MYSTERY, ALASKA (M). (Hollywood Pictures/Buena Vista International) Director: Jay Roach Stars: Russell Crowe, Hank Azaria, Colm Meaney, Mary McCormack, Burt Reynolds, Lolita Davidovich, Ron Eldard, Michael McKean, Maury Chaykin, Judith Ivey, Ryan Nortcutt, Mike Myers Running time: 120 minutes.
The good folk of Mystery, a small town in Alaska, live and breathe little else but ice hockey. The Saturday afternoon games on the local pond are a social highlight, and the men guard their positions in the team jealously. But after an article on this hick town team appears in the prestigious Sports Illustrated, they are offered a one-off opportunity to play an exhibition match against the New York Rangers.
The deal is the brainchild of celebrated sports journalist Charles Danner (Hank Azaria), who grew up in Mystery before seeking greener pastures. Most of the town rally around the team, with the exception of the crusty old judge (Burt Reynolds), who urges that Mystery cling to both its dignity and its illusions.
But as the match draws closer, tensions within the town simmer. The match represents something of a personal mission for John Biebe (Russell Crowe), the town's sheriff, who has just been dumped off the team after thirteen years. The town's mayor (Colm Meaney, from Con Air, etc) discovers that his wife (Lolita Davidovich) is having an affair with Shank (Ron Eldard), a member of the team, while Biebe becomes jealous of the former relationship that existed between his wife (Mary McCormack) and Danner.
Mystery's brief flirtation with fame also brings with it a number of problems, not the least of which is a sudden influx of money and interest from big corporations that threaten the harmony of this small town. Can the team pull off an unlikely fairy tale win against the best and most professional team in the business, or will defeat rip the heart out of this tight knit community? Mystery, Alaska is a classic tale about an underdog that eventually has its day, but this leisurely paced comedy takes its own sweet time about getting to the climactic hockey match. Director Jay Roach (the two Austin Powers comedies) and television writer David E Kelley paint a wonderful portrait of this small town, bringing to life its charming, laid back lifestyle and colourful gallery of eccentric inhabitants.
Roach has assembled a strong cast to bring the delightful residents of Mystery to life, and he has also coaxed a brief appearance from his Austin Powers star Mike Myers, who plays a vitriolic tv sports commentator.
As a film dealing with the fast but often violent sport of ice hockey, Mystery, Alaska is better than Disney's increasingly tiresome Mighty Ducks series, but somehow not as much fun as the ribald, raunchy comedy Slap Shot. Instead, this is another of those endearing comedies that explore the rich textures of life in small town America, and its quirky charms should win over most audiences.
*** greg king http://www.netau.com.au/gregking
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{% extends "base.html" %} {% block entire_body %}
~/.diffcommenter
:
[Diffcommenter] url = {{ settings.ROOT_URL }} login = {{ user.username }} password = your_password_here
.bashrc
add an alias:
alias to-review="python ~/to-review.py"
to-review
tool from inside your repository, like so:
# send commits from current branch to review to-review # send commits from specific branch to-review --branch origin/feature/mega_feature # send all commits as a single diff to-review --diff # submit only a specific commit to-review --only 1ef3c44 # submit several entire files, not diffs to-review -f file1.txt -f file2.txtLast option does not require the files to be inside a Git repository.


Unified Visualization for Bioinformatics Analyses, or UniViz is a joint effort between Clara Nguyen and Jason Kmec of San Jose State University.
Please enter valid, aligned or unaligned DNA sequence(s) in FASTA format below:


WITNESS LIST
Nominations
March 28, 2011 9:00 AM
Teacher Retirement System of Texas
ON:
Barth, Todd F. (Self), Houston, TX
Moss, Christopher S. (Self), Lufkin, TX
Sissney, F. Nanette (Self), Whitesboro, TX
Texas Education Agency
ON:
Scott, Robert P. (Self), Austin, TX
Registering, but not testifying:
For:
Hollingsworth, Jr., Lonnie (Robert P. Scott) (Texas Classroom Teachers Association),
Austin, TX
Texas Water Development Board
ON:
Bradford, Billy R. (Self), Brownsville, TX
Cluck, Monte D. (Self), Amarillo, TX
1


| Copyright | Copyright 1986-2019 Xilinx, Inc. All Rights Reserved. |
| Tool Version | xrcserver v.2019.2 (lin64) Build 0 |
| Date | Sun Jul 4 18:36:50 2021 |
| Host | Msoc running 64-bit Ubuntu 18.04.5 LTS |
| Command | Vitis IDE session HLS_Final_Project-Default |
| Table of Contents |
| 1 REPORT SUMMARY |
| 2 VIOLATION DETAILS |
| 3 CONFIG FILE DETAILS |
| 4 PERFORMANCE CHECKS MET |
Violations found: 2
Rule Specs Violated: 2
Rules Met: 1
Rule Specs Met: 1
| Id | Name | Severity | Full Name | Categories | Actual | Threshold | Details | Resolution |
|---|---|---|---|---|---|---|---|---|
| 98 | PLRAM_USAGE | advisory | PLRAM Usage | Kernel Data Transfer | 0 | > 0 | PLRAMs were used by 0 port(s). | Utilize PLRAMs to maximize performance. Click PLRAMs can increase your system performance by providing low-latency, high-bandwidth access to your data. For detailed explanation and recommendation: PLRAM_USAGE. |
| 99 | TRACE_MEMORY | warning | Trace Memory | Resource Usage | FIFO | != FIFO | Trace buffer memory was specified as FIFO. | Specify memory type to maximize trace buffer depth. Click Users can specify the trace buffer memory type for profiling. The default is FIFO:8K, which may not be large enough to hold all trace events on a given device. For detailed explanation and recommendation: TRACE_MEMORY. |
Config files found: 0
| Id | Name | Full Name | Categories | Actual | Threshold | Details | Rule Description |
|---|---|---|---|---|---|---|---|
| 100 | OBJECTS_RELEASED | Objects Released | Resource Usage | true | true | Check for OpenCL objects released by the host code. | This rule checks if OpenCL objects are properly released by the host code. |


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About
Nowadays companies are implementing new business models that depend heavily on Natural Language Processing (NLP) in textual documents to extract relevant informations from a variety of sources, including e-commerce, domain-specific documents, utilities, social media, etc. Implementing a NLP system requires, among other things, a considerable software engineering effort for: data structures for human language representation; the application of tools to textual representation through linguistic analysis at various levels (lexical, syntactic and semantic); reading and interpreting annotations generated from the corpus, the creation of linguistic resources, among others. While there are currently numerous NLP tools widely used in NLP language tasks, information extraction, annotation, and correction, each provides only partial coverage analysis. Besides that, these tools are developed in distinct programming languages and are made available without any standardization concerning data input and output, which makes their interoperability difficult due to incompatibility issues APIs, output data formats (representation) and basic tokenization, just to a few. Thus selecting tools and their language analysis according to a specific user interest, usually takes a plenty of time, especially when you want to use them together (tool integration). The goal of this paper is to analyze several NLP tools available in the literature and propose, implement and evaluate a new architecture that encapsulates several linguistic analyzes in one computational framework, allowing application developers not only to run linguistic analysis pipelines that involve in-depth treatment of the English language, but also an easy integration of various NLP tools. Besides that, it aims to provide a simple and intuitive graphical user interface (GUI) in order to fully exploit all language analysis already available, and that can easily be customized by the user himself later. In this way the functionality provided by the proposed DeepNLPF framework intends to help developers perform numerous textual data analyses more efficiently by guiding them in choosing the best, most appropriate and efficient language analysis task pipeline possible. The now proposed version of DeepNLPF is composed by several state-of-the-art NLP tools that were selected after a thorough literature review. Contributions of this work include: I) the selection of state-of-the-art NLP tools added to the framework; II) the proposal to standardize the inputs and outputs of NLP tools according to the linguistic annotation representation standards already established in the literature; III) the implementation of a computational architecture enabling multithreads and parallelism for the customization of NLP pipelines guided by an expressive but simple graphic interface; IV) and a module to facilitate the integration and updating of new third party NLP tools, as well as the generation of basic textual data statistics. Finally, several experiments show that DeepNLPF obtained better performance in relation to the sequential processing of the tested NLP tools when considered individually. More precisely, about 60% faster in terms of processing time.
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Changelog
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Soon the first version of DeepNLPF will be published.


