How much: $75 standard, $45 student
Where: Dunedin Public Art Gallery, The Octagon
When: 9am, Friday 31 October 2014
What: See the schedule (PDF)
UX Design Day 2014 is a one-day mini conference held at the Dunedin Public Art Gallery on Friday, 31st October, 2014. Attend if you have an interest in digital design or if you want your customers to love your business and products. Or both!
Our speakers will introduce User Experience Design, talk about what good design can do for your business and why it’s so important. With talks from some of the country’s top experts, this day will inspire and educate, helping you incorporate best practices & principles into your workflow.
If you’re an ace designer, never fear — you’re bound to learn something new from the wide variety of talks for all experience levels. UX Design Day is also a great opportunity to network with New Zealand’s vibrant design community.
Prepare for an action-packed day with a hands-on workshop and engaging talks. Of course we’ll make sure you’re well watered and fed (including dietary requirements)!
Questions? Contact us on Twitter, Facebook, or send an email.
See you there!
Veronica, Kitty, and Benjamin
How much: $75 standard, $45 student
Where: Dunedin Public Art Gallery, The Octagon
When: 9am, Friday 31 October 2014
What: See the schedule (PDF)
There are lots of great design resources available online and in bookstores. Here are just a few of them.
Click one below or play resource roulette!
Follow us on Twitter, Like us on Facebook, all that. Use hashtag #uxdd.


printim
The printim
command
was introduced in version 1.8. It produces a PNG, GIF, or JPG formatted image file based on the current
contents of the GrADS metabuffer, which is the stuff displayed in the
graphics window, minus any widgets. printim
will work in
batch mode.
outxwd
The outxwd
command draws the contents of the graphics display window to a file in XWD (X window dump) format. It does not work in batch mode.
wi
The wi
command was deprecated long ago and is not included in version 1.9 or later.
1. Set-up the GrADS metafile
The first step in creating hardcopy image output is to invoke the enable print
command -- this opens the output file
and enables
GrADS to direct image information to that file. If the file exists, it will be overwritten.
2. Display the image
The next step is to display the graphic that you want to print. When
you have finished, issue the print
command. GrADS copies the vector instructions used to create the current
display into the output file in a GrADS metacode format.
For multiple images in your metafile, use clear
, create the new image, and then print
again.
3. Close the GrADS metafile
There are three way to close the output file:
disable print
reinit
quit
GrADS metacode files may be translated into postscript using the GrADS
external utilities gxps
and gxeps
. Both utilities will
prompt for input and output filenames, unless they
are provided on the command line. The input filename should be the
file created by the enable
print
command. The output filename can be anything, but a
".ps" extension is conventional. Any existing file with this name will
be overwritten. Once the output file is created, you may print it
using UNIX print commands. Please consult the references pages for
gxps
and
gxeps
to see
all the command line arguments and options.
gxps
and gxeps
are not
GrADS commands. They must be executed from the UNIX command line, or
preceded by a !
and
executed as a shell command from the GrADS command line.
There is a shortcut for creating an encapsulated postscript (EPS) file directly from within a GrADS session: use the print
command without invoking the enable print
command first. This shortcut allows the user to skip the steps of creating the GrADS metafile and invoking the external utility gxeps
. However, using this shortcut means there can only be one image per file, and none of the options available when invoking gxeps
directly can be used.
GrADS metacode files may be displayed using the GrADS external utility
gxtran
. The input
filename should be the file created by the enable print
command. If the GrADS metafile contains more than one image, gxtran
will animate them.
The animation can be automatic or controlled by the user with carriage
returns. Please consult the gxtran reference page to
see all the command line arguments and options.
The GrADS metafile Viewer (GV) allows you to view and manipulate GrADS graphics output files using Windows 95/NT. There are two files to download:
To open the metafile simply double click on a file listed in the File Manager or Explorer, drag and drop the file onto GV, or use the standard Open dialog box. GV assumes that default extension of GRADS metafiles is GMF. If your file includes more than one picture you can browse through pages using the keyboard keys (PageDown and PageUp) or the toolbar buttons.
Use the View commands and the View/Options dialog box to customize the image -- display it as black-and-white or color, change the line thickness, or clip and enlarge any part of the image. Use the right mouse button to access the most commonly used features.
There are two ways to save separate pages of a GRADS metafile as Windows Metafile (WMF): 1) use the File/Save Page As command, or 2) use the Edit/Copy command to copy the current page to the Windows Clipboard and then Edit/Paste it in your favorite Windows application that handles Windows Metafiles.
Use File/Print command to print a current document to any printer (you do not need a Postscript printer). Use File/Print Preview to display the active metafile as it would appear when printed.


GeoMesa
1.2.6
User Manual
Introduction
Architecture Overview
Installation and Configuration
GeoMesa Data Stores
Accumulo Data Store
Kafka Data Store
HBase/Bigtable Data Stores
Cassandra Data Store
Modules and Libraries
GeoMesa GeoServer Plugins
GeoMesa Spark
Command Line Tools
Data Management
Examples
Developer Manual
Tutorials
GeoMesa
Docs
»
User Manual
View page source
User Manual
¶
Introduction
What is GeoMesa?
Community and Support
License
Architecture Overview
GeoMesa and GeoServer
Integration with GeoMesa
Key-value stores and Z-curves
GeoMesa’s Index
Installation and Configuration
Prerequisites and Platform
Versions and Downloads
Installing from the Binary Distribution
Building from Source
Installing the Accumulo Distributed Runtime Library
Setting up the Command Line Tools
Installing the GeoMesa GeoServer Plugins
Upgrading
Security Concerns
GeoMesa Data Stores
Reserved Words
Accumulo Data Store
Installation
Configuration
Creating a Data Store
Using the Accumulo Data Store in GeoServer
Visibilities
Indexing Strategies
Explaining: Query Plans
Iterator Stack
Kafka Data Store
Installation
Usage/Configuration
Data Producers
Data Consumers
Live Mode
Replay Mode
Using the Kafka Data Store in GeoServer
Command Line Tools
HBase/Bigtable Data Stores
HBase
Bigtable
Cassandra Data Store
Limitations
Index Structure
Query Planning
Using the Cassandra DataStore Programmatically
Using the Cassandra Data Store in Geoserver
How you can contribute
Modules and Libraries
GeoMesa Blob Store
GeoMesa Convert
GeoMesa Jobs
GeoMesa Metrics
GeoMesa Native API
GeoMesa Processes
GeoMesa Raster
GeoMesa Stream Processing
GeoMesa Utils
GeoMesa Web Data
GeoMesa GeoServer Plugins
Installation
Creating a Data Store
Publish a GeoMesa Layer
Preview a Layer
Analysis with WPS
Configuration
GeoMesa GeoServer Community Module
GeoMesa Spark
Usage
Examples
Command Line Tools
Installation
Installing SFT and Converter Definitions
Running the command line tools
Command overview
Kafka command line tools
Data Management
Index Structure
Attribute Indices
Customizing the Z-Index
Customizing Index Creation
Accumulo Visibilities
Splitting the Record Index
Moving and Migrating Data
Examples
Feature Type Management
Ingesting Data
Exporting Features


