public final class LayoutContext
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static int |
FIRST_AREA |
static int |
LAST_AREA |
static int |
NEW_AREA
Generated break possibility is first in a new area
|
static int |
RESOLVE_LEADING_SPACE |
static int |
SUPPRESS_BREAK_BEFORE
If this flag is set, it indicates that any break-before values other than "auto" should
not cause a mandatory break as this break was already handled by a parent layout manager.
|
| Modifier and Type | Method and Description |
|---|---|
void |
addPendingAfterMark(UnresolvedListElementWithLength element)
Adds a border or padding element to the pending list which will be used to generate
the right element list for break possibilities.
|
void |
addPendingBeforeMark(UnresolvedListElementWithLength element)
Adds a border or padding element to the pending list which will be used to generate
the right element list for break possibilities.
|
void |
clearKeepsPending()
Clears both keep-with-previous and keep-with-next strengths.
|
void |
clearKeepWithNextPending()
Clears any pending keep-with-next strength.
|
void |
clearKeepWithPreviousPending()
Clears any pending keep-with-previous strength.
|
void |
clearPendingMarks()
Clears all pending marks on the LayoutContext.
|
static LayoutContext |
copyOf(LayoutContext copy) |
void |
copyPendingMarksFrom(LayoutContext source) |
AlignmentContext |
getAlignmentContext() |
int |
getBPAlignment() |
int |
getBreakAfter()
Returns the value of the break after the element whose
LayoutManager.getNextKnuthElements(LayoutContext, int) method has just been
called. |
int |
getBreakBefore()
Returns the value of the break before the element whose
LayoutManager.getNextKnuthElements(LayoutContext, int) method has just been
called. |
int |
getCurrentSpan() |
int |
getDisableColumnBalancing()
Returns whether the column balancer should be disabled before a spanning block
|
HyphContext |
getHyphContext() |
double |
getIPDAdjust() |
Keep |
getKeepWithNextPending()
Returns the strength of a keep-with-next currently pending.
|
Keep |
getKeepWithPreviousPending()
Returns the strength of a keep-with-previous currently pending.
|
SpaceSpecifier |
getLeadingSpace() |
int |
getLineEndBorderAndPaddingWidth()
Get the width to be reserved for border and padding at the end of the line.
|
int |
getLineStartBorderAndPaddingWidth()
Get the width to be reserved for border and padding at the start of the line.
|
int |
getNextSpan() |
java.util.List |
getPendingAfterMarks() |
java.util.List |
getPendingBeforeMarks() |
int |
getRefIPD()
Returns the inline-progression-dimension of the nearest ancestor reference area.
|
double |
getSpaceAdjust() |
int |
getSpaceAfter()
Get the current amount of space after / end
|
int |
getSpaceBefore()
Get the current amount of space before / start
|
MinOptMax |
getStackLimitBP()
Returns the stack limit in block-progression-dimension.
|
SpaceSpecifier |
getTrailingSpace() |
WritingMode |
getWritingMode()
Get the writing mode of the relevant reference area.
|
boolean |
isFirstArea() |
boolean |
isKeepWithNextPending()
Indicates whether a keep-with-next constraint is pending.
|
boolean |
isKeepWithPreviousPending()
Indicates whether a keep-with-previous constraint is pending.
|
boolean |
isLastArea() |
boolean |
isStart() |
static LayoutContext |
newInstance() |
static LayoutContext |
offspringOf(LayoutContext parent)
Returns a descendant of the given layout context.
|
void |
resetAlignmentContext()
Reset alignment context.
|
boolean |
resolveLeadingSpace() |
void |
setAlignmentContext(AlignmentContext alignmentContext) |
void |
setBPAlignment(int alignment)
Sets the currently applicable alignment in BP direction.
|
void |
setBreakAfter(int breakAfter)
Sets the value of the break after the current element.
|
void |
setBreakBefore(int breakBefore)
Sets the value of the break before the current element.
|
void |
setDisableColumnBalancing(int disableColumnBalancing)
Sets whether the column balancer should be disabled before a spanning block
|
void |
setFlags(int flags) |
void |
setFlags(int flags,
boolean bSet) |
void |
setHyphContext(HyphContext hyph) |
void |
setIPDAdjust(double ipdA) |
void |
setLeadingSpace(SpaceSpecifier space) |
void |
setLineEndBorderAndPaddingWidth(int lineEndBorderAndPaddingWidth)
Set the width to be reserved for border and padding at the end of the line.
|
void |
setLineStartBorderAndPaddingWidth(int lineStartBorderAndPaddingWidth)
Set the width to be reserved for border and padding at the start of the line.
|
void |
setRefIPD(int ipd)
Sets the inline-progression-dimension of the nearest ancestor reference area.
|
void |
setSpaceAdjust(double adjust) |
void |
setSpaceAfter(int spaceAfter)
Set the amount of space after / end
|
void |
setSpaceBefore(int spaceBefore)
Set the amount of space before / start
|
void |
setStackLimitBP(MinOptMax limit)
Sets the stack limit in block-progression-dimension.
|
void |
setTrailingSpace(SpaceSpecifier space) |
void |
setTreatAsArtifact(boolean treatAsArtifact) |
void |
setWritingMode(WritingMode writingMode)
Set the writing mode.
|
void |
signalSpanChange(int span)
Used to signal the PSLM that the element list ends early because of a span change in
multi-column layout.
|
boolean |
startsNewArea() |
boolean |
suppressBreakBefore() |
java.lang.String |
toString() |
boolean |
treatAsArtifact() |
void |
unsetFlags(int flags) |
void |
updateKeepWithNextPending(Keep keep)
Updates the currently pending keep-with-next strength.
|
void |
updateKeepWithPreviousPending(Keep keep)
Updates the currently pending keep-with-previous strength.
|
public static final int NEW_AREA
public static final int SUPPRESS_BREAK_BEFORE
public static final int FIRST_AREA
public static final int LAST_AREA
public static final int RESOLVE_LEADING_SPACE
public static LayoutContext newInstance()
public static LayoutContext copyOf(LayoutContext copy)
public static LayoutContext offspringOf(LayoutContext parent)
newInstance()
, except for inheritable
properties that are passed on by the parent. At the moment, the only inheritable
property is the value returned by treatAsArtifact()
.public void copyPendingMarksFrom(LayoutContext source)
source
- from which pending marks are copiedpublic void setFlags(int flags)
flags
- to setpublic void setFlags(int flags, boolean bSet)
flags
- to set or clearbSet
- true to set, false to clearpublic void unsetFlags(int flags)
flags
- to clearpublic boolean isStart()
public boolean startsNewArea()
public boolean isFirstArea()
public boolean isLastArea()
public boolean suppressBreakBefore()
public Keep getKeepWithNextPending()
public Keep getKeepWithPreviousPending()
public void clearKeepWithNextPending()
public void clearKeepWithPreviousPending()
public void clearKeepsPending()
public void updateKeepWithNextPending(Keep keep)
keep
- the new strength to considerpublic void updateKeepWithPreviousPending(Keep keep)
keep
- the new strength to considerpublic boolean isKeepWithNextPending()
public boolean isKeepWithPreviousPending()
public void setLeadingSpace(SpaceSpecifier space)
space
- leading spacepublic SpaceSpecifier getLeadingSpace()
public boolean resolveLeadingSpace()
public void setTrailingSpace(SpaceSpecifier space)
space
- trailing spacepublic SpaceSpecifier getTrailingSpace()
public void addPendingAfterMark(UnresolvedListElementWithLength element)
element
- the border, padding or space elementpublic java.util.List getPendingAfterMarks()
addPendingAfterMark(UnresolvedListElementWithLength)
public void clearPendingMarks()
public void addPendingBeforeMark(UnresolvedListElementWithLength element)
element
- the border, padding or space elementpublic java.util.List getPendingBeforeMarks()
addPendingBeforeMark(UnresolvedListElementWithLength)
public void setStackLimitBP(MinOptMax limit)
limit
- the stack limitpublic MinOptMax getStackLimitBP()
public void setRefIPD(int ipd)
ipd
- of nearest ancestor reference areapublic int getRefIPD()
public void setHyphContext(HyphContext hyph)
hyph
- a hyphenation contextpublic HyphContext getHyphContext()
public void setBPAlignment(int alignment)
alignment
- one of EN_START, EN_JUSTIFY etc.public int getBPAlignment()
public void setSpaceAdjust(double adjust)
adjust
- space adjustmentpublic double getSpaceAdjust()
public void setIPDAdjust(double ipdA)
ipdA
- ipd adjustmentpublic double getIPDAdjust()
public void setAlignmentContext(AlignmentContext alignmentContext)
alignmentContext
- alignment contextpublic AlignmentContext getAlignmentContext()
public void resetAlignmentContext()
public int getLineStartBorderAndPaddingWidth()
public void setLineStartBorderAndPaddingWidth(int lineStartBorderAndPaddingWidth)
lineStartBorderAndPaddingWidth
- the width to be reservedpublic int getLineEndBorderAndPaddingWidth()
public void setLineEndBorderAndPaddingWidth(int lineEndBorderAndPaddingWidth)
lineEndBorderAndPaddingWidth
- the width to be reservedpublic int getNextSpan()
Constants.NOT_SET
, Constants.EN_NONE
Constants.EN_ALL
public int getCurrentSpan()
Constants.NOT_SET
, Constants.EN_NONE
Constants.EN_ALL
public void signalSpanChange(int span)
span
- the new span value (legal values: NOT_SET, EN_NONE, EN_ALL)public WritingMode getWritingMode()
public void setWritingMode(WritingMode writingMode)
writingMode
- the writing modepublic int getSpaceBefore()
public void setSpaceBefore(int spaceBefore)
spaceBefore
- the amount of space before / startpublic int getSpaceAfter()
public void setSpaceAfter(int spaceAfter)
spaceAfter
- the amount of space after / endpublic int getBreakBefore()
LayoutManager.getNextKnuthElements(LayoutContext, int)
method has just been
called.Constants.EN_AUTO
, Constants.EN_COLUMN
,
Constants.EN_PAGE
, Constants.EN_EVEN_PAGE
, or
Constants.EN_ODD_PAGE
public void setBreakBefore(int breakBefore)
breakBefore
- the value of the break-beforegetBreakBefore()
public int getBreakAfter()
LayoutManager.getNextKnuthElements(LayoutContext, int)
method has just been
called.Constants.EN_AUTO
, Constants.EN_COLUMN
,
Constants.EN_PAGE
, Constants.EN_EVEN_PAGE
, or
Constants.EN_ODD_PAGE
public void setBreakAfter(int breakAfter)
breakAfter
- the value of the break-aftergetBreakAfter()
public java.lang.String toString()
toString
in class java.lang.Object
public int getDisableColumnBalancing()
Constants.EN_TRUE
, Constants.EN_FALSE
public void setDisableColumnBalancing(int disableColumnBalancing)
disableColumnBalancing
- the value of the fox:disable-column-balancing propertygetDisableColumnBalancing()
public boolean treatAsArtifact()
public void setTreatAsArtifact(boolean treatAsArtifact)
Copyright 1999-2020 The Apache Software Foundation. All Rights Reserved.