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@* almostDone Template File *@
@(ageForm:Form[AgeCheck])(implicit request: MessagesRequestHeader, flash:Flash)
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Amazon EC2 Auto Scaling AutoScalingGroup LifecycleHookSpecification
The
LifecycleHookSpecification
property type defines lifecycle hooks for an Amazon EC2 Auto Scaling
group, which specify actions to perform when Amazon EC2 Auto Scaling launches or terminates
instances. For more
information, see Amazon EC2 Auto Scaling Lifecycle Hooks
in the Amazon EC2 Auto Scaling User Guide.
The
LifecycleHookSpecificationList
property of the AWS::AutoScaling::AutoScalingGroup resource contains
a list of LifecycleHookSpecification
property types.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "DefaultResult" :
String
, "HeartbeatTimeout" :Integer
, "LifecycleHookName" :String
, "LifecycleTransition" :String
, "NotificationMetadata" :String
, "NotificationTargetARN" :String
, "RoleARN" :String
}
YAML
DefaultResult:
String
HeartbeatTimeout:Integer
LifecycleHookName:String
LifecycleTransition:String
NotificationMetadata:String
NotificationTargetARN:String
RoleARN:String
Properties
For more information about each property, including constraints, see LifecycleHookSpecification in the Amazon EC2 Auto Scaling API Reference.
DefaultResult
-
The action that the Auto Scaling group should take when the lifecycle hook timeout elapses or if an unexpected failure occurs.
Valid values:
CONTINUE
,ABANDON
(default)Required: No
Type: String
Update requires: No interruption
HeartbeatTimeout
-
The maximum time, in seconds, that can elapse before the lifecycle hook times out. If the lifecycle hook times out, Auto Scaling performs the default action.
Required: No
Type: Integer
Update requires: No interruption
LifecycleHookName
-
The name of the lifecycle hook. For constraints, see LifecycleHookSpecification in the Amazon EC2 Auto Scaling API Reference.
Required: Yes
Type: String
Update requires: No interruption
LifecycleTransition
-
The state of the EC2 instance to attach the lifecycle hook to. For a list of lifecycle hook types, see DescribeLifecycleHookTypes in the Amazon EC2 Auto Scaling API Reference.
Required: Yes
Type: String
Update requires: No interruption
NotificationMetadata
-
Additional information to include when Amazon EC2 Auto Scaling sends a message to the notification target. For constraints, see LifecycleHookSpecification in the Amazon EC2 Auto Scaling API Reference.
Required: No
Type: String
Update requires: No interruption
NotificationTargetARN
-
The Amazon Resource Name (ARN) of the target that Amazon EC2 Auto Scaling sends notifications to when an instance is in the transition state for the lifecycle hook. The notification target can be either an Amazon SQS queue or an Amazon SNS topic.
Required: No
Type: String
Update requires: No interruption
RoleARN
-
The ARN of the IAM role that allows the Auto Scaling group to publish to the specified notification target.
Required: No
Type: String
Update requires: No interruption
Examples
The following snippet specifies a lifecycle hook for an
AWS::AutoScaling::AutoScalingGroup
resource.
JSON
{ "Resources": { "ASG": { "Type": "AWS::AutoScaling::AutoScalingGroup", "Properties": { "AvailabilityZones": [ { "Ref": "AZParameter" } ], "VPCZoneIdentifier": { "Ref": "Subnets" }, "DesiredCapacity": "0", "MaxSize": "0", "MinSize": "0", "LaunchConfigurationName": { "Ref": "LC" }, "LifecycleHookSpecificationList": [ { "LifecycleTransition": "autoscaling:EC2_INSTANCE_LAUNCHING", "LifecycleHookName": "myFirstLifecycleHook", "HeartbeatTimeout": 4800, "NotificationTargetARN": { "Fn::GetAtt": [ "SQS", "Arn" ] } } ] } }, "SQS": { "Type": "AWS::SQS::Queue" } } }
YAML
Resources: ASG: Type: 'AWS::AutoScaling::AutoScalingGroup' Properties: AvailabilityZones: - !Ref AZParameter VPCZoneIdentifier: !Ref Subnets DesiredCapacity: '0' MaxSize: '0' MinSize: '0' LaunchConfigurationName: !Ref LC LifecycleHookSpecificationList: - LifecycleTransition: 'autoscaling:EC2_INSTANCE_LAUNCHING' LifecycleHookName: 'myFirstLifecycleHook' HeartbeatTimeout: 4800 NotificationTargetARN: !GetAtt SQS.Arn SQS: Type: 'AWS::SQS::Queue'
See Also
-
Amazon EC2 Auto Scaling Lifecycle Hooks in the Amazon EC2 Auto Scaling User Guide
-
LifecycleHookSpecification in the Amazon EC2 Auto Scaling API Reference


Source code for pymodbus.constants
'''
Constants For Modbus Server/Client
----------------------------------
This is the single location for storing default
values for the servers and clients.
'''
from pymodbus.interfaces import Singleton
[docs]class Defaults(Singleton):
''' A collection of modbus default values
.. attribute:: Port
The default modbus tcp server port (502)
.. attribute:: Retries
The default number of times a client should retry the given
request before failing (3)
.. attribute:: RetryOnEmpty
A flag indicating if a transaction should be retried in the
case that an empty response is received. This is useful for
slow clients that may need more time to process a requst.
.. attribute:: Timeout
The default amount of time a client should wait for a request
to be processed (3 seconds)
.. attribute:: Reconnects
The default number of times a client should attempt to reconnect
before deciding the server is down (0)
.. attribute:: TransactionId
The starting transaction identifier number (0)
.. attribute:: ProtocolId
The modbus protocol id. Currently this is set to 0 in all
but proprietary implementations.
.. attribute:: UnitId
The modbus slave addrss. Currently this is set to 0x00 which
means this request should be broadcast to all the slave devices
(really means that all the devices should respons).
.. attribute:: Baudrate
The speed at which the data is transmitted over the serial line.
This defaults to 19200.
.. attribute:: Parity
The type of checksum to use to verify data integrity. This can be
on of the following::
- (E)ven - 1 0 1 0 | P(0)
- (O)dd - 1 0 1 0 | P(1)
- (N)one - 1 0 1 0 | no parity
This defaults to (N)one.
.. attribute:: Bytesize
The number of bits in a byte of serial data. This can be one of
5, 6, 7, or 8. This defaults to 8.
.. attribute:: Stopbits
The number of bits sent after each character in a message to
indicate the end of the byte. This defaults to 1.
.. attribute:: ZeroMode
Indicates if the slave datastore should use indexing at 0 or 1.
More about this can be read in section 4.4 of the modbus specification.
.. attribute:: IgnoreMissingSlaves
In case a request is made to a missing slave, this defines if an error
should be returned or simply ignored. This is useful for the case of a
serial server emulater where a request to a non-existant slave on a bus
will never respond. The client in this case will simply timeout.
'''
Port = 502
Retries = 3
RetryOnEmpty = False
Timeout = 3
Reconnects = 0
TransactionId = 0
ProtocolId = 0
UnitId = 0x00
Baudrate = 19200
Parity = 'N'
Bytesize = 8
Stopbits = 1
ZeroMode = False
IgnoreMissingSlaves = False
[docs]class ModbusStatus(Singleton):
'''
These represent various status codes in the modbus
protocol.
.. attribute:: Waiting
This indicates that a modbus device is currently
waiting for a given request to finish some running task.
.. attribute:: Ready
This indicates that a modbus device is currently
free to perform the next request task.
.. attribute:: On
This indicates that the given modbus entity is on
.. attribute:: Off
This indicates that the given modbus entity is off
.. attribute:: SlaveOn
This indicates that the given modbus slave is running
.. attribute:: SlaveOff
This indicates that the given modbus slave is not running
'''
Waiting = 0xffff
Ready = 0x0000
On = 0xff00
Off = 0x0000
SlaveOn = 0xff
SlaveOff = 0x00
[docs]class Endian(Singleton):
''' An enumeration representing the various byte endianess.
.. attribute:: Auto
This indicates that the byte order is chosen by the
current native environment.
.. attribute:: Big
This indicates that the bytes are in little endian format
.. attribute:: Little
This indicates that the bytes are in big endian format
.. note:: I am simply borrowing the format strings from the
python struct module for my convenience.
'''
Auto = '@'
Big = '>'
Little = '<'
[docs]class ModbusPlusOperation(Singleton):
''' Represents the type of modbus plus request
.. attribute:: GetStatistics
Operation requesting that the current modbus plus statistics
be returned in the response.
.. attribute:: ClearStatistics
Operation requesting that the current modbus plus statistics
be cleared and not returned in the response.
'''
GetStatistics = 0x0003
ClearStatistics = 0x0004
[docs]class MoreData(Singleton):
''' Represents the more follows condition
.. attribute:: Nothing
This indiates that no more objects are going to be returned.
.. attribute:: KeepReading
This indicates that there are more objects to be returned.
'''
Nothing = 0x00
KeepReading = 0xFF
#---------------------------------------------------------------------------#
# Exported Identifiers
#---------------------------------------------------------------------------#
__all__ = [
"Defaults", "ModbusStatus", "Endian",
"ModbusPlusOperation",
"DeviceInformation", "MoreData",
]


Data Analyst - Software Developer
Passionate about empowering education using new technologies. Skilled in Machine Learning, Data Analysis, Web Development, Android Development, and IoT Development.
PhD student in Computer Science with a focus on Intelligent Tutoring Systems at the University of Canterbury
Tyred was born in a hackathon in 2018. Tyred doesn't like biking in hilly areas of NZ! So its ambition is finding easy cycling routes in NZ for everyone who is like Tyred! You can learn more baout Tyred on my github repository
Github Repo DemoCrop-row detection project aims at finding the crop rows in different fields using simple image-processing methods and Mean-Shift clustering. This can be used for agricultural robots which should move between the crop rows. Check it out on my github repository if you're intrested to know how it works
Github RepoAs a part of my PhD thesis, I've been working on analysing comments in educational platforms. This analysis includes Natural Language Processing (NLP) to assess the written self-reports of the learners, modeling the learners based on their performance and providing personalized support to them. You can learn more about my work and this project on ResearchGate.
ResearchGateMarrun is a GPS-based snakes and ladders game which the player move on the virtual board by walking from one point to the other point. The goal of this game is .
A webscraper whcih reads data from NZ Covid19 website every day to get the number of new cases everyday and anlyses this data
Github


