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Antonio Rodrigues
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The SharpGit project provides frequent builds of the current development tree. These versions are not fully tested, but may contain bugfixes (and new bugs) that are not in released versions. If you are testing these daily builds (Thanks!) and find issues in it, please report those issues with the exact version.
The build script also supplies an x64 version of this Win32 build and a package of all binaries with some extras.
You can find older daily releases in the daily folder of documents & files. The previous version of this report is still available in our Subversion Repository.
Published daily builds will be available for at least a month after they are build.
| 2016-03-23 | |||
| [r1114] | rhuijben | src/SharpGit: AssemblyInfo.cpp | |
| Fix attribute name |


Their eyes and bodies are a monochrome mix of black and white, they never leave them equally colorless lands, that keep away all other imps. The sky is a constant shade of gray
A Pride Imp exploring the bones and having fun.
 Their need to be self-sufficient drives them to use the bones of their ancestors and relatives to create potions and weapons to fight with. They also are held in high regard, due to the bones, they wear on their bodies that come from the imp graveyard, where the grand imp of death lays. Their abilities allow them to take a bone from their body and grow a new one in its place. Their bodies are incapable of remaining incomplete as their unwavering will in their abilities have made them almost indestructible are less fearful of death. Their lands are barren on only have bones, so they really see anything with a color other than the colors in the sky. Anything that is not a monochromic color, that ventures into their territory is killed off, due to the imp of death feeding off of the vibrant colorful life coming from that creature. The Prideful imps are immune to this treatment and keet the grand imp of death starved of life, to keep it contained. The very fact that their vigilance over their land, halts the destruction of all of Impla, give them a lot of pride and makes them arrogant. The Prideful imps will only tolerate the company of other races of imps if they can be the leader and call the shots. Imps that leave the graveyard, like the imps the player has, are seen as lesser than the imps in the graveyard, by said imp. The imps that leave contribute their bodie’s magical healing properties to other imps, and despite their conceded attitudes, are welcomed by the other races of imps.
These fruits are grown from death plants and eaten by these imps, they are quite bitter.
These fruits are grown on death plants and taste very bland. They can be used as a glue-like substance.
The bones in the Pride territry are treated with respect and used in the Pride Imp's daily lives.
A house usually found in the Pride village, they are comprised of bones, the black tar-like fruit, and the white glue like fruit to keep the structure together.


Path template to use when creating a TMPDIR for this job. Overrides the global TMPDIR path template specified in the global Jenkins settings.
May contain the usual Jenkins environment/build variables. If relative, then it is
made absolute by prepending the value of the java.io.tmpdir
Java system property
on the node that is used to execute a particular build.
For example, ${BUILD_TAG}-tmp
may end up being something like
/var/tmp/foobar-123-tmp
if java.io.tmpdir
on the
node is set to /var/tmp/
.


In the step you can configure the size of your node, how much history you want to keep, what network you want to run on and what validators you want to trust.


My Name is Ammar , i study as a softwaer engineeering in ASAC i also worked as an Student Ambassador for ASAC also,
I like to solve problems even if I do not reach the root solution, but I try my best on it last thing i want you
to know about me is one of my favorite hobbies is eating all the time.
Top Ten Car;
- 1.Ferrari LaFerrari
- 2.McLaren P1
- 3.McLaren Senna
- 4.Porsche 918 Spyder
- 5.Koenigsegg One:1
- 6.Aston Martin Vulcan
- 7.Bugatti Chiron
- 8.Pagani Huayra
- 9.Rimac Concept One
- 10.Lamborghini Centenario
- My work experience and education
- Finish high school with GPA : B+
- Work as an Ambassador for ASAC
- Know programinh language like java
- have a good background in networking
- have a good skils at works like workunder pressure


Old Spice Homepage
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OLD SPICE BEER, PURE, SPORT SCENT, HIGH ENDURANCE
Old Spice was founded in 1995 in London, Ontario. And we are serving the best beer in town for over 25 years!
For more information click to “Learn More” button!
LEARN MORE
KNOW OUR BEERS!
Beer bottles
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Red Ale


_ = require 'underscore' if require?
Observatory-galileo is a nodejs / client logging framework with flexible architecture. It was developed as a basis for Observatory Logging & Monitoring Suite but can be used standalone independent of Meteor. Although you should really try Meteor. You'll love it, we promise :)
Below is the full API documentation, useful if you want to extend the framework. If you just want to jump to usage, start reading with Generic Emitter.
MessageEmitters
emit messages - either from explicit calls to logger.debug()
etc or by in turn listening or
monitoring some other provider: e.g., http connect module, external log service etc. Emitters use formatters
(Observatory.formatters
) -->Formatters
form or tranform messages into predefined json format that is acceptably by -->Loggers
receive formatted messages from emitters and either buffer them or process immediately,
by applying further output formatting - e.g., adding ANSI colors or html tags - and output them into
different out devices - console, mongo collection etc._ = require 'underscore' if require?
Observatory = Observatory ? {}
_.extend Observatory,
Log level (severity) definitions
LOGLEVEL:
SILENT: -1
FATAL: 0
ERROR: 1
WARNING: 2
INFO: 3
VERBOSE: 4
DEBUG: 5
MAX: 6
NAMES: ["FATAL", "ERROR", "WARNING", "INFO", "VERBOSE", "DEBUG", "MAX"]
settings:
maxSeverity: 3
printToConsole: true
Initializing the system - creating loggers, subscribing etc Currently creates 1 ConsoleLogger and subscribes it system-wide. Also initializes default logger (Generic Emitter). TODO: add tests and settings format
initialize: (s)->
@_loggers = []
if s?
@settings.maxSeverity = if s.logLevel? then @LOGLEVEL[s.logLevel] else 3
@settings.printToConsole = s.printToConsole ? true
@_consoleLogger = new Observatory.ConsoleLogger 'default'
@subscribeLogger @_consoleLogger
@_defaultEmitter = new Observatory.GenericEmitter 'default'
Returns default logger to use in the app via warn(), debug() etc calls
getDefaultLogger: -> @_defaultEmitter
Check if we are run on server or client. NOTE! To be overriden for Meteor based implementations!
isServer: -> not (typeof window isnt "undefined" and window.document)
Formatters - functions that take arbitrary json and format it into a message that loggers can accept. Formatters can be chained - will be useful when implementing Meteor related stuff.
formatters:
basicFormatter: (options)->
timestamp: new Date
severity: options.severity
textMessage: options.message
note that it's a function that gets passed around so this
will be what we need
module: options.module # should the priority be reversed?
object: options.obj
isServer: Observatory.isServer()
View formatters take a message accepted by loggers and further format it for nice output, e.g., adding ANSI colors or html markup.
viewFormatters:
_convertDate: (timestamp)->
timestamp.getUTCDate() + '/' + (timestamp.getUTCMonth()+1) + '/'+timestamp.getUTCFullYear()
_convertTime: (timestamp, ms=true)->
ts = timestamp.getUTCHours()+ ':' + timestamp.getUTCMinutes() + ':' + timestamp.getUTCSeconds()
ts += '.' + timestamp.getUTCMilliseconds() if ms
ts
_ps: (s)-> '['+s+']'
basicConsole: (o)->
t = Observatory.viewFormatters
ts = t._ps(t._convertDate(o.timestamp)) + t._ps(t._convertTime(o.timestamp))
full_message = ts + if o.isServer then "[SERVER]" else "[CLIENT]"
full_message+= if o.module then t._ps o.module else "[]"
full_message+= t._ps(Observatory.LOGLEVEL.NAMES[o.severity]) #TODO: RANGE CHECK!!!
full_message+= " #{o.textMessage}"
full_message+= " | #{JSON.stringify(o.object)}" if o.object?
full_message
array of system-wide subscribing loggers
_loggers: []
get all currently listening system-wide loggers
getLoggers: -> @_loggers
add new logger to listen to messages
subscribeLogger: (logger)->
@_loggers.push logger
remove logger from the listeners
unsubscribeLogger: (logger)->
@_loggers = _.without @_loggers, logger
class Observatory.MessageEmitter
_loggers = [] # array of subscribing loggers
_getLoggers: -> @_loggers
constructor: (@name)-> @_loggers = []
add new logger to listen to messages
subscribeLogger: (logger)->
@_loggers.push logger
remove logger from the listeners
unsubscribeLogger: (logger)->
@_loggers = _.without @_loggers, logger
Translates message to be logged to all subscribed loggers.
logger
has to respond to addMessage(msg)
call.
Normally, only system-wide loggers are used, subscription for specific emitters is to provide
finer-grained control.
emitMessage: (message)->
l.addMessage message for l in Observatory.getLoggers()
l.addMessage message for l in @_loggers if @_loggers.length > 0
message
Logger listens to messages and processes them, one by one or in batches. It also checks if the Emitters generate messages in the correct format described below.
class Observatory.Logger
messageBuffer = []
@name
is a module name@useBuffer
- whether to log the messages immediately or buffer them first@interval
- if using buffer, how often we should process it.
TODO: figure out how to use different interval-setting functions in pure js and Meteor.
TODO: actual interval setup in the constructor
TODO: tests for arguments shifting!!! constructor: (@name, @formatter = Observatory.viewFormatters.basicConsole, @useBuffer = false, @interval = 3000)->
if typeof formatter is 'boolean'
@interval = @useBuffer
@useBuffer = @formatter
@formatter = Observatory.viewFormatters.basicConsole
@messageBuffer = []
messageAcceptable
verifies that Emitters give messages in the format that
can be recognized by this logger. At the very minimum, we are checking for
timestamp, severity, client / server and either text or html formatted message to log.
messageAcceptable: (m)->
return (m? and m.timestamp? and m.severity? and m.isServer? and (m.textMessage? or m.htmlMessage?) )
addMessage
is the listening method that takes messages from Emitters
TODO: do we really need to throw an error??? add some kind of 'strict mode'?
addMessage: (message)->
throw new Error "Unacceptable message format in logger: #{@name}" if not @messageAcceptable message
if @useBuffer then @messageBuffer.push message else @log message
log
- 'virtual' function that does actual output of the message. Needs to be overriden by extending
classes with e.g. logging to console or inserting into Meteor Collection. Does nothing here.
log: (message)->
throw new Error "log() function needs to be overriden to perform actual output!"
processing the buffer
processBuffer: ->
return unless @messageBuffer.length > 0
@log obj for obj in @messageBuffer
@messageBuffer = []
Implements typical logging functionality to be used inside an app - log messages with various severity levels.
class Observatory.GenericEmitter extends Observatory.MessageEmitter
Creating a named emitter with maximum severity of the messages to emit equal to maxSeverity
and formatter
as a formatting function. This provides flexibility on how the message to be passed on to
loggers is formed. E.g., here it's given a basic format, when we'll use Meteor we'll provide a more
advanced formatter that will set userId, IP address etc.
constructor: (name, maxSeverity, formatter)->
@maxSeverity = maxSeverity
if formatter? and typeof formatter is 'function'
@formatter = formatter
else
@formatter = Observatory.formatters.basicFormatter
super name
some dynamic js magic - defining different severity method aliases programmatically to be DRY. TODO: need to keep in mind bind() doesn't work in IE8 and below, but there's a script to fix this
for m,i in ['fatal','error','warn','info','verbose','debug','insaneVerbose']
@[m] = @_emitWithSeverity.bind this, i
Trace a error - format stacktrace nicely and output with ERROR level
trace: (error, module)->
message = error.stack ? error
@_emitWithSeverity Observatory.LOGLEVEL.ERROR, message, module
Low-level emitting method that formats message and emits it
severity
- level with wich to emit a message. Won't be emitted if higher than @maxSeverity
message
- text message to include into the full log message to be passed to loggersmodule
- optional module name. If the emitter is named, its' name will be used instead in any case.obj
- optional arbitrary json-able object to be included into full log message, e.g. error object in the call to error
_emitWithSeverity: (severity, message, obj, module)->
return false if not severity? or severity > @maxSeverity
if typeof message is 'object'
module = obj
obj = message
message = JSON.stringify obj
if typeof obj is 'string'
module = obj
obj = null
options = severity: severity, message: message, obj: obj, module: module ? @name # explicit module overrides name
@emitMessage @formatter(options)
class Observatory.ConsoleLogger extends Observatory.Logger
Simply redefining log() to output messages to the console
log: (m)-> console.log @formatter m
(exports ? this).Observatory = Observatory