Client needs to animate an image to increase engagement and presentation appeal of some statistics
Client needs it to workin IE11 because ... reasons
Needs to work between 768 and 1200 pixels
How about 100% jQuery?
Ok Lets do 100% CSS3!
We need the additional functionality provided by javascript
Not really, but maybe we don't have to question my love
Photoshop right click layers, export as png
GIMP use this plugin
Other people are smarter and more capable than I am.
These are the people who's backs I trod apon in my ascent to greatness.
Maybe add in the top line somehow or resize some of the elements
Only include the parts of Animate.css that we are actually using
Don't use jQuery? ... Nah


Wood, Antony, et al. Best Tall Buildings: a Global Overview of 2013 Skyscrapers: CTBUH Awards.
Routledge, 2014.
Summary:
The chapter in this book "the power and the glory - strength and elegance in structure" specifically focuses
on the CCTV tower. In particular, it talks about how its form - being one continuous tube system allows for the building
to withstand the substantial seismic and wind events that it experiences. The source also talks about how the building is
reinforced with steel beams, arranged in a particular manner. Although undoubtedly providing a structural service, these
beams also act as a pleasing visual to the rhythm of the building. I also found it interesting that although the 243m tall building
is within the required height limit, it did not meet standards structurally, and had to be looked at in-depth by a panel of
Chinese engineers.
Ching, Francis D. K., et al. A Global History of Architecture, John Wiley & Sons, Incorporated, 2010.
ProQuest Ebook Central, https://ebookcentral-proquest-com.ezproxy.lib.utexas.edu/lib/utxa/detail.action?docID=698776.
Summary:
This source has examines thousands of years of the history of architecture. There is a chapter near the end
focusing on globalization, and China specifically. There are interesting facts here such as the global price
steel doubling due to demand for Chinese skyscrapers. There is also discussion of Rem Koolhaas' book, S, M, L, XL
and his musing about the contemporary city with specifics regard to the importance of scale in architecture. The article then
details some of Koolhaas' most famous works around the world, of course including the CCTV tower in Beijing. Something I found
particularly enlightening was Koolhaas's view that "the usual differences between architecture, the street, and the city are things
of the past. Instead, architecture has to embrace and combine all of these.
Ericson, Staffan, and Kristina Riegert. Media Houses: Architecture, Media and the Production of Centrality.
Peter Lang, 2010.
Summary:
This source discusses several aspects of the CCTV tower and what it represents for China and its future. There is a
big emphasis on the transparency it provides, both metaphorically and physically. The building is designed so that anyone can come
and witness the state-run television being produced, without any secrets. The sources also discusses how this new tower is a
"rebranding" of sorts for CCTV. The building is also designed in such a way that office workers will be more in-tune with what their
colleagues are working on, thereby establishing a greater sense of community within the organization through architecture. There is an
emphasis on the flow of communication between employees that is representative of the flow of information from CCTV itself to the Chinese citizens.


Module ROT.DijkstraMap
DijkstraMap Pathfinding.
Based on the DijkstraMap Article on RogueBasin, http://roguebasin.roguelikedevelopment.org/index.php?title=The_Incredible_Power_of_Dijkstra_Maps
Functions
| init (goalX, goalY, mapWidth, mapHeight, passableCallback) | Constructor. |
| compute () | Establish values for all cells in map. |
| addGoal (gx, gy) | Add new goal position. |
| removeGoals ([gx=nil[, gy=nil]]) | Remove all goals. |
| writeMapToConsole (returnString) | Output map values to console. |
| getWidth () | Get Width of map. |
| getHeight () | Get Height of map. |
| getDimensions () | Get Dimensions as table. |
| getMap () | Get the map table. |
| getGoals () | Get the goal cell as a table. |
| dirTowardsGoal (x, y) | Get the direction of the goal from a given position |
| iterateThroughMap (callback) | Run a callback function on every cell in the map |
Functions
- init (goalX, goalY, mapWidth, mapHeight, passableCallback)
-
Constructor.
Parameters:
- goalX int x-position of cell that map will 'roll down' to
- goalY int y-position of cell that map will 'roll down' to
- mapWidth int width of the map
- mapHeight int height of the map
- passableCallback function a function with two parameters (x, y) that returns true if a map cell is passable
- compute ()
- Establish values for all cells in map. call after ROT.DijkstraMap:new(goalX, goalY, mapWidth, mapHeight, passableCallback)
- addGoal (gx, gy)
-
Add new goal position.
Inserts a new cell to be used as a goal.
Parameters:
- gx int the new x-value of the goal cell
- gy int the new y-value of the goal cell
- removeGoals ([gx=nil[, gy=nil]])
-
Remove all goals.
Will delete all goal cells. You must insert another goal before computing.
You can specify one goal to be inserted after the goal remove takes place.
The method only checks that the coordinates are provided before setting the new goal cell.
Parameters:
- gx int Will use this value as the x-coordinate of a new goal cell to be inserted (default nil)
- gy int Will use this value as the y-coordinate of a new goal cell to be inserted (default nil)
- writeMapToConsole (returnString)
-
Output map values to console.
For debugging, will send a comma separated output of cell values to the console.
Parameters:
- returnString boolean[opt=false] Will return the output in addition to sending it to console if true.
- getWidth ()
-
Get Width of map.
Returns:
-
int
w width of map
- getHeight ()
-
Get Height of map.
Returns:
-
int
h height of map
- getDimensions ()
-
Get Dimensions as table.
Returns:
- table dimensions A table of width and height values
- int dimensions.w width of map
- int dimensions.h height of map
- getMap ()
-
Get the map table.
Returns:
-
table
map A 2d array of map values, access like map[x][y]
- getGoals ()
-
Get the goal cell as a table.
Returns:
- table goal table containing goal position
- int goal.x x-value of goal cell
- dirTowardsGoal (x, y)
-
Get the direction of the goal from a given position
Parameters:
- x int x-value of current position
- y int y-value of current position
Returns:
- int xDir X-Direction towards goal. Either -1, 0, or 1
- int yDir Y-Direction towards goal. Either -1, 0, or 1
- iterateThroughMap (callback)
-
Run a callback function on every cell in the map
Parameters:
- callback function A function with x and y parameters that will be run on every cell in the map


Unfortunately the page that you requested does not exist.
Don't worry, though, we have some great suggestions to help you on your way!
Based on the keywords found in the URL that you attempted to visit, we have suggested similar content and articles below.


Before Change
// Queries are duplicates only if there"s as least 2 of them.
// Also, to hide queries, we need to give all the duplicate groups an id
query_colors = contrasting_color_generator()
query_duplicates = dict(
(alias, dict(
(query, (duplicate_count, next(query_colors)))
for query, duplicate_count in queries.items()
if duplicate_count >= 2
))
for alias, queries in query_duplicates.items()
)
for alias, query in self._queries:
try:
duplicates_count, color = query_duplicates[alias][query["raw_sql"]]
After Change
// Queries are duplicates only if there"s as least 2 of them.
// Also, to hide queries, we need to give all the duplicate groups an id
query_colors = contrasting_color_generator()
query_duplicates = {
alias: {
query: (duplicate_count, next(query_colors))
for query, duplicate_count in queries.items()
if duplicate_count >= 2
}
for alias, queries in query_duplicates.items()
}
for alias, query in self._queries:
try:
duplicates_count, color = query_duplicates[alias][query["raw_sql"]]