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Author<EMAIL_ADDRESS>(Ron Small)
ERIN BROCKOVICH (2000)
Grade: B
Director: Steven Soderbergh
Screenplay: Susannah Grant
Starring: Julia Roberts, Albert Finny, Aaron Eckhart, Peter Coyote, Marg Helgenberger, Cherry Jones, Jamie Harold, Tracey Walter
ERIN BROCKOVICH is one of those rarities: a commercial star picture with something of a brain. It won't bring about world piece or even provide much of an emotional catharsis but, despite that we all know this story through and through and can pretty much predict how it'll turn out, it entertains without insulting anyone's intelligence. Perhaps what I appreciated most about the film was its director, Steven Soderbergh's, attention to detail.
 They simply tell their familiar story and rely on their star to bring audiences in. Soderbergh is an indie filmmaker at heart (and I mean "indie" in the best way possible). He came to fame with SEX, LIES AND VIDEOTAPE, delved into experimental film with SCHIZOPOLIS and THE UNDERNEATH, and most recently has began toying with high concept films (OUT OF SIGHT, the upcoming OCEAN 11 remake and TRAFFIC), imbuing them with a playful cleverness. With ERIN he has taken a product and put his own sensibilities all over it.  Problem is, they rarely are. These kind of pictures are grinded out by studio hacks like Damon Santoslefano and Brian Robbins without an once of joy or style. Films (PATCH ADAMS, STEPMOM, READY TO RUMBLE to name three) that trot forth before our glazed eyes, amping up woebegone tunes so we know when to cry, and wacky melodies cueing us to laugh. I think I can tell on my own, thank you very much. Ignorant pricks, those High Concept directors are.
For the most part, Soderbergh stays away from such Hollywood bullying tactics and tells this story in the cheeky, idiosyncratic manner for which he is known. All the typical Soderbergh elements are prevalent, in the neo realist direction, the hip score that doesn't call attention to itself the way most self consciously hip scores often do, and all the little components the director uses to flesh out his various characters.
As I said, ERIN is a star picture, and Julia Roberts is its incandescent star. After several light-as-Flockheart comedies, she steps into something a little divergent (but not much). ERIN isn't a heavy film by any means. In fact without Soderbergh's direction or Roberts in the lead it wouldn't be out of the realm of possibility that this picture would debut on SHOWTIME.
In BROCKOVICH Julia's attributes are constantly on display, from the very low cut dresses she wears (demonstrating the power of Personal Trainers), to that bright, wide smile that takes over her entire face. We always know we're watching Roberts and not Brockovich, but it works because the film builds on Roberts' sweet, snappy personality. Like Tom Cruise, the actress uses her smile as a bit of a weapon. It's one of her actors' "tricks". Similar to the peculiar George Clooney head bob, or Mark Wahlberg's intermittent scowl, it's something she flashes like a defense mechanism, a conceit that draws attention away from her performance, and into her attractiveness. I remember watching her in an interview on Howard Stern's E! show where she had that smile plastered across her face during the entire interrogation, all while Stern posed his usual litany of scatological queries. She seemed phony, like she was in pain but had to hide it with that wide smile. Like I said, it's a trick, but one that works its desired effect and, believe it or not, Stern soon acquiesced. It's important that actors be aware of their range, as Roberts seems to have become after some ill-fated attempts at character roles. After all, Cruise may be unable to do a passable Irish accent (see FAR AND AWAY, actually don't, just take my word for it), but he sure can paste on that affable grin whenever he's in trouble (which he did a whole lot in EYES WIDE SHUT).
And Roberts uses the smile well as Erin Brockovich, an accidental lawyer who stumbles upon a PG and E scandal, then, along with her grizzled boss, a veteran\amateur attorney (skillfully acted by Albert Finny in all his fog horn glory), she attempts to bring down the company. Nothing new here. We've seen this kind of thing in films from SILKWOOD to THE INSIDER, but this movie is full of the style and charm that most of the others lacked. THE INSIDER sunk because of its stubborn, unwavering attention to the procedure of its true story, hardly focusing on the human element. It was skillfully made, but ultimately dramatically inert. I saw SILKWOOD so long ago (I believe I was all of ten years old) that I won't even bother commenting on what it did right or wrong, though at ten anything without green mutated turtles bored me silly.
BROCKOVICH isn't academic, it's cheerful, and it's the kind of film that will make you smile. In fact I felt, at first, that the plot worked against the film (and maybe I still feel that a bit). It opens with Brockovich in a terrifically unexpected accident, shifting to her hectic home life, an attempted pass from a motorcycle jockey (played with a little too much smug intelligence by Aaron Eckheart) who falls to the ground after being "shot down" by Brockovich (a little detail I'm sure most directors would neglect to include), and her out spoken attempts to get a job. This stuff is handled with such gleeful naturalism that I might have given the film higher marks had it just gone along as a simple character study of this marvelously fascinating women. But I doubt most audiences would like that.
When the scandal is introduced the way it is I admit I was groaning for about ten minutes, but things pick up. Soderbergh stages terrifically mild confrontations, and the relationship between Finny and Roberts is subtly charming. Really the only reason I stayed involved in the picture was because of how fun Roberts makes the character of Brockovich. I would rather see that character (who's based on a real person) go through a familiar story than no story.
Though Soderbergh does make a TV movie-like plot more than watchable, I do wish he had taken it a couple steps further. For instance there's a scene where a PG and E lawyer comes down to the firm to meet with Finny and Erin. Right when we see this bookish, rather mousy looking fellow we immediately know that he is going to be played for laughs, and the good guys will come out on top. During the meeting Brockovich stares (one of those if looks could kill stares) directly at him, her eyes smoldering with disgust. Meanwhile the guy sort of notices, and stammers a bit.  Well why not do something a little different?  Instead we get a recognizable cartoon in a fairly recognizable scene.
The brief bits that feature the victims of the PG and E scandal (a scandal that I'm just realizing I haven't yet gone into, so here's a quick, fragmented summary for those of you who chose to read what I put in the parenthesis: company lies, water unhealthy, people sick + need money = law suit) are common as well. In one instance Soderbergh goes in for a slow, extraneous, extreme close-up of a cancer stricken youngster. Isn't there another way he could have conveyed the emotion, rather than doing what every director does when dealing with sickness: close-ups of sad faces. I dunno, maybe stuff like that is needed in big studio films, but surely Soderbergh could have imbued his victims with some of the multi-faceted character traits he gives nearly everyone else. Then I recall PATCH ADAMS, a movie so saccharine I had to shower after, and I can imagine what BROCKOVICH would be like in the hands of Tom Shadyac, that films director, or even Rob Reiner, who seems to be slowly losing all the talent he once had on a film by film basis.
But I doubt audiences will even care about the details Soderbegh plugs in, since the film would probably be just as successful if it was directed by a studio hack who would give it no personality. Audiences are like that, they go to see their favorite stars in familiar stories, and they generally don't care to be challenged. Of course BROCKOVICH doesn't challenge anybody, but it does raise the bar a bit on the quintessential star picture. I liked it, but I would have really liked it if Soderbergh aimed just an itsy bit higher.
http://www.geocities.com/incongruity98 Reeling (Ron Small)
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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de.digitalforger.smtl.util.internal
service method for a line ending
service method for a line ending
service method for a line ending
service method for a line ending
adding new function support to an eobject
adding new function support to an eobject
the object that will get extended metho support
Converting an ExtendedString to a normal String
Converting an ExtendedString to a normal String
The extended String that will be converted to a string
an enumeration of the parameter types for a case class parameter
adding new function support to a resource
adding new function support to a resource
the resource that will get extended method support
Getter of an ExtendedString, so the work with the return string if more efficient
Getter of an ExtendedString, so the work with the return string if more efficient
Generates a case class for an EClassifier
creates a new file and sets its context
creates a new file and sets its context
the name of the new file
the content of the new file
This method returns a FileSupport object with which a new file can be created
This method returns a FileSupport object with which a new file can be created
the name of the new file
A FileSupport object that offers methods to work with new to create files
creates two implicits for each classifier.
creates two implicits for each classifier. one to and one from the newly created case class
the classifier the implicits should belong to
start point of the M2T transformation
start point of the M2T transformation
returns the needed import for the factory methods of the model objects
returns the needed import for an attribute
returns the needed imports for classifier objects
returns the needed imports for reference objects
fills the 'existingClassifiers' map with the available data
A model to text generator that generates case classes with implicits (model -> case class and clase class -> model). Input is an ecore resource. The Output will be written to a file with the given destination path