I never liked that term ‘remote’. Whether in relation to me or to colleagues. It is exclusionary. And betrays a sense of ‘otherness’ that I feel is unhealthy for team cohesion. I understand that not everyone finds it easy or productive to work mostly apart from daily, in person, social interaction. But success is an arm in arm effort and the strategies to build successful teams work regardless of geography if we reward the right behaviors.
Every now and then, discussions around distributed teams, the hiring practices of a distributed team and all the details that go along with that come up whether in Twitter or in person with friends and co-workers.
If you follow me on twitter, it won’t be news that I work ‘remotely’ and have been for a few years since a couple of family moves. It has been a mostly successful experience that still goes on. I am not only still on the team, but I have since seen a promotion, have grown the team and am now growing it even more by hiring some level I DBAs to mentor and help grow.
So yes. It’s been and continues to be a success story. Even in a company that isn’t remote first.
What I do not talk about often is the story of when I didn’t succeed as a remote employee, resulting in being laid off years ago. And I think it’s important to talk about that as well. Because as gratifying as it may be to claim full credit at my current success, I’d be dishonest to pretend that success was all me.
The past few years were not the first time I’ve worked from home. However, when I’ve done it before it ended in lack of engagement and ultimately in my value being seen as dispensable, replaceable. I simply didn’t have a team around me that valued communication or the people management apparatus that understood what i did day to day besides just showing my face in meetings.
So what is it that makes the same person excel or fail miserably at the same thing? Let me share some of my experience around this and how I think it may matter to your organization.
The team
There is never a ‘successful remote employee’. There is always a ‘team that communicates well’ and this goes beyond locality and who’s physically where. I’ve seen Engineering teams in the same office fail spectacularly at basic communication around work in progress or around setting expectations for delivering feature work while other teams with three different timezones producing results efficiently. This is all a roundabout way for me to say that my success is really my team’s success. There are a number of habits that make my team so good at this and they are worth calling out
- Most conversations happen in chat. Keeps everyone in the loop
- Joining stand-up from laptops even for those in the office. Everyone is on the same communication channels
- In meetings where a large portion of the group is in a big room, someone is designated as representative of those calling in. That way we can still get the group’s attention and ask questions if we want to
These are not just behaviors that are useful when not the whole team is local. Life happens, co-workers who are parents have to work from home sometimes, errands, all sorts of things. Allowing for your people to still be part of important discussions while living life is a real sign of wanting life balance for your people and not just talking about it.
The effects of human laziness
Humans are by nature lazy, we do not seek what we do not see. And we make presumptions about things we do not know for a fact. These natural shortcuts our brains take will always show themselves in the systems we design. I have seen it multiple times where teams have developed and deployed software that did not actually meet all the needs of the business only for retrospectives later to declare the ‘root cause’ a failure in communication.
Noting the skepticism I have towards any mention of ‘root cause’, it amazes me how often organizations will over and over again prove Conway’s law in this specific dynamic. Teams that only communicate to humans in front of them will also fail to involve all stakeholders in decisions in design, will fail to notify other teams when they are about to ratchet up customer involvement in their new beta and will create products that ultimately are a reflection of how the team itself handles its communication. It has been my experience that teams that put the effort into being good at communication, at writing things down, at making everything explicit and as little as possible implicit, that also find the most alignment and find all their members rowing in the same direction. Having people work from wherever they live at this point becomes the cherry on top.
Diversity and inclusion
Here’s a harsh truth: If you require hiring in a specific geographic location but pinky swear that you value diversity and inclusion, then you are at best misguided as to how to create diverse teams and how to keep underrepresented groups included. Intent doesn’t matter when hiring practices have the opposite impact. Let’s say you are a company in the bay. Building a new product that has to solve large scale problems ,you know you need experienced distributed systems engineers. What average number of years experience do you think you will find in the bay?
If you decide to open the experienced roles to ‘anywhere US’ but none of your Engineering managers have managed people not local to them before or worse are not interested in that, you are hiring experienced engineers to see them fail. Remember, People quit managers not companies. Engineering managers who have not in the past honed their emotional intelligence to manage people not necessarily physically near them will not retain experienced engineers who have built the experience to know all the skeletons in your infrastructure. One thing I have learned in the last few years is that backchannels are a strong force in this industry. And people will tell each other whether that ‘Anywhere US’ position company Foo just posted is really something that is setup for success in a team that uses mature communication skills or if it is a Hail Mary because that position was open for nine months in the Bay and no one is applying and now that company is desperate to fill it.
We are in an industry that has far more demand for talent than is available to hire. And that’s an advantage for us as workers. Somehow I see companies clearly state hiring as a potential challenge and something that’s a competitive edge, yet they hamstring those same efforts out of the gate by requiring that people relocate. Not only does this make hiring harder but it is also not inclusive.


{% include meta.html %}
{% seo %}
Current time in NY, USA
{{ site.name }}
{{ site.description }}
Blog
About
{{ content }}
{% include analytics.html %}


Solar System Change question theme using top-right menu 00.00 Instructions Test your knowledge of the universe with our astonomy quiz! You will have 8 seconds to answer each question Your score will be displayed at the end. Play on your phone - I'm fully responsive. Question tracker


{% extends "base.html" %} {% block css %} {% endblock %} {% block js %} {% endblock %} {% block body %}
The air you breathe is filled with various pollutants released as a result of human activity. While a low pollution level may be safe, high levels of pollution carry health risks. In order to track air quality, the federal Environmental Protection Agency (EPA), the California Air Resources Board (CARB), and various district-level authorities have established permanent air quality monitoring stations to track air quality in real-time.
Environmental Justice is the idea that all people, regardless of race, national origin, or income, should be treated equally and fairly with respect to development, implementation, and enforcement of environmental laws. Environmental justice advocates seek to raise awareness about and create policy to remedy the undue environmental burdens suffered by low-income communities and communities of color.
Vulnerable communities are more likely to experence higher air pollution, and therefore greater health risks, than other communities. Before this injustice can be fixed through policy, however, vulnerable communities must have effective air monitoring to gather concrete evidence of the injustice. Unfortunately, over 50 million Americans live in areas without air monitoring, and vulnerable communities are more likely to live in such areas. This makes it difficult for communities to even realize that air quality problems even exist!
This mapping tool aims to raise awareness about the air monitoring (or lack of air monitoring) in your area. Input your address or ZIP code below to be shown a map of the air monitors in your community and a summary of air quality in your area.
This project is open-source! View or contribute on Github


Modern and Minimalistic
Bounce was built on the awesome Twitter Bootstrap so you have easy access to all of its features.
Awesome Typography
Bounce uses free open-source Google Web Fonts as well as the iconic Font Awesome.
Launch Ready
Bounce comes with several pages, giving you a head start on creating a unique design.
Header
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus ut felis arcu, sed iaculis eros.
Header
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus ut felis arcu, sed iaculis eros.
Header
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vivamus ut felis arcu, sed iaculis eros.