The configuration file
Workbench uses a YAML configuration whose location is indicated in the --config
argument. This file defines the various options it will use to create, update, or delete Islandora content (such as which CSV file to use and what directory your images are in). The simplest configuration file needs only the following four options:
task: create
host: "http://localhost:8000"
username: admin
password: islandora
In this example, the task
being performed is creating nodes (and optionally media). Other tasks are create_from_files
, update
, delete
, add_media
, and delete_media
. Some of the configuration settings documented below are used in all tasks, while others are only used in specific tasks.
Configuration settings
The settings defined in a configuration file are documented below, grouped into broad funcational categories for easier reference. The order of the options in the configuration file doesn't matter, and settings do not need to be grouped together in any specific way in the configuration file.
Required settings
| Setting | Required | Default value | Description |
|---|---|---|---|
| task | ✔ | One of 'create', 'create_from_files', update', delete', 'add_media', or 'delete_media' | |
| host | ✔ | The hostname, including http:// or https:// of your Islandora repository, and port number if not the default 80. This value must be wrapped in quotation marks. |
|
| username | ✔ | The username used to authenticate the requests. This Drupal user should be a member of the "Administrator" role. If you want to create nodes that are owned by a specific Drupal user, include their numeric user ID in the uid column in your CSV. |
|
| password | ✔ | The user's password. |
Drupal settings
| Setting | Required | Default value | Description |
|---|---|---|---|
| content_type | islandora_object | The machine name of the Drupal node content type you are creating or updating. | |
| drupal_filesystem | fedora:// | One of 'fedora://', 'public://', or 'private://'. Only used with Drupal 8.x - 9.1; starting with Drupal 9.2, the filesystem is automatically detected from the media's configuration. | |
| allow_adding_terms | false | Determines if Workbench will add taxonomy terms if they do not exist in the target vocabulary. See more information in the "Taxonomy reference fields" section. | |
| published | true | Whether nodes are published or not. Applies to create task only. Set to false if you want the nodes to be unpublished. Note that whether or not a node is published can also be set at a node level in the CSV file in the status base field, as described in the "Base Fields" section. Values in the CSV override the value of published set here. |
|
| validate_title_length | true | Whether or not to check if title values in the CSV exceed Drupal's maximum allowed length of 255 characters. Defaults to true. Set to false if you are using a module that lets you override Drupal's maximum title length, such as Node Title Length or Entity Title Length. Also, if your task is update , you should set this to false if title is not one of the fields you are updating. |
|
| list_missing_drupal_fields | false | Set to true to tell Workbench to provide a list of fields that exist in your input CSV but that cannot be matched to Drupal field names (or reserved column names such as "file"). If false , Workbench will still check for CSV column headers that it can't match to Drupal fields, but will exit upon finding the first such field. This option produces a list of fields instead of exiting on detecting the first field. |
Input data location settings
| Setting | Required | Default value | Description |
|---|---|---|---|
| input_dir | input_data | The full or relative path to the directory containing the files and metadata CSV file. | |
| input_csv | metadata.csv | Path to the CSV metadata file. Can be absolute, or if just the filename is provided, will be assumed to be in the directory named in input_dir . Can also be the URL to a Google spreadsheet (see the "Using Google Sheets as input data" section for more information). |
|
| google_sheets_csv_filename | google_sheet.csv | Local CSV filename for data from a Google spreadsheet. See the "Using Google Sheets as input data" section for more information. | |
| google_sheets_gid | 0 | The "gid" of the worksheet to use in a Google Sheet. See "Using Google Sheets as input data" section for more information. | |
| excel_worksheet | Sheet1 | If using an Excel file as your input CSV file, the name of the worksheet that the CSV data will be extracted from. |
Output CSV settings
| Setting | Required | Default value | Description |
|---|---|---|---|
| output_csv | The full or relative path to a CSV file with one record per node created by Workbench. See "The output CSV file" section for more information. |
CSV input file settings
| Setting | Required | Default value | Description |
|---|---|---|---|
| id_field | id | The name of the field in the CSV that uniquely identifies each record. | |
| delimiter | , [comma] | The delimiter used in the CSV file, for example, "," or "\t". If omitted, defaults to ",". | |
| subdelimiter | | [pipe] | The subdelimiter used in the CSV file to define multiple values in one field. If omitted, defaults to "|". | |
| csv_field_templates | Used in the create and update tasks only. A list of Drupal field machine names and corresponding values that are copied into the CSV input file. More detail provided in the "CSV field templates" section. |
||
| ignore_csv_columns | Used in the create and update tasks only. A list of CSV column headers that Workbench should ignore. |
Media settings
| Setting | Required | Default value | Description |
|---|---|---|---|
| nodes_only | false | Include this option in create tasks, set to true , if you want to only create nodes and not their accompanying media. See the "Creating nodes but not media" section for more information. 'fedora://'. |
|
| allow_missing_files | false | Determines if empty file values are allowed. If set to true, empty file values are allowed and will result in nodes without attached media. Defaults to false (which means all file values must contain the name of a file that exists in the input_data directory). |
|
| media_use_tid | http://pcdm.org/use#OriginalFile |
The term ID for the term from the "Islandora Media Use" vocabulary you want to apply to the media being created. You can provide a term URI instead of a term ID, for example "http://pcdm.org/use#OriginalFile" . You can specify multiple values for this setting by joining them with the subdelimiter configured in the subdelimiter setting; for example, media_use_tid: 17|18 . You can also set this at the object level by including media_use_tid in your CSV file; values there will override the value set in your configuration file. |
|
| media_type [singular] | Overrides, for all media being created, Workbench's default definition of whether the media being created is an image, file, document, audio, or video. Used in the create , add_media , and create_from_files tasks. More detail provided in the "[Setting Media Types] |
||
| media_types [plural] | Overrides default media type definitions on a per file extension basis. Used in the create , add_media , and create_from_files tasks. More detail provided in the "Setting Media Types" section. |
||
| delete_media_with_nodes | true | When a node is deleted using a delete task, by default, all if its media are automatically deleted. Set this option to false to not delete all of a node's media (you do not generally want to keep the media without the node). |
|
| use_node_title_for_media | false | For remote files, if set to true , name the downloaded copy using the parent node's title value. If false , downloaded file's name will depend on the application/website that is serving up the file. |
|
| delete_tmp_upload | false | For remote files, if set to true , remote file is deleted after it is used to create media. If false , downloaded file will remain in your input_dir . |
|
| media_file_fields | Defines the name of the field that refers to the file in an Islandora media. For more information, see this Troubleshooting entry. |
Islandora model settings
| Setting | Required | Default value | Description |
|---|---|---|---|
| model [singular] | Used in the create_from_files task only. Defines the term ID from the the "Islandora Models" vocabulary for all nodes created using this task. Note: one of model or models is required. More detail provided in the "Creating nodes from files" section. |
||
| models [plural] | Used in the create_from_files task only. Provides a mapping bewteen file extensions and terms in the "Islandora Models" vocabulary. Note: one of model or models is required. More detail provided in the Creating nodes from files" section. |
Paged and compound content settings
See the section "Creating paged content" for more information.
| Setting | Required | Default value | Description |
|---|---|---|---|
| paged_content_from_directories | false | Set to true if you are using the "Using subdirectories" method of creating paged content. | |
| paged_content_sequence_seprator | - [hyphen] | The character used to separate the page sequence number from the rest of the filename. Used when creating paged content with the "Using subdirectories" method. | |
| paged_content_page_model_tid | Required if paged_content_from_directories is true. The the term ID from the Islandora Models taxonomy to assign to pages. |
||
| paged_content_page_display_hints | The term ID from the Islandora Display taxonomy to assign to pages. If not included, defaults to the value of the field_display_hints in the parent's record in the CSV file. |
||
| paged_content_page_content_type | Set to the machine name of the Drupal node content type for pages created using the "Using subdirectories" method if it is different than the content type of the parent (which is specified in the content_type setting). | ||
| secondary_tasks | A list of configuration file names that are executed as secondary tasks after the primary task to create compound objects. See "Using a secondary task" for more information. |
Logging settings
See the "Logging" section for more information.
| Setting | Required | Default value | Description |
|---|---|---|---|
| log_file_path | workbench.log | The path to the log file, absolute or relative to workbench . |
|
| log_file_mode | a [append] | Set to "w" to overwrite the log file, if it exists. | |
| log_json | false | Whether or not to log the raw JSON POSTed, PUT, or PATCHed to Drupal. Useful for debugging. |
Miscellaneous settings
| Setting | Required | Default value | Description |
|---|---|---|---|
| pause | Defines the number of seconds to pause between each REST request to Drupal. Include it in your configuration to lessen the impact of Islandora Workbench on your site during large jobs, for example pause: 1.5. | ||
| user_agent | Islandora Workbench | String to use as the User-Agent header in HTTP requests. | |
| allow_redirects | true | Whether or not to allow Islandora Workbench to respond to HTTP redirects. | |
| bootstrap | Absolute path to one or more scripts that execute prior to Workbench connecting to Drupal. Scripts can be in any language, and need to be executable. For an example of using this feature to run a script that generates sample Islandora content, see the "Generating sample Islandora content" section. | ||
| progress_bar | false | Show a progress bar when running Workbench instead of row-by-row output. | |
| drupal_8 | false | Tells Workbench that it is communicating with a Drupal version lower than 9.2. Intended to be used in integration tests only. |
When you run Islandora Workbench with the --check
argument, it will verify that all configuration options required for the current task are present, and if they aren't tell you so.
Note
Islandora Workbench automatically converts any configuration keys to lowercase, e.g., Task
is equivalent to task
.
Validating the syntax of the configuration file
When you run Workbench, it confirms that your configuration file is valid YAML. This is a syntax check only, not a content check. If the file is valid YAML, Workbench then goes on to perform a long list of application-specific checks.
If this syntax check fails, some detail about the problem will be displayed to the user. The same information plus the entire Python stack trace is also logged to a file named "workbench.log" in the Islandora Workbench directory. This file name is Workbench's default log file name, but in this case (validating the config file's YAML syntax), that file name is used regardless of the log file location defined in the configuation's log_file_path
option. The reason the error is logged in the default location instead of the value in the configuration file (if one is present) is that the configuration file isn't valid YAML and therefore can't be parsed.
Example configuration files
These examples provide inline annotations explaining why the settings are included in the configuration file. Spaces are included for readability.
Create nodes only, no media
task: create
host: "http://localhost:8000"
username: admin
password: islandora
# This setting tells Workbench to create nodes with no media.
# Also, this tells --check to skip all validation of "file" locations.
# Other media settings, like "media_use_tid", are also ignored.
nodes_only: true
Use a custom log file location
task: create
host: "http://localhost:8000"
username: admin
password: islandora
# This setting tells Workbench to write its log file to the location specified
# instead of the default "workbench.log" within the "islandora_workbench" directory.
log_file_path: /home/mark/workbench_log.txt
Include some CSV field templates
task: create
host: "http://localhost:8000"
username: admin
password: islandora
# The values in this list of field templates are applied to every row in the
# input CSV file before the CSV file is used to populate Drupal fields. The
# field templates are also applied during the "--check" in order to validate
# the values of the fields.
csv_field_templates:
- field_member_of: 205
- field_model: 25
Create nodes and media from files (no input CSV file)
task: create_from_files
host: "http://localhost:8000"
username: admin
password: islandora
# The files to create the nodes from are in this directory.
input_dir: /tmp/sample_files
# This tells Workbench to write a CSV file containing node IDs of the
# created nodes, plus the field names used in the target content type
# ("islandora_object" by default).
output_csv: /tmp/sample_files.csv
# All nodes should get the "Model" value corresponding to this URI.
model: 'https://schema.org/DigitalDocument'


Software-Defined Virtual Network Security
From Theory to Practice.
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Certified Professional Developer Technology Evangelist | Developer | Data Analyst | Social Media Marketing Specialist | Blogger | Speaker | Dribbbler | Adventurer | Foodie


The Werecat Rules Engine is a freely available framework for building and evaluating simple, hierarchical rule structures. Using Java reflection, you can easily plug in your own data and functions to create a highly dynamic rules-based structure with very little coding effort. The rest of this document describes how to use the framework for your own projects.
Sample Rules
The rules format is simple JSON, which looks like this:
{ "top": "testOverride", "version": "0.0", "rules": { "testOverride": { "description": "Check for override", "condition": "setting != null", "accept": "doOverride", "decline": "testLocation" }, "testLocation": { "description": "Check if location is present", "condition": "location != null", "accept": "useLocation", "decline": "doNothing()", }, ...
The core structure is a list of tagged rules. The outer "top" tag identifies the default entry point for evaluating rules, and the "version" tag is merely for tracking changes. Each rule element has a name, used to reference the tree, and four attributes:
The condition is similar to an expression in C++, Java, or other programming languages, and may use all the similar operators (+,-,*,/,>,<, etc). You can work with both numbers and strings, including regular expression matching. Pseudo variables can be attached to your own data objects as described in the next section.
Actions (accept or decline), can consist of two components:
If you are using only a single action, simply include that action. If you are multiple actions, separate them by semicolons. (You may also omit the action entirely if not needed). A fully loaded action might look like this:
"action": "myMethod();nextTag"
Typically, the method will be used to update status in your Context object, as explained in the next section.
Sample Code
The most important class when using the Rule Engine is the RuleContext. Each time you evaluate a Rule, you should create a RuleContext that stores all the inputs needed in the rule engine, and has the ability to store any outputs you want. You should implement RuleContext on your own class to expose fields and methods you want to use in the rules, like this:
public class ExampleContext implements RuleContext { // Inputs int level; // Some sample value // Results int action = 0; // Getter public int getLevel() { return level; } // Action public void setBasicAction() { action = 1; } }
Because there is a method call getLevel, you may now use the variable level in your conditions (like "level != 2").
Actions can call any public method of your RuleContext. You can use any information you need from the context, including updating input values, or setting outputs.
Using the rule engine itself is very simple. First, import the package:import com.whicken.werecat.*;
Second, instantiate the engine with some JSON rules as input:
RuleFactory factory = new RuleFactory(ExampleContext.class); RuleEngine engine = new RuleEngine(); engine.load(file, factory);
The RuleContext being used must be passed in via the RuleFactory, so that the actions and conditions may be validated during instantiation. To evaluate a rule, create your context, and pass it in to the evaluate method. This will walk the rule structure, calling the appropriate actions, and return when completed.
ExampleContext context = new ExampleContext(); context.level = 4; engine.evaluate(context); System.out.println("Result: "+context.action);


GM recalls 640K pickups; seat belts can cause carpet fires
DETROIT (AP) — General Motors is recalling over 640,000 pickup trucks worldwide because hot gas from a high-tech seat belt can set the carpeting on fire.
The recall covers certain 2019 and 2020 Chevrolet Silverado and GMC Sierra 1500 trucks. Also included are some 2020 Silverado and Sierra 2500 and 3500 heavy-duty pickups. All have carpet as a floor covering, and most are in North America.
GM says it uses a small explosion to move a piston that tightens the belts before a crash. The explosion can release hot gas through an opening in a bracket, possibly setting the carpet on fire. The company reports two fires but no injuries.
Dealers will close the opening at no cost to owners. No date was given for the recall to start.