Awards to Leaders of Astronomy Camp
- "2012 Education Prize" of the American Astronomical Society to Dr. McCarthy, Camp Director. Citation
Astronomy Campers Garner Research & Teaching Awards
After Astronomy Camp, many high school students undertake research and teaching projects. Below is a summary of those students who have received prestigious national and international awards for their efforts. Many others have succeeded at local and regional Science Fairs. Some students have published educational articles and promoted astronomy in their local school classes, science museums, planetaria, and communities.
The Hubble Postdoctoral Fellowship
The Hubble Postdoctoral Fellowship
- Dr. Caitlin Casey (2010-2013)
- Finalists (The Top 40 Students)
- Samantha Scibelli (2013)
- Katherine Cordwell (2013)
- Sean Wahl (2007)
- Sarah Rapoport (2006)
- Robert Cordwell (2005; 4th place winner)
- Katherine Hartman (2004)
- Vera te Velde (2003)
- Ezra Rapoport (2002)
- Patrick Kelly (2000)
- Semi-Finalists (The Top 300 Students)
- Emily Low (2014)
- Abby Youngblood (1997)
- Katherine Cordwell (2013)
- Elan Mitchell (2004)
- Cynthia Gentry (1992, 1991)
- Harry Gaebler, Gayathri Cheran (2009)
- Christopher Limbach (2005)
- Max Moe, Molly Potter, Susana Hancock, Anna Czoski (2003)
- JoHanna Przybylowski and Stevi Fawcett (2001)
- Stevi Fawcett (2000)
- Patrick Kelly (2000) See Sky & Telescope magazine, Sept. 2000, p. 87.
- Joshua Neubert (1998, 1999)
- Elizabeth Waterhouse (1997)
Home | Information | Registration | Articles | Links
All Images, Media and Content - Copyright © 2019 Astronomy Camp
Site Design by Jacob Omann. Maintained by Astronomy Camp. Updated December 23,2018
All Images, Media and Content - Copyright © 2019 Astronomy Camp
Site Design by Jacob Omann. Maintained by Astronomy Camp. Updated December 23,2018