&$556;
Rune Township - 10000 Adena
Town of Goddard - 10000 Adena
Town of Aden - 53000 Adena
Town of Oren - 59000 Adena
Heine - 100000 Adena
The Town of Giran - 87000 Adena
The Town of Dion - 88000 Adena
The Town of Gludio - 85000 Adena
Orc Village - 13000 Adena
Dwarven Village - 4400 Adena
Den of Evil - 7500 Adena
Plunderous Plains - 1600 Adena
Iceman's Hut - 3500 Adena
Crypts of Disgrace - 9600 Adena
Pavel Ruins - 5300 Adena
Mithril Mines - 17000 Adena


HackCortona2017
menu
Face to sound
The recognizer of emoctions that sign for you!
Happiness
Anger
Webcam recognizer
Sadness
Neutral


黒の手番です。52 はどう動かしますか？
Unlimited
pips Black=158 White=144
(CorrectAnswer= 0/0, ErrorScore= 0)
1. 24/17 eq:+0.039 45.96% (G:12.15% B:0.59%) 54.04% (G:17.30% B:0.57%) 2. 22/17 8/6 eq:-0.152 (-0.191) 40.78% (G:10.99% B:0.57%) 59.22% (G:22.24% B:0.94%) 3. 22/17 4/2* eq:-0.213 (-0.253) 40.15% (G:10.26% B:0.47%) 59.85% (G:25.89% B:1.35%) 4. 22/15 eq:-0.225 (-0.264) 39.09% (G: 9.50% B:0.48%) 60.91% (G:23.88% B:1.13%) 5. 24/22 6/1 eq:-0.208 (-0.247) 37.86% (G: 9.16% B:0.33%) 62.14% (G:18.99% B:0.66%)


A jQuery plugin implementing table rows selection by mouse and keyboard comfortably.
Of course, there is a number of implementing the same kind of functionality as this one. But we needed something fairly light, mimicking the interface of Windows Explorer as much as possible with all the useful events fired... and writing it seemed easier (and more fun!) than bending anybody else's work.
It is a typical jQuery plugin built using the Widget Factoy, use it as such. On any selection change, select or unselect events are fired with some additional data that can be used to implement a variety of things. See the simple demo for the basic usage and the groups demo for something a bit more sophisticated.
It is a jQuery plugin, so you need jQuery and jQuery UI (widget at least).


This is How to Write a Blog Post Title
Posted by John Ansa March 18, 2020
Headeing H1
Headeing H2
Headeing H3
Headeing H4
Headeing H5
Headeing H6
Welcome to the EduAnsa Educational site.
The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.
Lorem ipsum dolor sit amet consectetur adipisicing elit. Voluptatum ratione soluta, quos quia maxime eos? Unde nisi hic amet ipsa quam distinctio porro laboriosam nam odio impedit non officiis sint, atque enim numquam nesciunt vero, cumque incidunt.


Revised 07/14/2005
Background ||| Techie-Stuff ||| Instructions
This page performs logistic regression, in which a dichotomous (two-value) outcome is predicted by one or more variables. It generates the coefficients of a prediction formula (and standard errors of estimate and significance levels), and odds ratios (with their 95% confidence intervals).
Ordinary regression deals with finding a function that relates a
continuous outcome variable (dependent variable y) to one or
more predictors (independent variables x1,
x2, etc.). Simple linear regression assumes a function
of the form:
y = c0 + c1 * x1 +
c2 * x2 +...
and finds the values of c0, c1, c2, etc.
(c0 is called the "intercept" or "constant term").
Logistic regression is a variation of ordinary regression, useful when the observed outcome is restricted to two values, which usually represent the occurrence or non-occurrence of some outcome event, (usually coded as 1 or 0, respectively). It produces a formula that predicts the probability of the occurrence as a function of the independent variables.
Logistic regression fits a special s-shaped curve by taking the linear regression
(above), which could produce any y-value between minus infinity and
plus infinity, and transforming it with the function:
p = Exp(y) / ( 1 + Exp(y) )
which produces p-values between 0 (as y approaches minus infinity)
and 1 (as y approaches plus infinity). This now becomes a special
kind of non-linear regression, which is what this page performs.
Logistic regression also produces Odds Ratios (O.R.) associated with each predictor value. The odds of an event is defined as the probability of the outcome event occurring divided by the probability of the event not occurring. The odds ratio for a predictor tells the relative amount by which the odds of the outcome increase (O.R. greater than 1.0) or decrease (O.R. less than 1.0) when the value of the predictor value is increased by 1.0 units.
Techie-stuff (for those who might be interested):
This page contains a straightforward JavaScript implementation of a standard iterative method to maximize the Log Likelihood Function (LLF), defined as the sum of the logarithms of the predicted probabilities of occurrence for those cases where the event occurred and the logarithms of the predicted probabilities of non-occurrence for those cases where the event did not occur.
Maximization is by Newton's method, with a very simple elimination algorithm to invert and solve the simultaneous equations. Central-limit estimates of parameter standard errors are obtained from the diagonal terms of the inverse matrix. Odds Ratios and their confidence limits are obtained by exponentiating the parameters and their lower and upper confidence limits (approximated by +/- 1.96 standard errors).
No special convergence-acceleration techniques are used. For improved precision, the independent variables are temporarily converted to "standard scores" ( value - Mean ) / StdDev. The Null Model is used as the starting guess for the iterations -- all parameter coefficients are zero, and the intercept is the logarithm of the ratio of the number of cases with y=1 to the number with y=0. The quantity -2*Ln(Likelihood) is displayed for the "Null model", for each step of the iteration, and for the "Final" (converged model). Convergence is not guaranteed, but this page should work properly with most practical problems that arise in real-world situations.
This implementation has no predefined limits for the number of independent variables or cases. The actual limits are probably dependent on your web browser's available memory and other browser-specific restrictions.
The fields below are pre-loaded with a very simple example.


Removing a Django App
In order to completely uninstall a Django application you have to not only delete the database tables - but you also need to remove the related contenttypes.
Let’s look at two ways to handle this: Manually and with South.
In this case, the application name is called customers
, and it contains the following tables within models.py:
class Student(models.Model):
name = models.CharField(max_length=30)
courses = models.ManyToManyField('Course')
def __unicode__(self):
return self.name
class Course(models.Model):
name = models.CharField(max_length=30)
def __unicode__(self):
return self.name
Also, I am using a sqlite3 database called test.db, which contains this data.
Let’s say we want to drop the Course table (for reasons unknown).
Contents
Manually
- Navigate to your project working directory and drop the table:
$ python manage.py sqlclear customers > drop_customers_customerprofile
-
Remove the app from the INSTALLED_APPS section in settings.py and delete any associated URL patterns from urls.py.
-
Drop the database tables:
$ sqlite3 test.db
sqlite> DROP TABLE customers_customerprofile;
equivalent MySQL command -
$ mysql -u root -p <database_name> < drop_<app_name>_<table_name>.sql
- Clean up the related contenttypes from the Shell:
$ python manage.py shell
>>> from django.contrib.contenttypes.models import ContentType
>>> ContentType.objects.filter(app_label='customers').delete()
- You can now delete the app folder as well as any associated media files and/or templates. Finally, make sure to uninstall any associated packages or dependencies using
pip uninstall <package_name>
. Make sure to use virtualenv.
South
I use South with all my Django projects, so I tend to prefer this method. Let’s take a look.
- Setup the initial migration and push it through:
$ python manage.py schemamigration customers --initial
$ python manage.py migrate customers
-
Remove the
CustomerProfile
class from models.py. -
Setup the migration to delete the table:
$ python manage.py schemamigration customers --auto
- Update the migration file 0002_auto__del_customerprofile.py, to clean up the related contenttypes as well as delete the table from the database, by updating the
forwards
function:
def forwards(self, orm):
# Deleting model 'CustomerProfile'
db.delete_table(u'customers_customerprofile')
from django.contrib.contenttypes.models import ContentType
ContentType.objects.filter(app_label='customers').delete()
- Push the migration through:
$ python manage.py migrate customers
- Fake a zero migration to remove the migration history and clear up the South tables:
$ python manage.py migrate customers zero --fake
- Remove the app from the INSTALLED_APPS section in settings.py and delete any associated URL patterns from urls.py. Then delete the app folder and any related media files and/or templates. Finally, make sure to uninstall any packages or dependencies using
pip unistall <package_name>
. Make sure to use virtualenv.
Comment if you have questions. Cheers!