Probability Basics¶
Equally likely events¶
If we flip a fair coin, the probability of getting heads or tails is equal. What this means is that if we flip a coin many times that the percentage of both heads and tails will go to 50%.
import random
from typing import Tuple
# Runs n trials of a coin flip and returns the number of heads and tails.
def runTrials(n: int) -> Tuple[int, int]:
heads = 0
tails = 0
for i in range(n):
# random.random() returns a random number between 0 and 1. Half the time this value is less than 0.5.
if random.random() < 0.5:
heads += 1
else:
tails += 1
return heads, tails
# Format the output of runTrials.
def printResults(heads: int, tails: int) -> None:
print("Trials:", heads + tails, "Proportion:", heads/(heads+tails),"Heads:", heads, "Tails:", tails)
# The * operator unpacks a list or tuple into separate variables.
printResults(*runTrials(10))
printResults(*runTrials(100))
printResults(*runTrials(1_000_000))
Trials: 10 Proportion: 0.5 Heads: 5 Tails: 5
Trials: 100 Proportion: 0.47 Heads: 47 Tails: 53
Trials: 1000000 Proportion: 0.500733 Heads: 500733 Tails: 499267
In our previous example we made a random selection between heads and tails. A random selection means that each value is equally likely. For example, a randomly selected card from a deck of \(52\) cards has probability \(1/52\) of being a 2 of clubs.
When every possible outcome has equal probability, the formula for the probability of some subset of the outcomes is:
For our coin flipping example, we have \(2\) possible outcomes: heads and tails. The number of selected outcomes is \(1\) (either heads or tails), so the probability is \(1/2\).
Playing cards example¶
What is the probability of drawing 4 aces when we randomly select 5 cards from a deck?
To draw the 4 aces, we must select 5 cards. 4 of them are aces and 1 of them is not. \({4 \choose 4}\) is the number of ways to select 4 aces. \({48 \choose 1}\) is the number of ways to select the non-ace. The number of ways to select the 4 aces is:
The number of ways to draw 5 cards is:
The answer is then:
Terminology¶
Experiments¶
When we flip a coin or roll a die that is an experiment. Experiments have a set of possible outcomes that happen with some probability.
Sample space¶
The set of all possible outcomes of an experiment is the sample space. When we flip a coin once the sample space is \(S=\{H,T\}\). When we flip a coin twice the sample space is \(S=\{HH,HT,TH,TT\}\).
Events¶
Events represent a set of possible outcomes from an experiment. When we flip two coins there is an event for flipping two heads, \(E_\text{both heads}=\{HH\}\). There is also an event for not flipping two heads \(E_\text{not both heads}=\{HT,TH,TT\}\). An event is a subset of the sample space. It may represent a single outcome of our experiment, or it may represent several of the possible outcomes:
We can rewrite our formula for probabilities when all outcomes of an experiment are equally likely using \(n(E)\) for the number of selected outcomes.
Example¶
We roll two dice and want to calculate the probability that the dice sum to 4. Define the experiment, sample space, event, and calculate the probability of the event.
The experiment is rolling two dice.
The sample space is the set of all possible ordered combinations of two dice. We can define it in set builder notation:
\(n(S) = 36\) because there are \(6\) possible outcomes for each die and we use the multiplication principle.
The event contains the elements of the sample space that sum to 4.
The probability is
Probability Functions¶
A probability function \(P\) calculates the probability of an event. When we flip a coin twice the probability of getting a head and a tail is:
For experiments where all outcomes are equally likely:
A probability is between \(0\) and \(1\) because an event can’t happen less than \(0\%\) of the time or more than \(100\%\) of the time. More formally, \(P\) is a function that takes an event as input and gives a number between \(0\) and \(1\) as output. In notation:
The rule about probabilities being between \(0\) and \(1\) is derived from some more basic assumptions and not intuition about the percentage of times something occurs. Let’s talk about these basic assumptions.
Probability Axioms¶
There are three fundamental assumptions (called axioms) about probability functions from which our other laws are derived.
First Axiom - For an event \(E\), and probability function \(P\):
Second Axiom - For a sample space \(S\):
Third Axiom - If \(E_1, E_2, ...E_n\) are mutually exclusive events:
Recall that mutually exclusive means that sets have empty intersections. We have already seen that \(n(E_1 \cup E_2) = n(E_1) + n(E_2)\) for mutually exclusive events. Our third axiom says something similar for the probability of mutually exclusive events, \(P(E_1 \cup E_2) = P(E_1) + P(E_2)\)
Probability Properties¶
From our axioms we can derive important properties of probability functions.
For example, We can show that \(P(A) + P(A^C) = 1\).
This implies that \(P(A^C) = 1 - P(A)\).
Here are some useful formulas that can be derived from these axioms.
As with counting, the formula for two unions gives us a formula for 3 unions.
Notice that these formulas are the same as our formulas for the size of the set if we swap out \(P\) for \(n\) and \(1\) for \(n(S)\).
Example: Coin Flipping and Axioms
If we flip a coin, heads and tails are mutually exclusive events.
We know that \(P(\{H\}), P(\{T\})=.5\) from our formula for equally likely events at the beginning of the chapter. Note that \(S = \{H\} \cup \{T\}\) so this example also illustrates that \(P(S)=1\).
Conditional Probability¶
Suppose we have some cats and dogs that are either fluffy or not fluffy.
Row title |
Fluffy |
Not fluffy |
Total |
|---|---|---|---|
Cats |
21 |
18 |
39 |
Dogs |
35 |
26 |
61 |
Total |
56 |
44 |
100 |
We can calculate the probability of a randomly selected pet being fluffy using our formula for equally likely events.
We can calculate the probability of a pet being a fluffy cat as:
We can also calculate the probability of a pet being fluffy, given that the pet is a cat. Notice that the denominator is not all the pets, but only the number of cats.
This is a conditional probability because it is conditioned on the pet being a cat. There is notation for this.
Notice that \(P(\text{Fluffy|Cat})\) does not mean the same thing as \(P(\text{Fluffy})\).
Formulas¶
The probability of event \(A\) given that event \(B\) has occurred is denoted \(P(A|B)\) and pronounced “the probability of A given B”. The general formula for this is
We can calculate \(P(A \cap B)\) by rearranging the formula.
Conceptuaize this as an event B happening with probability \(P(B)\) and then an event \(A\) happening with probability \(P(A|B)\), so \(P(A \cap B) = P(B) \cdot P(A|B)\)
For experiments where all outcomes have equal probabilities \(P(A|B)\) can be calculated with set sizes. Here is a derivation:
Independence¶
Events are independent if they do not influence each other. The second flip of a coin is not influenced by the first flip, so the two events are independent.
If two events influence each other they are not independent. Let the event that the first card pulled without replacement from a deck is a jack be \(J_1\) and the event that the second card pulled is a jack is \(J_2\).  The event \(J_1\) has an effect on \(J_2\) and these events are not independent.
Definition of Independence: Events \(A\) and \(B\) are said to be independent if \(P(A|B)=P(A)\).
This definition is equivalent to \(P(A \cap B) = P(A) \cdot P(B)\) after substitution with the conditional probability formulas. This identity has it’s own name:
Multiplication Rule for Independent Events: If \(A\) and \(B\) are independent events then \(P(A \cap B) = P(A) \cdot P(B)\).
Let \(H_n\) be the event that the nth flip of a coin is heads so that \(H_2\) means the second coin flip is heads. The multiplication rule for independent events works for more than two events if all events are mutually independent so that \(P(H_1 \cap H_2 \cap H_3 \cap H_4)=P(H_1)\times P(H_2)\times P(H_3)\times P(H_4) = (\frac{1}{2})^4\).
Bayes Theorem¶
Bayes Theorem Intuition¶
Suppose 1% of the population uses drugs. 98% of drug users test positive on a drug test and 2% of non-users test positive. What is the probability that a person testing positive for a drug test has used drugs?
Using the above tree diagram and the definition of conditional probability we calculate:
Let’s explain these steps in more detail.
Law of Total Probability¶
In the example above we use the following identity:
This identity works because:
There is a more general formulation of this known as the law of total probability:
Events \(A_1,A_2,...,A_n\) are said to be a partition of the sample space \(S\) if \(A_1 \cup A_2 \cup... \cup A_n=S\) and if for all \(i,j: \ A_i \cap A_j = \emptyset\). This just means that the sets \(A_1,A_2,...,A_n\) cover the whole set and there is no overlap between the sets.
If sets \(A_1,A_2,...,A_n\) partition \(S\) then for any event \(E\) we have the law of total probability:
This visual might be useful:
Consider how \(P(E) = P(E \cap (A_1 \cup A_2 \cup ... \cup A_n))\) and then revisit the algebra.
Bayes Theorem Formula¶
For a partition \(A_1,A_2,...,A_n\) of event \(E\):
Returning to our example of drug use, people either are drug users or aren’t. So the drug use partitions people that are tested for drugs.


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Introduction
In this tutorial, you'll learn how to set up and use animated sprites
in Torque 2D. An 'animated sprite' is a two-dimensional image that has
multiple frames, like a movie. This tutorial will focus on Torque 2D features
that are unique to animated sprites. In addition to these features,
animated sprites can be manipulated in many of the same ways as static
(non-animated) sprites, which you can learn about in the Static Sprite
Tutorial.
To prepare for this tutorial, run Torque 2D and create a new
project. Name the new project "AnimatedSprites" and use the Empty Game project template. This
creates a new project called AnimatedSprites in the location you
chose to save it to.
Loading The Image Files
We'll need to add a couple of images to the AnimatedSprites project for
this tutorial. To do this we will learn a convenient way to
bring images into Torque 2D. Simply drag Figure 1 and Figure 2 from
this html document into your Torque 2D window with your new project
loaded.
This should bring them into your object library, though as full
images.These images are "CELL" images, so double click their icons
in the object library and set them up according to the following instructions.
In the Image Builder dialog, change the Image Mode to CELL (as shown in
Figure 3). CELL mode is used for images that are evenly divided into
multiple frames. The default is four equally-sized frames, which is
correct for this image, so press Save.
Now it's time to adjust the FlourSack image. Double click on the
FlourSack imageMap. Choose CELL image mode. The frames don't look quite
right, so we're going to adjust the cell sizes. For starters, change the
Cell Width to "85" and press Enter.
That looks like the right width. Let's assume that the cells are as tall
as they are wide and change the Cell Height to "85" also. The result
does not look perfect. Enter "112" instead (as shown in Figure 4). It
should look a lot better. Click Save.
Creating A New Animation
Your sprites now appear in the Static Sprites section. To set them up
for the Animated Sprites section, we will use the Animation Builder. To
do this, click the Create a new Animation button under the Create tab
(as shown in Figure 5). In the Select Material dialog, select
imageMap1234ImageMap then press the Select button (as shown in
Figure 6).
This brings up the Animation Builder, with its default settings
selected. The biggest area of the Animation editor (center/lower left)
you can see all the available cells of the image map, which are used as
frames in your animation.
On the top is the Animation Storyboard where you can arrange these
cells. On the right side in the upper right you can see a preview
animation window, below that are various options and a save and cancel
button.
The Animation Builder
Now it's time to take a closer look at the Animation Builder
features. Let's take a look at the animation properties ...
- Frames Per Second: Number of frames you want played each
second (speed of the animation).
- Start Frame: This is the frame you want the animation to
start at.
- Total Time: The number of seconds in one cycle of the
animation. The frame times will be divided evenly, so, in this
animation, 4 frames in one second will result in .25 seconds per frame.
- Cycle Animation: If checked itmakes the
animation start back at the beginning after completing its
cycle.If this isn't checked it means the animation will play
once, then stop on the final frame.
- Name: The name of the animation.
The Animation Storyboard
To create your own animation drag your animation frames from the
Source ImageMap section into the Animation Storyboard (or just click the
green plus to add all of the frames). Let's make this one run
backwards...
Start by dragging the "4" image into the first frame in the storyboard.
Now continue dropping frames in... "3"... "2"... "1".You should
see the preview, in the top right, playing your animation as you build
it.Be sure to modify the Frame Per Second field if your animation
is going to fast.
The FlourSack Animation
Let's do one more animation as an example... one that's a little
cooler than flashing numbers. Back in the Create tab, click on Create a
new Animation and select FlourSackImageMap as the source
image.Click on the green plus (Add all the frames from the image)
button to add all of your frames. Hey, that looks pretty cool! But what
is that flash at the end of the animation?
Go back to the storyboard and scroll down to the end. You'll see that
there is an empty frame there . This is because when you import your
image using the Image Mode Cell, it divides the image into equal frames
per row based on the frame size you specify.
Since this animation has an odd number of frames, when the Image Map
Builder created it, there was a blank one at the end. Drag the last frame off of
the animation list and drop it on the trash can icon to remove it from the
animation, then preview it again. Now that looks really
good! Return to the storyboard and save the animation using the default
name.
Back in the Scene Editor, drag the animated flour sack from the Create
tab onto the left side of the Scene View. With that animated sprite
still selected, click the Edit tab on the right side of the screen and
open up the Physics section in that tab. Type "10" into the Velocity X
property and press Enter.
Save your level, then press the Play Level button. You should see the
sack walking across the screen.
In Conclusion
We've thoroughly explored the setup of animated sprites in Torque 2D. To
further your understanding, it will be worth your while to go through
the Image Maps tutorial. That will explain how to create the celled
image maps that are used to make these frame-based sprites.
Now that you have the tools, the possibilities for cool and interesting
animations are pretty much limitless! Have fun with it!
|


