{%extends "base.html"%} {%block title%}About{%endblock%} {%block body%} {%include 'messages.html' %} Home About About SLATE Portal SLATE (Service Layer At The Edge) is a system to enable sites to delegate service deployment and configuration to selected application administrators (learn more). Application Version {{ config.PORTAL_VERSION }} {%endblock%}
SLATE (Service Layer At The Edge) is a system to enable sites to delegate service deployment and configuration to selected application administrators (learn more).
{{ config.PORTAL_VERSION }}


Library for drawing 2D graphics to the screen in a game environment, used in conjunction with the Game library.
Static class containing utility functions to draw various shape primitives to the screen.
Draws a solid color ellipse to the screen at the given coordinates with the the given color and alpha.
| Name | Type | Optional | Description |
|---|---|---|---|
| left | integer | x coordinate of the left side of the bounding box that the ellipse will fill. |
|
| top | integer | y coordinate of the top side of the bounding box that the ellipse will fill. |
|
| width | integer | width of the bounding box that the ellipse will fill. |
|
| height | integer | height of the bounding box that the ellipse will fill. |
|
| red | integer | red color value of the ellipse (0-255). |
|
| green | integer | green color value of the ellipse (0-255). |
|
| blue | integer | blue color value of the ellipse (0-255). |
|
| alpha | integer | Optional | optional alpha value of the ellipse (0-255). Default is 255. |
Clears all drawn objects and fills the screen with a solid opaque color.
| Name | Type | Description |
|---|---|---|
| red | integer | red color value of the fill (0-255). |
| green | integer | green color value of the fill (0-255). |
| blue | integer | blue color value of the fill (0-255). |
Draws a line to the screen between the two given coordinates, with the given color, alpha, and thickness.
| Name | Type | Optional | Description |
|---|---|---|---|
| startX | integer | x coordinate of the start of the line |
|
| startY | integer | y coordinate of the start of the line |
|
| endX | integer | x coordinate of the end of the line |
|
| endY | integer | y coordinate of the end of the line |
|
| strokeWidth | integer | thickness of the line |
|
| red | integer | red color value of the line (0-255). |
|
| green | integer | green color value of the line (0-255). |
|
| blue | integer | blue color value of the line (0-255). |
|
| alpha | integer | Optional | optional alpha value of the line (0-255). Default is 255. |
Draws a solid color quadrilateral to the screen at the given coordinates with the the given color and alpha. Quadrilateral edges must not cross each other, otherwise behavior is undefined and may vary depending on platform.
| Name | Type | Optional | Description |
|---|---|---|---|
| ax | integer | x coordinate of the first quadrilateral point. |
|
| ay | integer | y coordinate of the first quadrilateral point. |
|
| bx | integer | x coordinate of the second quadrilateral point. |
|
| by | integer | y coordinate of the second quadrilateral point. |
|
| cx | integer | x coordinate of the third quadrilateral point. |
|
| cy | integer | y coordinate of the third quadrilateral point. |
|
| dx | integer | x coordinate of the fourth quadrilateral point. |
|
| dy | integer | y coordinate of the fourth quadrilateral point. |
|
| red | integer | red color value of the quadrilateral (0-255). |
|
| green | integer | green color value of the quadrilateral (0-255). |
|
| blue | integer | blue color value of the quadrilateral (0-255). |
|
| alpha | integer | Optional | optional alpha value of the quadrilateral (0-255). Default is 255. |
Draws a solid color rectangle to the screen at the given coordinates with the the given color and alpha.
| Name | Type | Optional | Description |
|---|---|---|---|
| left | integer | x coordinate of the left side of the bounding box that the rectangle will fill. |
|
| top | integer | y coordinate of the top side of the bounding box that the rectangle will fill. |
|
| width | integer | width of the bounding box that the rectangle will fill. |
|
| height | integer | height of the bounding box that the rectangle will fill. |
|
| red | integer | red color value of the retangle (0-255). |
|
| green | integer | green color value of the rectangle (0-255). |
|
| blue | integer | blue color value of the rectangle (0-255). |
|
| alpha | integer | Optional | optional alpha value of the rectangle (0-255). Default is 255. |
Draws a solid color triangle to the screen at the given coordinates with the the given color and alpha.
| Name | Type | Optional | Description |
|---|---|---|---|
| ax | integer | x coordinate of the first triangle point. |
|
| ay | integer | y coordinate of the first triangle point. |
|
| bx | integer | x coordinate of the second triangle point. |
|
| by | integer | y coordinate of the second triangle point. |
|
| cx | integer | x coordinate of the third triangle point. |
|
| cy | integer | y coordinate of the third triangle point. |
|
| red | integer | red color value of the triangle (0-255). |
|
| green | integer | green color value of the triangle (0-255). |
|
| blue | integer | blue color value of the triangle (0-255). |
|
| alpha | integer | Optional | optional alpha value of the triangle (0-255). Default is 255. |
Represents an image that can be drawn to the screen.
Draws the texture to the screen at the given coordinates. The image is positioned such that the top left corner is located at the (x, y) coordinates provided.
| Name | Type | Description |
|---|---|---|
| x | integer | x coordinate on the screen to draw to |
| y | integer | y coordinate on the screen to draw to |
Draws a portion of an image to the screen. The first x and y coordinates represent where on the screen to draw the image. The next 4 coordinates define a rectangular region on this texture to draw from.
| Name | Type | Description |
|---|---|---|
| x | integer | x coordinate on the screen to draw to |
| y | integer | y coordinate on the screen to draw to |
| sourceX | integer | x coordinate on the texture of the left side of the region to be drawn |
| sourceY | integer | y coordinate on the texture of the top side of the region to be drawn |
| sourceWidth | integer | width of the region on the texture to be drawn |
| sourceHeight | integer | height of the region on the texture to be drawn |
Draws a portion of an image to screen, stretching or shrinking it to fill a given region. The first 4 coordinates define a region on the screen to draw to. The last 4 coordinates define a region on the texture to sample the pixels from.
| Name | Type | Description |
|---|---|---|
| targetX | integer | x coordinate on the screen to draw to |
| targetY | integer | y coordinate on the screen to draw to |
| targetWidth | integer | width on the screen that the drawn region should stretch or shrink to |
| targetHeight | integer | height on the screen that the drawn region should stretch or shrink to |
| sourceX | integer | x coordinate on the texture of the left side of the region to be drawn |
| sourceY | integer | y coordinate on the texture of the top side of the region to be drawn |
| sourceWidth | integer | width of the region on the texture to be drawn |
| sourceHeight | integer | height of the region on the texture to be drawn |
Draws a portion of an image to screen, stretching or shrinking it to fill a given region and applies the given alpha. The first 4 coordinates define a region on the screen to draw to. The last 4 coordinates define a region on the texture to sample the pixels from. If the image contains per-pixel alpha values, those are compounded with the overall alpha.
| Name | Type | Description |
|---|---|---|
| targetX | integer | x coordinate on the screen to draw to |
| targetY | integer | y coordinate on the screen to draw to |
| targetWidth | integer | width on the screen that the drawn region should stretch or shrink to |
| targetHeight | integer | height on the screen that the drawn region should stretch or shrink to |
| sourceX | integer | x coordinate on the texture of the left side of the region to be drawn |
| sourceY | integer | y coordinate on the texture of the top side of the region to be drawn |
| sourceWidth | integer | width of the region on the texture to be drawn |
| sourceHeight | integer | height of the region on the texture to be drawn |
| alpha | integer | alpha value applied to the blitted result (0 to 255) |
Draws an image to the screen and rotates it the given amount. Increasing rotation values will rotate the image counter-clockwise. The image is centered over the given x and y coordinates and rotates from the center.
| Name | Type | Description |
|---|---|---|
| targetX | integer | x coordinate on the screen to draw to |
| targetY | integer | y coordinate on the screen to draw to |
| theta | float | angle to rotate the image (radians) |
Draws an image to the screen and rotates it the given amount and applies the given alpha. Increasing rotation values will rotate the image counter-clockwise. The image is centered over the given x and y coordinates and rotates from the center. If the image contains per-pixel alpha values, those are compounded with the overall alpha.
| Name | Type | Description |
|---|---|---|
| targetX | integer | x coordinate on the screen to draw to |
| targetY | integer | y coordinate on the screen to draw to |
| theta | float | angle to rotate the image (radians) |
| alpha | integer | alpha value applied to the blitted result (0 to 255) |
Draws the entire texture to the given portion of the screen, stretching or shrinking it to fill that given region.
| Name | Type | Description |
|---|---|---|
| targetX | integer | x coordinate on the screen to draw to |
| targetY | integer | y coordinate on the screen to draw to |
| targetWidth | integer | width on the screen that the drawn region should stretch or shrink to |
| targetHeight | integer | height on the screen that the drawn region should stretch or shrink to |
Draws the texture to the screen at the given coordinates and alpha value. The image is positioned such that the top left corner is located at the (x, y) coordinates provided. If the image contains per-pixel alpha values, those are compounded with the overall alpha.
| Name | Type | Description |
|---|---|---|
| x | integer | x coordinate on the screen to draw to |
| y | integer | y coordinate on the screen to draw to |
| alpha | integer | alpha value applied to the blitted result (0 to 255) |
Draws a portion of an image to screen, stretching or shrinking it to fill a given region and applies the given alpha. The first 4 coordinates define a region on the screen to draw to. The last 4 coordinates define a region on the texture to sample the pixels from. Increasing rotation values will rotate the image counter-clockwise. The rotation point is the center of the rectangle defined by the region denoted by the first 4 arguments. If the image contains per-pixel alpha values, those are compounded with the overall alpha.
| Name | Type | Description |
|---|---|---|
| targetX | integer | x coordinate on the screen to draw to |
| targetY | integer | y coordinate on the screen to draw to |
| targetWidth | integer | width on the screen that the drawn region should stretch or shrink to |
| targetHeight | integer | height on the screen that the drawn region should stretch or shrink to |
| sourceX | integer | x coordinate on the texture of the left side of the region to be drawn |
| sourceY | integer | y coordinate on the texture of the top side of the region to be drawn |
| sourceWidth | integer | width of the region on the texture to be drawn |
| sourceHeight | integer | height of the region on the texture to be drawn |
| theta | float | angle to rotate the image (radians) |
| alpha | integer | alpha value applied to the blitted result (0 to 255) |
Generates a new GraphicsTexture object that has been flipped. Passing in false for both arguments will return the original GraphicsTexture instance.
| Name | Type | Description |
|---|---|---|
| flipHorizontal | boolean | true to flip the image horizontally |
| flipVertical | boolean | true to flip the image vertically |
| Type | Description |
|---|---|
| Graphics2D.GraphicsTexture | A flipped texture. |
Loads a raw image resource into the graphics engine so that it can be drawn to the screen. What specifically happens under the hood depends on the platform implementation. For some platforms the raw image resource and the graphics texture will wrap identical native platform objects (such as JavaScript, which uses a regular HTML Canvas as its underlying implementation for both an ImageResource and GraphicsTexture). Other platforms make a distinction. For example, OpenGL-based platforms will use bitmap objects for the ImageResource, but the GraphicsTexture will be a handle to a GPU-loaded texture. These differences are abstracted away by the GraphicsTexture.
| Name | Type | Description |
|---|---|---|
| imageResource | ImageResources.ImageResource | An image resource to convert to a texture. |
| Type | Description |
|---|---|
| Graphics2D.GraphicsTexture | A texture object |
Generates a new GraphicsTexture object that has been scaled to a new size. Passing in the current width and height of the texture will return the original GraphicsTexture instance.
| Name | Type | Description |
|---|---|---|
| newWidth | integer | new width to stretch or shrink the image to |
| newHeight | integer | new height to stretch or shrink the image to |
| Type | Description |
|---|---|
| Graphics2D.GraphicsTexture | A resized texture. |