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{# # Homepage template # ----------------- # # This template gets loaded whenever the site's homepage is requested, because # the "Homepage" single-entry section's Template setting is set to "index". # # An `entry` variable will be automatically passed to this template, which will # be set to the Homepage entry. -#} {% extends "_layouts/site.html" %} {%block main%}


BUYProductViewController Class Reference
| Inherits from | BUYViewController : UIViewController |
|---|---|
| Conforms to | BUYThemeable |
| Declared in | BUYProductViewController.h |
– initWithClient:theme:
Creates a BUYProductViewController with a BUYClient and a theme
Note: Use this initializer to instatiate a BUYProdctViewController
with a custom theme. If you don’t need to customize the theme
use initWithClient:
- (instancetype)initWithClient:(BUYClient *)client theme:(BUYTheme *)theme
Return Value
Declared In
BUYProductViewController.h
– loadProduct:completion:
Loads the product details
- (void)loadProduct:(NSString *)productId completion:(void ( ^ ) ( BOOL success , NSError *error ))completion
Parameters
productId |
the product ID for the item to display |
|---|---|
completion |
a block to be called on completion of the loading of the product details. Will be called on the main thread. Upon success, the view controller should be presented modally |
Declared In
BUYProductViewController.h
– loadWithProduct:completion:
Alternative method when setting the product (and optionally, shop) directly on the view controller
- (void)loadWithProduct:(BUYProduct *)product completion:(void ( ^ ) ( BOOL success , NSError *error ))completion
Parameters
product |
the product to display |
|---|---|
completion |
block called when view controller is ready for display. Called on main thread |
Declared In
BUYProductViewController.h
productId
The loaded product ID
@property (nonatomic, strong, readonly) NSString *productId
Declared In
BUYProductViewController.h
product
The product to be displayed. This can be set before presentation instead of calling loadProduct:completion:
@property (nonatomic, strong, readonly) BUYProduct *product
Declared In
BUYProductViewController.h
isLoading
Returns YES when the view controller is loading data. loadProduct: or loadWithProduct: should not be called when data is already loading
@property (nonatomic, assign, readonly) BOOL isLoading
Declared In
BUYProductViewController.h
– presentPortraitInViewController:
This is a convenience method as an alternative to presentViewController: which will force portrait orientation. This method is only required when presenting from a landscape view controller.
- (void)presentPortraitInViewController:(UIViewController *)controller
Parameters
controller |
The view controller where the BUYProductViewController is to be presented on |
|---|
Declared In
BUYProductViewController.h


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Hi, I'm Nathalie.
I'm an Experienced Engineer with a demonstrated history of working in the oil & gas, security and telecommunications industries. I'm skilled in Systems Testing, Risk-Based Maintenance, Condition Monitoring, Consequence Classification, and Risk Analysis.
Here is my CV.
Relevant skills
JavaScript, CSS, HTML, Python, PostgreSQL, jQuery, Bootstrap, Sass, Git, Windows, Linux, MacOS
Featured Work
Memory Game
https://nmarquesin.github.io/apps/memory/
Arcade Game
https://nmarquesin.github.io/apps/frogger-clone/
Restaurant Reviews App
https://nmarquesin.github.io/restapp
Education
I have a MSc in Marine Technology from NTNU (Trondheim, Norway) and a BEng in Naval Architecture and Marine Engineering from UFRJ (Rio de Janeiro, Brazil).
I'm currently finishing Udacity's Front End Nanodegree.
I completed the following courses:
Free Code Camp's Responsive Web Design Certification (May 2019)
Free Code Camp's JavaScript Algorithms and Data Structures Certification (April 2019)
Udacity's Configuring Linux Web Servers (May 2017)
Udacity's Intro to Relational Databases (May 2017)
Free Code Camp's Front End Development Certification (March 2017)
Udacity's JavaScript Basics (March 2017)
Udacity's Intro to jQuery (March 2017)
Udacity's Responsive Web Design Fundamentals (February 2017)
Udacity's How to use Git and GitHub (January 2017)
Udacity's Intro to HTML and CSS (January 2017)
Udacity's Intro to Computer Science (November 2016)