Showing 1 to 5 of 5 entries
| Order # | Status | Vendor Name | From Location | Created By | Created At | Actions |
|---|---|---|---|---|---|---|
| 57019 | Completed | Foods India Limited | Warehouse | StoreERP | Aug 3, 2021 3:10 PM |
...
|
| 57019 | Completed | Foods India Limited | Warehouse | StoreERP | Aug 3, 2021 3:10 PM |
...
|
| 57019 | Completed | Foods India Limited | Warehouse | StoreERP | Aug 3, 2021 3:10 PM |
...
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| 57019 | Completed | Foods India Limited | Warehouse | StoreERP | Aug 3, 2021 3:10 PM |
...
|
| 57019 | Pending | Foods India Limited | Warehouse | StoreERP | Aug 3, 2021 3:10 PM |
...
|


[VisAPI Index] [VisAPI Hierarchy]
#include <VisApi/VisApi.h>
Inherits: visObject
Associate class with given attribute set.
Return associated attribute set.
True if attributes are equal according to specific rules.
Return average attribute calculated by specific algorithms.
| Documentation generated by breitlip@atzenger10 on Mon Jan 17 19:29:23 CET 2000 | Kdoc |


The Elm Language
Functional Front-end Programming
What are we going to talk about
What's great about Elm?
- Fully Functional
- Approachable
- Compiler
- Full code coverage
- Elm Architecture
Steps
- Hello world
- One row
- Two rows
- Two tables
- Toggle between tables
- Load legislators from api


- accepts(FSA, String) - Static method in class pt.up.fe.comp.fsa.Operations
-
Determines whether an automaton accepts a given string.
- accepts_alphabet_plus_closure(FSA) - Static method in class pt.up.fe.comp.fsa.Operations
-
Determines whether an automaton's accepts every string in it's alphabet
- accepts_alphabet_star_closure(FSA) - Static method in class pt.up.fe.comp.fsa.Operations
-
Determines whether an automaton's accepts every string in it's alphabet AND the empty set
- add_doublequote_to_alphabet(FSA) - Static method in class pt.up.fe.comp.fsa.Operations
-
Adds the double quote character to the alphabet.
- add_edge(FSA, String, String, String) - Static method in class pt.up.fe.comp.fsa.Operations
-
Adds an edge to the automaton.
- add_empty_to_alphabet(FSA) - Static method in class pt.up.fe.comp.fsa.Operations
-
Adds the empty string to the alphabet.
- add_final_state(FSA, String) - Static method in class pt.up.fe.comp.fsa.Operations
-
Adds a state to the set of final states of automaton.
- add_state(FSA, String) - Static method in class pt.up.fe.comp.fsa.Operations
-
Adds a state to the automaton as well as all associated edges.
- add_to_alphabet(FSA, String) - Static method in class pt.up.fe.comp.fsa.Operations
-
Adds all specified characters to an FSA's alphabet.
- and(Boolean, Boolean, Boolean...) - Static method in class pt.up.fe.comp.fsa.Operations
-
'AND' operator, used to check if all arguments evaluate to true.


I am a PhD Scholar at University of Brescia, Italy. As a scholar, my research interests are mostly focused on Corporate Social Responsibility, Human Resource Management, Business Ethics, Cross-Cultural Study, Strategy and Innovation and Leadership. I have always had an interest in projects that are interdisciplinary and which also foster a broad, social-environmental and political dialogue; I have published many papers in renowned journals, but I have also presented at conferences on CSR, Leadership and on sustainable performance. While my interests are vast, I have always found literary studies to satisfy my intellectual curiosity and provide a meaningful methodological foundation. Currently, I am working with International scholars with Nottingham Trent University, UK, University of Sharjah, UAE,University of Brescia, Italy and Quaid e Azam University, Pakistan to broaden my horizon.


How much are you willing to pay to cycle to work? Given that the bike and the helmet is free. This is not a trick question. As we live in a world with limited resources, everything we do and not do, has a cost. Therefore decision making boils down to how much you are willing to pay. And this is what I want to show you in this article below.
To make this explanation easier, let's say the cost of driving is $10 per km. Riding a bike is much cheaper but there are still costs associated with maintaining the bike, purchasing safety gear etc and let's say it costs someone $1 to cycle 1 km. And if we assume an average person is willing to pay up to $20 to cycle to work. This is the spatial catchment for those who could afford to cycle to work in Melbourne CBD.
Coverage across Metropolitan Melbourne with an approximate distance of 20km from the CBD.
However, varying road conditions across the catchment may make it more difficult for some to cycle to the CBD. For example, route difficulty, bike path safety etc could inherently increase the cost of cycling. A cycling budget of $20 may only get someone the distance of 10kms or less, if we take into account these costs.
Here, I came up with solution to estimate the cycling catchment based on a cycle budget of $20 using open source datasets from VicRoads portal. First, let's take a look at current road conditions within this 20km catchment. Blue roads indicate cycle paths that are "easier" for the rider therefore cheaper. Red lines indicate cycle paths that are more "difficult" to cycle on therefore may be more expensive to cycle on.
As a result, an average persons's bikeable distance may be cut short significantly for a budget of $20. The map below shows the "bikeable" distance after apply costs to travel origins of over 1700 locations in Melbourne Metropolitan area.
Another insight drawn from this is showing existing areas that are ideal for cycling. Knowing where the bottlenecks are, whether the whole path from A to B is diffult or if there are only small sections of the road that needs fixing, helps authorities make decisions about where or how they should invest in improving the overall city's wellbeing.


The SoS format is highly readable with almost verbatim inclusion of scripts in other languages. Anyone, including those who have no idea about SoS, would be able to read and modify workflows written in SoS format. This makes SoS an idea tool for the sharing of workflows with others, and for the creation of workflows that would be frequently updated for newer versions of tools.
As an interactive environment and notebook tool that promotes literate programming, SoS allows you to perform and record your analysis in different languages in a single Jupyter notebook, with seamless integration of multiple Jupyter kernels (e.g. python, and R). The ability toexchange data between different kernels allows you to use the most appropriate languages for different parts of your work.
SoS can work with all Jupyter kernels and currently provides native support (with data exchange) for the following languages:
| Bash |
| JavaScript |
| Julia |
| Matlab |
| Octave |
| Python 2 |
| Python 3 |
| R |
| SAS |
SoS follows your definition of workflow that allows you to define workflows in both foward and makefile style, even a mixture of both. More specifically, you can define workflows as ordered steps that would be executed sequentially step by step to process specified input files; or as a set of makefile-style rules that would be executed to generated specified output files; or as a sequence of steps with help from makefile-style steps. SoS automatically analyzes the input and output of steps and execute workflows using a dynamic DAG (Directed Acyclic Graph) that would be expanded and shrinked during the execution.
SoS separates the execution of workflows as individual execution units and record the signature of the execution units during the execution of the workflow.  The execution units are indepedent of workflows so some steps of your workflow could be ignored if they have been executed by another workflow.
SoS is very easy to learn so that you can start using it in minutes without having to learn any of the advanced features. As demonstrated in our Quick Start Guide, you can easily consolidate your scripts into an executable workflow using SoS and add more features to the script step by step if necessary. The multi-kernel feature of the Jupyter notebook is also easy to use with only two magics to remember.
SoS is released under a GPL 3 license and is available for Mac OSX, Linux, and Windows operating systems. SoS provides an interactive environment for interactive data analysis on your PC. With just a few additional steps, your script can be transformed to a workflow that deals with different input files and different processing logics, and can be run in batch mode on a cluster system or in a cloud environment.
It is technically not possible to execute workflows on all systems because of limits on hardware and software but using SoS's remote execution features, you can execute workflows locally while submitting tasks to external task queues such as computing servers, cluster systems, and RQ or Celery task queues. SoS automaticlly synchronize input and output files so your tasks could be executed easily on servers with their own filesystems.
SoS is open source and provides an API to work with aribitrary languages. Its data-exchange model allows the exachange of arbitrary data types between languages with results in native data types. If your favoriate language is not yet support and you are familiar with Python, feel free to add it to SoS.