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JURLIQUE
Jurlique is one of our newly engaged clients. I AGE MEDIA is so proud to be the chosen Chinese media partner with Jurlique, one of the most well-known Australian brand in the world.
Through a series of social media campaign, we have archived great results for this campaign and successfully aroused the interest in the Chinese community. In this particular campaign, Jurlique Rundle Mall store have SOLD OUT most of its products on the first day of the promotion.
ANZ BANK
ANZ Bank has been a long-term client and supporter of I AGE MEDIA since 2008. Through all these years, we have helped ANZ Bank and its Gouger St. Branch to reach more customers in Chinese community and maintain a very sustainable relationship with the Chinese community.
TELSTRA
I AGE MEDIA is delighted to be a part of TELSTRA’s ethnic community campaign. During the last couple of years, we have done a number of successful campaigns together with Telstra towards Chinese community.
ADELAIDE BMW
Chinese customer is one of Adelaide BMW’s major customer group, we have managed to deliver Adelaide BMW’s corporate branding and sales promotions successfully to its targeting customer base.
JR DUTY FREE
Our relationship with JR Duty Free started in 2009. In the last 5 years, we have worked closely with JR Duty Free and delivered many successful sales campaign and VIP events.
UNIQUE OPAL & DIAMOND
Unique Opal & Diamond is another local client of ours. We are pleased to work together to promote its amazing Australian Opal products to the Chinese community.
SA STATE GOVERNMENT
I AGE MEDIA is one of the major media Channel for South Australia State Government to connect with Chinese community. In the past years, we have done may media campaigns for State Government.
SA STATE GOVERNMENT
I AGE MEDIA is one of the major media channel for South Australia State Government to connect with the Chinese community. In the past years, we have done many media campaigns for State Government.
FOREX CT
Forex CT is one of our interstate clients. We assisted this client to promote its campaign and develop Adelaide customers by using our print and online media channels.


Judicial Service staff bounce back in court
by Gifty Amofa, GNAAccra, Nov. 16, GNA - The Judicial Service staff on Friday, November 15, returned to work after a two-day strike.
This was after Mr Alex Nartey, the President of the Judicial Service Association of Ghana (JUSAG) issued a press statement the previous day to suspend the strike action.
He said the abrogation of the action was on the premise that the leadership of both JUSAG and Management of the Judicial Service met on the issues that necessitated the action.
The President said Justice Jones Victor Dotse, the Acting Chief Justice had approved that matters raised at the meeting would be forwarded to the Judicial Council.
Visits at the various courts in Accra by the Ghana News Agency (GNA) saw most staff at post and the courts back to normal.
Lawyers, litigants and other court users were there to patronise the court’s services.
As usual, cases were called with a number of them adjourned, but the place looked a little bit deserted because some people did not know service had resumed.
Mr Nartey in an interview with GNA denied that the court gave an order to the members to go back to work.
JUSAG last week threatened an indefinite strike over the need to review their salaries and allowances.
The members on Wednesday and Thursday, November 13 and 14, respectively started the action until the release on Thursday evening.
Meanwhile, the National Labour Commission deemed their action illegal.
GNA


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Coding Quiz Challenge
Rules of the quiz, Code Quiz Try to answer the following questions within the time limit. Keep in mind that an incorrect answers will penalize your score/time by ten seconds!
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CosEventDomainAdmin_EventDomain
Module
Module Summary
Description
To get access to all definitions include necessary hrl files by using:
-include_lib("cosEventDomain/include/*.hrl").
This module also exports the functions described in:
- CosNotification_QoSAdmin
- CosNotification_AdminPropertiesAdmin
Exports
add_channel(EventDomain, Channel) -> MemberID
Types
Adds the given channel to the target domain. The channel must be a CosNotifyChannelAdmin::EventChannel.
get_all_channels(EventDomain) -> MemberIDSeq
Types
Returns a a sequence of all channels associated with the target object.
get_channel(EventDomain, MemberID) -> Reply
Types
If the target domain have a CosNotifyChannelAdmin::EventChannel represented by the given id this channel is returned. Otherwise, an exception is raised.
remove_channel(EventDomain, MemberID) -> Reply
Types
If a CosNotifyChannelAdmin::EventChannel with the MemberID exists it will removed and all its Connections terminated. Otherwise an exception is raised.
add_connection(EventDomain, Connection) -> Reply
Types
The Connection parameter must contain valid data to enable the target domain to setup a connection between two channels. The struct members supplier_id and consumer_id determines which channel should produce and consume events. which type of events and if the supplier should push or the consumer pull events is determined by ctype and notification_style respectively.
If the target domain is not able to setup the connection the appropriate exception is raised.
get_all_connections(EventDomain) -> ConnectionIDSeq
Types
This operation returns a sequence of all connections within the target domain.
get_connection(EventDomain, ConnectionID) -> Reply
Types
If a connection identified by the given id exists within the target domain, a #'CosEventDomainAdmin_Connection'{} which describe the connection is returned. Otherwise, an exception is raised.
remove_connection(EventDomain, ConnectionID) -> Reply
Types
If the supplied connection id exists, the connection the id represents is terminated. Otherwise, an exception is raised.
get_offer_channels(EventDomain, MemberID) -> Reply
Types
This operation returns a sequence, containing the member id's of all channels within the target domain which will supply events to the channel identified by the given id. But, if no such id exists in this domain, an exception is raised.
get_subscription_channels(EventDomain, MemberID) -> Reply
Types
This operations behaves like get_subscription_channels; the difference is that the id's returned identifies channels which will consume events supplied by the channel associated with the given id.
Types
Calling this operation will terminate all connections within the target domain. The domain will terminate but all channels will not be affected.
get_cycles(EventDomain) -> RouteSeq
Types
Returns a list of all cycles within the target domain.
get_diamonds(EventDomain) -> DiamondSeq
Types
Returns a list of all diamonds within the target domain
set_default_consumer_channel(EventDomain, MemberID) -> Reply
Types
If the given id represents a channel within the target domain, this channel will be used when connection a supplier client without specifying a certain channel. If no such channel exists an exceptions is raised.
set_default_supplier_channel(EventDomain, MemberID) -> Reply
Types
If the given id represents a channel within the target domain, this channel will be used when connection a consumer client without specifying a certain channel. If no such channel exists an exceptions is raised.
connect_push_consumer(EventDomain, Consumer) -> Reply
Types
If a default Channel have been set, this operation connects the given PushConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_pull_consumer(EventDomain, Consumer) -> Reply
Types
If a default Channel have been set, this operation connects the given PullConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_push_supplier(EventDomain, Supplier) -> Reply
Types
If a default Channel have been set, this operation connects the given PushSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_pull_supplier(EventDomain, Supplier) -> Reply
Types
If a default Channel have been set, this operation connects the given PullSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_structured_push_consumer(EventDomain, Consumer) -> Reply
Types
If a default Channel have been set, this operation connects the given StructuredPushConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_structured_pull_consumer(EventDomain, Consumer) -> Reply
Types
If a default Channel have been set, this operation connects the given StructuredPullConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_structured_push_supplier(EventDomain, Supplier) -> Reply
Types
If a default Channel have been set, this operation connects the given StructuredPushSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_structured_pull_supplier(EventDomain, Supplier) -> Reply
Types
If a default Channel have been set, this operation connects the given StructuredPullSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_sequence_push_consumer(EventDomain, Consumer) -> Reply
Types
If a default Channel have been set, this operation connects the given SequencePushConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_sequence_pull_consumer(EventDomain, Consumer) -> Reply
Types
If a default Channel have been set, this operation connects the given SequencePullConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_sequence_push_supplier(EventDomain, Supplier) -> Reply
Types
If a default Channel have been set, this operation connects the given SequencePushSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_sequence_pull_supplier(EventDomain, Supplier) -> Reply
Types
If a default Channel have been set, this operation connects the given SequencePullSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_push_consumer_with_id(EventDomain, Consumer, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given PushConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_pull_consumer_with_id(EventDomain, Consumer, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given PullConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_push_supplier_with_id(EventDomain, Supplier, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given PushSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_pull_supplier_with_id(EventDomain, Supplier, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given PullSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_structured_push_consumer_with_id(EventDomain, Consumer, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given StructuredPushConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_structured_pull_consumer_with_id(EventDomain, Consumer, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given StructuredPullConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_structured_push_supplier_with_id(EventDomain, Supplier, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given StructuredPushSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_structured_pull_supplier_with_id(EventDomain, Supplier, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given StructuredPullSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_sequence_push_consumer_with_id(EventDomain, Consumer, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given SequencePushConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_sequence_pull_consumer_with_id(EventDomain, Consumer, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given SequencePullConsumer to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_sequence_push_supplier_with_id(EventDomain, Supplier, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given SequencePushSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.
connect_sequence_pull_supplier_with_id(EventDomain, Supplier, MemberID) -> Reply
Types
If a Channel associated with the given MemberID exists within the target Domain, this operation connects the given SequencePullSupplier to it. Otherwise, the #'CosNotifyChannelAdmin_ChannelNotFound'{} exception is raised.