ArtFolio is a service that gives artists a platform to showcase their work to the world. In doing so we give the world a glimpse into our world the world of ArtFolio.


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If the cockroach wants to rule over the chicken, then it must hire the fox as a body-guard.
~ Sierra Leone proverb


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More code will be added here, stay tuned and keep accessing this webpage.
Webpage Maintained & Designed by Deepak Mittal
Happy Coding :)🖤🖤🖤


In 2008, Penn State University was designated a Center of Academic Excellence in Information Assurance by the NSA, in its managing role for the Committee on National Security Systems (CNSS). This designation came after a thorough examination of the Security and Risk Analysis major, and mapping of SRA/IST courses to criteria set by NSA. This designation, while in effect, allowed us to provide SRA graduates with CNSS 4011 (Information Assurance) certification. That designation was set to expire at the end of 2013.
In 2011, in anticipation of the need for re-certification of CAE institutions, NSA and DHS began a lengthy process of re-thinking the certifications and re-developing the CAE designation. Meetings were held with current CAE institutions at a number of locations during 2011 and 2012. In late 2013, the new procedures for CAE designation were established. After another in-depth examination, this time with new requirements, Penn State and the College of IST were re-certified as a CAE in Information Assurance and Cyber Defense during summer, 2014.
The FBI has a wide variety of career paths for individuals from all backgrounds and experiences. Explore the options below to start a career like no other with the FBI.
FBI Career Page
The CIA is an independent agency responsible for providing national security intelligence to senior US policymakers
CIA Career Page


| RESTART_SYSCALL(2) | Linux Programmer's Manual | RESTART_SYSCALL(2) |
Note: There is no glibc wrapper for this system call; see NOTES.
restart_syscall() is used for restarting only those system calls that, when restarted, should adjust their time-related parameters—namely poll(2) (since Linux 2.6.24), nanosleep(2) (since Linux 2.6), clock_nanosleep(2) (since Linux 2.6), and futex(2), when employed with the FUTEX_WAIT (since Linux 2.6.22) and FUTEX_WAIT_BITSET (since Linux 2.6.31) operations. restart_syscall() restarts the interrupted system call with a time argument that is suitably adjusted to account for the time that has already elapsed (including the time where the process was stopped by a signal). Without the restart_syscall() mechanism, restarting these system calls would not correctly deduct the already elapsed time when the process continued execution.
The kernel uses restart_syscall() to ensure that when a system call is restarted after a process has been stopped by a signal and then resumed by SIGCONT, then the time that the process spent in the stopped state is counted against the timeout interval specified in the original system call. In the case of system calls that take a timeout argument and automatically restart after a stop signal plus SIGCONT, but which do not have the restart_syscall() mechanism built in, then, after the process resumes execution, the time that the process spent in the stop state is not counted against the timeout value. Notable examples of system calls that suffer this problem are ppoll(2), select(2), and pselect(2).
From user space, the operation of restart_syscall() is largely invisible: to the process that made the system call that is restarted, it appears as though that system call executed and returned in the usual fashion.
| 2017-09-15 | Linux |


Project: Android
Description: Library of QuickCalls and response maps for automating Android mobile phones via the Android Debug Bridge, ADB
Category: library
Class: Community
1 QuickCall Library in project://di_ADB
clearLog
Clear the logcat logs
Return: Block
success: 1-success; 0-failue
dumpLog
Dump the logcat logs
Return: Logfile response
getAirplaneModeStatus
Return the status of airplane mode for the handset
Return Value: Block
status: 0 - not in airplane mode; 1 - airplane mode
getCallStatus
Get the call status for the mobile - must be in the ADB shell to execute this command
Return Value: Block
callStatus: 0 - not in call; 1 - Connecting; 2 - In call
getDevices
This procedure will return a list of ADB devices and the corresponding model of each device.
getScreenshot
Send the screenshot to the specified file on the host machine
Return: Block
status: 0 - failure; 1 - success;
| Argument |
Description |
| filename |
The filename (full path) on the host machine of the resulting screenshot file. |
| tempfile |
The location (full path) of the temporary file on the handset |
| deviceId |
ADB device ID |
getSMS
Get the specified SMS from the database
Return: table (response mapped)
id, sender, timestamp, subject, message body
| Argument |
Description |
| sendingNumber |
if specified, the quickcall will filter only on SMS from this phone number |
| text_body |
if specified, filter only texts containing specific keywords |
| latest_only |
If set, only get the latest text message
0 - get all messages 1 - get only latest text message |
| device_id |
The ADB id for this device |
getVersion
Return the ADB version information
Return: JSON Block
version: 0
isDeviceActive
Is this device currently active in the list of ADB devices?
Return Value: Block
active: 0 - inactive; 1 - active
| Argument |
Description |
| expectedDevice |
The device ID of the device expected in the active list |
reboot
Reboot the device
Return Value: Block
success: 0 - failure; 1 - success
| Argument |
Description |
| ADB_ID |
The ID of the ADB device |
restartUSB
Restart the USB service on the linux box. This should result in the handset re-appearing if it is missing.
NOTE: **** Must have run sudo command previously to not have to re-input sudo password ****
Return Value: Block
success: 0 - failure; 1 - success
startShell
Enter the ADB shell on the specified handset
Return Values: JSON Block
success: 0 - Unsuccessful; 1 - Successful
| Argument |
Description |
| device_id |
The deviceId of the Android device - usually from getDevices |
| rooted |
true: The handset is rooted false: The handset is not rooted |
shellAnswerCall
Send the answer call key event in the ADB shell
shellBrowseWebPage
In the ADB shell, browse to the webpage specified
| Argument |
Description |
| page |
The webpage URL |
shellDeleteFile
Delete the file at the specified location
Return Value: Block
Success: 1-success; 0-failure;
| Argument |
Description |
| location |
location: Complete path of the directory where file is present |
| file_name |
file_name: Name of the file to be deleted |
shellDownloadFile
Download the specified file to the specified location
Return Value: Block
Success: 1-success; 0-failure;
| Argument |
Description |
| location |
location: Complete path on the handset where downloaded file need to be stored. exmaple: /storage/self/primary/Download |
| file_url |
file_url: URL for the file
Exmaple: "http://www.spirent.com/-/media/Datasheets/Broadband/PAB/CLEAR/Spirent_iTest_datasheet.pdf" |
shellEndCall
End a call in progress
shellExit
Exit the adb command shell
Return Value: Block
Success: 1-success; 0-failure;
| Argument |
Description |
| rooted |
true: UE is rooted false: UE is not rooted |
shellGetFileSize
Return the size of a file from the specified location
Return Value: Block
fileSize: integer from 0 - ?
| Argument |
Description |
| location |
location: Complete path on the handset where downloaded file need to be stored. exmaple: /storage/self/primary/Download |
| file_name |
file_name: Name of the file whose size needs to be determined. |
shellGetSMS
Get the specified SMS from the database
Return: table (response mapped)
id, sender, timestamp, subject, message body
| Argument |
Description |
| sendingNumber |
if specified, the quickcall will filter only on SMS from this phone number |
| text_body |
if specified, filter only texts containing specific keywords |
| latest_only |
If set, only get the latest text message
0 - get all messages 1 - get only latest text message |
shellHomeButton
Press the KEYCODE_HOME button
Return Value: Block
Success: 1-success; 0-failure;
shellKickstartMonSub
This procedure is designed to "kick start" monitor subscriber. We noticed that monitor subscriber performs better after the UE registers/deregisters.
Return Value: Block
success: 1 - success; 0 - failure
shellPowerButton
Simulate the power button
Return Value: Block
Success: 1-success; 0-failure;
shellReboot
From within the ADB shell, reboot the device
Return Values: JSON Block
success: 1 - success; 0 - failure
shellStartCall
Call a specified phone number
Response: Block
success: 0 - failed, 1 - success
| Argument |
Description |
| phoneNumber |
The phone number to be called |
shellSendSMS
Send the specified SMS message to the specified phone number
Return value: Block
success: 0 - failure, 1 - success
| Argument |
Description |
| message |
The body of the SMS message |
| phoneNumber |
The "to" phone number for the SMS |
shellViewVideo
This procedure is designed to stream a large video from YouTube
Return Value: Block
Success: 1-success; 0-failure;
| Argument |
Description |
| video |
The URL of a YouTube video to stream |
setAirplaneMode
Set airplane mode
Return Value: Block
failure: 0 - success; 1 - failure
| Argument |
Description |
| status |
Airplane mode ON = 1 OFF = 0 |
shellSetLTE
Set LTE via pressing *#2263#
Return value: Block
success: 0-Failue; 1-Success
shellSetTerminalWidth
Set the terminal width
Return value: Block
success: 0-Failue; 1-Success
| Argument |
Description |
| width |
The width of the terminal window |
shellServiceList
Return a table of the services enabled for this device
Return value:
Table of services
shellReadAPN
Return the APN of the handset
| Argument |
Description |
| carrier_name |
The carrier name to update in the carrier database. Telus is the default. |
| APN |
If specified, use the APN value rather than the carrier to retrieve APN information. |
shellGetCarrierInformation
Get the carrier database information
| Argument |
Description |
| carrier |
The name of the carrier |
shellSetVoLTE
Set the handset for VoLTE
| Argument |
Description |
| mode |
0 - Turn off VoLTE 1 - Turn on VoLTE |
shellSetVoLTEPressMenu
| Argument |
Description |
| volte_on_off |
on or off |
shellSetNetwork
Set the handset for VoLTE and return the network mode:
0: GSM/WCDMA
1: GSM
2: WCDMA
9: LTE, GSM/WCDMA
11: LTE
12: LTE/WCDMA
Return Values:
JSON Block:
network_mode
| Argument |
Description |
| network_type |
0: GSM/WCDMA 1: GSM 2: WCDMA 9: LTE, GSM/WCDMA 11: LTE 12: LTE/WCDMA |
shellSetNetworkPressMenu
| Argument |
Description |
| network_mode |
0 = LTE/WCDMA/GSM (auto connect) 1 = WCDMA/GSM (auto connect) 2 = WCDMA only 3 = GSM only |
shellGetNetwork
Get the handset network mode
0: GSM/WCDMA
1: GSM
2: WCDMA
9: LTE, GSM/WCDMA
11: LTE
12: LTE/WCDMA
changeAPN
Change the APN of the handset to the specified value - note that this is NOT called from a shell
| Argument |
Description |
| new_APN |
The value of the new APN |
| new_APN_protocol |
If desired, pass in a new_APN_protocol: IPV4, IPV6, IPV4V6. Leave as NA to make no changes. |
| device_id |
The deviceId of the Android device - usually from getDevices |
| rooted |
true: this UE is rooted false: this UE is not rooted |
| carrier_name |
The carrier name to change in the telephony database |
shellChangeAPN
Change the APN of the handset to the specified value - note that the APN carrier type must exist in the database
| Argument |
Description |
| new_APN |
The value of the new APN |
| new_APN_protocol |
If desired, pass in a new_APN_protocol: IP, IPV6, IPV4V6. Leave as NA to make no changes. |
| carrier_name |
The carrier name to change in the telephony database |
shellAddAPN
Add the APN - if it already exists, an error is returned.
| Argument |
Description |
| APN |
The value of the new APN |
| APN_protocol |
IP, IPV6, IPV4V6 |
| carrier_name |
The carrier name to change in the telephony database |
| mcc |
Mobile carrier code |
| mnc |
Mobile network code |
| type |
The APN type |
shellDeleteAPN
Delete the specified APN
| Argument |
Description |
| APN |
The value of the new APN |
| carrier_name |
The carrier name to change in the telephony database |
shellClearAPN
Clear all rows from the carriers table that have the specified APN.
| Argument |
Description |
| APN |
The value of the new APN |
shellGetInternalId
Returns the internal ADB ID for the session
shellGetSessionADBId


