---|---|
| ptr | pointer to a location with enough storage soace to contain an element of type value_type |
| val | value to initialize the constructed element to |
template<typename T>
template<typename U>
bool tf:: cudaUSMAllocator<T>:: operator==(const cudaUSMAllocator<U>&) const noexcept
compares two allocator of different types using ==
USM allocators of different types are always equal to each other because the storage allocated by the allocator a1
can be deallocated through a2
.
template<typename T>
template<typename U>
bool tf:: cudaUSMAllocator<T>:: operator!=(const cudaUSMAllocator<U>&) const noexcept
compares two allocator of different types using !=
USM allocators of different types are always equal to each other because the storage allocated by the allocator a1
can be deallocated through a2
.


Blood is universally recognized as the most precious element that sustains life. It saves innumerable lives across the world in a variety of conditions. The need for blood is great - on any given day, approximately 39,000 units of Red Blood Cells are needed. More than 29 million units of blood components are transfused every year. Donate Blood Despite the increase in the number of donors, blood remains in short supply during emergencies, mainly attributed to the lack of information and accessibility. We positively believe this tool can overcome most of these challenges by effectively connecting the blood donors with the blood recipients.


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iPhone camera evolution: How does the iPhone 6 camera compare to previous ...
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How does the iPhone 6 camera stack up against previous hardware? In her ongoing series, professional iPhone photographer Lisa Bettany takes a hard ...
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Europe Edition - Latest News, Breaking Stories, Top Headlines From Europe ...
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WSJ coverage of today's breaking news and headlines for Europe. Top stories, photos, videos and detailed news analysis and reporting about Europe.
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Venture Capitalist Sounds Alarm on Startup Investing
online.wsj.com
Silicon Valley may have taken on more risk than it can handle, according to prominent venture capitalist Bill Gurley, who cited potential cracks in ...
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Docker Hub Official Repos: Announcing Language Stacks | Docker Blog
blog.docker.com
With Docker containers fast becoming the standard for building blocks for distributed apps, we’re working with the Docker community to make it easier ...
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Etsy CEO to Businesses: If Net Neutrality Perishes, We Will Too | WIRED
www.wired.com
As the CEO of a young company, I spend most of my time thinking about serving our community of one million sellers, leading 600 employees, building ...
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The ‘Output From PhenologyMMS’ screen contains the information for the model run, as well as the output from the run.
The top portion of the screen, as seen in the next two images, contains all inputs provided to the model including the parameters from the Growth Stages screen.
The middle portion of the Output screen shows the Note concerning the display of 999 in the output which may be due to the planting date being selected outside the range of the weather years in the selected weather file (meaning weather data were not available). A portion of the Leaf Number table is then displayed which shows the increase in number of leaves and the corresponding day of year (DOY) when this amount occurred. The number of leaves is incremented only when the next integer is reached, therefore there is a gap in time between DOY of one leaf and DOY of the next leaf. The number of leaves continues to increment until the end of leaf growth. The growth stage at which this occurs varies between crops.
At the bottom of the Output screen, the Phenology table is displayed. This table shows for each Phenological Event (i.e., Growth stage) the day of year (DOY), date, days after planting (DAP), days after emergence (DAE), days after vernalization (DAV), GDD from planting (GDD AP), GDD from emergence (GDD AE), GDD from vernalization (GDD AV), and number of leaves that had appeared (LN) when that event occurred. Not all columns apply to all crops. For example, those crops not requiring vernalization will not have a DAV or GDD AV column. The number of leaves is displayed for each growth stage up to and including the stage where leaf growth ends. For every stage thereafter, the number remains the same as the maximum number achieved at the end of the leaf growth stage.
The Maximum Canopy Height for the crop is displayed at the bottom of the table.
There are several buttons at the bottom of the screen:
The ‘Back…’ button - takes you back to the first screen, ‘Begin Setup’. You can then set up a new run and run a different scenario or Load a Saved Scenario.
The ‘Save Output …’ button - opens the ‘Save Output From Current Run’ dialog box where you can save the output from the current run. It is usually saved in the 'results' folder and the saved file contains only the Leaf Number table and the Phenology table.
The ‘Save Scenario…’ button opens the ‘Save Current Scenario’ dialog box to the ‘saves’ folder. This saves all the input data used to run the current crop and produce the current output. This saved file will then be in the list of scenarios that you can choose from in the ‘Begin Setup’ screen. NOTE: to see this new scenario in the listing, exit the program and restart it to update the list so that it includes the new scenario.
Press the ‘Exit’ button to close the program.
Links to: Overview