This page collects the evolutionary algorithms for deep learning
Please email me<EMAIL_ADDRESS>if you find new or mistakes for these information
Stepwise PathNet: a layer-by-layer knowledge-selection-based transfer learning algorithm [2020, Imai et al.] [Scientific Reports]
A particle swarm optimization-based flexible convolutional auto-encoder for image classification [2018, Sun et al.][IEEE Transactions on Neural Networks and Learning Systems, DOI:10.1109/TNNLS.2018.2881143.] BibTex
Evolving unsupervised deep neural networks for learning meaningful representations [2018, Sun et al.] [IEEE Transactions on Evolutionary Computation]BibTex
Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification [2020, Sun et al.] [IEEE Transactions on Cybernetics]BibTex (early access)
Evolving deep convolutional neural networks for image classification [2020, Sun et al.] [IEEE Transactions on Evolutionary Computation]BibTex
An experimental study on hyper-parameter optimization for stacked auto-encoders [2018, Sun et al.] [2018 IEEE Congress on Evolutionary Computation (CEC)]BibTex
Completely Automated CNN Architecture Design Based on Blocks [2020, Sun et al.] [IEEE transactions on neural networks and learning systems]BibTex
Surrogate-assisted evolutionary deep learning using an end-to-end random forest-based performance predictor [2020, Sun et al.] [IEEE Transactions on Evolutionary Computation]BibTex
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks [2019, Wang et al.] [Springer PRICAI 2019: Trends in Artificial Intelligence]BibTex
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification [2018, Wang et al.] [Springer AI 2018: Advances in Artificial Intelligence]BibTex
Evolving deep convolutional neural networks by variable-length particle swarm optimization for image classification [2018, Wang et al.] [CEC 2018]BibTex
Evolving deep neural networks by multi-objective particle swarm optimization for image classification [2019, Wang et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]BibTex
Evolving deep convolutional neural networks for hyperspectral image denoising [2020, Liu et al.] [IJCNN 2020]
Hierarchical representations for efficient architecture search [2018, Liu et al.] [ICLR2018]
Genetic CNN [2017, Xie et al.] [ICCV 2017]
A genetic programming approach to designing convolutional neural network architectures [2017, Suganuma et al.] [GECCO 2017]
Large-scale evolution of image classifiers [2017, Real et al.] [ICML2017]
A hypercube-based encoding for evolving large-scale neural networks [2009, Stanley et al.] [MIT Press Artificial life]
Structure discovery of deep neural network based on evolutionary algorithms [2015, Shinozaki et al.] [2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)]
Genetic deep neural networks using different activation functions for financial data mining [2015, Zhang et al.] [2015 IEEE International Conference on Big Data (Big Data)]
Automated structure discovery and parameter tuning of neural network language model based on evolution strategy [2016, Tanaka et al.] [2016 IEEE Spoken Language Technology Workshop (SLT)]
Boosting Neuro Evolutionary Techniques for Speech Recognition [2019, Anwar et al.] [2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)]
An Evolutionary Approach to Compact DAG Neural Network Optimization [2019, Chiu et al.] [IEEE Access]
Evolutionary Design of Deep Neural Networks [2019, Radu et al.] [2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)]
Evolving deep neural networks using coevolutionary algorithms with multi-population strategy [2020, Tirumala et al.] [Springer Neural Computing and Applications]
Hybrid evolution of convolutional networks [2011, Cheung et al.] [2011 10th International Conference on Machine Learning and Applications and Workshops]
Structure learning for deep neural networks based on multiobjective optimization [2017, Liu et al.] [IEEE transactions on neural networks and learning systems]
Evolution-based configuration optimization of a Deep Neural Network for the classification of Obstructive Sleep Apnea episodes [2019, Falco et al.] [Elsevier Future Generation Computer Systems]
A Multi-objective Particle Swarm Optimization for Neural Networks Pruning [2019, Wu et al.] [2019 IEEE Congress on Evolutionary Computation (CEC)]
Effective long short-term memory with differential evolution algorithm for electricity price prediction [2018, Peng et al.] [Elsevier Energy]
Evolving memory cell structures for sequence learning [2009, Bayer et al.] [Springer International Conference on Artificial Neural Networks]
Learning Structural Similarity with Evolutionary-GAN: A New Face De-identification Method [2019, Song et al.] [IEEE 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC)]
Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data [2017, Ye et al.] [Public Library of Science PloS one]
Evolving ensemble models for image segmentation using enhanced particle swarm optimization [2019, Tan et al.] [IEEE access]
Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm [2019, Li et al.] [Elsevier Knowledge-Based Systems]
Differential evolution optimization for resilient stacked sparse autoencoder and its applications on bearing fault diagnosis [2018, Saufi et al.] [IOP Publishing Measurement Science and Technology]
A GPSO-optimized convolutional neural networks for EEG-based emotion recognition [2020, Gao et al.] [Elsevier Neurocomputing]
Particle swarm optimization--deep belief network--based rare class prediction model for highly class imbalance problem [2017, Kim et al.] [Wiley Online Library Concurrency and Computation: Practice and Experience]
Ant-based Neural Topology Search (ANTS) for Optimizing Recurrent Networks [2020, ElSaid et al.] [Springer International Conference on the Applications of Evolutionary Computation (Part of EvoStar)]
NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search [2020, Wei et al.] [arXiv]
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch [2020, Real et al.] [arXiv]
Automatically Searching for U-Net Image Translator Architecture [2020, Shu et al.] [arXiv]
Searching for accurate binary neural architectures [2019, Shen et al.] [Proceedings of the IEEE International Conference on Computer Vision Workshops]
Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB [2019, Johner et al.] [2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)]
Neural Architecture Search for Skin Lesion Classification [2020, Kwasigroch et al.] [IEEE Access]
Neural Architecture Search for Deep Image Prior [2020, Ho et al.] [arXiv]
DeepMaker: A Multi-Objective Optimization Framework for Deep Neural Networks in Embedded Systems [2020, Loni et al.] [Elsevier Microprocessors and Microsystems]
Automated Neural Network Construction with Similarity Sensitive Evolutionary Algorithms [2019, Tian et al.] [2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)]
A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network [2019, Singh et al.] [arXiv]
Neural architecture search for image saliency fusion [2020, Bianco et al.] [Elsevier Information Fusion]
Artificial Neural Network and Accelerator Co-design using Evolutionary Algorithms [2019, Colangelo et al.] [2019 IEEE High Performance Extreme Computing Conference (HPEC)]
ImmuNeCS: Neural Committee Search by an Artificial Immune System [2019, Frachon et al.] [arXiv]
ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures [2020, Chen et al.] [arXiv]
Enhancing Neural Architecture Search with Speciation and Inter-Epoch Crossover [2019, Baughman et al.]
Deep neural network architecture search using network morphism [2019, Kwasigroch et al.] [IEEE 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR)]
A Novel Automatic CNN Architecture Design Approach Based on Genetic Algorithm [2019, Ahmed et al.] [Springer International Conference on Advanced Intelligent Systems and Informatics]
Automated design of error-resilient and hardware-efficient deep neural networks [2019, Schorn et al.] [arXiv]
Genetic Neural Architecture Search for automatic assessment of human sperm images [2019, Miahi et al.] [arXiv]
Cars: Continuous evolution for efficient neural architecture search [2019, Yang et al.] [arXiv]
Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm [2019, Litzinger et al.] [Springer International Conference on Artificial Neural Networks]
Self-adaptive 2D-3D ensemble of fully convolutional networks for medical image segmentation [2020, Calisto et al.] [International Society for Optics and Photonics Medical Imaging 2020: Image Processing]
Neural network architecture search with differentiable cartesian genetic programming for regression [2019, M{\"a}rtens et al.] [Proceedings of the Genetic and Evolutionary Computation Conference Companion]
Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search [2019, Chu et al.] [arXiv]
Evolving Robust Neural Architectures to Defend from Adversarial Attacks [2019, Vargas et al.] [arXiv]
Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents [2019, Behjat et al.] [IEEE 2019 International Conference on Robotics and Automation (ICRA)]
Automatic Modulation Recognition Using Neural Architecture Search [2019, Wei et al.] [IEEE 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD\&IS)]
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks [2019, An et al.] [arXiv]
EENA: efficient evolution of neural architecture [2019, Zhu et al.] [Proceedings of the IEEE International Conference on Computer Vision Workshops]
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search [2019, Saltori et al.] [Springer International Conference on Image Analysis and Processing]
Automatic Design of Artificial Neural Networks for Gamma-Ray Detection [2019, Assun{\c{c}}{\~a}o et al.] [IEEE Access]
AdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation [2019, Baldeon-Calisto et al.] [Elsevier Neurocomputing]
Fast denser: Efficient deep neuroevolution [2019, Assun{\c{c}}{\~a}o et al.] [Springer European Conference on Genetic Programming]
Incremental Evolution and Development of Deep Artificial Neural Networks [2020, Assun{\c{c}}ao et al.] [Springer Genetic Programming: 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15--17, 2020, Proceedings 23]
Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach [2019, Cetto et al.] [Springer INNS Big Data and Deep Learning conference]
Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming [2020, Suganuma et al.] [MIT Press Evolutionary Computation]
Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising [2019, Liu et al.] [Elsevier Medical image analysis]
Evolutionary Cell Aided Design for Neural Network Architectures [2019, Colangelo et al.] [arXiv]
Evolutionary neural automl for deep learning [2019, Liang et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
GeneCAI: Genetic Evolution for Acquiring Compact AI [2020, Javaheripi et al.] [arXiv]
Investigating recurrent neural network memory structures using neuro-evolution [2019, Ororbia et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
Fast, accurate and lightweight super-resolution with neural architecture search [2019, Chu et al.] [arXiv]
Joint neural architecture search and quantization [2018, Chen et al.] [arXiv]
Gradient based evolution to optimize the structure of convolutional neural networks [2018, Mitschke et al.] [2018 25th IEEE International Conference on Image Processing (ICIP)]
Lamarckian evolution of convolutional neural networks [2018, Prellberg et al.] [Springer International Conference on Parallel Problem Solving from Nature]
Optimizing deep learning hyper-parameters through an evolutionary algorithm [2015, Young et al.] [Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments]
CMA-ES for hyperparameter optimization of deep neural networks [2016, Loshchilov et al.] [arXiv]
Neuroevolution: from architectures to learning [2008, Floreano et al.] [Springer Evolutionary intelligence]
An evolutionary algorithm that constructs recurrent neural networks [1994, Angeline et al.] [IEEE transactions on Neural Networks]
Evolving deep neural networks [2019, Miikkulainen et al.] [Elsevier Artificial Intelligence in the Age of Neural Networks and Brain Computing]
Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model [2020, Mart{\'\i}nez-{\'A}lvarez et al.] [arXiv]
Evolving recurrent neural networks for time series data prediction of coal plant parameters [2019, ElSaid et al.] [Springer International Conference on the Applications of Evolutionary Computation (Part of EvoStar)]
Using ant colony optimization to optimize long short-term memory recurrent neural networks [2018, ElSaid et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration [2018, ElSaid et al.] [Elsevier Applied Soft Computing]
Evolving deep recurrent neural networks using ant colony optimization [2015 Desell et al.] [Springer European Conference on Evolutionary Computation in Combinatorial Optimization]
Spatial evolutionary generative adversarial networks [2019, Toutouh et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
Differential-Evolution-Based Generative Adversarial Networks for Edge Detection [2019, Zheng et al.] [Proceedings of the IEEE International Conference on Computer Vision Workshops]
Evolutionary generative adversarial networks [2019, Wang et al.] [IEEE Transactions on Evolutionary Computation]
Effective Mutation and Recombination for Evolving Convolutional Networks [2020, Dahal et al.] [Proceedings of the 3rd International Conference on Applications of Intelligent Systems]
A novel channel pruning method for deep neural network compression [2018, Hu et al.] [arXiv]
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs) [2019, He et al.] [arXiv]
Deep neural networks compression learning based on multiobjective evolutionary algorithms [2020, Huang et al.] [Elsevier Neurocomputing]
Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics [2016, Zhang et al.] [IEEE transactions on neural networks and learning systems]
Deep learning using genetic algorithms [2012, Lamos-Sweeney et al.]
Evolution strategy based neural network optimization and LSTM language model for robust speech recognition [2016, Tanaka et al.] [Cit. on]
Multiobjective evolution of deep learning parameters for robot manipulator object recognition and grasping [2018, Hossain et al.] [Taylor \& Francis Advanced Robotics]
Designing neural networks using genetic algorithms with graph generation system [1990, Kitano et al.] [Complex systems]
Designing Neural Networks using Genetic Algorithms [1989, Miller et al.] [ICGA]
Arabic sentiment classification using convolutional neural network and differential evolution algorithm [2019, Dahou et al.] [Hindawi Computational intelligence and neuroscience]
Particle swarm optimisation for evolving artificial neural network [2000, Zhang et al.] [Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics.'cybernetics evolving to systems, humans, organizations, and their complex interactions'(cat. no. 0)]
Evolving autoencoding structures through genetic programming [2019, Rodriguez-Coayahuitl et al.] [Springer Genetic Programming and Evolvable Machines]
Evolving Efficient Deep Neural Networks for Real-time Object Recognition [2019, Lan et al.] [2019 IEEE Symposium Series on Computational Intelligence (SSCI)]
Development of an Evolutionary Deep Neural Net for Materials Research [2020, Roy et al.] [Springer TMS 2020 149th Annual Meeting \& Exhibition Supplemental Proceedings]
Emotion Estimation by Joint Facial Expression and Speech Tonality Using Evolutionary Deep Learning Structures [2019, Chung et al.] [2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)]
An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification [2019, Bi et al.] [2019 IEEE Congress on Evolutionary Computation (CEC)]
Evolutionary deep learning: A genetic programming approach to image classification [2018, Evans et al.] [2018 IEEE Congress on Evolutionary Computation (CEC)]
Recognizing the order of four-scene comics by evolutionary deep learning [2018, Fujino et al.] [Springer International Symposium on Distributed Computing and Artificial Intelligence]
The evolutionary deep learning based on deep convolutional neural network for the anime storyboard recognition [2017, Fujino et al.] [Springer International Symposium on Distributed Computing and Artificial Intelligence]
Deep convolutional networks for human sketches by means of the evolutionary deep learning [2017, Fujino et al.] [IEEE 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS)]
Evolutionary LSTM-FCN networks for pattern classification in industrial processes [2020, Ortego et al.] [Elsevier Swarm and Evolutionary Computation]
An evolutionary hyper-heuristic to optimise deep belief networks for image reconstruction [2019, Sabar et al.] [Elsevier Applied Soft Computing]
An Automated Approach for Developing a Convolutional Neural Network Using a Modified Firefly Algorithm for Image Classification [2020, Sharaf et al.] [Springer Applications of Firefly Algorithm and its Variants]
An Evolutionary Optimization Algorithm for Gradually Saturating Objective Functions [2020, Sapra et al.]
Constrained Evolutionary Piecemeal Training to Design Convolutional Neural Networks [2020, Sapra et al.] [International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer]
Auto-creation of Effective Neural Network Architecture by Evolutionary Algorithm and ResNet for Image Classification [2019, Chen et al.] [2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)]
PSO-based optimized CNN for Hindi ASR [2019, Passricha et al.] [Springer International Journal of Speech Technology]
Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net [2019, Zhang et al.] [Springer International Conference on Medical Image Computing and Computer-Assisted Intervention]
Efficient network architecture search via multiobjective particle swarm optimization based on decomposition [2020, Jiang et al.] [Elsevier Neural Networks]
Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data [2019, Qiang et al.] [Springer International Workshop on Multiscale Multimodal Medical Imaging]
An Evolutionary Approach to Variational Autoencoders [2020, Hajewski et al.] [IEEE 2020 10th Annual Computing and Communication Workshop and Conference (CCWC)]
Efficient Neural Network Space with Genetic Search [2019, Kang et al.] [Springer International Conference on Bio-Inspired Computing: Theories and Applications]
Regularized evolution for image classifier architecture search [2019, Real et al.] [Proceedings of the aaai conference on artificial intelligence]
Reinforced evolutionary neural architecture search [2018, Chen et al.] [arXiv]
Nsga-net: A multi-objective genetic algorithm for neural architecture search [2018, Lu et al.]
Evolutionary-neural hybrid agents for architecture search [2018, Maziarz et al.] [arXiv]
Evolutionary Neural Architecture Search for Image Restoration [2019, van et al.] [IEEE 2019 International Joint Conference on Neural Networks (IJCNN)]
NSGA-Net: neural architecture search using multi-objective genetic algorithm [2019, Lu et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
Multi-objective reinforced evolution in mobile neural architecture search [2019, Chu et al.] [arXiv]
Real-time Federated Evolutionary Neural Architecture Search [2020, Zhu et al.] [arXiv]
Evolutionary Neural Architecture Search for Retinal Vessel Segmentation [2020, Fan et al.] [arXiv]
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search [2020, Zhang et al.] [arXiv]
Evolutionary neural architecture search for deep learning [2019, Liang et al.]
Searching toward pareto-optimal device-aware neural architectures [2018, Cheng et al.] [Proceedings of the International Conference on Computer-Aided Design]
Evolving Knowledge And Structure Through Evolution-based Neural Architecture Search [2019, Wang et al.] [NTNU]
Multi-objective evolutionary federated learning [2019, Zhu et al.] [IEEE transactions on neural networks and learning systems]
Particle swarm optimization of deep neural networks architectures for image classification [2019, Junior et al.] [Elsevier Swarm and Evolutionary Computation]
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks [2019, Wang et al.] [arXiv]
EEGNAS: Neural Architecture Search for Electroencephalography Data Analysis and Decoding [2019, Rapaport et al.] [Springer International Workshop on Human Brain and Artificial Intelligence]
Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization [2019, Teng et al.] [Elsevier Bioresource technology]
Evolving image classification architectures with enhanced particle swarm optimisation [2018, Fielding et al.] [IEEE Access]
Designing compact convolutional neural network for embedded stereo vision systems [2018, Loni et al.] [2018 IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)]
Memetic evolution of deep neural networks [2018, Lorenzo et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization [2019, Zhang et al.] [arXiv]
Automatic Model Selection for Neural Networks [2019, Laredo et al.] [arXiv]
Deepswarm: Optimising convolutional neural networks using swarm intelligence [2019, Byla et al.] [Springer UK Workshop on Computational Intelligence]
The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks [2019, ElSaid et al.] [arXiv]
Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search [2018, Suganuma et al.] [arXiv]
From nodes to networks: Evolving recurrent neural networks [2018, Rawal et al.] [arXiv]
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch [2019, Ren et al.] [Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition]
Evolutionary architecture search for deep multitask networks [2018, Liang et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
Single path one-shot neural architecture search with uniform sampling [2019, Guo et al.] [arXiv]
The evolved transformer [2019, So et al.] [arXiv]
A Graph-Based Encoding for Evolutionary Convolutional Neural Network Architecture Design [2019, Irwin-Harris et al.] [2019 IEEE Congress on Evolutionary Computation (CEC)]
Evolutionary stochastic gradient descent for optimization of deep neural networks [2018, Cui et al.] [Advances in neural information processing systems]
Multi-Criterion Evolutionary Design of Deep Convolutional Neural Networks [2019, Lu et al.] [arXiv]
Efficient residual dense block search for image super-resolution [2019, Song et al.] [arXiv]
DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning [2018, Qi et al.] [arXiv]
A specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks [2019, Camero et al.] [arXiv]
Evolution of convolutional highway networks [2018, Kramer et al.] [Springer International Conference on the Applications of Evolutionary Computation]
Evolving space-time neural architectures for videos [2019, Piergiovanni et al.] [Proceedings of the IEEE International Conference on Computer Vision]
Evolutionary deep learning for car park occupancy prediction in smart cities [2018, Camero et al.] [Springer International Conference on Learning and Intelligent Optimization]
Evolutionary deep learning-based energy consumption prediction for buildings [2018, Almalaq et al.] [IEEE Access]
Automated problem identification: Regression vs classification via evolutionary deep networks [2017, Dufourq et al.] [Proceedings of the South African Institute of Computer Scientists and Information Technologists]
EvoDeep: a new evolutionary approach for automatic deep neural networks parametrisation [2018, Mart{\'\i}n et al.] [Elsevier Journal of Parallel and Distributed Computing]
An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm [2020, Neshat et al.] [arXiv]
Evolutionary Deep Learning to Identify Galaxies in the Zone of Avoidance [2019, Jones et al.] [arXiv]
EvoU-Net: an evolutionary deep fully convolutional neural network for medical image segmentation [2020, Hassanzadeh et al.] [Proceedings of the 35th Annual ACM Symposium on Applied Computing]
Evolutionary Deep Learning Approaches for Financial Prediction [2019, 정혜정 et al.] [이화여자대학교 대학원]
Waste generation prediction under uncertainty in smart cities through deep neuroevolution [2019, Camero et al.] [Universidad de Antioquia Revista Facultad de Ingenier{\'\i}a Universidad de Antioquia]
A genetic algorithm for convolutional network structure optimization for concrete crack detection [2018, Gibb et al.] [2018 IEEE Congress on Evolutionary Computation (CEC)]
Genetic Programming and Gradient Descent: A Memetic Approach to Binary Image Classification [2019, Evans et al.] [arXiv]
Efficient multi-objective neural architecture search via lamarckian evolution [2018, Elsken et al.] [arXiv]
Simple and efficient architecture search for convolutional neural networks [2017, Elsken et al.] [arXiv]
Deep learning architecture search by neuro-cell-based evolution with function-preserving mutations [2018, Wistuba et al.] [Springer Joint European Conference on Machine Learning and Knowledge Discovery in Databases]
TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning [2016, Olson et al.] [Workshop on Automatic Machine Learning]
Particle swarm optimization for hyper-parameter selection in deep neural networks [2017, Lorenzo et al.] [Proceedings of the genetic and evolutionary computation conference]
Evolving artificial neural networks [1999, Yao et al.] [Proceedings of the IEEE]
Evolving neural networks through augmenting topologies [2002, Stanley et al.] [MIT Press Evolutionary computation]
Efficient Evolutionary Deep Neural Architecture Search (NAS) by Noisy Network Morphism Mutation [2019, Chen et al.] [Springer International Conference on Bio-Inspired Computing: Theories and Applications]