Friday, March 21st @ 7pm in front of Koerner Library
bikerave (\bahykreyv\): boisterous conglomeration
def'n: badass swippersnappers gettin' their grunge hipster on to heavy bass, neon lights, and fast bikes.

Thought up in the dark corners of first year residence, the BikeRave is back this year with more bikes, louder jams, and brighter colours.
Fact: biking is radical.
Fact: raves are radical.
Fact: you are radical, and we don't want the radical you to be hurt.
Please wear a helmet, bring water to stay hydrated, and ride at a slow pace, paying mind to other bikers around you!
Please arrive at 7pm.
You will have ~30 minutes to decorate with free glowsticks and shinys at Koerner Plaza.
Look for t-shirts, glowsticks, prizes, and refreshments.
Then at 7:30pm, we will unleash the kraken and begin biking!
Contact for volunteers<EMAIL_ADDRESS>Other inquiries (including partnership<EMAIL_ADDRESS>or<PHONE_NUMBER>.


The tsearch2 Reference
Brandon Craig Rhodes
30 June 2003 (edited by Oleg Bartunov, 2 Aug 2003).
This Reference documents the user types and functions
of the tsearch2 module for PostgreSQL.
An introduction to the module is provided
by the tsearch2 Guide,
a companion document to this one.
You can retrieve a beta copy of the tsearch2 module from the
GiST for PostgreSQL
page -- look under the section entitled Development History
for the current version.
Vectors and queries both store lexemes,
but for different purposes.
A tsvector stores the lexemes
of the words that are parsed out of a document,
and can also remember the position of each word.
A tsquery specifies a boolean condition among lexemes.
Any of the following functions with a configuration argument
can use either an integer id or textual ts_name
to select a configuration;
if the option is omitted, then the current configuration is used.
For more information on the current configuration,
read the next section on Configurations.
-
to_tsvector( [configuration,]
document TEXT) RETURNS tsvector
-
Parses a document into tokens,
reduces the tokens to lexemes,
and returns a tsvector which lists the lexemes
together with their positions in the document.
For the best description of this process,
see the section on Parsing and Stemming
in the accompanying tsearch2 Guide.
-
strip(vector tsvector) RETURNS tsvector
-
Return a vector which lists the same lexemes
as the given vector,
but which lacks any information
about where in the document each lexeme appeared.
While the returned vector is thus useless for relevance ranking,
it will usually be much smaller.
-
setweight(vector tsvector, letter) RETURNS tsvector
-
This function returns a copy of the input vector
in which every location has been labelled
with either the letter
'A', 'B', or 'C',
or the default label 'D'
(which is the default with which new vectors are created,
and as such is usually not displayed).
These labels are retained when vectors are concatenated,
allowing words from different parts of a document
to be weighted differently by ranking functions.
-
vector1 || vector2
-
concat(vector1 tsvector, vector2 tsvector)
RETURNS tsvector
-
Returns a vector which combines the lexemes and position information
in the two vectors given as arguments.
Position weight labels (described in the previous paragraph)
are retained intact during the concatenation.
This has at least two uses.
First,
if some sections of your document
need be parsed with different configurations than others,
you can parse them separately
and concatenate the resulting vectors into one.
Second,
you can weight words from some sections of you document
more heavily than those from others by:
parsing the sections into separate vectors;
assigning the vectors different position labels
with the setweight() function;
concatenating them into a single vector;
and then providing a weights argument
to the rank() function
that assigns different weights to positions with different labels.
-
tsvector_size(vector tsvector) RETURNS INT4
-
Returns the number of lexemes stored in the vector.
-
text::tsvector RETURNS tsvector
-
Directly casting text to a tsvector
allows you to directly inject lexemes into a vector,
with whatever positions and position weights you choose to specify.
The text should be formatted
like the vector would be printed by the output of a SELECT.
See the Casting
section in the Guide for details.
Query Operations
-
to_tsquery( [configuration,]
querytext text) RETURNS tsvector
-
Parses a query,
which should be single words separated by the boolean operators
"&" and,
"|" or,
and "!" not,
which can be grouped using parenthesis.
Each word is reduced to a lexeme using the current
or specified configuration.
-
querytree(query tsquery) RETURNS text
-
This might return a textual representation of the given query.
-
text::tsquery RETURNS tsquery
-
Directly casting text to a tsquery
allows you to directly inject lexemes into a query,
with whatever positions and position weight flags you choose to specify.
The text should be formatted
like the query would be printed by the output of a SELECT.
See the Casting
section in the Guide for details.
A configuration specifies all of the equipment necessary
to transform a document into a tsvector:
the parser that breaks its text into tokens,
and the dictionaries which then transform each token into a lexeme.
Every call to to_tsvector() (described above)
uses a configuration to perform its processing.
Three configurations come with tsearch2:
- default -- Indexes words and numbers,
using the en_stem English Snowball stemmer for Latin-alphabet words
and the simple dictionary for all others.
- default_russian -- Indexes words and numbers,
using the en_stem English Snowball stemmer for Latin-alphabet words
and the ru_stem Russian Snowball dictionary for all others.
- simple -- Processes both words and numbers
with the simple dictionary,
which neither discards any stop words nor alters them.
The tsearch2 modules initially chooses your current configuration
by looking for your current locale in the locale field
of the pg_ts_cfg table described below.
You can manipulate the current configuration yourself with these functions:
-
set_curcfg( id INT | ts_name TEXT
) RETURNS VOID
-
Set the current configuration used by to_tsvector
and to_tsquery.
-
show_curcfg() RETURNS INT4
-
Returns the integer id of the current configuration.
Each configuration is defined by a record in the pg_ts_cfg table:
create table pg_ts_cfg (
id int not null primary key,
ts_name text not null,
prs_name text not null,
locale text
);
The id and ts_name are unique values
which identify the configuration;
the prs_name specifies which parser the configuration uses.
Once this parser has split document text into tokens,
the type of each resulting token --
or, more specifically, the type's tok_alias
as specified in the parser's lexem_type() table --
is searched for together with the configuration's ts_name
in the pg_ts_cfgmap table:
create table pg_ts_cfgmap (
ts_name text not null,
tok_alias text not null,
dict_name text[],
primary key (ts_name,tok_alias)
);
Those tokens whose types are not listed are discarded.
The remaining tokens are assigned integer positions,
starting with 1 for the first token in the document,
and turned into lexemes with the help of the dictionaries
whose names are given in the dict_name array for their type.
These dictionaries are tried in order,
stopping either with the first one to return a lexeme for the token,
or discarding the token if no dictionary returns a lexeme for it.
Function ts_debug allows easy testing of your current configuration.
You may always test another configuration using set_curcfg function.
Example:
apod=# select * from ts_debug('Tsearch module for PostgreSQL 7.3.3');
ts_name | tok_type | description | token | dict_name | tsvector
---------+----------+-------------+------------+-----------+--------------
default | lword | Latin word | Tsearch | {en_stem} | 'tsearch'
default | lword | Latin word | module | {en_stem} | 'modul'
default | lword | Latin word | for | {en_stem} |
default | lword | Latin word | PostgreSQL | {en_stem} | 'postgresql'
default | version | VERSION | 7.3.3 | {simple} | '7.3.3'
Here:
- tsname - configuration name
- tok_type - token type
- description - human readable name of tok_type
- token - parser's token
- dict_name - dictionary used for the token
- tsvector - final result
Each parser is defined by a record in the pg_ts_parser table:
create table pg_ts_parser (
prs_name text not null,
prs_start regprocedure not null,
prs_nexttoken regprocedure not null,
prs_end regprocedure not null,
prs_headline regprocedure not null,
prs_lextype regprocedure not null,
prs_comment text
);
The prs_name uniquely identify the parser,
while prs_comment usually describes its name and version
for the reference of users.
The other items identify the low-level functions
which make the parser operate,
and are only of interest to someone writing a parser of their own.
The tsearch2 module comes with one parser named default
which is suitable for parsing most plain text and HTML documents.
Each parser argument below
must designate a parser with prs_name;
the current parser is used when this argument is omitted.
-
CREATE FUNCTION set_curprs(parser) RETURNS VOID
-
Selects a current parser
which will be used when any of the following functions
are called without a parser as an argument.
-
CREATE FUNCTION token_type(
[ parser ]
) RETURNS SETOF tokentype
-
Returns a table which defines and describes
each kind of token the parser may produce as output.
For each token type the table gives the tokid
which the parser will label each token of that type,
the alias which names the token type,
and a short description descr for the user to read.
-
CREATE FUNCTION parse(
[ parser, ] document TEXT
) RETURNS SETOF tokenout
-
Parses the given document and returns a series of records,
one for each token produced by parsing.
Each token includes a tokid giving its type
and a lexem which gives its content.
Dictionaries take textual tokens as input,
usually those produced by a parser,
and return lexemes which are usually some reduced form of the token.
Among the dictionaries which come installed with tsearch2 are:
- simple simply folds uppercase letters to lowercase
before returning the word.
- en_stem runs an English Snowball stemmer on each word
that attempts to reduce the various forms of a verb or noun
to a single recognizable form.
- ru_stem runs a Russian Snowball stemmer on each word.
Each dictionary is defined by an entry in the pg_ts_dict table:
CREATE TABLE pg_ts_dict (
dict_name text not null,
dict_init regprocedure,
dict_initoption text,
dict_lexize regprocedure not null,
dict_comment text
);
The dict_name
serve as unique identifiers for the dictionary.
The meaning of the dict_initoption varies among dictionaries,
but for the built-in Snowball dictionaries
it specifies a file from which stop words should be read.
The dict_comment is a human-readable description of the dictionary.
The other fields are internal function identifiers
useful only to developers trying to implement their own dictionaries.
The argument named dictionary
in each of the following functions
should be dict_name
identifying which dictionary should be used for the operation;
if omitted then the current dictionary is used.
-
CREATE FUNCTION set_curdict(dictionary) RETURNS VOID
-
Selects a current dictionary for use by functions
that do not select a dictionary explicitly.
-
CREATE FUNCTION lexize(
[ dictionary, ] word text)
RETURNS TEXT[]
-
Reduces a single word to a lexeme.
Note that lexemes are arrays of zero or more strings,
since in some languages there might be several base words
from which an inflected form could arise.
Ranking attempts to measure how relevant documents are to particular queries
by inspecting the number of times each search word appears in the document,
and whether different search terms occur near each other.
Note that this information is only available in unstripped vectors --
ranking functions will only return a useful result
for a tsvector which still has position information!
Both of these ranking functions
take an integer normalization option
that specifies whether a document's length should impact its rank.
This is often desirable,
since a hundred-word document with five instances of a search word
is probably more relevant than a thousand-word document with five instances.
The option can have the values:
- 0 (the default) ignores document length.
- 1 divides the rank by the logarithm of the length.
- 2 divides the rank by the length itself.
The two ranking functions currently available are:
-
CREATE FUNCTION rank(
[ weights float4[], ]
vector tsvector, query tsquery,
[ normalization int4 ]
) RETURNS float4
-
This is the ranking function from the old version of OpenFTS,
and offers the ability to weight word instances more heavily
depending on how you have classified them.
The weights specify how heavily to weight each category of word:
{D-weight, C-weight, B-weight, A-weight}
If no weights are provided, then these defaults are used:
{0.1, 0.2, 0.4, 1.0}
Often weights are used to mark words from special areas of the document,
like the title or an initial abstract,
and make them more or less important than words in the document body.
-
CREATE FUNCTION rank_cd(
[ K int4, ]
vector tsvector, query tsquery,
[ normalization int4 ]
) RETURNS float4
-
This function computes the cover density ranking
for the given document vector and query,
as described in Clarke, Cormack, and Tudhope's
"Relevance Ranking for One to Three Term Queries"
in the 1999 Information Processing and Management.
The value K is one of the values from their formula,
and defaults to K=4.
The examples in their paper K=16;
we can roughly describe the term
as stating how far apart two search terms can fall
before the formula begins penalizing them for lack of proximity.
-
CREATE FUNCTION headline(
[ id int4, | ts_name text, ]
document text, query tsquery,
[ options text ]
) RETURNS text
-
Every form of the the headline() function
accepts a document along with a query,
and returns one or more ellipse-separated excerpts from the document
in which terms from the query are highlighted.
The configuration with which to parse the document
can be specified by either its id or ts_name;
if none is specified that the current configuration is used instead.
An options string if provided should be a comma-separated list
of one or more 'option=value' pairs.
The available options are:
- StartSel, StopSel --
the strings with which query words appearing in the document
should be delimited to distinguish them from other excerpted words.
- MaxWords, MinWords --
limits on the shortest and longest headlines you will accept.
- ShortWord --
this prevents your headline from beginning or ending
with a word which has this many characters or less.
The default value of 3 should eliminate most English
conjunctions and articles.
Any unspecified options receive these defaults:
StartSel=<b>, StopSel=</b>, MaxWords=35, MinWords=15, ShortWord=3


Instead of cooking pasta in a huge pot of water, here we use just 3.5 cups for this one-pot pasta recipe. When the pasta is al dente, most of the water has evaporated and the bit that's left is thickened with the starch that cooks off the pasta. With just a few add-ins like lemon and Parmesan cheese you have a delicious silky sauce. Want to use up your veggie stash in the freezer? Swap in 8 ounces frozen spinach for fresh.


Kidambi Srikanth, Sameer Verma progress in Korea Open, Sourabh Verma bows out
The former world no.1 Srikanth now enjoys a dominant 11-3 head-to-head record over the Hong Kong shuttler. He will face Kanta Tsuneyama of Japan in the second round
by PTIIndia made a promising start at the Korea Open with shuttlers Kidambi Srikanth and Sameer Verma advancing to the second round of the men’s singles competition here on Wednesday.
The sixth seed Srikanth defeated Hong Kong’s Wong Wing Ki Vincent 21-18, 21-17 in a match that lasted 37 minutes.
The former world no.1 Srikanth now enjoys a dominant 11-3 head-to-head record over the Hong Kong shuttler. He will face Kanta Tsuneyama of Japan in the second round.
Sameer Verma also reached the second round after his opponent Japan’s Kazumasa Sakai retired midway with the score reading 11-8 in favour of the Indian.
However, it was the end of the road for Sourabh Verma, who went down after winning the first game to local favourite Kim Donghun 21-13 12-21 13-21.
Sameer will take on Donghun in the second round


Ever had trouble remembering numbers? Worry not, you are only a human, and Drillmajor is here to help. This simple game will teach you the mnemonic major system, and allow you to hone your skills and track your progress anywhere.
Simply click the big red button when you remember what word is associated with the number. Click the back arrow to give up and to not store your time.
As abstract symbols, numbers themselves are very difficult for the human mind to store. However, if you associated
a more concrete object for each number, remembering even long strings of integers would be easy. For
example, think of the number
007 and you might instantly imagine
James Bond. You can think of this text a week
later, remember James Bond, and effortlessly also recall the number I used in this example: 007. The
major system itself works by associating specific sounds to each digit between 0 and 9, so that you can
systematically turn any number into words and any word into a number. It is quite powerful:
For example
34729401215721110 translates to
American Presidential Candidates. I wonder which one is easier to remember?
The sounds for each digit are the following:
To make numbers easier to remember on the fly, here is a list of words for each number from 0 to 100. Remembering each number - word pair will allow you to store and recall numbers inhumanly fast.


PicoBramble
A lightweight library for running jobs across multiple machines. Originally designed for a cluster of Raspberry Pis.
Built as a learning exercise - you should probably use something like the JPPF framework for serious projects.
This project is probably not secure. I recommend running this on a private, wired network which is not connected to the internet.