AD320 WEEK 1 Assignment
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I'm a software engineer passionate about solving problems and creating innovative solutions. Based in Gdańsk, Poland, I bring a combination of technical skills and creative thinking to every project. When I'm not coding, I love to hit the open road on my bike.
I specialize in coding with Elixir, Phoenix LiveView, Ruby, React, Typescript, PostgreSQL and PostGIS, and I love using these technologies to build scalable and reliable applications. I also have a passion for creating maps using MapLibre and Leaflet, which allows me to bring my love for technology and my love for outdoors together.
I believe in using technology to make a positive impact, and I take pride in everything I do. If you have a problem to solve or an idea to bring to life, I'd love to work with you. Let's make something amazing together.
Projects
TravelerMap
TravelerMap is a website that showcases the beauty of National Parks and other outdoor destinations. Built using the powerful combination of Elixir and Phoenix LiveView, this site offers a dynamic and interactive user experience, with real-time features such as live search. To make sure photos are looking their best, I also created a custom AWS S3 + Lambda solution that automatically processes and optimizes images for display.
Tech: Elixir/Phoenix/LiveView, TypeScript, Maplibre, Postgres/Postgis.
Velomapa
It is one of the most popular websites for cyclists in Poland. With a user base of ~80 000 unique visitors and almost 300 000 sessions per month during peak The site features a wealth of helpful information, including GPX track downloads. It has social features which allows users to upload photos, add reviews, and like routes.Tech: Elixir/Phoenix, TypeScript, Leaflet, Postgres/Postgis, AWS S3 and Lambda for photo processing.
Map of bicycle infrastructure
The Bicycle Infrastructure Map project is a prime example of using technology to improve the cycling experience in Poland. The objective of the project was to provide cyclists with a tool for planning safe and efficient routes using designated bicycle infrastructure. To achieve this goal, the project leveraged data from OpenStreetMap which are updated daily. The solution also aimed to help OpenStreetMap editors identify missing or incorrect data.
To deliver this project, I developed a lightweight tile server using Go lang, resulting in fast and efficient map rendering.
Tech: TypeScript, Maplibre, custom Go mbtiles server, OSM tools.


Ticoult1282.github.io
See something you like? We get new stock in on a regular basis so even if there is something you cant find just give us a call and we will be happy View and Download Plantronics K100 get started online. Bluetooth, In-car. K100 Telephone pdf manual download.
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 on 'Jimmy Kimmel Live' and the boys made a surprise appearance on the November 13 broadcast of the show.
The boys appeared in the "Mean Tweets" segment. The "Mean Tweets" segment features the Jimmy Kimmel team searching through a lot of tweets to find some of the not-so-nice things people write and then turn them over to the people they were written about. As you can see in the segment, this was filmed with all 7 members including the recently departed Wonho.
You can check out MONSTA X at 1:29 in the video above.


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This Data represents the call history from San Francisco's 311 Customer Service Center. These calls were taken and recorded on March 13th, 2016 between 12:00am and 12:00pm.
The category of service calls over time.


What are you minimizing = Cross-Entropy – distance between the two vectors – the softmax from the output layer and the expected one-hot encoding
Big picture (note no deep learning, just a simple NN). Take in the inputs X (matrix = size of samples * dimension or representation of each sample). Multiply by some linear model Weights * X + biases
Now take these scores (LOGITS) and covert them to probabilities using a softmax function
Bigger picture
Where again cross entropy is the difference between the actual (one-hot encoding) w.r.t. what the model is telling you. You want to essentially minimize this loss throughout your sample size.
How do you do it – SCD – Stochastic Gradient descent
1. Inputs => Mean = 0 & Equal Small variance
2. Initial weights => Random, mean = 0, Equal Small variance
3. Momentum i.e. keep history of the past and then move accordingly.
4. Learning rate – slowly reduce it.
5.
Final few pointers. “Adagrad” takes care of the first 3 but nothing beats hand tuned optimization.
Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units (nn.relu()) and 1024 hidden nodes. This model should improve your validation / test accuracy.
Note: Depicted above is
a "2-layer" neural network:
1.
The first layer effectively consists of the set of weights and
biases applied to X and passed through ReLUs. The
output of this layer is fed to the next one, but is not observable outside the
network, hence it is known as a hidden layer.
2. The second layer
consists of the weights and biases applied to these intermediate outputs,
followed by the softmax function to generate
probabilities.
Now, let’s look at the actual code + explanation for tensorflow.
//Turn the logistic
regression example with SGD into a 1-hidden layer neural network with rectified
linear units (nn.relu()) and
1024 hidden nodes. This model should improve your validation / test accuracy.
##START same as before
batch_size = 128
graph = tf.Graph()
##Now you create the structure in tensorflow, it’s a graph (generic structure)
with graph.as_default(): ##similar to using in c#,
get the default graph. SO there can be more than one graph
#
Input data. For the training data, we use a placeholder that will be fed
# at run
time with a training minibatch.
tf_train_dataset
= tf.placeholder(tf.float32, shape=(batch_size, image_size * image_size))
tf_train_labels
= tf.placeholder(tf.float32,
shape=(batch_size, num_labels))
#notice
two kind of variables, constant that’s already bound and “placeholder” that
will be bound
tf_valid_dataset
= tf.constant(valid_dataset)
tf_test_dataset
= tf.constant(test_dataset)
##END same
as before
## START creation
of new hidden layer
#
new hidden layer, this is another HYPERPARAMETER
that can be tuned.
hidden_nodes
= 1024
##
NOTE:: instead of num_lables
you have this as 784*1024. And then later the weigths
is 1024 * 10. Earlier, the weigths we 784 * 10
## When you train a model, you use variables
to hold and update parameters. Variables are in-memory buffers containing tensors.
They must be explicitly
initialized and can be saved to disk during and after training. You can
later restore saved values to exercise or analyse the
model.
The
difference is that with tf.Variable
you have
to provide an initial value when you declare it. With tf.placeholder
you don't
have to provide an initial value and you can specify it at run time with the feed_dict
argument
inside Session.run
hidden_weights
= tf.Variable(tf.truncated_normal([image_size *
image_size, hidden_nodes])
)
hidden_biases
= tf.Variable( tf.zeros([hidden_nodes]))
hidden_layer
= tf.nn.relu( tf.matmul( tf_train_dataset, hidden_weights)
+ hidden_biases)
#
Variables.
weights = tf.Variable( tf.truncated_normal([hidden_nodes, num_labels]))
biases = tf.Variable(tf.zeros([num_labels]))
## END creation
of new hidden layer
# Training
computation.
logits = tf.matmul(hidden_layer,
weights) + biases
## what is
numpy axis --
http://www.sam.math.ethz.ch/~raoulb/teaching/PythonTutorial/intro_numpy.html
##
example: 0 = rows, 1 = columns for 2 dimensional matrix.
## what
does reduce mean do: http://stackoverflow.com/questions/34236252/difference-between-np-mean-and-tf-reduce-mean-numpy-tensorflow
## reduce
mean = average mean across the axis or dimensions of the entire batch size.
loss = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits,
tf_train_labels) )
##maybe a
better name for loss would be average_loss???
#
Optimizer.
# 0.5 =
learning rate
optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
#
Predictions for the training, validation, and test data.
train_prediction
= tf.nn.softmax(logits)
valid_relu
= tf.nn.relu( tf.matmul(tf_valid_dataset, hidden_weights)
+ hidden_biases)
valid_prediction
= tf.nn.softmax( tf.matmul(valid_relu, weights) +
biases)
test_relu
= tf.nn.relu( tf.matmul( tf_test_dataset, hidden_weights) + hidden_biases)
test_prediction
= tf.nn.softmax(tf.matmul(test_relu, weights) +
biases)
### same as before....
num_steps = 3001
with tf.Session(graph=graph)
as session:
tf.initialize_all_variables().run()
print("Initialized
all variables. ONLY do it ONCE for the session.")
for step in range(num_steps):
# Pick
an offset within the training data, which has been randomized.
# Note:
we could use better randomization across epochs.
offset = (step * batch_size) % (train_labels.shape[0]
- batch_size)
#
Generate a minibatch.
batch_data
= train_dataset[offset:(offset + batch_size), :]
batch_labels
= train_labels[offset:(offset + batch_size), :]
#
Prepare a dictionary telling the session where to feed the minibatch.
# The
key of the dictionary is the placeholder node of the graph to be fed,
# and
the value is the numpy array to feed to it.
#
PLACEHOLDER – bound to actual values in RUNTIME
feed_dict
= {tf_train_dataset : batch_data, tf_train_labels
: batch_labels}
_, l, predictions = session.run(
[optimizer, loss, train_prediction], feed_dict=feed_dict)
if (step % 500 == 0):
print("Minibatch loss at step %d: %f" % (step, l))
print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
print("Validation
accuracy: %.1f%%" % accuracy(
valid_prediction.eval(), valid_labels))
print("Test accuracy:
%.1f%%" % accuracy(test_prediction.eval(), test_labels))


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Welcome to the documentation of fault-diagnosis¶
MATLAB libraries for fault-diangosis in dynamic, large-scale systems¶
fault-diagnosis is research software in MATLAB, targeted for fault diagnosis in large-scale systems. It employs methodologies of Structural Analysis for qualitative abstraction and efficient extraction of residual-generators.
This software is open-source and its source code can be found in this github repository: https://github.com/Georacer/fault-diagnosis
Overview¶
fault-diangosis uses the methodology of Structural Analysis to perform Fault Diagnosis, which performs a qualitative abstraction of the mathematical model of a system into a bipartite Structural Graph. Graph methodologies are then applied onto that graph for the extraction of residual generators.
Graph representation of the bipartite graph is favoured, against the biadjacency matrix one, to allow more natural handling and processing of graph meta-information.
The codebase is split into several logical parts. There is dedicated documentation for each one:
- Structural Graph
- Graph Interface
- Display engine
- Subgraph Generator
- Matcher
Demos are available in the dedicated section.
System Compatibility¶
This software is developed and tested under Ubuntu Linux
External Software Requirements¶
fault-diagnosis requires the following software to operate:
- Fault Diagnosis Toolbox - Research software on fault diagnosis using structural graphs. Used for the generation of PSO sets and its interface of the Dulmage-Mendelsohn decomposition.
- matlab_networks_routines - Library for graph operations.
- munkres - Fast implementaiton of the Hungarian weighted assignment algorithm.
The above software is distributed alognside fault-diagnosis, according to the copyright set by their respective authors/owners. Licence files for each software are included in the corresponding directories.
Also the following software is required to be installed on the system:
- The dot language compiler, part of the GraphViz package - Used for graph visualization.