hunch_net hunch_net-2007 hunch_net-2007-226 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: It’s conference season once again. Conference Due? When? Where? double blind? author feedback? Workshops? AAAI February 1/6 (and 27) July 22-26 Vancouver, British Columbia Yes Yes Done UAI February 28/March 2 July 19-22 Vancouver, British Columbia No No No COLT January 16 June 13-15 San Diego, California (with FCRC ) No No No ICML February 7/9 June 20-24 Corvallis, Oregon Yes Yes February 16 KDD February 23/28 August 12-15 San Jose, California Yes No? February 28 The geowinner this year is the west coast of North America. Last year ‘s geowinner was the Northeastern US, and the year before it was mostly Europe. It’s notable how tightly the conferences cluster, even when they don’t colocate.
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1 February 28 The geowinner this year is the west coast of North America. [sent-9, score-0.659]
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3 It’s notable how tightly the conferences cluster, even when they don’t colocate. [sent-11, score-0.28]
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same-blog 1 1.0 226 hunch net-2007-01-04-2007 Summer Machine Learning Conferences
Introduction: It’s conference season once again. Conference Due? When? Where? double blind? author feedback? Workshops? AAAI February 1/6 (and 27) July 22-26 Vancouver, British Columbia Yes Yes Done UAI February 28/March 2 July 19-22 Vancouver, British Columbia No No No COLT January 16 June 13-15 San Diego, California (with FCRC ) No No No ICML February 7/9 June 20-24 Corvallis, Oregon Yes Yes February 16 KDD February 23/28 August 12-15 San Jose, California Yes No? February 28 The geowinner this year is the west coast of North America. Last year ‘s geowinner was the Northeastern US, and the year before it was mostly Europe. It’s notable how tightly the conferences cluster, even when they don’t colocate.
2 0.49685621 422 hunch net-2011-01-16-2011 Summer Conference Deadline Season
Introduction: Machine learning always welcomes the new year with paper deadlines for summer conferences. This year, we have: Conference Paper Deadline When/Where Double blind? Author Feedback? Notes ICML February 1 June 28-July 2, Bellevue, Washington, USA Y Y Weak colocation with ACL COLT February 11 July 9-July 11, Budapest, Hungary N N colocated with FOCM KDD February 11/18 August 21-24, San Diego, California, USA N N UAI March 18 July 14-17, Barcelona, Spain Y N The larger conferences are on the west coast in the United States, while the smaller ones are in Europe.
3 0.40406615 387 hunch net-2010-01-19-Deadline Season, 2010
Introduction: Many conference deadlines are coming soon. Deadline Double Blind / Author Feedback Time/Place ICML January 18((workshops) / February 1 (Papers) / February 13 (Tutorials) Y/Y Haifa, Israel, June 21-25 KDD February 1(Workshops) / February 2&5 (Papers) / February 26 (Tutorials & Panels)) / April 17 (Demos) N/S Washington DC, July 25-28 COLT January 18 (Workshops) / February 19 (Papers) N/S Haifa, Israel, June 25-29 UAI March 11 (Papers) N?/Y Catalina Island, California, July 8-11 ICML continues to experiment with the reviewing process, although perhaps less so than last year. The S “sort-of” for COLT is because author feedback occurs only after decisions are made. KDD is notable for being the most comprehensive in terms of {Tutorials, Workshops, Challenges, Panels, Papers (two tracks), Demos}. The S for KDD is because there is sometimes author feedback at the decision of the SPC. The (past) January 18 de
4 0.3782765 331 hunch net-2008-12-12-Summer Conferences
Introduction: Here’s a handy table for the summer conferences. Conference Deadline Reviewer Targeting Double Blind Author Feedback Location Date ICML ( wrong ICML ) January 26 Yes Yes Yes Montreal, Canada June 14-17 COLT February 13 No No Yes Montreal June 19-21 UAI March 13 No Yes No Montreal June 19-21 KDD February 2/6 No No No Paris, France June 28-July 1 Reviewer targeting is new this year. The idea is that many poor decisions happen because the papers go to reviewers who are unqualified, and the hope is that allowing authors to point out who is qualified results in better decisions. In my experience, this is a reasonable idea to test. Both UAI and COLT are experimenting this year as well with double blind and author feedback, respectively. Of the two, I believe author feedback is more important, as I’ve seen it make a difference. However, I still consider double blind reviewing a net wi
5 0.34658018 11 hunch net-2005-02-02-Paper Deadlines
Introduction: Itâ€™s conference season, and smell of budding papers is in the air. IJCAI 2005 , January 21 COLT 2005 , February 2 KDD 2005 , February 18 ICML 2005 , March 8 UAI 2005 , March 16 AAAI 2005 , March 18
6 0.26476753 145 hunch net-2005-12-29-Deadline Season
7 0.23338267 130 hunch net-2005-11-16-MLSS 2006
8 0.15000534 71 hunch net-2005-05-14-NIPS
9 0.14362407 242 hunch net-2007-04-30-COLT 2007
10 0.13966861 452 hunch net-2012-01-04-Why ICML? and the summer conferences
11 0.13752034 17 hunch net-2005-02-10-Conferences, Dates, Locations
12 0.11874551 116 hunch net-2005-09-30-Research in conferences
13 0.11797123 379 hunch net-2009-11-23-ICML 2009 Workshops (and Tutorials)
14 0.11443709 470 hunch net-2012-07-17-MUCMD and BayLearn
15 0.11368788 283 hunch net-2008-01-07-2008 Summer Machine Learning Conference Schedule
16 0.10895687 65 hunch net-2005-05-02-Reviewing techniques for conferences
17 0.10756002 472 hunch net-2012-08-27-NYAS ML 2012 and ICML 2013
18 0.10741237 198 hunch net-2006-07-25-Upcoming conference
19 0.1037793 266 hunch net-2007-10-15-NIPS workshops extended to 3 days
20 0.097852945 326 hunch net-2008-11-11-COLT CFP
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same-blog 1 0.99123842 226 hunch net-2007-01-04-2007 Summer Machine Learning Conferences
Introduction: It’s conference season once again. Conference Due? When? Where? double blind? author feedback? Workshops? AAAI February 1/6 (and 27) July 22-26 Vancouver, British Columbia Yes Yes Done UAI February 28/March 2 July 19-22 Vancouver, British Columbia No No No COLT January 16 June 13-15 San Diego, California (with FCRC ) No No No ICML February 7/9 June 20-24 Corvallis, Oregon Yes Yes February 16 KDD February 23/28 August 12-15 San Jose, California Yes No? February 28 The geowinner this year is the west coast of North America. Last year ‘s geowinner was the Northeastern US, and the year before it was mostly Europe. It’s notable how tightly the conferences cluster, even when they don’t colocate.
2 0.89247942 422 hunch net-2011-01-16-2011 Summer Conference Deadline Season
Introduction: Machine learning always welcomes the new year with paper deadlines for summer conferences. This year, we have: Conference Paper Deadline When/Where Double blind? Author Feedback? Notes ICML February 1 June 28-July 2, Bellevue, Washington, USA Y Y Weak colocation with ACL COLT February 11 July 9-July 11, Budapest, Hungary N N colocated with FOCM KDD February 11/18 August 21-24, San Diego, California, USA N N UAI March 18 July 14-17, Barcelona, Spain Y N The larger conferences are on the west coast in the United States, while the smaller ones are in Europe.
3 0.80064088 331 hunch net-2008-12-12-Summer Conferences
Introduction: Here’s a handy table for the summer conferences. Conference Deadline Reviewer Targeting Double Blind Author Feedback Location Date ICML ( wrong ICML ) January 26 Yes Yes Yes Montreal, Canada June 14-17 COLT February 13 No No Yes Montreal June 19-21 UAI March 13 No Yes No Montreal June 19-21 KDD February 2/6 No No No Paris, France June 28-July 1 Reviewer targeting is new this year. The idea is that many poor decisions happen because the papers go to reviewers who are unqualified, and the hope is that allowing authors to point out who is qualified results in better decisions. In my experience, this is a reasonable idea to test. Both UAI and COLT are experimenting this year as well with double blind and author feedback, respectively. Of the two, I believe author feedback is more important, as I’ve seen it make a difference. However, I still consider double blind reviewing a net wi
4 0.80005723 387 hunch net-2010-01-19-Deadline Season, 2010
Introduction: Many conference deadlines are coming soon. Deadline Double Blind / Author Feedback Time/Place ICML January 18((workshops) / February 1 (Papers) / February 13 (Tutorials) Y/Y Haifa, Israel, June 21-25 KDD February 1(Workshops) / February 2&5 (Papers) / February 26 (Tutorials & Panels)) / April 17 (Demos) N/S Washington DC, July 25-28 COLT January 18 (Workshops) / February 19 (Papers) N/S Haifa, Israel, June 25-29 UAI March 11 (Papers) N?/Y Catalina Island, California, July 8-11 ICML continues to experiment with the reviewing process, although perhaps less so than last year. The S “sort-of” for COLT is because author feedback occurs only after decisions are made. KDD is notable for being the most comprehensive in terms of {Tutorials, Workshops, Challenges, Panels, Papers (two tracks), Demos}. The S for KDD is because there is sometimes author feedback at the decision of the SPC. The (past) January 18 de
5 0.79907119 145 hunch net-2005-12-29-Deadline Season
Introduction: Many different paper deadlines are coming up soon so I made a little reference table. Out of curiosity, I also computed the interval between submission deadline and conference. Conference Location Date Deadline interval COLT Pittsburgh June 22-25 January 21 152 ICML Pittsburgh June 26-28 January 30/February 6 140 UAI MIT July 13-16 March 9/March 16 119 AAAI Boston July 16-20 February 16/21 145 KDD Philadelphia August 23-26 March 3/March 10 166 It looks like the northeastern US is the big winner as far as location this year.
6 0.78964269 11 hunch net-2005-02-02-Paper Deadlines
7 0.55899167 130 hunch net-2005-11-16-MLSS 2006
8 0.53062534 184 hunch net-2006-06-15-IJCAI is out of season
9 0.51921088 283 hunch net-2008-01-07-2008 Summer Machine Learning Conference Schedule
10 0.44857922 17 hunch net-2005-02-10-Conferences, Dates, Locations
11 0.40262395 65 hunch net-2005-05-02-Reviewing techniques for conferences
12 0.37865144 470 hunch net-2012-07-17-MUCMD and BayLearn
13 0.36610183 452 hunch net-2012-01-04-Why ICML? and the summer conferences
14 0.34613553 66 hunch net-2005-05-03-Conference attendance is mandatory
15 0.33940449 326 hunch net-2008-11-11-COLT CFP
16 0.33613735 357 hunch net-2009-05-30-Many ways to Learn this summer
17 0.30788618 71 hunch net-2005-05-14-NIPS
18 0.29582891 116 hunch net-2005-09-30-Research in conferences
19 0.29319519 242 hunch net-2007-04-30-COLT 2007
20 0.27463278 379 hunch net-2009-11-23-ICML 2009 Workshops (and Tutorials)
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[(32, 0.469), (53, 0.118), (55, 0.238), (94, 0.014)]
simIndex simValue blogId blogTitle
same-blog 1 0.88258713 226 hunch net-2007-01-04-2007 Summer Machine Learning Conferences
Introduction: It’s conference season once again. Conference Due? When? Where? double blind? author feedback? Workshops? AAAI February 1/6 (and 27) July 22-26 Vancouver, British Columbia Yes Yes Done UAI February 28/March 2 July 19-22 Vancouver, British Columbia No No No COLT January 16 June 13-15 San Diego, California (with FCRC ) No No No ICML February 7/9 June 20-24 Corvallis, Oregon Yes Yes February 16 KDD February 23/28 August 12-15 San Jose, California Yes No? February 28 The geowinner this year is the west coast of North America. Last year ‘s geowinner was the Northeastern US, and the year before it was mostly Europe. It’s notable how tightly the conferences cluster, even when they don’t colocate.
2 0.83373249 433 hunch net-2011-04-23-ICML workshops due
Introduction: Lihong points out that ICML workshop submissions are due April 29.
3 0.46683288 387 hunch net-2010-01-19-Deadline Season, 2010
Introduction: Many conference deadlines are coming soon. Deadline Double Blind / Author Feedback Time/Place ICML January 18((workshops) / February 1 (Papers) / February 13 (Tutorials) Y/Y Haifa, Israel, June 21-25 KDD February 1(Workshops) / February 2&5 (Papers) / February 26 (Tutorials & Panels)) / April 17 (Demos) N/S Washington DC, July 25-28 COLT January 18 (Workshops) / February 19 (Papers) N/S Haifa, Israel, June 25-29 UAI March 11 (Papers) N?/Y Catalina Island, California, July 8-11 ICML continues to experiment with the reviewing process, although perhaps less so than last year. The S “sort-of” for COLT is because author feedback occurs only after decisions are made. KDD is notable for being the most comprehensive in terms of {Tutorials, Workshops, Challenges, Panels, Papers (two tracks), Demos}. The S for KDD is because there is sometimes author feedback at the decision of the SPC. The (past) January 18 de
4 0.46002233 331 hunch net-2008-12-12-Summer Conferences
Introduction: Here’s a handy table for the summer conferences. Conference Deadline Reviewer Targeting Double Blind Author Feedback Location Date ICML ( wrong ICML ) January 26 Yes Yes Yes Montreal, Canada June 14-17 COLT February 13 No No Yes Montreal June 19-21 UAI March 13 No Yes No Montreal June 19-21 KDD February 2/6 No No No Paris, France June 28-July 1 Reviewer targeting is new this year. The idea is that many poor decisions happen because the papers go to reviewers who are unqualified, and the hope is that allowing authors to point out who is qualified results in better decisions. In my experience, this is a reasonable idea to test. Both UAI and COLT are experimenting this year as well with double blind and author feedback, respectively. Of the two, I believe author feedback is more important, as I’ve seen it make a difference. However, I still consider double blind reviewing a net wi
5 0.44110787 326 hunch net-2008-11-11-COLT CFP
Introduction: Adam Klivans , points out the COLT call for papers . The important points are: Due Feb 13. Montreal, June 18-21. This year, there is author feedback.
6 0.44110787 465 hunch net-2012-05-12-ICML accepted papers and early registration
7 0.43462542 472 hunch net-2012-08-27-NYAS ML 2012 and ICML 2013
8 0.43099239 271 hunch net-2007-11-05-CMU wins DARPA Urban Challenge
9 0.42777073 20 hunch net-2005-02-15-ESPgame and image labeling
10 0.42403731 90 hunch net-2005-07-07-The Limits of Learning Theory
11 0.42370406 446 hunch net-2011-10-03-Monday announcements
12 0.42205197 448 hunch net-2011-10-24-2011 ML symposium and the bears
13 0.41790715 302 hunch net-2008-05-25-Inappropriate Mathematics for Machine Learning
14 0.41726613 49 hunch net-2005-03-30-What can Type Theory teach us about Machine Learning?
15 0.40693012 356 hunch net-2009-05-24-2009 ICML discussion site
16 0.40620822 422 hunch net-2011-01-16-2011 Summer Conference Deadline Season
17 0.39713988 57 hunch net-2005-04-16-Which Assumptions are Reasonable?
18 0.3971335 292 hunch net-2008-03-15-COLT Open Problems
19 0.38987753 71 hunch net-2005-05-14-NIPS
20 0.38868949 159 hunch net-2006-02-27-The Peekaboom Dataset