Azpilicueta: I wish Mourinho all the best at Tottenham
by Sports DeskChelsea captain Cesar Azpilicueta wished former manager Jose Mourinho all the best as Tottenham head coach.
Mourinho was appointed by Spurs on Wednesday, a day after the Premier League club – who are 14th in the table – sacked Mauricio Pochettino.
The Portuguese tactician led Chelsea to three league titles across two spells at the helm, but will now look to salvage their rivals' season.
Mourinho may have joined Chelsea's rivals, but Azpilicueta – who won one of his two Premier League titles under the 56-year-old – sent well wishes.
"I can talk only when Jose was here at Chelsea with us," he told Sky Sports News.
"We won trophies, and now football moves on. I wish him all the best even though he goes to a rival.
"I have good memories with him and I have played many times under him and against him.
"Obviously when we play against each other we both want to win for our teams, but this is football. I wish him well."
Mourinho's first game in charge of Spurs will come against another London club, West Ham, on Saturday.


{% extends "dashboard/layouts/base.html" %} {% block content %} {% if next %} {% if user.is_authenticated %}
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--- layout: main title: LFHA News tagline: Newsletters and announcement regarding activities in the neighborhood ---


Before Change
import optuna
try:
from catalyst.dl import Callback
_available = True
except ImportError as e:
_import_error = e
// CatalystPruningCallback is disabled because Catalyst is not available.
_available = False
Callback = object
class CatalystPruningCallback(Callback):
Catalyst callback to prune unpromising trials.
Args:
After Change
from optuna._imports import try_import
with try_import() as _imports:
from catalyst.dl import Callback
class CatalystPruningCallback(Callback):
Catalyst callback to prune unpromising trials.
Args:


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Random Forest
1. Bagging and Decision Trees
Bagging, short for bootstrap aggregating, is the process by which we train an ensemble of machine learning models using datasets sampled from the empirical distribution. This process helps reduce variance and overfitting. Given a dataset $S$, we generate $m$ samples $S’$ of size $n$, by drawing datapoints from $S$ uniformly and with replacement. We can then create an ensemble by fitting $m$ models on each sample and averaging (regression) or voting (classification) the result of each of the models. If each of these models is a decision tree, then this ensemble is a random forest.
To take advantage of the bootstrapping mechanism, each of the ensemble models must be independent of each other. This is not always the case because usually there are features the model learns more strongly than others, effectively making the different models depend on each other. To remediate this, we do not allow the decision tree to learn all the features. Instead, each of the models knows different subsets of features. After fitting the models, the predicted class is determined by the majority vote. In the case of regression, we average each of the predictions.
2. Ensembles and Random forest
We analyze the effect of bootstrapping decision trees on the generalization error and bias/variance tradeoff.
Suppose we have $m$ models $V^{a}$, with $a=1\ldots m$. In the case of regression, consider the model average
\[\bar{V}(x)=\sum_a \omega_a V^a(x)\]where $\omega_a$ are some weights. The ambiguity $A(x)^a$ for the model $a$ is defined as
\[A^a(x)=(V^a(x)-\bar{V}(x))^2\]and the ensemble ambiguity $A(x)$ is obtained by taking the ensemble average
\[A(x)=\sum_a \omega_aA^a(x)=\sum_a \omega_a(V^a(x)-\bar{V}(x))^2\]The error of a model and the ensemble, respectively $\epsilon^a$ and $\epsilon$, are
\[\begin{equation*}\begin{split}&\epsilon^a(x)=(y(x)-V^a(x))^2 \\ &\epsilon= (y(x)-\bar{V}(x))^2 \end{split}\end{equation*}\]One can easily show that
\[A(x)=\sum_a \omega_a\epsilon^a(x)-\epsilon(x)=\bar{\epsilon}(x)-\epsilon(x)\]where we defined the ensemble average $\bar{\epsilon}=\sum_a \omega_a\epsilon^a$. Averaging this quantities over the distribution of $x$, $D(x)$, we obtain an equation involving the generalization error of the ensemble and of the individual components, that is
\[E=\bar{E}-A\]where $E=\int dx \epsilon(x) D(x)$ is the generalization error and $A=\int dx A(x) D(x)$ is the total ambiguity.
Note that the ambiguity $A$ only depends on the models $V^a$ and not on labeled data. It measures how the different models correlate with the average. Since $A$ is always positive, we can conclude that the generalization error is smaller than the average error.
If the models are highly biased, we expect similar predictions across the ensemble, making $A$ small. In this case, the generalization error will be essentially the same as the average of the generalization errors. However, if the predictions vary a lot from one model to another, the ambiguity will be higher, making the generalization smaller than the average. So we want the models to disagree! Random forests implement this by letting each decision tree learn on a different subset of every split feature. This results in a set of trees with different split structure:
Another important aspect of ensemble methods is that they do not increase the bias of the model. For instance
\[\begin{equation*}\begin{split}\text{Bias}=f(x)-\mathbb{E}\bar{V}(x)=\sum_a \omega_a (f(x)-\mathbb{E}V^a(x))=\sum_a \omega_a \text{Bias}^a=\text{bias} \end{split}\end{equation*}\]where $\text{bias}$ is the bias of an individual model, assuming that each model has similar bias. On the other hand, the variance
\[\mathbb{E}(\bar{V}(x)-\mathbb{E}\bar{V}(x))^2=\sum_a \omega_a^2(V^a-\mathbb{E}V^a)^2+\sum_{a\neq b}\omega_a\omega_b(V^a-\mathbb{E}V^a)(V^b-\mathbb{E}V^b)\]We do not expect the quantities $(V^a-\mathbb{E}V^a)^2$ and $(V^a-\mathbb{E}V^a)(V^b-\mathbb{E}V^b)$ to differ significantly across the models, and so defining
\[\text{Var}\equiv (V^a-\mathbb{E}V^a)^2,\; \rho(x)\equiv\frac{(V^a-\mathbb{E}V^a)(V^b-\mathbb{E}V^b)}{\text{Var}(x)}\]we obtain
\[\begin{equation*}\begin{split}\mathbb{E}(\bar{V}(x)-\mathbb{E}\bar{V}(x))^2&=\text{Var}(x)\sum_a \omega_a^2 + \rho(x)\text{Var}(x) \sum_{a\neq b}\omega_a\omega_b\\ &=\text{Var}(x)(1-\rho(x))\sum_a\omega_a^2+\rho(x)\text{Var}(x)<\text{Var}(x)\end{split}\end{equation*}\]This quantity has a lower bound at $\omega_a=1/m$, the uniform distribution. This means that
\[\text{Var}(x)\frac{(1-\rho(x))}{m}+\rho(x)\text{Var}(x)\leq \mathbb{E}(\bar{V}(x)-\mathbb{E}\bar{V}(x))^2\leq \text{Var}(x)\]If the models are averaged with constant weights, then $\sum_a \omega_a^2$ tends to zero as $m\rightarrow \infty$, and the variance is the product of the correlation $\rho(x)$ and the individual model variance.
3. Python Implementation
class RandomForest:
def __init__(self,n_estimators,params):
self.n_estimators=n_estimators
self.params=params
self.trees=None
def fit(self,x,y):
n_instances=x.shape[0]
self.classes=sorted(set(y))
self.trees=[]
for n in range(self.n_estimators):
idx=np.arange(n_instances)
idx_sample=np.random.choice(idx,n_instances,replace=True)
xsample=x[idx_sample]
ysample=y[idx_sample]
tree=DecisionTreeClassifier(**self.params,max_features='auto')
tree.fit(xsample,ysample)
self.trees.append(tree)
def predict(self,x):
classes=self.trees[0].classes_
dic={i:cl for i,cl in enumerate(classes)}
ypred=self.trees[0].predict_proba(x)
for tree in self.trees[1:]:
ypred+=tree.predict_proba(x)
ypred=ypred
ypred=ypred.argmax(axis=1)
ypred=np.vectorize(dic.get)(ypred)
return ypred


Userinfo
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Client
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I know HTML
I know CSS
I know Javacript
Server
I know PHP
I know C#
I know Django


Accepted formats, in order of preference
Accepted formats for figures
- PostScript (PS/EPS)
- JPG (best for photographs)
- GIF or PNG
- PDF figures (only with PDFLaTeX)
Accepted file properties
- Names containing a-z A-Z 0-9 . , - _
- Total compressed package size limit: 6MB
- Total uncompressed package size limit: 18MB
- Individual PDF size limit: 15MB
TeX style and class files


| QuickCreate archive (Tracker AddOn) |
The QuickCreate Archive is a Tracker add-on that lets you create archives with minimal clicks :) The basic idea was to do a replacement for Zip-O-Matic but instead of just supporting zip format alone, QuickCreate would allow control over the format, and all the options that are supported in Beezer.
The QuickCreate Archive is actually a sort of hack. It resides within Beezer but is invoked using a Tracker add-on. You cannot launch QuickCreate explicitly from Beezer as it isn't listed under "Tools". This is not what it is meant for and hence isn't listed there.
How to Use
Using QuickCreate is very similar to using Zip-O-Matic. Here's a quick run-through in anycase...
- In Tracker, select the files you want to archive.
- Right-click and choose Add-Ons –> Beezer as shown in the picture above
- Now you are all ready to archive the files you have chosen, and the QuickCreate window will be shown as shown in the picture below
Archive Name Archive Type
This shows the path and name of the Archive that will is to be created. You can chop and change elements of the path (such as modifying destination directories, and file name).
Settings
Allows you to choose what format archives you are going to make. Please note, as always, the necessary binaries needs to be installed in your system and the "workers" folder must have valid (non-broken) links to the binaries.
Password
For the chosen archive type, this is the list of available settings. Usually, you needn't keep changing these but in cases where you need to alter the options before creating the archive (such as modifying compression level, modifying storage of attributes etc.) you need to use this.
The status line
For those archive types that supports passwords (and supported by Beezer) you can input passwords. Currently, zip passwords are not supported due to the nature of the zip binary. But in anycase for real security purposes you should use a full-fledged encryption tool such as Cryptic for BeOS.
The status line at the very bottom of the QuickCreate window shows you some information to assist you in creating archives. For example, if the archive name chosen already exists, the status line shows a warning text reminding you the existing file will be overwritten and so on.
Once you are all set to create an archive just press ENTER or hit the "Create" button!! Cancelling the operation is allowed, but doing so will almost always delete the incomplete archive rather than get an archive with partially added files....
! Tip
You can specify the default archive type to make the quick create process even faster! That can be done from Preferences: Miscellaneous.
| The other way of creating archives | Welcome window |


What is the current Goodreads average rating?
Design Score:
50 Page Score:
Tired Score:
Prose Score:
Conclusion Score:
Length Score:
Reread Score:
Recommend Score:
Thought-Provoking Score:


Model cell type heterogeneity of bulk renal cell carcinoma. The observed gene expression in bulk tumor sample is modeled by a log-normal distribution with the location parameter structured as a linear combination of the component-specific gene expressions.
| Version: | 1.0.0 |
| Depends: | R (≥ 2.15.0), gtools, matrixStats |
| Published: | 2018-05-25 |
| Author: | Tao Wang |
| Maintainer: | Tao Wang <tao.wang at utsouthwestern.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | DisHet results |
| Reference manual: | DisHet.pdf |
| Package source: | DisHet_1.0.0.tar.gz |
| Windows binaries: | r-devel: DisHet_1.0.0.zip, r-release: DisHet_1.0.0.zip, r-oldrel: DisHet_1.0.0.zip |
| OS X binaries: | r-release: DisHet_1.0.0.tgz, r-oldrel: DisHet_1.0.0.tgz |
| Old sources: | DisHet archive |
Please use the canonical form https://CRAN.R-project.org/package=DisHet to link to this page.