Codegen Options
This document outlines some useful knobs for running codegen on an XLS design. Codegen is the process of generating RTL from IR and is where operations are scheduled and mapped into RTL constructs. The output of codegen is suitable for simulation or implementation via standard tools that understand Verilog or SystemVerilog.
Input specification
<input.ir>
is a positional argument giving the path to the ir file.--top
specifies the top function or proc to codegen.
Output locations
The following flags control where output files are put. In addition to Verilog, codegen can generate files useful for understanding or integrating the RTL.
--output_verilog_path
is the path to the output Verilog file.--output_schedule_path
is the path to a textproto that shows into which pipeline stage the scheduler put IR ops.--output_schedule_ir_path
is the path to the "IR" representation of the design, a post-scheduling IR that includes any optimizations or transforms during the scheduling pipeline.--output_block_ir_path
is the path to the "block IR" representation of the design, a post-scheduling IR that is timed and includes registers, ports, etc.--output_signature_path
is the path to the signature textproto. The signature describes the ports, channels, external memories, etc.--output_verilog_line_map_path
is the path to the verilog line map associating lines of verilog to lines of IR.
Pipelining and Scheduling Options
The following flags control how XLS maps IR operations to RTL, and if applicable control the scheduler.
--generator=...
controls which generator to use. The options arepipeline
andcombinational
. Thepipeline
generator runs a scheduler that partitions the IR ops into pipeline stages.--delay_model=...
selects the delay model to use when scheduling. See the page here for more detail.--clock_period_ps=...
sets the target clock period. See scheduling for more details on how scheduling works. Note that this option is optional, without specifying clock period XLS will estimate what the clock period should be.--pipeline_stages=...
sets the number of pipeline stages to use when--generator=pipeline
.--clock_margin_percent=...
sets the percentage to reduce the target clock period before scheduling. See scheduling for more details.--period_relaxation_percent=...
sets the percentage that the computed minimum clock period is increased. May not be specified with--clock_period_ps
.--worst_case_throughput=...
sets the worst-case throughput bound to use when--generator=pipeline
. If set, allows scheduling a pipeline with worst-case throughput no slower than once per N cycles (assuming no stallingrecv
s).--additional_input_delay_ps=...
adds additional input delay to the inputs. This can be helpful to meet timing when integrating XLS designs with other RTL.--ffi_fallback_delay_ps=...
Delay of foreign function calls if not otherwise specified. If there is no measurement or configuration for the delay of an invoked modules, this is the value used in the scheduler.--io_constraints=...
adds constraints to the scheduler. The flag takes a comma-separated list of constraints of the formfoo:send:bar:recv:3:5
which means that sends on channelfoo
must occur between 3 and 5 cycles (inclusive) before receives on channelbar
. Note that for a constraint likefoo:send:foo:send:3:5
, no constraint will be applied between a node and itself; i.e.: this means all different pairs of nodes sending onfoo
must be in cycles that differ by between 3 and 5. If the special minimum/maximum valuenone
is used, then the minimum latency will be the lowest representableint64_t
, and likewise for maximum latency. For an example of the use of this, see this example and the associated BUILD rule.
Feedback-driven Optimization (FDO) Options
The following flags control the feedback-driven optimizations in XLS. For now,
an iterative SDC scheduling method is implemented, which can take low-level
feedbacks (typically from downstream tools, e.g., OpenROAD) to guide the delay
estimation refinements in XLS. For now, FDO is disabled by default
(--fdo_iteration_number=1
).
--fdo_iteration_number=...
The number of FDO iterations during the pipeline scheduling. Must be an integer >= 1.--fdo_delay_driven_path_number=...
The number of delay-driven subgraphs in each FDO iteration. Must be a non-negative integer.--fdo_fanout_driven_path_number=...
The number of fanout-driven subgraphs in each FDO iteration. Must be a non-negative integer.--fdo_refinement_stochastic_ratio=...
*path_number over refinement_stochastic_ratio paths are extracted and *path_number paths are randomly selected from them for synthesis in each FDO iteration. Must be a positive float <= 1.0.--fdo_path_evaluate_strategy=...
Support path, cone, and window for now.--fdo_synthesizer_name=...
Only support yosys for now.--fdo_yosys_path=...
Absolute path of Yosys.--fdo_sta_path=...
Absolute path of OpenSTA.--fdo_synthesis_libraries=...
Synthesis and STA libraries.
Naming
Some names can be set at codegen via the following flags:
--module_name=...
sets the name of the generated verilog module- For functions,
--input_valid_signal=...
and--output_valid_signal=...
adds and sets the name of valid signals when--generator
is set topipeline
. --manual_load_enable_signal=...
adds and sets the name of an input that sets the load-enable signals of each pipeline stage.- For procs,
--streaming_channel_data_suffix=...
,--streaming_channel_valid_suffix=...
, and--streaming_channel_ready_suffix=...
set suffixes to be used on their respective signals in ready/valid channels. For example,--streaming_channel_valid_suffix=_vld
for a channel namedABC
would result in a valid port calledABC_vld
.
Reset Signal Configuration
--reset=...
sets the name of the reset signal. If not specified, no reset signal is used.--reset_active_low
sets if the reset is active low or high. Active high by default.--reset_asynchronous
sets if the reset is synchronous or asynchronous (synchronous by default).--reset_data_path
sets if the datapath should also be reset. True by default.
Codegen Mapping
--use_system_verilog
sets if the output should use SystemVerilog constructs such as SystemVerilog array assignments,@always_comb
,@always_ff
, asserts, covers, etc. True by default.--separate_lines
causes every subexpression to be emitted on a separate line. False by default.
Format Strings
For some XLS ops, flags can override their default codegen behavior via format string. These format strings use placeholders to fill in relevant information.
--gate_format=...
sets the format string forgate!
ops. Supported placeholders are:-
{condition}
: Identifier (or expression) of the gate. -{input}
: Identifier (or expression) for the data input of the gate. -{output}
: Identifier for the output of the gate. -{width}
: The bit width of the gate operation.For example, consider a format string which instantiates a particular custom AND gate for gating:
my_and gated_{output} [{width}-1:0] (.Z({output}), .A({condition}), .B({input}))
And the IR gate operation is:
the_result: bits[32] = gate(the_cond, the_data)
This results in the following emitted Verilog:
my_and gated_the_result [32-1:0] (.Z(the_result), .A(the cond), .B(the_data));
To ensure valid Verilog, the instantiated template must declare a value named
{output}
(e.g.the_result
in the example).
--assert_format=...
sets the format string for assert statements. Supported placeholders are:-
{message}
: Message of the assert operation. -{condition}
: Condition of the assert. -{label}
: Label of the assert operation. It is an error not to use thelabel
placeholder. -{clk}
: Name of the clock signal. It is an error not to use theclk
placeholder. -{rst}
: Name of the reset signal. It is an error not to use therst
placeholder.For example, the format string:
{label}: `MY_ASSERT({condition}, "{message}")
could result in the following emitted Verilog:
my_label: `MY_ASSERT(foo < 8'h42, "Oh noes!");
--smulp_format=...
and--umulp_format=...
set the format strings forsmulp
andumulp
ops respectively. These ops perform partial (or split) multiplies. Supported placeholders are:-
{input0}
and{input1}
: The two inputs. -{input0_width}
and{input1_width}
: The width of the two inputs -{output}
: Name of the output. Partial multiply IP generally produces two outputs with the property that the sum of the two outputs is the product of the inputs.{output}
should be the concatenation of these two outputs. -{output_width}
: Width of the output.For example, the format string:
multp #( .x_width({input0_width}), .y_width({input1_width}), .z_width({output_width}>>1) ) {output}_inst ( .x({input0}), .y({input1}), .z0({output}[({output_width}>>1)-1:0]), .z1({output}[({output_width}>>1)*2-1:({output_width}>>1)})]) );
could result in the following emitted Verilog:
multp #( .x_width(16), .y_width(16), .z_width(32>>1) ) multp_out_inst ( .x(lhs), .y(rhs), .z0(multp_out[(32>>1)-1:0]), .z1(multp_out[(32>>1)*2-1:(32>>1)]) );
Note the arithmetic performed on
output_width
to make the two-outputmultp
block fill the concatenated output expected by XLS.
I/O Behavior
--flop_inputs
and--flop_outputs
control if inputs and outputs should be flopped respectively. These flags are only used by the pipeline generator.For procs, inputs and outputs are channels with ready/valid signalling and have additional options controlling how inputs and outputs are registered.
--flop_inputs_kind=...
and--flop_outputs_kind=...
flags control what the logic around the outputs and inputs look like respectively. The list below enumerates the possible kinds of output flopping and shows what logic is generated in each case.-
flop
: Adds a pipeline stage at the beginning or end of the block to hold inputs or outputs. This is essentially a single-element FIFO.
- `skid`: Adds a skid buffer at the inputs or outputs of the block. The
skid buffer can hold 2 entries.
- `zerolatency`: Adds a zero-latency buffer at the beginning or end of the
block. This is essentially a single-element FIFO with bypass.
--flop_single_value_channels
control if single-value channels should be flopped.
--add_idle_output
adds an additional output port namedidle
.idle
is the NOR of:1. Pipeline registers storing the valid bit for each pipeline stage.
2. All valid registers stored for the input/output buffers.
3. All valid signals for the input channels.
RAMs (experimental)
XLS has experimental support for using proc channels to drive an external RAM. For an example usage, see this delay implemented with a single-port RAM (modeled here). Note that receives on the response channel must be conditioned on performing a read, otherwise there will be deadlock.
The codegen option --ram_configurations
takes a comma-separated list of
configurations in the format ram_name:ram_kind[:kind-specific-configuration]
.
For a 1RW
RAM, the format is
ram_name:1RW:req_channel_name:resp_channel_name[:latency]
, where latency is 1
if unspecified. For a 1RW
RAM, there are several requirements these channels
must satisfy:
- The request channel must be a tuple type with 4 entries corresponding to
(addr, wr_data, we, re)
. All entries must have typebits
, andwe
andre
must be a single bit. - The response channel must be a tuple type with a single entry corresponding
to
(rd_data)
.rd_data
must have the same width aswr_data
.
Instead of the normal channel ports, the codegen option will produce the following ports:
{ram_name}_addr
{ram_name}_wr_data
{ram_name}_we
{ram_name}_re
{ram_name}_rd_data
Note that there are no ready/valid signals as RAMs have fixed latency. There is an internal buffer to catch the response and apply backpressure on requests if needed.
When using --ram_configurations
, you should generally add a scheduling
constraint via --io_constraints
to ensure the request-send and
response-receive are scheduled to match the RAM's latency.
Optimization
--gate_recvs
emits logic to gate the data value of a receive operation in Verilog. In the XLS IR, the receive operation has the semantics that the data value is zero when the predicate isfalse
. Moreover, for a non-blocking receive, the data value is zero when the data is invalid. When set to true, the data is gated and has the previously described semantics. However, the latter does utilize more resource/area. Setting this value to false may reduce the resource/area utilization, but may also result in mismatches between IR-level evaluation and Verilog simulation.
--array_index_bounds_checking
: With this option set, an out of bounds array access returns the maximal index element in the array. If this option is not set, the result relies on the semantics of out-of-bounds array access in Verilog which is not well-defined. Setting this option totrue
may result in more resource/area. Setting this value tofalse
may reduce the resource/area utilization, but may also result in mismatches between IR-level evaluation and Verilog simulation.
--mutual_exclusion_z3_rlimit
controls how hard the mutual exclusion pass will work to attempt to prove that sends and receives are mutually exclusive. Concretely, this roughly limits the number ofmalloc
calls done by the Z3 solver, so the output should be deterministic across machines for a given rlimit.