[gecode-users] gecode
Martin Mann
mmann at informatik.uni-freiburg.de
Fri Mar 16 20:51:05 CET 2007
> I use gecode and I want to print just the solutions which are different , not all, what can I do?
???
what is your problem?
using a branching in your space?
usually all solutions found by a search engine like DFS or BAB are
different if you specified a branching ?!?!?
sorry, but at least for me you gave too less information to understand
the concrete problem and to provide some suggestions.
yours,
Martin
--
Martin Mann, Dipl. Bioinf.
Bioinformatics - Inst. of Computer Science
Albert-Ludwigs-University Freiburg
Tel: ++49-761-203-8259
Fax: ++49-761-203-7462
http://www.bioinf.uni-freiburg.de/~mmann/
More information about the gecode-users
mailing list


Generate Keys
512 bit
1024 bit
2048 bit
4096 bit
Other
Private Key
Public Key
Text > RSA
RSA > Text
File > RSA
RSA File > Text
Plaintext
RSA


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|
ADC-Bluetooth
2.0
Nico Lingg
|
Makrodefinitionen | |
| #define | CLKPWR_CLKSRCSEL_CLKSRC_IRC ((uint32_t)(0x00)) |
| #define | CLKPWR_CLKSRCSEL_CLKSRC_MAINOSC ((uint32_t)(0x01)) |
| #define | CLKPWR_CLKSRCSEL_CLKSRC_RTC ((uint32_t)(0x02)) |
| #define | CLKPWR_CLKSRCSEL_BITMASK ((uint32_t)(0x03)) |
| #define | CLKPWR_CLKOUTCFG_CLKOUTSEL_CPU ((uint32_t)(0x00)) |
| #define | CLKPWR_CLKOUTCFG_CLKOUTSEL_MAINOSC ((uint32_t)(0x01)) |
| #define | CLKPWR_CLKOUTCFG_CLKOUTSEL_RC ((uint32_t)(0x02)) |
| #define | CLKPWR_CLKOUTCFG_CLKOUTSEL_USB ((uint32_t)(0x03)) |
| #define | CLKPWR_CLKOUTCFG_CLKOUTSEL_RTC ((uint32_t)(0x04)) |
| #define | CLKPWR_CLKOUTCFG_CLKOUTDIV(n) ((uint32_t)((n&0x0F)<<4)) |
| #define | CLKPWR_CLKOUTCFG_CLKOUT_EN ((uint32_t)(1<<8)) |
| #define | CLKPWR_CLKOUTCFG_CLKOUT_ACT ((uint32_t)(1<<9)) |
| #define | CLKPWR_CLKOUTCFG_BITMASK ((uint32_t)(0x3FF)) |
| #define | CLKPWR_PLL0CON_ENABLE ((uint32_t)(0x01)) |
| #define | CLKPWR_PLL0CON_CONNECT ((uint32_t)(0x02)) |
| #define | CLKPWR_PLL0CON_BITMASK ((uint32_t)(0x03)) |
| #define | CLKPWR_PLL0CFG_MSEL(n) ((uint32_t)(n&0x7FFF)) |
| #define | CLKPWR_PLL0CFG_NSEL(n) ((uint32_t)((n<<16)&0xFF0000)) |
| #define | CLKPWR_PLL0CFG_BITMASK ((uint32_t)(0xFF7FFF)) |
| #define | CLKPWR_PLL0STAT_MSEL(n) ((uint32_t)(n&0x7FFF)) |
| #define | CLKPWR_PLL0STAT_NSEL(n) ((uint32_t)((n>>16)&0xFF)) |
| #define | CLKPWR_PLL0STAT_PLLE ((uint32_t)(1<<24)) |
| #define | CLKPWR_PLL0STAT_PLLC ((uint32_t)(1<<25)) |
| #define | CLKPWR_PLL0STAT_PLOCK ((uint32_t)(1<<26)) |
| #define | CLKPWR_PLL0FEED_BITMASK ((uint32_t)0xFF) |
| #define | CLKPWR_PLL1CON_ENABLE ((uint32_t)(0x01)) |
| #define | CLKPWR_PLL1CON_CONNECT ((uint32_t)(0x02)) |
| #define | CLKPWR_PLL1CON_BITMASK ((uint32_t)(0x03)) |
| #define | CLKPWR_PLL1CFG_MSEL(n) ((uint32_t)(n&0x1F)) |
| #define | CLKPWR_PLL1CFG_PSEL(n) ((uint32_t)((n&0x03)<<5)) |
| #define | CLKPWR_PLL1CFG_BITMASK ((uint32_t)(0x7F)) |
| #define | CLKPWR_PLL1STAT_MSEL(n) ((uint32_t)(n&0x1F)) |
| #define | CLKPWR_PLL1STAT_PSEL(n) ((uint32_t)((n>>5)&0x03)) |
| #define | CLKPWR_PLL1STAT_PLLE ((uint32_t)(1<<8)) |
| #define | CLKPWR_PLL1STAT_PLLC ((uint32_t)(1<<9)) |
| #define | CLKPWR_PLL1STAT_PLOCK ((uint32_t)(1<<10)) |
| #define | CLKPWR_PLL1FEED_BITMASK ((uint32_t)0xFF) |
| #define | CLKPWR_CCLKCFG_BITMASK ((uint32_t)(0xFF)) |
| #define | CLKPWR_USBCLKCFG_BITMASK ((uint32_t)(0x0F)) |
| #define | CLKPWR_IRCTRIM_BITMASK ((uint32_t)(0x0F)) |
| #define | CLKPWR_PCLKSEL0_BITMASK ((uint32_t)(0xFFF3F3FF)) |
| #define | CLKPWR_PCLKSEL1_BITMASK ((uint32_t)(0xFCF3F0F3)) |
| #define | CLKPWR_PCLKSEL_SET(p, n) _SBF(p,n) |
| #define | CLKPWR_PCLKSEL_BITMASK(p) _SBF(p,0x03) |
| #define | CLKPWR_PCLKSEL_GET(p, n) ((uint32_t)((n>>p)&0x03)) |
| #define | CLKPWR_PCON_PM0 ((uint32_t)(1<<0)) |
| #define | CLKPWR_PCON_PM1 ((uint32_t)(1<<1)) |
| #define | CLKPWR_PCON_BODPDM ((uint32_t)(1<<2)) |
| #define | CLKPWR_PCON_BOGD ((uint32_t)(1<<3)) |
| #define | CLKPWR_PCON_BORD ((uint32_t)(1<<4)) |
| #define | CLKPWR_PCON_SMFLAG ((uint32_t)(1<<8)) |
| #define | CLKPWR_PCON_DSFLAG ((uint32_t)(1<<9)) |
| #define | CLKPWR_PCON_PDFLAG ((uint32_t)(1<<10)) |
| #define | CLKPWR_PCON_DPDFLAG ((uint32_t)(1<<11)) |
| #define | CLKPWR_PCONP_BITMASK 0xEFEFF7DE |
| #define CLKPWR_CCLKCFG_BITMASK ((uint32_t)(0xFF)) |
Macro defines for CPU Clock Configuration RegisterCPU Clock configuration bit mask
| #define CLKPWR_CLKOUTCFG_BITMASK ((uint32_t)(0x3FF)) |
Clock source selection bit mask
| #define CLKPWR_CLKOUTCFG_CLKOUT_ACT ((uint32_t)(1<<9)) |
CLKOUT activity indication
| #define CLKPWR_CLKOUTCFG_CLKOUT_EN ((uint32_t)(1<<8)) |
CLKOUT enable control
| #define CLKPWR_CLKOUTCFG_CLKOUTDIV | ( | n | ) | ((uint32_t)((n&0x0F)<<4)) |
Integer value to divide the output clock by, minus one
| #define CLKPWR_CLKOUTCFG_CLKOUTSEL_CPU ((uint32_t)(0x00)) |
Macro defines for Clock Output Configuration RegisterSelects the CPU clock as the CLKOUT source
| #define CLKPWR_CLKOUTCFG_CLKOUTSEL_MAINOSC ((uint32_t)(0x01)) |
Selects the main oscillator as the CLKOUT source
| #define CLKPWR_CLKOUTCFG_CLKOUTSEL_RC ((uint32_t)(0x02)) |
Selects the Internal RC oscillator as the CLKOUT source
| #define CLKPWR_CLKOUTCFG_CLKOUTSEL_RTC ((uint32_t)(0x04)) |
Selects the RTC oscillator as the CLKOUT source
| #define CLKPWR_CLKOUTCFG_CLKOUTSEL_USB ((uint32_t)(0x03)) |
Selects the USB clock as the CLKOUT source
| #define CLKPWR_CLKSRCSEL_BITMASK ((uint32_t)(0x03)) |
Clock source selection bit mask
| #define CLKPWR_CLKSRCSEL_CLKSRC_IRC ((uint32_t)(0x00)) |
Macro defines for Clock Source Select RegisterInternal RC oscillator
| #define CLKPWR_CLKSRCSEL_CLKSRC_MAINOSC ((uint32_t)(0x01)) |
Main oscillator
| #define CLKPWR_CLKSRCSEL_CLKSRC_RTC ((uint32_t)(0x02)) |
RTC oscillator
| #define CLKPWR_IRCTRIM_BITMASK ((uint32_t)(0x0F)) |
Macro defines for IRC Trim RegisterIRC Trim bit mask
| #define CLKPWR_PCLKSEL0_BITMASK ((uint32_t)(0xFFF3F3FF)) |
Macro defines for Peripheral Clock Selection Register 0 and 1Peripheral Clock Selection 0 mask bit
| #define CLKPWR_PCLKSEL1_BITMASK ((uint32_t)(0xFCF3F0F3)) |
Peripheral Clock Selection 1 mask bit
| #define CLKPWR_PCLKSEL_BITMASK | ( | p | ) | _SBF(p,0x03) |
Macro to mask peripheral clock of each type
| #define CLKPWR_PCLKSEL_GET | ( | p, | |
| n | |||
| ) | ((uint32_t)((n>>p)&0x03)) |
Macro to get peripheral clock of each type
| #define CLKPWR_PCLKSEL_SET | ( | p, | |
| n | |||
| ) | _SBF(p,n) |
Macro to set peripheral clock of each type p: position of two bits that hold divider of peripheral clock n: value of divider of peripheral clock to be set
| #define CLKPWR_PCON_BODPDM ((uint32_t)(1<<2)) |
Brown-Out Reduced Power Mode
| #define CLKPWR_PCON_BOGD ((uint32_t)(1<<3)) |
Brown-Out Global Disable
| #define CLKPWR_PCON_BORD ((uint32_t)(1<<4)) |
Brown Out Reset Disable
| #define CLKPWR_PCON_DPDFLAG ((uint32_t)(1<<11)) |
Deep Power-down entry flag
| #define CLKPWR_PCON_DSFLAG ((uint32_t)(1<<9)) |
Deep Sleep entry flag
| #define CLKPWR_PCON_PDFLAG ((uint32_t)(1<<10)) |
Power-down entry flag
| #define CLKPWR_PCON_PM0 ((uint32_t)(1<<0)) |
Macro defines for Power Mode Control RegisterPower mode control bit 0
| #define CLKPWR_PCON_PM1 ((uint32_t)(1<<1)) |
Power mode control bit 1
| #define CLKPWR_PCON_SMFLAG ((uint32_t)(1<<8)) |
Sleep Mode entry flag
| #define CLKPWR_PCONP_BITMASK 0xEFEFF7DE |
Macro defines for Power Control for Peripheral RegisterPower Control for Peripherals bit mask
| #define CLKPWR_PLL0CFG_BITMASK ((uint32_t)(0xFF7FFF)) |
PLL 0 Configuration bit mask
| #define CLKPWR_PLL0CFG_MSEL | ( | n | ) | ((uint32_t)(n&0x7FFF)) |
Macro defines for PPL0 Configuration RegisterPLL 0 Configuration MSEL field
| #define CLKPWR_PLL0CFG_NSEL | ( | n | ) | ((uint32_t)((n<<16)&0xFF0000)) |
PLL 0 Configuration NSEL field
| #define CLKPWR_PLL0CON_BITMASK ((uint32_t)(0x03)) |
PLL 0 control bit mask
| #define CLKPWR_PLL0CON_CONNECT ((uint32_t)(0x02)) |
PLL 0 control connect
| #define CLKPWR_PLL0CON_ENABLE ((uint32_t)(0x01)) |
Macro defines for PPL0 Control RegisterPLL 0 control enable
| #define CLKPWR_PLL0FEED_BITMASK ((uint32_t)0xFF) |
Macro defines for PPL0 Feed RegisterPLL0 Feed bit mask
| #define CLKPWR_PLL0STAT_MSEL | ( | n | ) | ((uint32_t)(n&0x7FFF)) |
Macro defines for PPL0 Status RegisterPLL 0 MSEL value
| #define CLKPWR_PLL0STAT_NSEL | ( | n | ) | ((uint32_t)((n>>16)&0xFF)) |
PLL NSEL get value
| #define CLKPWR_PLL0STAT_PLLC ((uint32_t)(1<<25)) |
PLL status Connect bit
| #define CLKPWR_PLL0STAT_PLLE ((uint32_t)(1<<24)) |
PLL status enable bit
| #define CLKPWR_PLL0STAT_PLOCK ((uint32_t)(1<<26)) |
PLL status lock
| #define CLKPWR_PLL1CFG_BITMASK ((uint32_t)(0x7F)) |
USB PLL configuration bit mask
| #define CLKPWR_PLL1CFG_MSEL | ( | n | ) | ((uint32_t)(n&0x1F)) |
Macro defines for PLL1 Configuration RegisterUSB PLL MSEL set value
| #define CLKPWR_PLL1CFG_PSEL | ( | n | ) | ((uint32_t)((n&0x03)<<5)) |
USB PLL PSEL set value
| #define CLKPWR_PLL1CON_BITMASK ((uint32_t)(0x03)) |
USB PLL control bit mask
| #define CLKPWR_PLL1CON_CONNECT ((uint32_t)(0x02)) |
USB PLL control connect
| #define CLKPWR_PLL1CON_ENABLE ((uint32_t)(0x01)) |
Macro defines for PLL1 Control RegisterUSB PLL control enable
| #define CLKPWR_PLL1FEED_BITMASK ((uint32_t)0xFF) |
Macro defines for PLL1 Feed RegisterPLL1 Feed bit mask
| #define CLKPWR_PLL1STAT_MSEL | ( | n | ) | ((uint32_t)(n&0x1F)) |
Macro defines for PLL1 Status RegisterUSB PLL MSEL get value
| #define CLKPWR_PLL1STAT_PLLC ((uint32_t)(1<<9)) |
USB PLL status Connect bit
| #define CLKPWR_PLL1STAT_PLLE ((uint32_t)(1<<8)) |
USB PLL status enable bit
| #define CLKPWR_PLL1STAT_PLOCK ((uint32_t)(1<<10)) |
USB PLL status lock
| #define CLKPWR_PLL1STAT_PSEL | ( | n | ) | ((uint32_t)((n>>5)&0x03)) |
USB PLL PSEL get value
| #define CLKPWR_USBCLKCFG_BITMASK ((uint32_t)(0x0F)) |
Macro defines for USB Clock Configuration RegisterUSB Clock Configuration bit mask