安德魯網頁設計團隊
GETTING YOUR WEBSITE RIGHT MAKES EVERYTHING ELSE EASIER
ABOUT RANGER
Thank you for supporting us!


INSTINCT (1999)
Rating: 2 stars (out of 4.0) ******************************** Key to rating system: 2.0 stars - Debatable 2.5 stars - Some people may like it 3.0 stars - I liked it 3.5 stars - I am biased in favor of the movie 4.0 stars - I felt the movie's impact personally or it stood out ********************************* A Movie Review by David Sunga
Directed by: Jon Turteltaub
Written by: Gerald Di Pego, based on the novel "Ishmael" by Daniel Quinn
Starring: Cuba Gooding Jr., Anthony Hopkins, Donald Sutherland, Maura Tierney
Synopsis: Theo Caulder (Cuba Gooding Jr.) is a young psychiatrist who has been given an interesting case to study: An anthropologist named Ethan Powell (Anthony Hopkins) who has lost his humanity living in the Rwandan wilderness among mountain gorillas has been caught, deported, and sent to an American prison psychiatric ward after killing two rangers. Caulder makes regular therapy visits to converse with Powell and to try to figure out his mentality. Is Powell a brutal killer, an ape man, or a downtrodden naturalist with a legitimate complaint against society?
Opinion: INSTINCT plays like a mystery. Mountain gorillas are gentle herbivores who only attack in self defense, so what could possibly cause an eminent scientist with the mentality and friendship of herbivores to go out and attack rangers? Psychiatrist Theo Caulder takes the whole movie to find out the reason when the answer is obvious.
INSTINCT isn't very convincing. As Powell Anthony Hopkins seems too educated and studious to have ape-like episodes of superhuman strength and brutish behavior. It makes the character seem more like a split personality than a scientist with a secret reason. That a keen mind such as Powell's would relish an alternate identity as a primitive, grass-eating forest creature instead of using his wits and tool-making abilities in the jungle is barely credible. Similarly unconvincing is the bond between Theo Caulder and Ethan Powell. Their psychiatric relationship is a clinical, teacher/student thing. But as Caulder Cuba Gooding Jr. seems overly emotional, as if the two protagonists had shared more happy moments than they actually had.
The film's major theme is the innocence of nature versus the corruption of humanity. This is a noble goal, but INSTINCT hits you over the head with it. As Powell Anthony Hopkins even draws a colored map of ancient Earth and diagrams man's historic exploitation of nature. Much more compelling is the side plot involving Maura Tierney as Powell's neglected daughter, trying to understand why her father had abandoned the family. To me, a more intriguing INSTINCT would be a complex movie about a daughter trying to rediscover the lost father she never knew.
Reviewed by David Sunga June 7, 1999
Copyright © 1999 by David Sunga This review and others like it can be found at THE CRITIC ZOO: http://www.criticzoo.com email<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


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UK inflation falls faster than expected in October
LONDON, Nov 13 — British annual inflation dropped faster than expected in October to a near three-year low at 1.5 per cent as lower energy prices offset rising prices for clothes, official data showed today.
The Consumer Prices Index 12-month rate, the lowest since November 2016, compared with 1.7 per cent in September, the Office for National Statistics (ONS) said in a statement published ahead of next month’s UK general election.
Analysts’ consensus forecast had been for a drop to 1.6 per cent.
“Admittedly, the drop mainly reflected a slump in energy inflation and so was not a reflection of a weakening in underlying inflationary pressures,” noted Ruth Gregory, senior UK economist at Capital Economics research group.
“Overall, the figures do little to change our view that inflation will spend more time below (the Bank of England’s target of) 2.0 per cent than above it in 2020 and that if Brexit is delayed further, interest rates will be cut, in May,” she added.
The pound meanwhile showed little reaction to the latest inflation data.
“For the third day running we’ve had some news that could have caused some selling in the pound, but the currency has remained largely unperturbed with economic data still playing second fiddle to political developments in terms of moving the markets,” said David Cheetham, chief market analyst at XTB trading group.
Official data yesterday showed British unemployment hitting 3.8 per cent, a fresh 45-year low.
And on Monday, the ONS said that Britain’s economy avoided recession in the third quarter, with growth of 0.3 per cent. — AFP


Chloe Price
Chloe Elizabeth Price (born March 11, 1994).
"Max Caulfield... Don't you forget about me"
She was Max's best friend before she moved to Seattle, leaving Chloe in their hometown of Arcadia Bay, Oregon. They rekindle their friendship during the events of the game.
After Max left, Chloe became best friends with Rachel Amber before she went missing. She is the daughter of William and Joyce Price, and the step-daughter of David Madsen.
Chloe is a stubborn, mischievous and rebellious young woman. During her time with Max, Chloe's personality seems to grow a lot, since she finds new strength in Max's support and the resurgence of their friendship.
At the end of the game, Chloe admits her selfishness towards her family over all the years, and even offers to sacrifice herself to save the town from destruction.
See photos of ChloeChloe has a very aggressive side to her.
"This shit-pit has taken everyone I've ever loved...
Chloe suffers from both drug and alcohol addiction.
"I hope you checked the perimeter, as my step-ass would say. Now, let's talk bidness."
Chloe shows signs of depression during her time with Max.
"I know I've been selfish, but for once I think I should accept my fate... our fate..."
Chloe has a very toxic relationship with her step-father.
"Welcome to 'The Real Step-douches of Arcadia Bay'."
Take a look at some photos of Chloe.


Creatively translate poems from Spanish to English in a line-by-line format with word-by-word Spanish to English translations provided by the Collins Spanish to English Dictionary.
Creatively translate poems from Spanish to English in a line-by-line format with word-by-word Spanish to English translations provided by the Collins Spanish to English Dictionary.


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The Squish effect makes the target element disappear into the upper-left corner of the page. The Squish effect produces the same effect as the Grow effect when the Grow effect’s growCenter option is set to false.
You can only attach this effect to the following HTML elements: address, dd, div, dl, dt, form, img, p, ol, ul, applet, center, dir, menu, or pre.
The following table lists optional available options for the Squish effect.
|
Option |
Description |
|---|---|
|
duration |
Duration of the effect in milliseconds. The default value is 1000. |
|
from |
Start size in % or pixels. The default value is 0%. |
|
to |
End size in % or pixels. The default value is 100%. |
|
toggle |
Lets you create a toggle effect. The default value is false. |
|
useCSSBox |
Modifies the border, margin and padding proportionally to the element’s inner content box. Default value is false. |
|
transition |
Determines the type of transition. The default is Spry.squareTransition. |
|
fps |
Determines the number of frames per second (fps) of the animation. The default is 60. |
|
setup |
Lets you define a function that is called before the effect begins, e.g., setup:function (element,effect){/* ... */}. |
|
finish |
Lets you define a function that is called after the effect finishes, e.g., finish:function (element,effect){/* ... */}. |
Sample code:
|
var effect = new Spry.Effect.Squish("targetID",{duration: 2000, from: "100%", to: "0%"}); |
|
effect.start(); |