| Overview | Package | Class | Index | No Frames | Frames | Prev | Next |
Genero Calendar library
This library implements a set of functions to create a calendar area in a WEBCOMPONENT field.
| CONSTANT Summary |
|---|
|
FGLCALENDAR_THEME_AMAZON INTEGER |
|
FGLCALENDAR_THEME_CHILI INTEGER |
|
FGLCALENDAR_THEME_DEFAULT INTEGER |
|
FGLCALENDAR_THEME_PACIFIC INTEGER |
|
FGLCALENDAR_THEME_SAHARA INTEGER |
|
FGLCALENDAR_THEME_VIOLA INTEGER |
|
FGLCALENDAR_TYPE_DEFAULT INTEGER |
|
FGLCALENDAR_TYPE_DOTS INTEGER |
|
FGLCALENDAR_TYPE_ICON INTEGER |
|
FGLCALENDAR_TYPE_TEXT INTEGER |
| FUNCTION Summary | |
|---|---|
|
addSelectedDate(id SMALLINT, value DATE) Adds a date to the selected dates of the given calendar |
|
|
clearSelectedDates(id SMALLINT) Clears the list of selected dates for the given calendar |
|
|
create(name STRING) RETURNS INTEGER Create a new calendar web component and return its ID |
|
|
destroy(id INTEGER) Destroy a calendar object |
|
|
display(id SMALLINT, cy SMALLINT, cm SMALLINT) Displays the calendar in the web component field |
|
|
finalize() Library finalization function. |
|
|
getSelectedDateFromValue(id SMALLINT, value STRING) Extracts selected date info from the web component field value. |
|
|
getViewMonth(id SMALLINT) Returns the current month set for the last calendar display. |
|
|
getViewYear(id SMALLINT) Returns the current year set for the last calendar display. |
|
|
initialize() Library initialization function. |
|
|
isSelectedDate(id SMALLINT, value DATE) Check if a date is selected for the given calendar |
|
|
removeSelectedDate(id SMALLINT, value DATE) Remove a date from the selected dates of the given calendar |
|
|
setColorTheme(id SMALLINT, theme SMALLINT) Defines the view type of the calendar. |
|
|
setDayCellColor(id SMALLINT, color STRING) Defines the color for the day cells in the current month. |
|
|
setDayCellSize(id SMALLINT, rw SMALLINT, rh SMALLINT) Defines the relative size of calendar cells. |
|
|
setDayNames(id SMALLINT, names STRING) Defines the names for the days (for localization). |
|
|
setDayOffCellColor(id SMALLINT, color STRING) Defines the color for the days off (Sat / Sun) |
|
|
setDayOutCellColor(id SMALLINT, color STRING) Defines the color for the days out of current month |
|
|
setDaySelectedCellColor(id SMALLINT, color STRING) Defines the color for the selected days |
|
|
setDayTodayCellColor(id SMALLINT, color STRING) Defines the color for the current day cell (today). |
|
|
setViewType(id SMALLINT, type SMALLINT) Defines the view type of the calendar. |
|
|
showDayNames(id SMALLINT, show BOOLEAN) Defines if day names must be displayed in the calendar. |
|
|
showDayNumbers(id SMALLINT, show BOOLEAN) Defines if day numbers must be displayed in the calendar. |
|
|
showToday(id SMALLINT, show BOOLEAN) Defines if today must be rendered in the calendar. |
|
|
showWeekNumbers(id SMALLINT, show BOOLEAN) Defines if the calendar must show week numbers on the left. |
Adds a date to the selected dates of the given calendar
id
- The calendar idvalue
- A DATE valueClears the list of selected dates for the given calendar
id
- The calendar idCreate a new calendar web component and return its ID
This function create a new calendar object and returns its ID. The calendar ID will be used in other functions to identify a calendar object. The function requires the name of the form field defining the WEBCOMPONENT form item.
DEFINE id INTEGER
LET id = fglcalendar.create("formonly.mycalendar")
name
- The name of the WEBCOMPONENT form field.Destroy a calendar object
This function releases all resources allocated for the calendar.
id
- The calendar idDisplays the calendar in the web component field
id
- The calendar idcy
- The current year (4 digits)cm
- The current month (1-12)Library finalization function.
This function has to be called when the library is not longer used.
Extracts selected date info from the web component field value.
This function can be called with the value of the web component field as parameter, after the selection action was fired, in order to get the last date selected by the user.
CALL getSelectedDateFromValue(id, value)
id
- The calendar idvalue
- The JSON-formatted string value returned in the calendar web component field.Returns the current month set for the last calendar display.
id
- The calendar idReturns the current year set for the last calendar display.
id
- The calendar idLibrary initialization function.
This function has to be called before using other functions of this module.
Check if a date is selected for the given calendar
id
- The calendar idvalue
- A DATE valueRemove a date from the selected dates of the given calendar
id
- The calendar idvalue
- A DATE valueDefines the view type of the calendar.
id
- The calendar idtheme
- The color theme, can be: FGLCALENDAR_THEME_DEFAULT, FGLCALENDAR_THEME_SAHARA, FGLCALENDAR_THEME_PACIFIC, FGLCALENDAR_THEME_AMAZON, FGLCALENDAR_THEME_VIOLA, FGLCALENDAR_THEME_CHILIDefines the color for the day cells in the current month.
id
- The calendar idcolor
- The color (#RRGGBB)Defines the relative size of calendar cells.
id
- The calendar idrw
- Relative widthrh
- Relative heihtDefines the names for the days (for localization).
By default, texts are in English, using this function to define localized day names, in the character set of your choice. The list of day names must be provided as string contained a comma-separated names.
CALL setDayNames(id, "M|T|W|T|F|S|S")
id
- The calendar idnames
- The list of day namesDefines the color for the days off (Sat / Sun)
id
- The calendar idcolor
- The color (#RRGGBB)Defines the color for the days out of current month
id
- The calendar idcolor
- The color (#RRGGBB)Defines the color for the selected days
id
- The calendar idcolor
- The color (#RRGGBB)Defines the color for the current day cell (today).
id
- The calendar idcolor
- The color (#RRGGBB)Defines the view type of the calendar.
id
- The calendar idtype
- The view type, can be: FGLCALENDAR_TYPE_DEFAULT, FGLCALENDAR_TYPE_ICON, FGLCALENDAR_TYPE_TEXT, FGLCALENDAR_TYPE_DOTSDefines if day names must be displayed in the calendar.
Default is TRUE.
id
- The calendar idshow
- A boolean to indicate if day names must be displayedDefines if day numbers must be displayed in the calendar.
Default is TRUE.
id
- The calendar idshow
- A boolean to indicate if day numbers must be displayedDefines if today must be rendered in the calendar.
Default is TRUE.
id
- The calendar idshow
- A boolean to indicate if today must be renderedDefines if the calendar must show week numbers on the left.
Default is FALSE.
id
- The calendar idshow
- A boolean to indicate if week numbers must be rendered| Overview | Package | Class | Index | No Frames | Frames | Prev | Next |