Christine Camou
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Introduction
I am currently planning a replication and extension of an experimental study that had a count-based dependent variable. The aim is to submit it as a registered report before collecting the data and so I need to have an analysis plan and estimated sample size/stopping rule.
One of the ways that this can be achieved is by simulating the data that you expect to see if your hypothesis is correct. This was suggested by Stan Lazic at the Cumberland Lodge Reproducibility Retreat this year and simulation was covered also by Dorothy Bishop and Daniel Lakens.
In essence, the point of simulation-based power analysis is to bash out a huge number of ‘replications’ by drawing random numbers that represent the participants in your study. You then count the number of instances where p < 0.051.
There are a number of ways in which this can be achieved, but the one I found most intuitive was reverse engineering the general linear model equation.
The following text and code will cover the following:
- Deciding on baseline values.
- Thinking about how manipulations will affect these values.
- Using these two ideals and the equation for a straight line to represent sets of idealised participants.
- Run analyses on these imaginary participants.
- Repeat the above thousands of times and calculate the power of your study.
- Show how sample size affects power.
Chosing parameters
Think of task. It can be anything. If you have choice paralysis let’s say reaction time on an attentional task. If you were to take 1000 participants and test them on this task without changing anything, what would you expect the mean reaction time to be? Let us for now say 900ms \(\pm\) 100ms.
How imagine that we take 500 of these participants and load them with some sort of depressant. Let’s say 1000ml of finest Diamond White Cider2.
How do you think these participants will respond? Do you think they’ll get slower? Perhaps their reaction time will increase by 200ms, so the mean in this group is 900ms 100ms.
You have just made assumptions that can be represented by the general linear model.
General Linear Model
The form of a general linear model is \(y = \alpha + \beta_{1}x_{1} + \beta_{2}x_{2}...+\beta_{n}x_{n} + \epsilon\)3
Beta is the amount of change in y for any value of x. In our above example that is the difference between the two conditions - in this case 200ms. Alpha is the value of y when all beta’s are 0. So 900ms in the group who are not drinking horrible cider. Epsilon accounts for the fact that we are not dealing with machines but random data and is just noise added to the model representing unexplained variation.
You can have as many betas as you like in your equation (or as many as is meaningful) but they are meaningless without something (x) to multiply them by.
X can refer to conditions in an experiment (0 = control (no nasty booze), and 1 = experimental (too much nasty booze)) or some scale that you predict will alter y by a certain amount (average alcohol consumption?).
So applying this to our chosen values above, this can be nicely represented by the model above such that \(y_{nothing} = 900 + 200*0 + \epsilon\) or just the base score with error. Making x = 1 means that 200 is added to y.
Thom Bagueley4 describes the lefthand side of the equation as the random element and the righthand side the systematic element. That is, y depends on both the random attributes of the participant and the random assignment within in the study whereas the righthand side of the equation entirely describes y.
One of the general goals of regression analysis is to estimate each \(\beta_i\) given \(X_i\) and \(y_i\)5.
Reverse Engineering
When reading around how to simulate data I stumbled across a StackOverflow post that suggests reverse engineering this equation to simulate the data. That is, rather than estimating \(\beta_{i}\) given y and x, you calculate y given a set of \(\beta\)’s and x and adding some random error to it.
This approach helped a lot, since before that I was relying on example of full factorial designs using matrix multiplication^[I had no idea what this was or how to do it - this link was really valuable if you are interested.
Lets do an example in R:
#set parameters
a = 900 # Value at intercept when X = 0
b1 = 200 # The about that X is multiplied by
#create a vector of conditions
x <- c(1) #X is one here - if it was 0 then it would be an intercept only model/control condition
#calculate a single observation of y
y <- a + b1*x[1]
This creates a value of y that is 900 + 200 = 1100ms. Of course, because we’re dealing with randomness, the parameters will not be perfectly reflected when collecting data from participants, so we add some random noise to y. This is taken from a normal distribution with a mean of 0 and a standard deviations of 100ms:
y <- a + b1*x[1] + rnorm(1,0,100)
y
## [1] 1134.854
In this observation our simulated participant was only exposed to one condition.
We can expand this to an entire set of participants easily by just changing a few of the lines that we already have:
a = 900
b1 = 200
# create a vector of conditions for 100 participants
x <- rep(c(1,0),50)
y <- a + b1 * x + rnorm (100,0,100) #compute y for 100 people with normal error
paste("Mean of Control Group =", round(mean(y[x==0]), digits = 2))
## [1] "Mean of Control Group = 895.96"
paste("Mean of Cider Group =", round(mean(y[x==1]), digits = 2))
## [1] "Mean of Cider Group = 1081.65"
You can explore what is going on here by typing the individual variable names into the R console.
Running Analyses (Welch’s t Test)
We now have 100 participants drawn from the control condition (intercept only) and an experimental condition (mean difference = 2).
To keep things neat, we can create a dataframe to store all of the variables in one place:
dat <- as.data.frame(cbind(y,x,pp = 1:100))
We can then run a t-test on this equation to see if there is a significant difference:
t.test(y ~ x, data = dat)
##
## Welch Two Sample t-test
##
## data: y by x
## t = -9.5625, df = 97.38, p-value = 1.141e-15
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -224.2272 -147.1502
## sample estimates:
## mean in group 0 mean in group 1
## 895.9576 1081.6463
As you can see the mean in group 0 is close to 900 and the mean in group1 is close to 200 which is what we coded our betas as.
Analysis (ANOVA)
This can easily be extended to multiple variables and then analysed using Analysis of Variance. Perhaps we give a second group a similar dose of Buckfast6:
a = 900
b1 = 200
b2 = 500
#three groups, 33 a piece
grp1 <- c(rep(0,33),rep(1,33),rep(0,33))
grp2 <- c(rep(0,66),rep(1,33))
y <- a + b1*grp1 + b2 * grp2 + rnorm(99,0,100)
dat <- as.data.frame(cbind(y,grp1,grp2,pp = 1:99))
summary(aov(y ~ grp1 * grp2, data = dat))
## Df Sum Sq Mean Sq F value Pr(>F)
## grp1 1<PHONE_NUMBER>34 17.62 6.02e-05 ***
## grp2 1 4199935 4199935 476.44 < 2e-16 ***
## Residuals 96<PHONE_NUMBER>
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
If you run a regression analysis on the same data you will find the values that we set are retrieved:
summary(lm(y~grp1 * grp2, data = dat))
##
## Call:
## lm(formula = y ~ grp1 * grp2, data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -219.231 -55.990 3.896 59.342 225.858
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 895.95 16.34 54.818 < 2e-16 ***
## grp1 168.23 23.11 7.278 9.25e-11 ***
## grp2 504.52 23.11 21.827 < 2e-16 ***
## grp1:grp2 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 93.89 on 96 degrees of freedom
## Multiple R-squared: 0.8373, Adjusted R-squared: 0.8339
## F-statistic: 247 on 2 and 96 DF, p-value: < 2.2e-16
Power Analysis
I wont go too far into what Power Analysis is, other than that it tells you the probability of you finding an effect, if there is one, given three parameters:
- Sample size.
- Effect Size.
- Type 1 error rate (how willing you are to find an effect when there isn’t one).
Typically it is used for sample size estimation using software like G*Power and you would input the smallest effect size that you would deem to be meaningful, your type one error rate (usually 0.05, but see the link above to find out how to justify your this value) and how willing you are to miss an effect if there is one (type two error rate).
Another way to run a power analysis that doesn’t necessarily require you to know how to compute Cohen’s \(F^2\) is to run simulations as we have above. This way we have to think about what change from the intercept value we think is meaningful. It also means that we have to know how to analyse our data before we collect it because as we have seen, the same model (GLM) is used to both generate fake samples and analyse the real data.
Brute Force Power Analysis
Using the methods above, you can run the simulation thousands of times and find out what power your model has:
#Create an empty dataframe
P <- data.frame()
#Set a loop to repeat the code in Curley brackets ten thousand times
for(i in 1:1e4){
a = 900
b1 = 200
b2 = 500
#three groups, 33 a piece
grp1 <- c(rep(0,33),rep(1,33),rep(0,33))
grp2 <- c(rep(0,66),rep(1,33))
y <- a + b1*grp1 + b2 * grp2 + rnorm(99,0,100)
dat <- as.data.frame(cbind(y,grp1,grp2,pp = 1:99))
#The right side of this looks horrible but it is just an index to the p values in the summary object
P <- rbind(P,P<-summary(aov(y ~ grp1 * grp2, data = dat))[[1]][["Pr(>F)"]][[1]][[1]])
}
#Calculate the power
Power <- length(P[P<0.05])/1e4*100
So given the provided mean differences, sample size and number of groups, this simulated study had a power of 64.27.
To explore this further you can create a function and pass different sample sizes into the model. So, below we start with 9 participants and then gradually work up to over 300:
Power_Calc <- function(n){
a = 900
b1 = 200
b2 = 500
#three groups, n/3 a piece
grp1 <- c(rep(0,n/3),rep(1,n/3),rep(0,n/3))
grp2 <- c(rep(0,(n/3*2)),rep(1,n/3))
y <- a + b1*grp1 + b2 * grp2 + rnorm(n,0,100)
dat <- as.data.frame(cbind(y,grp1,grp2,pp = 1:n))
P <- summary(aov(y ~ grp1 * grp2, data = dat))[[1]][["Pr(>F)"]][[1]][[1]]
return(P)
}
Power <- data.frame() # Empty dataframe
n = 9 # starting value of 9 participants)
for(i in 1:100){
Power <- rbind(Power,cbind(Power_Calc(n),n))
n = n+3 #Keeping equal groups
}
colnames(Power) <- c("P_Value", "Participants")
plot(P_Value~Participants, data = Power)
We can then plot the data, as above, to show what p-values you can expect for different sample sizes. It is also a good demonstration of how even though we know there is a mean difference of between 200-500, you can still get values way above 0.05 even with a lot of participants. Try running the code a few times and playing with the different beta values.
This is by no means the best or most efficient method for running power analysis using simulation (I would suggest working through the documents on the OSF page referenced above to see some more elegant methods), but hopefully it seems intuitive enough to get started.
In the following weeks I intend to expand on this method, demonstrating how you can use it to run generalised linear models such as count and logistic regression studies. Since I am quite in to Bayesian statistics and the purpose of me doing this analysis was to plan a Bayesian study, I may throw in some of that too.
Thanks for reading!
This is what many UK teenagers first discover hangovers with^ [My first draft of this mentioned White Lightening which was notorious when I was at school - but I have since found that it was discontinued in 2009]↩
I am using Greek letters below because this is the terminology used in regression equations.↩
I recently bought his book Serious Stats and it is really accessible given the content - definitely one to buy if you’re ready to get serious with your statistics!↩
oughta!↩
this one is more for the students and those north of Hadrian’s wall↩