public class Label
extends java.lang.Object
Label
indicates the topological relationship of a component
of a topology graph to a given Geometry
.
This class supports labels for relationships to two Geometry
s,
which is sufficient for algorithms for binary operations.
Topology graphs support the concept of labeling nodes and edges in the graph.
The label of a node or edge specifies its topological relationship to one or
more geometries. (In fact, since JTS operations have only two arguments labels
are required for only two geometries). A label for a node or edge has one or
two elements, depending on whether the node or edge occurs in one or both of the
input Geometry
s. Elements contain attributes which categorize the
topological location of the node or edge relative to the parent
Geometry
; that is, whether the node or edge is in the interior,
boundary or exterior of the Geometry
. Attributes have a value
from the set {Interior, Boundary, Exterior}
. In a node each
element has a single attribute <On>
. For an edge each element has a
triplet of attributes <Left, On, Right>
.
It is up to the client code to associate the 0 and 1 TopologyLocation
s
with specific geometries.
| Constructor and Description |
|---|
Label(int onLoc)
Construct a Label with a single location for both Geometries.
|
Label(int geomIndex,
int onLoc)
Construct a Label with a single location for both Geometries.
|
Label(int onLoc,
int leftLoc,
int rightLoc)
Construct a Label with On, Left and Right locations for both Geometries.
|
Label(int geomIndex,
int onLoc,
int leftLoc,
int rightLoc)
Construct a Label with On, Left and Right locations for both Geometries.
|
Label(Label lbl)
Construct a Label with the same values as the argument Label.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
allPositionsEqual(int geomIndex,
int loc) |
void |
flip() |
int |
getGeometryCount() |
int |
getLocation(int geomIndex) |
int |
getLocation(int geomIndex,
int posIndex) |
boolean |
isAnyNull(int geomIndex) |
boolean |
isArea() |
boolean |
isArea(int geomIndex) |
boolean |
isEqualOnSide(Label lbl,
int side) |
boolean |
isLine(int geomIndex) |
boolean |
isNull(int geomIndex) |
void |
merge(Label lbl)
Merge this label with another one.
|
void |
setAllLocations(int geomIndex,
int location) |
void |
setAllLocationsIfNull(int location) |
void |
setAllLocationsIfNull(int geomIndex,
int location) |
void |
setLocation(int geomIndex,
int location) |
void |
setLocation(int geomIndex,
int posIndex,
int location) |
void |
toLine(int geomIndex)
Converts one GeometryLocation to a Line location
|
static Label |
toLineLabel(Label label) |
java.lang.String |
toString() |
public Label(int onLoc)
public Label(int geomIndex, int onLoc)
public Label(int onLoc, int leftLoc, int rightLoc)
public Label(int geomIndex, int onLoc, int leftLoc, int rightLoc)
public Label(Label lbl)
public void flip()
public int getLocation(int geomIndex, int posIndex)
public int getLocation(int geomIndex)
public void setLocation(int geomIndex, int posIndex, int location)
public void setLocation(int geomIndex, int location)
public void setAllLocations(int geomIndex, int location)
public void setAllLocationsIfNull(int geomIndex, int location)
public void setAllLocationsIfNull(int location)
public void merge(Label lbl)
public int getGeometryCount()
public boolean isNull(int geomIndex)
public boolean isAnyNull(int geomIndex)
public boolean isArea()
public boolean isArea(int geomIndex)
public boolean isLine(int geomIndex)
public boolean isEqualOnSide(Label lbl, int side)
public boolean allPositionsEqual(int geomIndex, int loc)
public void toLine(int geomIndex)
public java.lang.String toString()
toString
in class java.lang.Object


You have only one minute to answer 10 javaScript questions
For every wrong answer you will be penalized 10 senconds, good luck!
You have only one minute to answer 10 javaScript questions
For every wrong answer you will be penalized 10 senconds, good luck!


Some python libraries that are relevant for Bayesian Neural Networks (as part of the general trend towards Probabilistic Programming) are:
The training of the network implies feeding it with training data and finding the sets of weights and biases that minimizes a loss function that has been selected for that particular problem. Consider, e.g., a classification problem where the single output \( y \) of the final network layer is a real number \( \in [0,1] \) that indicates the (discrete) probability for input \( \boldsymbol{x} \) belonging to either class \( t=1 \) or \( t=0 \): $$ \begin{align} p_{t=1} \equiv p(t=1 | \boldsymbol{w},\boldsymbol{x}) &= y \label{_auto1}\\ p_{t=0} \equiv p(t=0 | \boldsymbol{w},\boldsymbol{x}) &= 1-y, \label{_auto2} \end{align} $$ where we have simplified the notation, not using boldface anymore for the arrays \( w \) and \( x \). A simple neural network classifier can be trained by minimizing the loss function $$ C_W(\boldsymbol{w}) = C(\boldsymbol{w}) + \alpha E_W(\boldsymbol{w}), $$ made up of an error function $$ C(\boldsymbol{w}) = -\sum_n \left[ t^{(n)} \log ( y(\boldsymbol{x}^{(n)},\boldsymbol{w})) + (1 - t^{(n)}) \log (1 - y(\boldsymbol{x}^{(n)},\boldsymbol{w})) \right], $$ where \( t^{(n)} \) is the training data, and the regularizer $$ E_W(\boldsymbol{w}) = \frac{1}{2} \sum_i w_i^2, $$ that is designed to avoid overfitting. The error function can be interpreted as minus the log likelihood $$ p(\mathcal{D}|\boldsymbol{w}) = \exp\left[ - C(\boldsymbol{w}) \right]. $$ Similarly the regularizer can be interpreted in terms of a log prior probability distribution over the parameters $$ p(\boldsymbol{w} | \alpha) = \frac{1}{Z_W(\alpha)} \exp \left[ -\alpha E_W \right]. $$ If \( E_W \) is quadratic as given above, then the corresponding prior distribution is a Gaussian with variance \( \sigma_W^2 = 1/\alpha \) and \( 1/Z_W = (\alpha/2\pi)^{K/2} \), where \( K \) is the number of parameters in \( w \). The objective function \( C_W(w) \) then corresponds to the inference of the parameters \( \boldsymbol{w} \) given the data $$ p(\boldsymbol{w} | \mathcal{D}, \alpha) = \frac{p(D|\boldsymbol{w}) p(\boldsymbol{w}|\alpha)}{p(\mathcal{D}|\alpha)} = \frac{1}{Z_M} \exp [ -C_W(\boldsymbol{w}) ]. $$ We show the evolution of the probability distribution for a sequence of an increasing number of training data in the following figure. The network parameters \( \boldsymbol{w} \) that are found by minimizing \( C_W(\boldsymbol{w}) \) can be interpreted as the (locally) most probable parameter vector \( \boldsymbol{w}^* \).
Instead, we will use the Bayesian approach and consider the information that is contained in the actual probability distribution. In fact, there are different uncertainties that should be addressed:
We will use \( y \) to denote the output from the neural network. For classification problems, \( y \) will give the categorical (discrete) distribution of probabilities \( p_{t=c} \) of belonging to class \( c \). For regression problems, \( y \) is a continuous variable. It could also, in general, be a vector of outputs. The neural network can be seen as a non-linear mapping \( y(x; w) \): \( x \in \mathbb{R}^p \to y \in \mathbb{R}^m \).
A Bayesian neural network can be viewed as probabilistic model in which we want to infer \( p(y \lvert \boldsymbol{x},\mathcal{D}) \) where \( \mathcal{D} = \left\{\boldsymbol{x}^{(i)}, y^{(i)}\right\} \) is a given training dataset.
We construct the likelihood function \( p(\mathcal{D} \lvert \boldsymbol{w}) = \prod_i p(y^{(i)} \lvert \boldsymbol{x}^{(i)}, \boldsymbol{w}) \) which is a function of parameters \( \boldsymbol{w} \). Maximizing the likelihood function gives the maximimum likelihood estimate (MLE) of \( \boldsymbol{w} \). The usual optimization objective during training is the negative log likelihood. For a categorical distribution this is the cross entropy error function, for a Gaussian distribution this is proportional to the sum of squares error function. MLE can lead to severe overfitting though.
Multiplying the likelihood with a prior distribution \( p(\boldsymbol{w}) \) is, by Bayes theorem, proportional to the posterior distribution \( p(\boldsymbol{w} \lvert \mathcal{D}) \propto p(\mathcal{D} \lvert \boldsymbol{w}) p(\boldsymbol{w}) \). Maximizing \( p(\mathcal{D} \lvert \boldsymbol{w}) p(\boldsymbol{w}) \) gives the maximum a posteriori (MAP) estimate of \( \boldsymbol{w} \). Computing the MAP estimate has a regularizing effect and can prevent overfitting. The optimization objectives here are the same as for MLE plus a regularization term coming from the log prior.