Verification code status
Status of a verification code previously shared with your patient.
| UUID | Status | Expires |
|---|---|---|
| {{.uuid}} | {{.status}} | {{.expires}} |
{{define "code/show"}}
{{template "head" .}} {{template "navbar" .}}Status of a verification code previously shared with your patient.
| UUID | Status | Expires |
|---|---|---|
| {{.uuid}} | {{.status}} | {{.expires}} |


Kittens have sharp little teeth and claws.Unfortunately they can’t differentiate between climbing the lovely cat tree you bought for them and climbing the drapes, couch, or your leg. And kitten teeth might miss the toy you’re playing with and chomp down on your hand.
Kittens are adorable. It’s difficult to resist these funny, lively little bundles of fur and energy. But when you adopt a kitten, you get a baby who needs a lot of time and attention from you. You must teach a kitten how to be a good companion.
When you adopt an adult cat, you know exactly what you are getting. You know what they look like, their body type, and coat. You can tell whether the cat is laid-back or active, vocal or quiet, cuddly, curious or shy.


Class: QueryCSS
| Defined in: | coffee/epoch.coffee |
Overview
Singelton class used to query CSS styles by way of reference elements. This allows canvas based visualizations to use the same styles as their SVG counterparts.
Variables Summary
- cache =
-
{}
- styleList =
-
['fill', 'stroke', 'stroke-width']
- container =
-
null
Class Method Summary
- . (void) purge() Purges the selector to style cache
- . (void) load() Called on load to insert the css reference element container.
- . (String) containerId(container) @param container The containing element for a chart.
- . (?) getStyles(selector, container) @param container HTML containing element in which to place the reference SVG.
Class Method Details
.
(void)
purge()
Purges the selector to style cache
.
(void)
load()
Called on load to insert the css reference element container.
.
(String)
containerId(container)
@param container The containing element for a chart.
.
(?)
getStyles(selector, container)
@param container HTML containing element in which to place the reference SVG.