Man Utd dealt Erling Haaland transfer blow by RB Leipzig's reduced fee option
Man Utd have tracked Red Bull Salzburg ace Erling Haaland but they could lose out to Leipzig who can get the marksman on the cheap
by Sam MeadeManchester United's hopes of signing Erling Haaland have been dealt a blow after it emerged Red Bull Leipzig can get the forward in a cut price deal.
Haaland has become one of Europe's hottest properties this season after scoring 26 goals in all competitions.
His exploits have attracted United's interest as well as a host of other top European sides.
But Leipzig, who share the same owners as the Austrian side, can bring Haaland to the Bundesliga for just €30m (£25.7m) according to Bild .
United have been quoted a fee of around £85m - more than three times what the German side can sign him for.
Haaland's father Alf-Inge, who played in the Premier League with Leeds and Manchester City , has told his son it is better to be playing than be sat on the bench.
That further increases the appeal of Leipzig who, after heavy investment, have become a challenger in Germany with Julian Nagelsmann at the helm.
Markus Krösche, Leipzig's sporting director, admits steps are already being considered which could see the 19-year-old leave Austria.
He said: "The boy is a very good player. Both in the league and in the Champions League, he does an exceptionally good job. Of course, we are already looking around for the summer. "
Haaland only made the switch from Norwegian side Molde last year for £4.5m, but has proved to be a hit.
Mino Raiola is also said to be advising the young forward who has scored goals in all three of his Champions League appearances this season.
Ole Gunnar Solskjaer has been assured of the funds to sign top class players and is eyeing a frontman at the earliest possible option.
Mario Mandzukic is a name that has long been linked given his availability but the Red Devils' transfer policy places an emphasis on young talent.


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Secant Method¶
Overview¶
In numerical analysis, the secant method is a root-finding algorithm that uses a succession of roots of secant lines to better approximate a root of a function \(f\). The secant method can be thought of as a finite-difference approximation of Newton’s method. However, the method was developed independently of Newton’s method and predates it by over 3000 years.
The Method¶
The secant method is defined by the recurrence relation
As can be seen from the recurrence relation, the secant method requires two initial values, \(x_0\) and \(x_1\), which should ideally be chosen to lie close to the root.
Derivation Of The Method¶
Starting with initial values \(x_0\) and \(x_1\), we construct a line through the points \((x0, f(x0))\) and \((x1, f(x1))\), as shown in the picture above. In slope–intercept form, the equation of this line is
The root of this linear function, that is the value of \(x\) such that \(y=0\) is
We then use this new value of \(x\) as \(x_2\) and repeat the process, using \(x_1\) and \(x_2\) instead of \(x_0\) and \(x_1\). We continue this process, solving for \(x_3\), \(x_4\), etc., until we reach a sufficiently high level of precision (a sufficiently small difference between \(x_n\) and \(x_n-1\)):
Usage¶
Imagine that we want to minimize the following function:
Then the code will look like this:
// example_root_secant.cpp
#include <iostream>
#include "../src/numerary.hpp" // Numerary library
using namespace std;
using namespace numerary;
/* Function to found the root */
double f(double x) {
return sin(x);
}
/* The main function */
int main() {
const double eps = 1.e-9; // eps value for method (1.e-9 by default)
double a = -1; // "a" value of segment [a, b]
double b = 1; // "b" value of segment [a, b]
double root;
short int is_found;
is_found = Numerary::root(f, a, b, &root, "secant", eps);
if (is_found == 1) {
cout << "Root is in x = " << root << endl;
} else {
cout << "Method not allowed!" << endl;
}
return 0;
}


The multiple instance data set consists of many independent subjects (called bags) and each subject is composed of several components (called instances). The outcomes of such data set are binary or categorical responses, and, we can only observe the subject-level outcomes. For example, in manufacturing processes, a subject is labeled as "defective" if at least one of its own components is defective, and otherwise, is labeled as "non-defective". The 'milr' package focuses on the predictive model for the multiple instance data set with binary outcomes and performs the maximum likelihood estimation with the Expectation-Maximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection.
| Version: | 0.3.0 |
| Depends: | R (≥ 3.2.3) |
| Imports: | utils, pipeR (≥ 0.5), numDeriv, glmnet, Rcpp (≥ 0.12.0), RcppParallel |
| LinkingTo: | Rcpp, RcppArmadillo, RcppParallel |
| Suggests: | testthat, knitr, knitcitations, rmarkdown, data.table, ggplot2, plyr |
| Published: | 2017-06-08 |
| Author: | Ping-Yang Chen [aut, cre], ChingChuan Chen [aut], Chun-Hao Yang [aut], Sheng-Mao Chang [aut] |
| Maintainer: | Ping-Yang Chen <pychen.ping at gmail.com> |
| BugReports: | https://github.com/PingYangChen/milr/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/PingYangChen/milr |
| NeedsCompilation: | yes |
| SystemRequirements: | GNU make |
| CRAN checks: | milr results |
| Reference manual: | milr.pdf |
| Vignettes: |
milr\: Multiple-Instance Logistic Regression with Lasso Penalty |
| Package source: | milr_0.3.0.tar.gz |
| Windows binaries: | r-devel: milr_0.3.0.zip, r-release: milr_0.3.0.zip, r-oldrel: milr_0.3.0.zip |
| OS X binaries: | r-release: milr_0.3.0.tgz, r-oldrel: milr_0.3.0.tgz |
| Old sources: | milr archive |
Please use the canonical form https://CRAN.R-project.org/package=milr to link to this page.


This section delves more deeply into the things that you can do with the various {_parameter parameters}. For more complete parameter documentation, go {All_parameters here}. This section covers most of the features of the Digital Chroma, including explaining its enhancements over the original Chroma.