There is a (somewhat jenky) script that lets players of Anime Music Quiz record the data from matches they play. It can be found here. This webpage merges data from any number of recorded sessions, and gives a more convenient way of viewing it.
The page lists each unique song that was encountered. It gives the anime name, the song's type (opening, ending, or insert), the song's name and artist, and all media links that have been collected for the song (there may be multiple links for different quality options). The main title of the anime will be based on the "Show Names" (English or Romaji) option that was in place during recording. The same option should be used for all sessions, or else the same song can be listed twice under the English and Romaji names.
Each listed song can also give the accuracy of the players in answering that song correctly. Since this statistic is based on a fairly small sample of plays with a relatively small sample of players, it should generally be taken with a grain of salt. The accuracy is really only relevant to the group of players that played during the recorded sessions. For others, the statistic is at best unhelpful, and at worst misleading (don't expect it to match the difficulty of the song in game). As such, the accuracies are hidden by default.
Finally, each song has a list of known valid alternative answers, and a list of incorrect answers. The known alternatives are on a per-song basis (instead of a per-anime basis) because some songs play in multiple seasons, or even multiple different anime. The only incorrect answers that are shown by default are answers which are known to be valid for some other song. The other garbage answers can be still be seen, but are hidden by default. Note that hidden incorrect answers may actually be valid answers that have not been seen yet.


Jon Favreau confirms filming has already started on Season 2 of ‘The Mandalorian’
The first episode of the show debuted on new streaming service Disney+ yesterday (November 12)
Jon Favreau has revealed the second season of new Star Wars spin-off TV show The Mandalorian has already begun filming.
The man behind the series along with The Jungle Book and The Lion King remakes, took to Instagram tonight (November 13) to post a picture of the titular’s character’s helmet along with the following message: “Greetings from the set of The Mandalorian Season 2.”
The first episode of the show debuted on new streaming service Disney+ yesterday (November 12).
The story marks the first time Disney has taken one of its most popular franchises into the world of live-action television.
Fans and journalists discussed the first episode on social media, with one writer describing it as “very, very good” and many specifically referencing the ending, which includes a massive cliffhanger.
Star Wars fans in the UK though aren’t able to watch The Mandalorian episode 1 yet – Disney+ launches on these shores on 31 March 2020 – and expressed their disappointment.
One wrote: “The true gauntlet is that people in the UK have to avoid Americans spoiling The Mandalorian for FOUR MONTHS.”
The first season will run for eight episodes and appear weekly on the streaming service.
Meanwhile, Star Wars fans have been reacting after a notorious scene from A New Hope was changed again upon the launch of Disney+.
The scene in question has prompted plenty of debate over whether Han or Greedo shoot first in their battle during A New Hope.
In a newly tweaked version of the scene that has made it onto the streaming service, neither of the pair are shown shooting first – they fire at about the same time.
Star Wars episode 9 – The Rise Of Skywalker – also comes to cinemas December 20.


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Given a fn
, we may wish to run it on a filepath
, determine
its success, and try again with the parent directory of filepath
,
until we obtain result that is not NULL
. If this does not occur for
any parent directory, we halt with the string error
.
traverse_parent_directories(filepath, fn, error)
| filepath | character. The filepath to traverse along. The |
|---|---|
| fn | function. A one-argument function called on |
| error | character or function. A string to error if |
The result of fn
on the first parent directory of
filepath
on which it is not NULL
.


Executes a provided function once for each array element, starting from the array’s last element.
Array.prototype.slice()
to clone the given array and Array.prototype.reverse()
to reverse it.Array.prototype.forEach()
to iterate over the reversed array.