Appendix 4: Installing in AWS-EB and Docker¶
“Cloud” servers are an increasingly popular low-cost alternative to locally-administered servers. A large number of these options are available [1] and as time goes by we hope to have instructions for multiple platforms and, of course, would greatly appreciate any community contributions along these lines.
Update August-2016: These instructions worked in August, 2015: there’s a pretty good chance one or more require some changes, or can be done a lot more easily, now.
Amazon Web Services Elastic Beanstalk¶
Amazon Web Services “Elastic Beanstalk” (AWS-EB) was the first cloud service where we successfully deployed CIVET: At the time of this writing AWS provides a generous free trial option, and their instructions worked the first time I tried. [2]
A coherent set of instructions can be found at http://docs.aws.amazon.com/elasticbeanstalk/latest/dg/create-deploy-python-django.html. Then follow these steps:
- Get an AWS account: https://aws.amazon.com/
2. In the instructions, skip the steps prior to the heading Configure your Django application for AWS Elastic Beanstalk unless you also want to try out the system locally (which is probably a good idea)
- Download the CIVET system from GitHub: https://github.com/civet-software/CIVET-Django [3]
- In the file djcivet_site/djciv_data/civet_settings.py set
PRODUCTION_MODE = True
[4]
5. Create a directory that you will use to deploy the system: for consistency with the remaining instructions it should be called AWS-CIVET though once you are comfortable with these instructions it could be named something different.
6. In that directory, copy the directory djcivet_site. Following the instructions, create directories named .elasticbeanstalk
and .ebextensions [5]
and create a file named requirements.txt Just copy the contents from the section below; you don’t need the pip freeze
step. Copy the code in the AWS-EB Configuration Files section below into the various files
Your directory will now look like
AWS-CIVET |-- .ebextensions | `-- 01-django_eb.config |-- .elasticbeanstalk | `-- config.yml |-- djcivet_site | |-- db.sqlite3 | |-- djcivet_data | |-- docs | |-- djcivet_site | | |-- __init__.py | | |-- settings.py | | |-- urls.py | | |-- wsgi.py | `-- manage.py `-- requirements.txt
7. Install the “eb” command-line tool per the instructions found at http://docs.aws.amazon.com/elasticbeanstalk/latest/dg/eb-cli3.html. Initializing this will require AWS access credentials, a process described at http://docs.aws.amazon.com/general/latest/gr/getting-aws-sec-creds.html.
8. Follow the instructions in the Deploy your site using AWS Elastic Beanstalk section to use “eb” in a terminal
application. It will take a minute or so for the eb create
process to complete—there’s plenty of feedback—and there is an
additional lag before the URL will be recognized.
9. You should now see the CIVET home page at the URL http://aws-civet-dev.elasticbeanstalk.com. This should take you to the
login page unless you’ve set REQUIRE_LOGIN = False
. Run through the options with the demonstration files to make sure
the site is working. If the site doesn’t come up at your first attempt, try reloading a couple of times until AWS
recognizes the URL.
10. When you are finished, enter eb terminate
and respond to the confirmation prompt with “AWS-CIVET-dev” in order to
stop the program.
AWS-EB Configuration Files¶
In
Django==1.8.3
In AWS-CIVET/.elasticbeanstalk/ create the file config.yml
branch-defaults:
default:
environment: AWS-CIVET-dev
global:
application_name: AWS_CIVET
default_ec2_keyname: aws-civet
default_platform: Python 2.7
default_region: us-east-1
profile: eb-cli
sc: null
In AWS_CIVET/.ebextensions/ create the file 01-djcivet_site.config
container_commands:
01_collectstatic:
command: "source /opt/python/run/venv/bin/activate && python djcivet_site/manage.py collectstatic --noinput"
option_settings:
"aws:elasticbeanstalk:application:environment":
DJANGO_SETTINGS_MODULE: "djcivet_site.settings"
PYTHONPATH: "/opt/python/current/app/djcivet_site:$PYTHONPATH"
"aws:elasticbeanstalk:container:python":
WSGIPath: "djcivet_site/djcivet_site/wsgi.py"
In djcivet_site/djcivet_site/settings.py
- Set
DEBUG = False
- Change SECRET_KEY since the downloaded version isn’t exactly secret
Handling of static files¶
As even a brief perusal of the web will affirm, the handling of static files in production versions of Django is, well, complicated. After joining legions of programmers past, present and future in beating my head against the wall on trying to get CIVET to access files internally in production as it does in the development mode, I gave up [6] and put the static resources referenced from inside templates on a directory on an external server, specifically http://civet.parusanalytics.com/civet_static/ Files that are read in views.py remain in the static/djciv_data folder, which works in both the development and production modes.
If you would like to modify the static files in the system—the main target would be CKEditor, unless you find our
mascot too insufferably cute—you can move this material (the contents of the directory static/djciv_data in the distribution) to
the server of your choice: just change the address in settings.STATIC_SOURCE
to point to the new location. [7]
Docker¶
Docker (https://www.docker.com/) is a highly popular, rapidly evolving [8] “containerization” system which will ultimately simplify the secure deployment of software in a wide variety of different systems. Briefly, “containers” are a more efficient extension of the concept of virtual machines —computers running programs which simulate the operation of other computers—by packaging all of the required software in an “image” file that is able to run on any system capable of running Docker. Because the operations within a container can be isolated from the host machine, and the contents of the container can be inspected and verified, this should provide a more secure (and efficient) environment than situations where a variety of software needs to be installed in order for a system to run, and that software potentially has access to all of the resources of the system. [10] Hence the excitement.
To date, I have successfully gotten the Docker container described below to run CIVET in development mode as a container on my Macintosh; I attempted to get it running on the Google Cloud but was unsuccessful; I have not tried any other configurations. As always, I will be happy to incorporate any additional suggestions into this documentation.
The guide I used for the deployment is http://michal.karzynski.pl/blog/2015/04/19/packaging-django-applications-as-docker-container-images/. This was not the first one I tried, and as indicated above, Docker is still evolving so you should make certain you are using a recent set of guides (and the instructions here may break sooner rather than later.)
Using Karzynski as a guide, here are the steps:
1. If you aren’t already using Docker, get a Docker account—there is a free option—and install Docker: the instructions for this will vary depending on your operating system; Karzynski’s instructions are just for Linux.
2. Set-up a directory to hold the Docker project—I called this Docker-CIVET, which corresponds to Karzynski’s local directory dockyard. I’ll be using Karzynski’s Docker image name DOCKYARD.
- Copy the directory djcivet_site into Docker-CIVET.
4. In Docker-CIVET, create the docker-entrypoint.sh and Dockerfile files from the code given below. Your directory will now look like
Docker-CIVET |-- docker-entrypoint.sh |-- Dockerfile |-- djcivet_site | |-- db.sqlite3 | |-- djcivet_data | |-- docs | |-- djcivet_site | | |-- __init__.py | | |-- settings.py | | |-- urls.py | | |-- wsgi.py | `-- manage.py `-- requirements.txt
5. Follow the remaining instructions to build and test the container with the user-name
from your Docker account and the
image-name
of your choice (e.g. docker-civet
).
Contents of docker-entrypoint.sh¶
#!/bin/bash
python manage.py migrate # Apply database migrations
python manage.py collectstatic --noinput # Collect static files
# Prepare log files and start outputting logs to stdout
touch /srv/logs/gunicorn.log
touch /srv/logs/access.log
tail -n 0 -f /srv/logs/*.log &
# Start Gunicorn processes
echo Starting Gunicorn.
exec gunicorn djcivet_site.wsgi:application \
--name djcivet_site \
--bind <IP_ADDRESS>:8000 \
--workers 3 \
--log-level=info \
--log-file=/srv/logs/gunicorn.log \
--access-logfile=/srv/logs/access.log \
"$@"
Contents of Dockerfile¶
############################################################
# Dockerfile to run a Django-based web application
# Based on an Ubuntu Image
############################################################
# Set the base image to use to Ubuntu
FROM ubuntu:14.04
# Set the file maintainer (your name - the file's author)
MAINTAINER Parus Analytics
# Set env variables used in this Dockerfile (add a unique prefix, such as DOCKYARD)
# Local directory with project source
ENV DOCKYARD_SRC=djcivet_site
# Directory in container for all project files
ENV DOCKYARD_SRVHOME=/srv
# Directory in container for project source files
ENV DOCKYARD_SRVPROJ=/srv/djcivet_site
# Update the default application repository sources list
RUN apt-get update && apt-get -y upgrade
RUN apt-get install -y python python-pip
# Create application subdirectories
WORKDIR $DOCKYARD_SRVHOME
RUN mkdir media static logs
VOLUME ["$DOCKYARD_SRVHOME/media/", "$DOCKYARD_SRVHOME/logs/"]
# Copy application source code to SRCDIR
COPY $DOCKYARD_SRC $DOCKYARD_SRVPROJ
# Install Python dependencies
#RUN pip install -r $DOCKYARD_SRVPROJ/requirements.txt
RUN pip install Django
RUN pip install gunicorn
# Port to expose
EXPOSE 8000
# Copy entrypoint script into the image
WORKDIR $DOCKYARD_SRVPROJ
COPY ./docker-entrypoint.sh /
ENTRYPOINT ["/docker-entrypoint.sh"]
Footnotes
| [1] | In particular, Heroku (https://www.heroku.com/) appears to be another Django-friendly option, and also offers free accounts. Using Heroku requires a [free] GitHub account. With the recent revelations about the sheer brutality of Amazon’s corporate culture, I’ve got some ethical issues with recommending this vendor, but for the moment it is the one widely-available cloud option where I know I can get CIVET to work. |
| [2] | Which, ahem, cannot be said for my multiple attempts to get the system running on the comparable Google service, though I’m sure it is possible to do this and would be happy to add instructions once someone has figured it out. |
| [3] | At some point I’ll put a “turn-key” directory on GitHub that will have all of the appropriate files. But not yet. |
| [4] | You can also leave PRODUCTION_MODE = False and set STATIC_SOURCE = "http://civet.parusanalytics.com/civet_static/" :
key here is that AWS needs to read static files from a remote server. |
| [5] | The ‘.’ in front of the file name means these will probably be invisible in most standard views of the AWS-CIVET directory: this is a Unix feature, not a bug. |
| [6] | Or simply took the approach that the Django system clearly prefers, depending on your perspective |
| [7] | An apparently popular approach for handling this is to use an AWS S3 server instance for external storage of static files: there are multiple descriptions on the Web describing how to do this. As it involves quite a few steps and I’ve got a perfectly good server already set up in the cloud, I went with that route instead. |
| [8] | Which is to say, a whole lot of moving parts which don’t quite always play well together and inconsistently documented: see http://blog.circleci.com/its-the-future/, http://blog.circleci.com/it-really-is-the-future/, and https://valdhaus.co/writings/docker-misconceptions/ [9] |
| [9] | Thanks to John Beieler for the links. |
| [10] | Or as the situation was recently explicated at our local software development meet-up, in reference to a certain institution that does not have a campus but “Grounds”, and I am not referring to Starbucks, “So which is it with your sysadmins? They want to make sure Docker is deployed securely? Well, there are plenty of ways to do that. Or they just don’t want to do any work? Then you’ve got a different set of problems.” |


This page cannot be found
{{ handler.request.uri }}
Details are on the server log.
HTTP 404: Not Found