nips nips2011 nips2011-94 nips2011-94-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Matthew D. Zeiler, Graham W. Taylor, Leonid Sigal, Iain Matthews, Rob Fergus
Abstract: We present a type of Temporal Restricted Boltzmann Machine that deﬁnes a probability distribution over an output sequence conditional on an input sequence. It shares the desirable properties of RBMs: efﬁcient exact inference, an exponentially more expressive latent state than HMMs, and the ability to model nonlinear structure and dynamics. We apply our model to a challenging real-world graphics problem: facial expression transfer. Our results demonstrate improved performance over several baselines modeling high-dimensional 2D and 3D data. 1


Resume
Objective
Internship related to Computer Science with an interest in traveling
Education
Bachelor of Science Computer Science, May 2023
- Minor in Japanese
- Rose-Hulman Institute of Technology, Terre Haute, IN
Related Courses: Introduction to Software, OO Software, Introduction to Computer Systems, Data structures and Algorithms Analysis, Computer Architecture I, Operating Systems, Database Systems, Technical and Professional Communication Skills: Software experience: Python, Java, C, Assembly
Skills
- Software Experience: Python, Java, C, Assembly, Html, CSS, Javascript
- Language:
Work Experience
ID Tech- Instructor, Summer 2020
- Taught the basic fundamentals of Java, Python, and Scratch to various age groups around the U.S.
- Due to the Covid-19 outbreak, I taught remotely through Zoom
Kanazawa Institute of Technology (Study Abroad), Summer 2019
- Studied Japanese language and culture while traveling Japan
- Student Survey
- Conducted research concerning the college student’s study habits
- Presented information in Japanese at the end of the program
Projects
Raspberry Pi, Fall 2019
- Coding with Raspberry Pi
- Working with Git Bash
- Programming Hardware
Bubble Bobble Spring 2019
- Team-lead project
- Implemented arcade game using cohesion and coupling
- Improved communication skills
Activities
- National Society of Black Engineers Membership Chair (2020-present)
- National Society of Black Engineers Senator (2020)
- Cyber-Security Club (2019-present)
- National Society of Black Engineers Member (2019-present)
- Salvation Army Volunteer (2019)
- Rose-Hulman Collegiate Basketball Athlete (2018-2019)
Honors and Certificates
- 4.0 GPA Kanazawa Institute of Technology Program


b e a t r i c e a i s p u r o |
11.24.09 | ||
I am interested in applying for the graphics/web design position with Baron Services. I was told by an employee at your company that a design position is opening up there. I received my Bachelor of Arts degree from California State University, Long Beach in Journalism, Public Relations, and Spanish in 1991 and completed coursework at Art Center College of Design in 1997. Throughout my fifteen-year career, I have been involved in the planning and management of hundreds of outstanding design and outreach projects. I have extensive experience designing print and web collateral and re-branding corporate identities. I have developed successful marketing and outreach campaigns, and produced numerous web sites. In addition to superior research and analytical skills, I am capable of managing multiple projects and delivering quality work, while meeting deadlines successfully. As the first webmaster and designer at Caltech, I collaborated extensively with staff and faculty in creating and developing promotional material, both online and in print. I served as designer and editor of the original Caltech Alumni Newsletter, supervised staff, and taught classes in design. Also, as a native Spanish speaker, I translated documents and facilitated bilingual discussions for geologists conducting research in Mexico. I translated several storefront sites for MLB.com, Copeland's Sports, and Victory Sweepers into Spanish. I am fluent in HTML/CSS, Adobe CS3, and optimizing for mac/pc/unix platforms. I am extremely well-organized, resourceful, proactive, and energetic. I believe my design skills, project management experience, and effective communication skills will be a valuable asset to your group. I look forward to meeting with you soon to discuss my qualifications.Sincerely, |
|||
:: cv |
|||
| :: recent sites | |||


Primary responsibilities include the analysis of business and functional requirements, assisting with the design and architecture of technical solutions, and development of applications and systems. Must help provide technical direction & design as well as independently develop technical solutions using sound development principles. Must communicate technical and non-technical information clearly, answer questions from customers, and be able provide efficient technical solutions to problems.
General: Object-oriented development, web development, systems analysis & design, data modeling, process modeling, requirements analysis, database design & development, JavaScript / jQuery development, Visio, source control management, testing Platform Skills: Team Foundation Server (TFS), ALM, AIM Software Development Skills: OO Development, Unit Testing, Technical Documentation (e.g. Functional Specification, Design Specification, Technical Reference), User Documentation, Data Design, Normalization
Force.com platform, Sales Cloud, Service Cloud, Apex, VisualForce, Salesforce Integration, Angular, jQuery, NodeJS, Sails (support only), object-oriented development, data modeling, process modeling, requirements analysis, database design & development (SQL Server, Postgres, MongoDB).
Departmental & small-business systems ($200K)
Example: Since I took over HRTK as the main developer, we've had more ~$200k of work done on it, in my estimation.
Three
My only assignment to this point has been Eli Lilly. However, I've had the pleasure of working with Protect Lilly (URF), Six Sigma (myLSS), HR (HRTK), Ethics & Compliance (myIntegrity, 7 Elements, IRA 2.0), Corporate Affairs (Lilly.com), and Global Learning Services (SKB) departments.
Two
During my time with Allegient I've only worked in the pharmaceutical industry to this point. However, I've had experiences in travel (Cornerstone Informations Systems) and education industries (Franklin College).
Assists
I've helped Joel Spittal with multiple estimations for HRTK and COI(Conflict of Interest - new project) work orders.
Also, I've estimated 25+ enhancements for HRTK, myLSS, 7 Elements, and myIntegrity and suggested potential enhancements to HRTK, myLSS, and myIntegrity clients that turned into work for Allegient.
None
None
Bachelors, 1 of Certifications, publications, or speaking
Bachelors (Computer Science, Software Engineering, Economics from Franklin College). Salesforce Certified Administrator, Salesforce Certified Platform Developer, and Salesforce Certified App Builder.
Advanced knowledge, successful project implementations as a key contributor
I have been a key contributor for one or more releases on each of these project - HRTK, myLSS, URF, IRA 2.0, myIntegrity, 7 Elements.
Component lead
Multiple HRTK, myLSS, myIntegrity, 7 Elements releases being the sole developer. Also, I've helped getting a senior develop up to speed on one of the HRTK release. Took the inititative of putting together an onboarding document for new Salesforce resources at Lilly to alleviate the onboarding pain.
Client advisor
I have advised HRTK and myIntegrity teams on how to leverage the existing fuctionality of Salesforce - i.e. reports, dashboards and Chatter.
Fulfill multiple career track roles effectively
I have stepped in IRA 2.0 and URF as a UI/UX developer. I am present and engaged at every business meeting with the client to get a better understanding of requirements and to help the client understand what they want.
2 years positive feedback
Kudos from Allegient current and former team members (Andy Mathews, Eli Collis, Cameron Ames, Derek Schutz). Positive performance feedback from BAs - Monty Birge and Zach Miklosy. Positive personal performance feedback from Gina Cromwell - the main business contact for HRTK. Pending personal performance feedback from Emily Sexson and Hyeyeon Lee - main business client on myIntegrity and 7 Elements - by the end of this week.
2+ projects overall positive team feedback
I have recieved personal kudos on my work on HRTK, myLSS, URF, and 7 Elements from senior developers and project managers on the team (Eli Collis, Andy Mathews, Derek Schultz).
Received 2+ instances of client praise, praise received from client's director level management
I have received personal positive performance reviews from client on HRTK, myIntegrity and 7 Elements (Emily Sexson and Hyeyeon Lee).