Both MLE and MAP give point estimates of parameters. If we instead had a full posterior distribution over parameters we could make predictions that take weight uncertainty into account. This is covered by the posterior predictive distribution \( p(y \lvert \boldsymbol{x},\mathcal{D}) = \int p(y \lvert \boldsymbol{x}, \boldsymbol{w}) p(\boldsymbol{w} \lvert \mathcal{D}) d\boldsymbol{w} \) in which the parameters have been marginalized out. This is equivalent to averaging predictions from an ensemble of neural networks weighted by the posterior probabilities of their parameters \( \boldsymbol{w} \).
Returning to the binary classification problem, \( y^{(n+1)} \) corresponds to the probability \( p_{t^{(n+1)}=1} \) and a Bayesian prediction of a new datum \( y^{(n+1)} \) will correspond to a pdf and involves marginalizing over the weight and bias parameters $$ p(y^{(n+1)} | x^{(n+1)}, D, \alpha) = \int d \boldsymbol{w} p( y^{(n+1)} | x^{(n+1)}, w, \alpha) p(w|D,\alpha), $$ where we have also included the weight decay hyperparameter \( \alpha \) from the prior (regularizer). Marginalization could, of course, also be performed over this parameter.
We show an example of such inference, comparing the point estimate \( y(x; w^*, \alpha) \) and the Bayesian approach, in the following figure.
The Bayesian classifier is based on sampling a very large number of neurons with different parameters. The distribution of these samples will be proportional to the posterior pdf for the parameters. The decision boundary shown in the figure is obtained as the mean of the predictions of the sampled neurons evaluated on a grid. It is clear that the Bayesian classifier is more uncertain about its predictions in the lower left and upper right corners, where there is little training data.
This becomes even more clear when we plot the standard deviation of the predictions of the Bayesian classifier.
The predictions are rather certain along a diagonal line (close to the training data). Note that the interpretation of the prediction in the center of the figure (near \( x_1,x_2 = 0,0 \)) is the following: The Bayesian binary classifier predicts a probability of \( \sim 0.5 \) for this point in the input parameter space to belong to class 1 (i.e. the decision is very uncertain). The Baysian classifier is also very certain about this uncertainty (the standard deviation is small).
In contrast, predictions for points in the upper left or lower right corners are very certain about the class label (and there is little uncertainty about this certainty).
In short, there are three different approaches:
Bayesian neural networks differ from plain neural networks in that their weights are assigned a probability distribution instead of a single value or point estimate. These probability distributions describe the uncertainty in weights and can be used to estimate uncertainty in predictions. Training a Bayesian neural network via variational inference learns the parameters of these distributions instead of the weights directly.
Unfortunately, an analytical solution for the weight posterior \( p(\boldsymbol{w} \lvert \mathcal{D}) \) in neural networks is intractable. We therefore have to approximate the true posterior with a proxy variational distribution \( q(\boldsymbol{w} \lvert \boldsymbol{\theta}) \) whose parameters we want to estimate.
This can be done by minimizing the Kullback-Leibler divergence between \( q(\boldsymbol{w} \lvert \boldsymbol{\theta}) \) and the true posterior \( p(\boldsymbol{w} \lvert \mathcal{D}) \) w.r.t. \( \boldsymbol{\theta} \).
The specific goal is then to replace \( p(\boldsymbol{w} \lvert \mathcal{D}) \), which we don't know, with the known proxy distribution \( q(\boldsymbol{w} \lvert \boldsymbol{\theta}^*) \), where \( \boldsymbol{\theta}^* \) is the optimal set of variational parameters.
Intuitively there are three scenarios:
The task at hand is therefore to find the set of parameters \( \boldsymbol{\theta}^* \) that maximizes \( J_\mathrm{ELBO}(\boldsymbol{\theta}) \). The hardest term to evaluate is obviously the expectation of the log-likelihood $$ \mathbb{E}_{q} \left[ \log \, p(\mathcal{D} \lvert \boldsymbol{w}) \right] = \sum{i=1}^N \mathbb{E}_{q} \left[ \log \, p( y^{(i)} \lvert \boldsymbol{x}^{(i)}, \boldsymbol{w}) \right]. $$ This problem constitutes a new and active area of research in machine learning and it permeates well with the overarching theme of this course which is inference from data. We will end by giving two pointers to further readings on this subject.
See also
The well-cited paper paper: Weight Uncertainty in Neural Networks (Bayes by Backprop) has been well described in the blog entry by Martin Krasser. The main points of this blog entry are reproduced below with some modifications and some adjustments of notation.
All three terms in equation \eqref{eq:elbo} are expectations w.r.t. the variational distribution \( q(\boldsymbol{w} \lvert \boldsymbol{\theta}) \). In this paper they use the variational free energy \( \mathcal{F}(\mathcal{D},\boldsymbol{\theta}) \equiv -J_\mathrm{ELBO}(\boldsymbol{\theta}) \) as a cost function (since it should be minimized). This quantity can be approximated by drawing Monte Carlo samples \( \boldsymbol{w}^{(i)} \) from \( q(\boldsymbol{w} \lvert \boldsymbol{\theta}) \). $$ \begin{equation} \mathcal{F}(\mathcal{D},\boldsymbol{\theta}) \approx {1 \over N} \sum_{i=1}^N \left[ \log \, q(\boldsymbol{w}^{(i)} \lvert \boldsymbol{\theta}) - \log \, p(\boldsymbol{w}^{(i)}) - \log \, p(\mathcal{D} \lvert \boldsymbol{w}^{(i)})\right] \label{eq:VariationalFreeEnergy} \end{equation} $$
In the example used in the blog post, they use a Gaussian distribution for the variational posterior, parameterized by \( \boldsymbol{\theta} = (\boldsymbol{\mu}, \boldsymbol{\sigma}) \) where \( \boldsymbol{\mu} \) is the mean vector of the distribution and \( \boldsymbol{\sigma} \) the standard deviation vector. The elements of \( \boldsymbol{\sigma} \) are the elements of a diagonal covariance matrix which means that weights are assumed to be uncorrelated. Instead of parameterizing the neural network with weights \( \boldsymbol{w} \) directly, it is parameterized with \( \boldsymbol{\mu} \) and \( \boldsymbol{\sigma} \) and therefore the number of parameters are doubled compared to a plain neural network.
A training iteration consists of a forward-pass and and backward-pass. During a forward pass a single sample is drawn from the variational posterior distribution. It is used to evaluate the approximate cost function defined by equation \eqref{eq:VariationalFreeEnergy}. The first two terms of the cost function are data-independent and can be evaluated layer-wise, the last term is data-dependent and is evaluated at the end of the forward-pass. During a backward-pass, gradients of \( \boldsymbol{\mu} \) and \( \boldsymbol{\sigma} \) are calculated via backpropagation so that their values can be updated by an optimizer.
Since a forward pass involves a stochastic sampling step we have to apply the so-called re-parameterization trick for backpropagation to work. The trick is to sample from a parameter-free distribution and then transform the sampled \( \boldsymbol{\epsilon} \) with a deterministic function \( t(\boldsymbol{\mu}, \boldsymbol{\sigma}, \boldsymbol{\epsilon}) \) for which a gradient can be defined. In the blog post they choose \( \boldsymbol{\epsilon} \) to be drawn from a standard normal distribution i.e. \( \boldsymbol{\epsilon} \sim \mathcal{N}(\boldsymbol{0}, \boldsymbol{I}) \) and the function \( t \) is taken to be \( t(\boldsymbol{\mu}, \boldsymbol{\sigma}, \boldsymbol{\epsilon}) = \boldsymbol{\mu} + \boldsymbol{\sigma} \odot \boldsymbol{\epsilon} \), i.e., it shifts the sample by mean \( \boldsymbol{\mu} \) and scales it with \( \boldsymbol{\sigma} \) where \( \odot \) is element-wise multiplication.
For numerical stability the network is parametrized with \( \boldsymbol{\rho} \) instead of \( \boldsymbol{\sigma} \) and \( \boldsymbol{\rho} \) is transformed with the softplus function to obtain \( \boldsymbol{\sigma} = \log(1 + \exp(\boldsymbol{\rho})) \). This ensures that \( \boldsymbol{\sigma} \) is always positive. As prior, a scale mixture of two Gaussians is used \( p(\boldsymbol{w}) = \pi \mathcal{N}(\boldsymbol{w} \lvert 0,\sigma_1^2) + (1 - \pi) \mathcal{N}(\boldsymbol{w} \lvert 0,\sigma_2^2) \) where \( \sigma_1 \), \( \sigma_2 \) and \( \pi \) are shared parameters. Their values are learned during training (which is in contrast to the paper where a fixed prior is used).
See Martin Krasser's blog entry for results and further details.