| boolean |
_isFormPost
Property modified by setForm() to determine if the transaction
should be processed as a HTML form POST.
|
| int |
_polling_interval
The polling frequency, in milliseconds, for HandleReadyState.
|
| string |
_sFormData
Property modified by setForm() to the HTML form POST body.
|
| int |
_transaction_id
A transaction counter that increments the transaction id for each transaction.
|
Class YAHOO.util.Connect
Object | +--YAHOO.util.Connect
class
YAHOO.util.Connect
Field Summary [top]
Method Summary [top]
| Object |
abort
(<object> o)
Public method to terminate a transaction, if it has not reached readyState 4.
|
| object |
asyncRequest
(<string> method, <string> uri, callback, <string> postData)
Method for initiating an asynchronous request via the XHR object.
|
| Object |
createExceptionObject
(<int> tId, callbackArg)
If a transaction cannot be completed due to dropped or closed connections,
there may be not be enough information to build a full response object.
|
| Object |
createResponseObject
(<object> o, callbackArg)
This method evaluates the server response, creates and returns the results via
its properties.
|
| object |
createXhrObject
(<int> transactionId)
Instantiates a XMLHttpRequest object and returns an object with two properties:
the XMLHttpRequest instance and the transaction id.
|
| Object |
getConnectionObject
()
This method is called by asyncRequest and syncRequest to create a
valid connection object for the transaction.
|
| Object |
handleReadyState
(<object> o, callback)
This method serves as a timer that polls the XHR object's readyState
property during a transaction, instead of binding a callback to the
onreadystatechange event.
|
| Object |
handleTransactionResponse
(<object> o, <function> callback)
This method attempts to interpret the server response and
determine whether the transaction was successful, or if an error or
exception was encountered.
|
| Object |
initHeader
(<string> label,<string> value)
Accessor that stores the HTTP headers for each transaction.
|
| Object |
isCallInProgress
(<object> o)
Accessor to check if the transaction associated with the connection object
is still being processed.
|
| Object |
releaseObject
(<object> o)
Dereference the XHR instance and the connection object after the transaction is completed.
|
| Object |
setForm
(<string> formName)
This method assembles the form label and value pairs and
constructs an encoded POST body.
|
| Object |
setHeader
(<object> o)
Accessor that sets the HTTP headers for each transaction.
|
| Object |
setProgId
(id)
Member to add an ActiveX id to the existing xml_progid array.
|
Field Detail [top]
_isFormPost
boolean
_isFormPost
Property modified by setForm() to determine if the transaction
should be processed as a HTML form POST.
_polling_interval
int
_polling_interval
The polling frequency, in milliseconds, for HandleReadyState.
when attempting to determine a transaction's XHR readyState.
The default is 300 milliseconds.
_sFormData
string
_sFormData
Property modified by setForm() to the HTML form POST body.
_transaction_id
int
_transaction_id
A transaction counter that increments the transaction id for each transaction.
Constructor Detail [top]
YAHOO.util.Connect
YAHOO.util.Connect
Method Detail [top]
abort
Object
abort
(<object> o)
Public method to terminate a transaction, if it has not reached readyState 4.
- Parameters:
-
o
- The connection object returned by asyncRequest.
- Returns:
- void
asyncRequest
object
asyncRequest
(<string> method, <string> uri, callback, <string> postData)
Method for initiating an asynchronous request via the XHR object.
- Parameters:
-
method
- HTTP transaction method -
uri
- Fully qualified path of resource -
callback
- User-defined callback function or object -
postData
- POST body -
callbackArg
- User-defined callback arguments
- Returns:
- Returns the connection object
createExceptionObject
Object
createExceptionObject
(<int> tId, callbackArg)
If a transaction cannot be completed due to dropped or closed connections,
there may be not be enough information to build a full response object.
The object's property "type" value will be "failure", and two additional
unique, properties are added - errorCode and errorText.
- Parameters:
-
tId
- Transaction Id -
callbackArg
- The user-defined arguments -
errorCode
- Error code associated with the exception. -
errorText
- Error message describing the exception.
- Returns:
- object
createResponseObject
Object
createResponseObject
(<object> o, callbackArg)
This method evaluates the server response, creates and returns the results via
its properties. Success and failure cases(and exceptions) will differ in their defined properties
but property "type" will confirm the transaction's status.
- Parameters:
-
o
- The connection object -
callbackArg
- User-defined arguments to be passed to the callback -
isSuccess
- Indicates whether the transaction was successful or not.
- Returns:
- object
createXhrObject
object
createXhrObject
(<int> transactionId)
Instantiates a XMLHttpRequest object and returns an object with two properties:
the XMLHttpRequest instance and the transaction id.
- Parameters:
-
transactionId
- Property containing the transaction id for this transaction.
- Returns:
- connection object
getConnectionObject
Object
getConnectionObject
()
This method is called by asyncRequest and syncRequest to create a
valid connection object for the transaction. It also passes a
transaction id and increments the transaction id counter.
- Returns:
- object
handleReadyState
Object
handleReadyState
(<object> o, callback)
This method serves as a timer that polls the XHR object's readyState
property during a transaction, instead of binding a callback to the
onreadystatechange event. Upon readyState 4, handleTransactionResponse
will process the response, and the timer will be cleared.
- Parameters:
-
o
- The connection object -
callback
- User-defined callback object -
callbackArg
- User-defined arguments passed to the callback
- Returns:
- void
handleTransactionResponse
Object
handleTransactionResponse
(<object> o, <function> callback)
This method attempts to interpret the server response and
determine whether the transaction was successful, or if an error or
exception was encountered.
- Parameters:
-
o
- The connection object -
callback
- - User-defined callback object -
callbackArg
- - User-defined arguments to be passed to the callback
- Returns:
- void
initHeader
Object
initHeader
(<string> label,<string> value)
Accessor that stores the HTTP headers for each transaction.
- Parameters:
-
label
- The HTTP header label -
value
- The HTTP header value
- Returns:
- void
isCallInProgress
Object
isCallInProgress
(<object> o)
Accessor to check if the transaction associated with the connection object
is still being processed.
- Parameters:
-
o
- The connection object returned by asyncRequest
- Returns:
- boolean
releaseObject
Object
releaseObject
(<object> o)
Dereference the XHR instance and the connection object after the transaction is completed.
- Parameters:
-
o
- The connection object
- Returns:
- void
setForm
Object
setForm
(<string> formName)
This method assembles the form label and value pairs and
constructs an encoded POST body. Both syncRequest()
and asyncRequest() will automatically initialize the
transaction with a HTTP header Content-Type of
application/x-www-form-urlencoded.
- Parameters:
-
formName
- value of form name attribute
- Returns:
- void
setHeader
Object
setHeader
(<object> o)
Accessor that sets the HTTP headers for each transaction.
- Parameters:
-
o
- The connection object for the transaction.
- Returns:
- void
setProgId
Object
setProgId
(id)
Member to add an ActiveX id to the existing xml_progid array.
In the event(unlikely) a new ActiveX id is introduced, it can be added
without internal code modifications.
- Parameters:
-
string
- id The ActiveX id to be added to initialize the XHR object.
- Returns:
- void


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Web basics
You (and your browser) request to the Internet, which requests to servers (closet computers hosting files)
HTML: “HyperText Markup Language” – base for all web pages
Made up of: text content (what you see), markup (what it looks like)
Can contain references to other documents (images/videos/etc), links to other pages
HTML tags - in all cases, replace parentheses with greater/less than signs to make functional
Bold (b)bold(/b)
Emphasis (em)italicize(/em)
Image tag: (img): (img src=”url” alt=”text”) [nice to include alt just in case url doesn’t work][“void” tag doesn’t need a closing tag]
Break tag (br) makes a line break [another void tag], stack if you want multiple breaks
Paragraph tag (p) content (/p) will render as a paragraph
Difference between break and paragraph tags: former is inline (just text), whereas paragraph makes a block (with height and width)
Span and div both contain content (inline and block, respectively): (span) text (/span), (div) text (/div)
Attributes: (tag ATTR=“value”)contents(/tag)
Anchors make links (a href = www.reddit.com)derp(/a) links to Reddit and displays “derp”
Complete HTML document format
(!DOCTYPE HTML)
(html)
(head)
(title)Title text(/title)
(/head)
(body)
(b)content(/b)
(/body)
(/html)


Motivating the trainees
Alex Lakatos,
@lakatos88
or
<EMAIL_ADDRESS>Alex Lakatos, Training of Trainers, EBS, 26.03.2014
Types of motives
Vocational
Self Development
Social
Hierarchy of needs
Physical needs
Security needs
Social needs
Self-fulfilment
Needs Theory
No need can ever be completely satisfied
Needs are constantly changing
Methods of Motivation
Motivation by force
Motivation by enticement
Motivation by identification or ego involvement
Importance of Motivation in Learning
Techniques of Motivating Trainees
Techniques of Motivating Trainees
Show the need for the lesson
Arouse and maintain interest
Increase chances of early success
Give recognition and credit where due
Techniques of Motivating Trainees
Praise, avoid blame
Avoid emotional responses
Be professional
Set clear goals
Techniques of Motivating Trainees
Provide necessary facilities/resources
Give responsibilities
Treat trainees as special individuals
Give correct guidance
Gamification
Gamification Techniques
Rewards
Competition
Feel more like games
Top 5 Rules
Get executive buy-in and make it count
Explain the rules of the game
Create a master communication plan
Reward employees who spread the word
Ask for feedback, and do something with it
Top 5 Pitfalls
Forgetting about motivation
Just “tacking on” gamification to your current strategy
Not getting senior alignment from the get-go
Not giving people access to share their success
Being predictable
Thanks
Alex Lakatos @lakatos88
Slides
: alexlakatos.github.io/slides/motivating-your-trainees
Social


Naveenkumar
Adi Raman
- 0438 9797 869
-<EMAIL_ADDRESS>- arnaveenkumar.github.io/cv/
- Montreal, QC, Canada
I am a self-motivated professional has a passion to learn and work on the new technologies which would shape my professional career. Being a team player in my previous work gave me an exposure to work in teams which helped me in improving my interpersonal skills. Right from my bachelors, I showed great zeal in exploring the topics in AI which motivated me to motivated me to pursue a Masters in Computer Science at the University of Montreal. Some of my coursework includes Data Science, Machine Learning, Deep Learning, and Blockchain. My previous work and academic background built confidence in me to look for an opportunity to expand my skills and interests in related areas.
Employment
Programmer Analyst Trainee
Cognizant, India
Developed an application to maintain the creation, subscription & premium payment of various insurance policies for customers.
- Modified the existing project and developed a proposed application with required changes.
- Tested in real-time environment.
- Conducted several code reviews to make it a bug-free application.
- Worked on a team in designing the technical details.
- Languages used: C#.NET, SQL, HTML, CSS3.
Academic projects
Blockchain-based Digital Certification and Certification Revocation, University of Montreal
The proposed work is to maintain a list of transaction details of certificate revocation and granting verifier ability to view all the versions of a revoked certificate and preserving the simplicity of multiple certificate verification at verifier application.
- Tools: Ubuntu command-line terminal, Ropsten Ethereum TestNet and IPFS Linux package.
- Project link: https://github.com/arnaveenkumar/blockchain_cert
Facial expression recognition using Convolutional Neural Networks, University of Montreal
Implemented using Convolutional Neural Networks to predict and recognize human facial expressions. Inclusion of Adagrad optimization algorithm improved the results.
- Dataset: CK+ Dataset
- Test accuracy: 88.97%
- Tools: Spyder IDE, Google Colab.
- Project link: https://github.com/arnaveenkumar/mlproj
Kaggle (Joint competition - McGill University and University of Montreal)
The goal of this project is to predict the human-drawn images. Developed using Convolutional Neural Networks. The feature design includes normalization of image pixel intensity and data augmentation.
- Dataset: Human-drawn images dataset.
- Test accuracy: 77.14%
- Tools: Spyder IDE, Google Colab.
- Project link: https://www.kaggle.com/c/ift3395-6390-f2018
Certifications
- Certified EMC Academic Associate, EMC Cloud Infrastructure and Services
- Artificial Intelligece Planning, University of Edinburgh
- Business English Level B1, University of Cambridge
- French Level 1, Here and Now-The French Institute
Volunteer experience
Worked as a 'Flood relief volunteer' to help flood victims during Dec' 2015 South Indian floods. Organized medical camp for the affected communities.