The cabin on Fog Hill was full of junk and rusted metal, the kids on the bus said to Flip. They said a mad man was living in it, and that he sang songs with the crickets. Flip's mom said the kids were messing with him, but that he still must not go up to the cabin. "It's just an empty cabin," his mom said, "But I went to it as a kid, and got bit by a bug."
in-context


A1: GTEx v8 MASHR-based models are parsimonious and exhibit the greatest power. These are the best option. However, they require GWAS preprocessing on older GWAS as detailed here.
A2: GTEx v8 UTMOST models are a robust but less effective option. They are based on HapMap snps and as such have a good overlap with most GWAS' snp sets.
A3: If your GWAS population is non-European, you might be interested in population-specific models like the MESA models.
A: 1KG and HapMap are the labels for the set of SNPs which were used to train the prediction models. As you probably have guessed, 1KG refers to the 1000 Genomes SNP set and the HapMap refers to the HapMap SNP set. Careful consideration of the SNPs in your genotype data should help you decide which of the models to use. The 1000 Genomes models make use of many more SNPs, so we believe these models to have greater prediction accuracy and some preliminary investigation into out-of-sample data suggests this to be the case. If you are working with older data though, or you would have to impute a significant amount of your genotype data to achieve more complete coverage of the models, we recommend using the HapMap models. For most use cases, the HapMap models are a good starting point. Bear in mind that v6 models are deprecated
Click on the following links to download the complete SNP annotation files we used to train the different models. These are tab-delimited text files with info on chromosome, position and allele info in addition to the rsid numbers.
HapMap (36MB)
1000 Genomes (150MB)
Alternatively, you can query the sqlite database to get a list of all the SNPs it uses for prediction. See here for a python function to create a file containing all SNPs in a database.
A: To build Elastic Net models, the code is available here: PredictDB_Pipeline_GTEx_v7. An introductory documentation is available here Google Group.
The newer GTEx v8 models were generaed with code available here. The documentation is still a work in progress, don't hesitate to use the PrediXcan/MetaXcan Google Group to ask for support.
The older Elastic Net pipeline for v6 models is available here. It is deprecated and exists solely for reference purposes.


The method of calculating synteny in Ensembl Genomes depends on the division, to account for differences in the underlying data such as genome size and evolutionary distance.
Synteny is calculated using a modified form of the algorithm as specified in Ensembl, based on pairwise whole genome alignments. The synteny algorithm has two phases. First, orthologous regions that are closer than 1.5kbp are grouped. In the second phase, the regions that are in synteny are linked provided that no more than 2 non-syntenic groups are found between them, and they are less than 3kbp apart.
Current synteny data is available from:
Nematode synteny is based on pairwise whole genome alignments; Dipteran synteny is based on orthology. In the latter case, all orthologues between the species were used to generate syntenic blocks, with the restriction that at least two orthologs were present in each block. Both sets of synteny use the standard Ensembl pipeline parameters and a two-stage synteny algorithm. First, orthologous regions that are closer than 200kbp are grouped. In the second stage, the regions that are in synteny are linked provided that no more than 2 non-syntenic groups are found between them, and they are less than 3Mbp apart.
Current synteny data is available from Ensembl Metazoa.
Ensembl Plants includes synteny analyses generated in house, based on pairwise genome alignments, and some generated by our collaborators Gramene. The Gramene method is based on orthology. For each species combination, the orthologues are assigned a gene index (1..last) depending on order along the chromosome (non-orthologous genes are skipped). DAGchainer software is used to detect collinear genes contained in syntenic blocks, and the coordinates of the syntenic block are derived from the outermost genes within each block. There is no post-processing, so syntenic blocks can sometimes overlap or be nested. The method was used in The B73 Maize Genome: Complexity, Diversity, and Dynamics. Schnable PS et al. 2009. Science 326(5956):1112-1115.
Current synteny data is available from Ensembl Plants.


Quaternion
Interpolation (Slerp)
slerp: Spherical Linear intERPolation
Smoothly interpolate between two quaternions.
Slerp takes 3 parameters: $q_0$ and $q_1$ which are the starting and
ending quaternions and $t$, which is the interpolation parameter ($0$ to $1$).
Standard linear interpolation:
$\Delta a = a_1 - a_0$
$lerp(a_0, a_1, t) = a_0 + t\Delta a$
Starts at $a_0$ and add $t^{th}$ differ between $a_1$ and $a_0$
- Compute difference
- Take a fraction of the difference
- Take original value and adjust by fraction of the diffence
Compute the difference
Angular displacement:
$\Delta q = q_0^{-1}q_1$
Take a fraction of the difference
Quaternion exponentiation:
$\Delta q^t$
Take original value and adjust by fraction of the diffence
Use quaternion multiplication to adjust the initial value:
$q_0 \Delta q^t$
Slerp (theoretical formula):
$slerp(q_0,q_1,t) = q_0(q_0^{-1}q_1)^t$
In practice, its much simpler.
All quaternions exist on the same 4D "sphere" (because they're all unit
quaternions). We can connect two quats by connecting the arc of the sphere.
Plane example with rotation
Two 2D Vectors: $v_0$ and $v_1$ (unit length). $t$ fraction, with
$\omega$ angle between $v_0$ and $v_1$
Plane example with rotation
$v_t = k_0v_0 + k_1v_1$
Plane example with rotation
Solving for $k_1$ and $k_0$ with trig:
$sin\ \omega = \frac{sin\ t\omega}{k_1}$
$k_1 = \frac{sin\ t\omega}{sin\ \omega}$
$k_0 = \frac{sin(1 - t)\omega}{sin\ \omega}$
$v_t = k_0v_0 + k_1v_1$
$v_t = \frac{sin(1 - t)\omega}{sin\ \omega}v_0 + \frac{sin\ t\omega}{sin\ \omega}v_1$
With slerp:
$slerp(q_0,q_1,t) =
\frac{sin(1 - t)\omega}{sin\ \omega}q_0 +
\frac{sin\ t\omega}{sin\ \omega}q_1$
Use dot product to compute the 'angle' between the quats.
Because $q$ and $-q$ aren't the same orientation, we need to choose $q_0$ and
$q_1$ so the dot product between them is nonnegative.
Also, if $q_0$ and $q_1$ are too close, $sin\ \theta$ will be very small which
will be an issue in division. In this case, we'll use linear interpolation.


{% load static %}
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There are many browsers but there is only a handset of rendering engines and these are the base for all browsers. This is of course good to keep in mind when testing for browser compability.
These are the main rendering engines used today:
And these are the JavaScript engines:
JavaScript is a modern programming language that was misunderstood for many years. Douglas Crockford wrote the article JavaScript: The World's Most Misunderstood Programming Language in 2011 reasoning around why he thinks this has been the case. The official name for JavaScript is actually EcmaScript, often abbreviated ES.
EcmaScript 5.1 (ES5) was a main update that was released in 2011 that enhanced
JavsScript in many ways. I'm using the added Array
functions all
the time and getters/setters are used in the data binding implementation.
Object.create
is used easily implement proper inheritance and
Object.keys
makes it possible to list the properties of an object.
It is safe to use most ES5 features if you're not concerned with old browers like IE6/7/8. The combined market share for IE6/7/8 varies from time to time but is currently (July 2015) around 5%. The cost for supporting this 5% of users is dramatic. You should probably assume that JavaScript 5.1 is supported by your users browser unless you have an un-proportional large amount of users sitting with old browsers.
EcmaScript Edition 6 (ES6), also called Harmony was released in June 2015. es6-features.org provides a good summary of the new features. Implementation of Harmony is far from complete in all browsers. These features must be used with care for some time to come. You can use Traceur iframe you want to start using ES6 already now.
Features for EcmaScript 7 were
listed already before EcmaScript 6 was apporved. One feature I'm looking forward
to is Object.observe
. It will make data-binding a breeze.


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