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CVGenerator by Arsim Hyseni
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Part of my role as an open source advocate for Scala projects at Twitter involves talking to developers outside of Twitter about how we can make our open source projects more widely useful and accessible, and one of the most common requests for Finagle is for more introductory tutorials and examples.
One of the steps we’re taking in this direction is a major overhaul of
finagle-example
,
which we’ll be moving out of the main Finagle repository and
into its own project under the Finagle organization
on GitHub. At the same time we’ll be
filling out the top-level introduction to the examples (which is currently a
little bare),
adding more detailed API documentation, providing better example coverage for
Finagle subprojects,
and creating a larger set of
Java examples
to show off our new work on improving Java compatibility.
We’re also working on several stand-alone example projects and tutorials, including the Finagle Name Finder, which provides a simple Scala wrapper for a few pieces of OpenNLP, a Java library for natural language processing, and uses that wrapper to define a named entity recognition RPC service built on Finagle and Thrift. You can start up a name finding service in your SBT console with just a few lines of code:
import com.twitter.finagle.Thrift
import com.twitter.finagle.examples.names.thriftscala._
val server = SafeNameRecognizerService.create(
langs = Seq("en"),
numThreads = 4,
numRecognizers = 4
) map { service =>
Thrift.serveIface("localhost:9090", service)
} onSuccess { _ =>
println("Server started successfully")
} onFailure { ex =>
println("Could not start the server: " + ex)
}
You can then create a client (either locally in the same console, or on another machine):
import com.twitter.finagle.Thrift
import com.twitter.finagle.examples.names.thriftscala._
val client =
Thrift.newIface[NameRecognizerService.FutureIface]("localhost:9090")
And feed it a document:
val doc = """
An anomaly which often struck me in the character of my friend Sherlock Holmes
was that, although in his methods of thought he was the neatest and most
methodical of mankind, and although also he affected a certain quiet primness of
dress, he was none the less in his personal habits one of the most untidy men
that ever drove a fellow-lodger to distraction. Not that I am in the least
conventional in that respect myself. The rough-and-tumble work in Afghanistan,
coming on the top of a natural Bohemianism of disposition, has made me rather
more lax than befits a medical man.
"""
client.findNames("en", doc) onSuccess { response =>
println("People: " + response.persons.mkString(", "))
println("Places: " + response.locations.mkString(", "))
} onFailure { ex =>
println("Something bad happened: " + ex.getMessage)
}
And the service will tokenize the document, identify parts of speech, attempt to find all the names of people, places, and organizations, and promptly return the results:
People: Sherlock Holmes
Places: Afghanistan
When you start a service, you can have OpenNLP recognizers loaded into memory
for a given set of languages, but the service can also load languages from disk
for individual requests—for example, we’ve only loaded the English-language
models for the service we started above, but we could also copy a set of
Spanish-language models into our models
directory, and then our client could
ask to use them:
val esDoc = """
Alrededor de 1902 fue el primero en aplicar una descarga eléctrica en un tubo
sellado y con gas neón con la idea de crear una lámpara. Inspirado en parte por
la invención de Daniel McFarlan Moore, la lámpara de Moore, Claude inventó la
lámpara de neón mediante la descarga eléctrica de un gas inerte comprobando que
el brillo era considerable.
"""
val result = client.findNames("es", esDoc)
We’re using the Name Finder project in a new “Finagle Essentials” course at Twitter University, and the slides for that course go into more detail about how the implementation works. The project is currently designed to fit into an overview of the fundamental abstractions behind Finagle, and to demonstrate just how easy it is to get a basic (but useful) Finagle service up and running, but over the next few months we’ll be expanding it to show off more of Finagle’s capabilities.
We’re also working on a tutorial that walks through the implementation of a Finagle protocol for scodec, a Scala binary serialization library that’s part of the Typelevel group of projects. If you’re interested in helping out with this kind of tutorial development, or if you have a Finagle example project, blog post, etc. that you’d like us to feature, or if you just have questions, please get in touch via @finagle or the Finaglers mailing list, and be sure to watch this space for updates as we continue to improve our documentation and examples.