Day 1 - Icosahedron
Day 2 - Rectangular Prism
Day 3 - Laughing Man
Day 4 - Intelligent Qube
Day 5 - A Tale of Two Cubes (Orthographic Camera)
Day 6 - A Tale of Two Cubes (Perspective Camera)
Day 7 - Harmony of the Knots
Day 8 - Nuka-Cola
Day 9 - Hell's Kitchen
Day 10 - Spotlight and Shadows
Day 11 - Shadow DOM
Day 12 - Walking and Strafing
Day 13 - Extruded Triangle
Day 14 - Haunted Spheres
Day 15 - Golfball and Tee
Day 16 - Clickable Cubes and Walking
Day 17 - Ninja Brewing Bottle
Day 18 - Reposition Objects
Day 19 - Destroy Objects
Day 20 - Create Objects


SVcurator
Home
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Variant: {{variant_info.variant_name}}
Size:{{variant_info.variant_size}}
{{bar_complete}}/{{bar_total}}
<
>
Q1:
Does a
{{variant_info.del_ins}}
exist at this site that has a size between
{{var_minus_20}}
bp and
{{var_plus_20}}
bp?
{% for subfield in form.radios %}
{{ subfield }} {{ subfield.label.text }}
{% endfor %}
Q2:
Does any other variant >= 20bp exist at this site?
{% for subfield in form.radios2 %}
{{ subfield }} {{ subfield.label.text }}
{% endfor %}
Q3: Genotype:
Select the genotype for this variant
{% for subfield in form.radios3 %}
{{ subfield }} {{ subfield.label.text }}
{% endfor %}
Q4: Genotype Confidence:
Note confidence in the selected genotype:
{% for subfield in form.radios4 %}
{{ subfield }} {{ subfield.label.text }}
{% endfor %}


Afterschool helps parents find fun, educational and safe activities for their children to engage in. With afterschool, parents can help their children use their most crucial developmental years to get involved with their community, learn new skills and mature.
Get Started
Elementary school students spend a significant amount of their time after school. These students can potentially do beneficial activities that they cannot experience from sitting through their school classes.
1 in 3 American children are overweight or obese.
What was not a problem decades ago is a problem today because children live an inactive lifestyle. Children rarely need to walk, and most time after school is spent on the screen.
1 in 4 American children are on their own after school.
That is 1,133,989 children on their own in the afternoon hours who are in need of finding beneficial activities to do.
Students spend 9 hours on media every day.
Studies have shown that excessive media use can lead to attention problems, school difficulties, sleep and eating disorders, and obesity.
Aids Development
Children who participate in after school activities that engage their intellect or physical bodies benefit much more socially, physically, and psychologically than the children who spend their time on media.
Decreases Stress
Children feel stressed as adults do, but the issue is that they do not know how to deal with it. After school activities can play a significant role in relieving stress through a fun and low pressure environment.
Develops Skills
Children can grow in their self-esteem by learning new skills and interacting with their peers. For example, reading as a child has a positive correlation with the folowing: communication skills, logical thinking skills, concentration, and discipline.
Sources: Afterschool Alliance, Pew Research Center, She Knows, Early Moments, Let's Move


Module: Forwarder::Meta
Defined Under Namespace
Classes: ObjectContainer
Instance Method Summary (collapse)
- - (Object) eval_body(application, as, to, with)
- - (Object) eval_chain(names, context)
- - (Object) eval_receiver(name, context)
- - (Object) eval_receiver_body
- - (Object) eval_symbolic_receiver(name, context)
Instance Method Details
- (Object) eval_body(application, as, to, with)
4 5 6 7 8 9 10 11 12 |
# File 'lib/forwarder/meta.rb', line 4 def eval_body application, as, to, with lambda do |*args, &blk| arguments = ( [ with ].flatten + args ).compact # rcv = __eval_receiver__( to ) rcv = Meta.eval_receiver to, self # p as: as, to: to, rcv: rcv, args: arguments, app: application, self: self rcv.send( as, *arguments, &(application||blk) ) end end |
- (Object) eval_chain(names, context)
14 15 16 17 18 |
# File 'lib/forwarder/meta.rb', line 14 def eval_chain names, context names.inject context do | ctxt, name | Meta.eval_symbolic_receiver name, ctxt end end |
- (Object) eval_receiver(name, context)
20 21 22 23 24 25 26 27 28 29 30 31 32 |
# File 'lib/forwarder/meta.rb', line 20 def eval_receiver name, context case name when Proc context.instance_eval( &name ) when String, Symbol # p name: name, context: context Meta.eval_symbolic_receiver name, context when ObjectContainer name.object when Array Meta.eval_chain name, context end end |
- (Object) eval_receiver_body
33 34 35 36 |
# File 'lib/forwarder/meta.rb', line 33 def eval_receiver_body lambda do | name | end end |
- (Object) eval_symbolic_receiver(name, context)
38 39 40 41 42 43 44 45 |
# File 'lib/forwarder/meta.rb', line 38 def eval_symbolic_receiver name, context case "#{name}" when /\A@/ context.instance_variable_get name else context.send name end end |


TIFF CHANGE INFORMATION
This document describes the changes made to the software between the
previous and current versions (see above). If you don't
find something listed here, then it was not done in this timeframe, or
it was not considered important enough to be mentioned. The following
information is located here:
MAJOR CHANGES:
- Now builds with CMake 2.8.9 and newer (previously required 3.0.0)
CHANGES IN THE SOFTWARE CONFIGURATION:
- CMakeLists.txt / CMake
- Supports CMake 2.8.9 and later.
- Add missing file which wasn't being distributed, causing
unit tests to fail.
- Make shared/static library building configurable.
- CMake reads all version information directly from
configure.ac to avoid duplication of values.
- CMake builds are now included in 'distcheck' target.
- Makefile.am
- Autotools 'make distcheck' now tests the CMake-based build
if CMake is available.
CHANGES IN LIBTIFF:
- Fixes to avoid undefined behaviour of signed types (C
standard compliance).
- Fixes to avoid possible isses when casting to unsigned char.
- Fixes to avoid undefined behaviour with shifts.
- Fix generation of output with 16 bit or 32 bit integer, when
byte swapping is needed, in horizontal predictor (#2521).
- Fix decoding when there is a single pixel to decode (unlikely
case...) and byte swapping is involved.
- Add add explicit masking with 0xff before casting to uchar in
floating-point horizontal differencing and accumulation routines.
- Eliminate requirement for and use of 64-bit constant values.
CHANGES IN THE TOOLS:
- tiffgt
- Silence glut API deprecation warnings on MacOS X.
- fax2ps
- Detect failure to write to temporary file.
CHANGES IN THE CONTRIB AREA:
Last updated $Date: 2016-09-25 20:05:47 $.


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Patrick Galligan
Digital Archivist, Rockefeller Archive Center
www.rockarch.org
What it is
Why we need it
Put a screenshot here of Find-It results
Ref_id search
Real-time AS API query
Lightweight
Open source
Engage and Involve
Solution-centered
Small iterations
Kill your darlings
No duplication of data
Let machines do the work
Making workflows efficient
Just say no to monoliths
Flexibility, agility, and maintability
Find-It's not a long-term solution
Still more steps than necessary
Need a requesting system with an API
Find me on Twitter: @p_galligan


Crash Course in web2py (part 2 - web forms)
In the last tutorial I showed you the basics of web2py, and we even created a web app in less than ten minutes! In Part 2, I’ll show you how to create a basic web form that stores the submitted data in a SQLite database.
Assumptions:
- You installed web2py
- You know what a web form is
Anyway - let’s get going …
MVC Architecture
Real quick, web2py utilizes the Model View Control (MVC) architecture:
- Model - application data
- View - portions of the application data that the end user is allowed to see
- Controller - controls the application workflow and the logic used to display the data to the end user
Wikipedia has a great diagram of how each part works together-
web2py essentially makes it easier for developers to design each part and create the overall workflow to ensure that each part works together. I hope that’s clear. If not, please comment, and I’ll go a little more in depth. It’s important to understand this, as we will be utilizing this form of development from this point on.
Okay - Go ahead and start the web2py server, go to the admin interface, and then create a new application. I named mine “form”. Feel free to come up with something a bit more creative.
Model*
Open db.py in the Model’s section and append the following code below, which defines the database schema. There are three required fields, plus a unique ID that is automatically created by web2py.
db = DAL('sqlite://webform.sqlite')
db.define_table('register',
Field('first_name', requires=IS_NOT_EMPTY()),
Field('last_name', requires=IS_NOT_EMPTY()),
Field('email', requires=IS_NOT_EMPTY()))
View
Create a new HTML file named default/display_your_form.html
And then add the following code to call the form-

Web Form

Inputs:

{{=form}}

Submitted variables:

{{=BEAUTIFY(request.vars)}}

Controller
Finally, comment out all the current code in the default.py file and add in this new function to define the form-
def display_your_form():
form = SQLFORM(db.register)
return dict(form=form)
Test
Check out your amazing form at http://<IP_ADDRESS>:8000/form/default/display_your_form.html. Now, Go ahead and test the form to make sure it works. If done correctly, after you input the data and hit submit, the values are displayed.
Recap
We used the MVC approach to make this form:
- Model: defines database table and schema
- View: calls the form
- Controller: constructs the form to be called
Alright - Hold off on deploying the app for now, as we will continue to add features in the next part of this series. Pretty soon we’re going to have the best form on the Internet. Give yourself a pat on the back. You went from creating a useless “hello world” app, to creating something functional - all in less than 20 minutes!
You can find the code for db.py, display_your_form.html, and default.py here.
In the next tutorial we’ll be adding in form validation. Brace yourself.


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