Despite India’s often confronting, chaotic cities assaulting the first-time visitor with an ambush of disorienting sensory confusion, on the banks of the Ganges, in the foothills of the Himalayas and by the Goan coast, you’ll find places of pure stillness. Even within her livewire cities, temples and monuments dedicated to spiritual devotion offer solace to those searching for a holiday interwoven with a little self-contemplation. The cities of Varanasi, Amritsar, Ajmer and Goa are good places to start for a soul-searching pilgrimage to spiritual India, with opportunities for introspection aplenty.
Vaidyanatha Jyotirlinga temple, also known as Baba Baidyanath dham and Baidyanath dham is one of the twelve Jyotirlingas, the most sacred abodes of Shiva. It is located in Deoghar in the Santhal Parganas division of the state of Jharkhand, India. It is a temple complex consisting of the main temple of Baba Baidyanath, where the Jyotirlinga is installed, and 21 other temples.[1] According to Hindu beliefs, the demon king Ravana worshipped Shiva at the current site of the temple to get the boons that he later used to wreak havoc in the world. Ravana offered his ten heads one after another to Shiva as a sacrifice. Pleased with this, Shiva descended to cure Ravana who was injured. As he acted as a doctor, he is referred to as Vaidhya ("doctor"). From this aspect of Shiva, the temple derives its name.
"Jagannath Temple" redirects here. For other uses, see Jagannath Temple (disambiguation). Shree Jagannath Temple at Puri Jagannath Temple, Puri The Shree Jagannath Temple at Puri Religion Affiliation Hinduism District Puri Deity Jagannath Festivals Ratha YatraChandan YatraSnana YatraNabakalebara Governing body Shree Jagannath Temple Office, Puri, Odisha, Shree Jagannath Temple Managing Committee, Puri Location Location Puri State Odisha Country India Jagannath Temple, Puri is located in OdishaJagannath Temple, Puri Location in Odisha Geographic coordinates 19°48′17″N 85°49′6″ECoordinates: 19°48′17″N 85°49′6″E Architecture Type Kalinga Architecture Creator Indradyumna
The Padmanabhaswamy temple is a Hindu temple located in Thiruvananthapuram, the state capital of Kerala, India. The name of the city of Thiruvananthapuram in Malayalam translates to "The City of Lord Ananta",[1] referring to the deity of the Padmanabhaswamy temple. The temple is built in an intricate fusion of the Chera style and the Dravidian style of architecture, featuring high walls, and a 16th-century gopura.[2][3] While the Ananthapura temple in Kumbla is considered the original seat of the deity ("Moolasthanam"), architecturally to some extent, the temple is a replica of the Adikesava Perumal temple in Thiruvattar.[4]


This class is used to as a singleton to retrieve the current user after each new request.
Located in /application/Bootstrap.php (line 123)
Zend_Application_Bootstrap_Bootstrap | --Bootstrap | --Ivc
Ivc singleton constructor
Inherited From Bootstrap
Bootstrap::_initAcl()
Bootstrap::_initConfig()
Bootstrap::_initNavigation()
Bootstrap::_initProfiling()
Bootstrap::_initUser()
Documentation generated on Tue, 21 Feb 2012 06:45:21 +0800 by phpDocumentor 1.4.1


Streaming Analytics
Concepts, Architecture, Technology
Bas Geerdink | March 11, 2020 | Big Data & AI World
Who am I?
{
"name": "Bas Geerdink",
"role": "Technology Lead",
"company": ["Aizonic"],
"background": ["Artificial Intelligence",
"Informatics"],
"mixins": ["Software engineering",
"Architecture",
"Innovation"],
"twitter": "@bgeerdink",
"linked_in": "bgeerdink"
}
Fast Data Use Cases
| Sector |
Data source |
Pattern |
Notification |
| Retail |
Clickstream |
Product interest |
Recommendation |
| Finance |
Payment data |
Fraud detection |
Block money transfer |
| Insurance |
Page visits |
Trend analysis |
Actionable insight |
| Healthcare |
Patient data |
Heart failure |
Alert doctor |
| Traffic |
Cars passing |
Traffic jam |
Update route info |
| Internet of Things |
Machine logs |
System failure |
Alert to sys admin |
Fast Data Pattern
There is a common pattern in all these scenarios:
- Prepare raw data:
- Process each event separately; or
- Detect pattern by combining events (CEP)
- Transform the data:
- Process each event / combination; and/or
- Determine relevancy (ML)
- Produce follow-up action
Architecture
A possible software stack
- Data stream storage: Kafka
- Cache: NoSQL database, e.g. Cassandra or Ignite
- Stream processing: Spark or Flink
- Model scoring: PMML and Openscoring.io
Architecture
Fast Data applications
- A Fast Data application is a running job that processes events in a data store (Kafka)
- Jobs can be deployed as ever-running pieces of software in a big data cluster (Spark, Flink, ...)
- The basic pattern of a job is:
- Connect to the stream and consume events (ingestion)
- If needed, group the events (windowing)
- Process each event or window (aggregation)
- Write the results to a data store or stream (sink)
Parallelism
- To get high throughput, we have to process the events in parallel
- Parallelism can be configured on cluster level and on job level (number of worker threads)
val conf = new SparkConf()
.setMaster("local[8]")
.setAppName("FraudNumberOfTransactions")
./bin/spark-submit --name "LowMoneyAlert" --master local[4]
--conf "spark.dynamicAllocation.enabled=true"
--conf "spark.dynamicAllocation.maxExecutors=2" styx.jar
Event time
- Events occur at certain time
⇛ event time
- ... but arrive a little while later
⇛ ingestion time
- ... and are processed later
⇛ processing time
Out-of-orderness
Windows
- In processing infinite streams, we usually look at a time window
- A windows can be considered as a bucket of time
- There are different types of windows:
- Sliding window
- Tumbling window
- Session window
Windows
Window considerations
- Size: large windows lead to big state and long calculations
- Number: many windows (e.g. sliding, session) lead to more calculations
- Evaluation: do all calculations within one window, or keep a cache across multiple windows (e.g. when comparing windows, like in trend analysis)
- Timing: events for a window can appear early or late
Watermarks
- Watermarks are timestamps that trigger the computation of the window
- They are generated at a time that allows a bit of slack for late events
Model scoring
- To determine the follow-up action of a aggregated business event (e.g. pattern), we have to enrich the event with customer data
- The resulting data object contains the characteristics (features) as input for a model
PMML
- PMML is the glue between data science and data engineering
- Data scientists can export their machine learning models to PMML (or PFA) format
from sklearn.linear_model import LogisticRegression
from sklearn2pmml import sklearn2pmml
events_df = pandas.read_csv("events.csv")
pipeline = PMMLPipeline(...)
pipeline.fit(events_df, events_df["notifications"])
sklearn2pmml(pipeline, "LogisticRegression.pmml", with_repr = True)
PMML
Alternative stacks
| Message bus |
Streaming technology |
Database |
| Kafka |
Spark Structured Streaming |
Ignite |
| Kafka |
Flink |
Cassandra |
| Azure Event Hubs |
Azure Stream Analytics |
Cosmos DB |
| AWS Kinesis Data Streams |
AWS Kinesis Data Firehose |
DynamoDb |
Wrap-up
- Stream processing is a form of big data that is becoming increasingly popular
- The common pattern is: CEP → ML → Notification
- Pick the right tools for the job; Kafka, Flink, and Cassandra are amongst the best
- Be aware of typical fast data issues: late events, state management, windows, etc.


{% extends 'lifetab/base.html' %} {% load staticfiles %} {% block main %} {% if user and not user.is_anonymous %}
The lifelogging app that let's you keep your thoughts in one place.
Login with Google


Edgewood College soccer, lacrosse teams moving to new venue
by Stephen CohnMADISON, Wis. - Edgewood College will have its first dedicated home venue for its soccer and lacrosse teams next fall.
According to a news release from the school, Edgewood's teams will practice and play at Reddan Soccer Park in Verona starting in the fall of 2020.
The new home is a result of a partnership with the Madison Area Youth Soccer Association, and by the fall, the new field will have stands, a press box and a scoreboard. Reddan Field will use existing lighting and parking, according to the release.
Edgewood's baseball and softball teams already have their home fields in Verona.


{% trans %} Tiers of government refer to distinct levels of government which are set up in geographically vast countries with heterogeneity in population. The purpose of such classification is to ensure efficient governance and an equitable provisioning of public goods through co-operation, co-ordination and competition between the different tiers. In India, there are three tiers of government: Union, State and Local. The latter can be either urban or rural. Each of these tiers has constitutionally mandated responsibilities to raise resources and spend the same. An annual statement of receipts and expenditures are presented by all tiers of government, together constituting Government Budget in India. {% endtrans %}


--- layout: page title: Lean Prover Zulip Chat Archive permalink: archive/113488general/09702Completelatticeredundantparameter.html ---
Isn't bot and top provable from supremum and infimum, respectively?
Yes, and Sup
is definable from Inf
, and sup
is definable from Sup
. The reason these additional definitions are in the structure is that sometimes you want a different definitional reduction for the expression than the lattice definition gives you, i.e. Prop
is a complete lattice with top true
, but if it was defined with Sup
then that would be some cumbersome forall false thing


Below are two “sets” of generated numbers for demonstration. The subsequent set types are then listed out. Click to see new ones!
Generate again!Element of ∈
Not element of ∉
Subset of ⊂
Cardinality |A|
Element of ∈
Not element of ∉
Subset of ⊂
Cardinality |A|
Credits: Symbols source


--- layout: post title: no title date: '2016-11-04T02:03:01+01:00' categories: - Storyboards picture_url: https://film-Storyboards.tumblr.com/post/152719038804 ---


(6)
Nice 1 day ban
(4)
Helpmedad
(7)
Hello skids
(4)
Downtown rp
(2)
Ro Ghoul Script
(6)
Buying Hexus?
(5)


Click the button to get a new array with the full name of each person in the array.
Try it
Get Session
Session:


Overriding the Display Style
Every element has a default display value. However, you can override this.
Changing an inline element to a block element, or vice versa, can be useful for making the page look a specific way, and still follow the web standards.
Note: Setting the display property of an element only changes how the element is displayed, NOT what kind of element it is.
So, an inline element with display: block; is not allowed to have other block elements inside it.
Making List elements li as inline
- One
- Two
- Three
Making Link elements as block
Git


The LOOP package provides a library of classes implemented using its models. All classes included in its library were created or derived from actual implementations used in either academic or commercial applications, and many are reused in more than one application. Therefore, even though the classes presented here are provided mainly as use examples of LOOP models they are very useful for construction of software applications.
Most of these classes were initially created as Lua conventional modules and were later converted to classes so it is straightforward to convert them back to modules that don't lay on LOOP. Some of these classes can even be used as an instance of itself thus avoiding the creation of singleton objects. However, in some scenarios, it is interesting to be able to have multiple instances. The LOOP Class Library is organized in six groups as presented below.
This group contains classes that implement a flexible mechanism that serializes almost any value into a Lua code that can be concatenated with other chunks of code to produce complex automatic generated scripts. However, the serialization mechanism provided by these class face a strong limitation imposed by current implementation of Lua that does not provide means to restore function upvalues properly.
Class of objects that serialize values by creating a chunk of Lua code that when executed creates an isomorphic image of the serialized value, i.e. an identical copy of the original value, that may include self-referecing tables and functions.
However there are some limitations, specifically related to function upvalues.
It is also possible to define custom serialization for userdata
.
This class is useful to implement persistence mechanisms or remote communication infrastructure.
Subclass of Serializer
that serializes values into a string.
It is also used to restore values serialized in a string using the serialization mechanism provided by Serializer
.
This class is useful to pack values into strings that can be stored in run-time memory or other sort of storage.
Subclass of Serializer
that serializes values into a file.
It is also used to restore values serialized in a file using the serialization mechanism provided by Serializer
.
This class is useful to implement persitence mechanisms.
Subclass of Serializer
that serializes values into a socket object like the ones provided by the LuaSocket library.
It is also used to restore values serialized using the serialization mechanism provided by Serializer
that are transmitted by a socket.
This class is useful to implement communication infrastructures.


> Created a website using Wordpress for a local nonprofit organization.
> Volunteered to help girls (ages 9-12) develop a video game from start to finish using Scratch.


If you have any queries regarding any products or our business please feel free to contact on below number. We will be more than happy to serve you better.
Address: 10332 Malcolm Cr, Cockeysville, MD 21030
Email<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>


If you'd like the full game experience, please play on a desktop. If you'd like you continue on mobile, please rotate to landscape.
Lines:
Food:
Controls: Arrows/W,A,S,D and Space


--- layout: post title: Composite primary keys for Active Record categories: - sightings author: David published: true date: 2006-07-30 16:37:00.000000000 +01:00 ---
Dr Nic Williams has charged one of the major strongholds of legacy database compatibility with Rails, composite primary keys support. By installing his nimble plugin, you get a very natural API for mapping those blasted composites using set_primary_keys
and an array of keys.
It’s still a work in progress, but if you have to deal with a legacy database and want to use Rails, this might just be your ticket in.


Serialization
[Reflection and serialization]
Detailed Description
The serialization API lets you produce text or binary representations of a space, and recreate a copy of the original space from that representation. It provides funcionality for creating variables and posting propagators in a space using the specifications obtained through reflection.Requires
#include "gecode/serialization.hh"
Functions | |
| void | Gecode::emitFlatzinc (Space *home, std::ostream &os) |
| Produce FlatZinc-like representation of home
A FlatZinc-like representation of the space home is written to os, respecting the variable names established by m. | |
| void | Gecode::emitJavaScript (Space *home, std::ostream &os) |
| Produce JavaScript representation of home
A JavaScript representation of the space home is written to os, respecting the variable names established by m. |
Function Documentation
| GECODE_SERIALIZATION_EXPORT void Gecode::emitFlatzinc | ( | Space * | home, | |
| std::ostream & | os | |||
| ) |
Produce FlatZinc-like representation of home
A FlatZinc-like representation of the space home is written to os, respecting the variable names established by m.
The generated code is not completely FlatZinc-compatible, as propagator names are represented as C++-style template names.
Definition at line 333 of file flatzinc.cc.
| GECODE_SERIALIZATION_EXPORT void Gecode::emitJavaScript | ( | Space * | home, | |
| std::ostream & | os | |||
| ) |
Produce JavaScript representation of home
A JavaScript representation of the space home is written to os, respecting the variable names established by m.
Definition at line 140 of file javascript.cc.


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--- title: "Using fstatfs from a 32-bit program on a 64-bit system" date: "2013-06-02" redirect_from: "/post/51952799861/using-fstatfs-from-a-32-bit-program-on-a-64-bit-system" categories: - "fstatfs" - "fstatfs64" - "EOVERFLOW" - "x86_64" - "i386" - "amd64" - "glibc" ---
CentOS 5.9, Linux 2.6.18-308.24.1.el5.centos.plus, glibc 2.5-107
Recently at work, Zack Dannar and I ran into an issue on a 64-bit system where a 32-bit process was making a call to fstatfs() which was failing. An strace of the process showed the following:
... 01:00:28.335337 fstatfs(3, 0xffccebf0) = -1 EOVERFLOW (Value too large for defined data type) ...
The call was failing with an errno of EOVERFLOW. Odd. We wrote a simple program to see if the issue would reproduce easily:
#include <fcntl.h> #include <stdio.h> #include <stdlib.h> #include <sys/stat.h> #include <sys/types.h> #include <sys/vfs.h> int main(void) { int fd; if((fd=open("test.txt", O_RDONLY)) == -1) { perror("open()"); exit(1); } struct statfs buf; if(fstatfs(fd, &buf) == -1) perror("fstatfs()"); close(fd); }
and compiled it as a 32-bit binary:
$ gcc fstatfstest.c -o fstatfstest -m32 $ /tmp/fstatfstest fstatfs(): Value too large for defined data type
Searching the Linux man pages, we find that fstatfs() and its associated structure are defined as:
int fstatfs(int fd, struct statfs *buf); struct statfs { long f_type; /* type of filesystem (see below) */ long f_bsize; /* optimal transfer block size */ long f_blocks; /* total data blocks in file system */ long f_bfree; /* free blocks in fs */ long f_bavail; /* free blocks avail to non-superuser */ long f_files; /* total file nodes in file system */ long f_ffree; /* free file nodes in fs */ fsid_t f_fsid; /* file system id */ long f_namelen; /* maximum length of filenames */ };
The only problem is that a long is 4 bytes on i386 and 8 bytes on x86_64, so when a 32-bit program passes a statfs structure into a fstatfs() call, an attempt is made to stuff the result into a structure that is not big enough (in some cases). This issue occurred on a CentOS 5 system with an XFS file system that is heavy on the metadata (it creates and removes lots of files often). On our CentOS 5 systems, by default the inode max percent (imaxpct) is 25%.
$ xfs_info /dev/sdc1 meta-data=/dev/sdc1 isize=256 agcount=32, agsize=49194800 blks = sectsz=512 attr=1 data = bsize=4096 blocks=1574233600, imaxpct=25 = sunit=16 swidth=352 blks, unwritten=1 naming =version 2 bsize=4096 log =internal bsize=4096 blocks=32768, version=2 = sectsz=512 sunit=16 blks, lazy-count=0 realtime =none extsz=4096 blocks=0, rtextents=0
We know that the ceiling of a 4 byte long is 2,147,483,647, so let's see if we have more inodes than that:
$ df -i /dev/sdc1 Filesystem Inodes IUsed IFree IUse% Mounted on /dev/sdc1<PHONE_NUMBER> 118578362<PHONE_NUMBER> 2% /exports
Yep, 6 billion is certainly more than ~ 2 billion, so my best guess is that when fstatfs() tries to copy 6,296,934,400 into f_files, it is overrunning. We know b_blocks is ok from the xfs_info output, but f_bfree, f_bavail and f_ffree are also candidates. Checking our Oracle Enterprise Linux 6 systems, I saw that they have imaxpct set to 5% by default (Oracle Enterprise Linux 6 is based on Red Hat Enterprise Linux 6). Hopefully having the imaxpct lower will prevent the same situation from occurring on those systems.
Long story short, if you run into this problem and must use fstatfs, then defining your own struct and passing that to fstatfs64() instead of fstatfs() should work:
#include <stdio.h> #include <sys/types.h> #include <sys/stat.h> #include <fcntl.h> #include <errno.h> #include <sys/vfs.h> #include <unistd.h> #include <string.h> struct kernel_statfs64 { long int f_type; /* type of file system (see below) */ long int f_bsize; /* optimal transfer block size */ long long f_blocks; /* total data blocks in file system */ long long f_bfree; /* free blocks in fs */ long long f_bavail; /* free blocks available to unprivileged user */ long long f_files; /* total file nodes in file system */ long long f_ffree; /* free file nodes in fs */ long long f_fsid; /* file system id */ long int f_namelen; /* maximum length of filenames */ long int f_frsize; /* fragment size (since Linux 2.6) */ long int f_spare[5]; }; int main(void) { unsigned int fd = open("test.txt", O_RDONLY); if(fd == -1) perror("open()"); struct kernel_statfs64 buf; memset(&buf, 0, sizeof(&buf)); int result; result = fstatfs64(fd, &buf); printf("fstatfs64 returned %d\n", result); if(result == -1) perror("statfs()"); switch(buf.f_type) { case 0x58465342: printf("XFS file system\n"); break; ;; case 0x6969: printf("NFS file system\n"); break; ;; default: printf("Unknown file system type (0x%x)\n", buf.f_type); ;; } return 0; }
Compiling and running confirms the fix:
$ gcc fstatfstest.c -o fstatfstest -m32 $ /tmp/fstatfstest fstatfs returned 0 XFS file system


actionslacks
01-Sep-19 01:16 AM
I have feeling it's not gonna stop until Binance shuts out the ussa. after that d-day. true price discovery may begin again.
if the kmd logo had been on the binance usa press release. this likely would not be happeningz
atomic dex can't come soon enough.
oh man, I thought I was a downer.


I'm researching more about Komodo competitors, reading Ethereum 2.0 plans... I didn't fully realize what level of clustefuck it is compared to Komodo! There's nothing else to do than continuing improving the tech and creating migrations tools, they're doing the job for us.
Polkadot has a broken model, a complete non-starter. Cosmos seems the best of the bunch, still reading...
jl777c24-Jul-19 12:42 PM
what are the main problems with polkadot?
KomodoWorld24-Jul-19 12:44 PM
limited number of parachains available, the get sold on auction and their availability expires without paying the rent
jl777c24-Jul-19 12:44 PM
?
KomodoWorld24-Jul-19 12:45 PM
i.e. you're renting a parachain
jl777c24-Jul-19 12:45 PM
seems to be creating artificial scarcity
i guess appeals to the "what is the point of holding your token" crowd, nice tax scheme
KomodoWorld24-Jul-19 12:45 PM
the number of parachains increases in time but still limited
jl777c24-Jul-19 12:46 PM
cosmos i believe requires some deposit
one of them was permissioned block producers
and these are the best of the competition in the many chains space
KomodoWorld24-Jul-19 12:49 PM
All of them want some tax to make the VC and token holders feel happy.. I don't believe any of them would ever adopt a similar model to Komodo, it's far from their comprehension
Hollowman900024-Jul-19 12:54 PM
All these ecosystems are being built
Do any real businesses see value in using any of them?
Even ours here at KMD
jl777c24-Jul-19 12:58 PM
most businesses have just forked ETH
the ETH gas prevents them from using the actual ETH chain
with komodo, there is no need to make a source level fork to avoid fees
Hollowman900024-Jul-19 01:01 PM
What are they using it for though
Private network?
What secures it?
jl777c24-Jul-19 01:02 PM
why to secure it?
Hollowman900024-Jul-19 01:02 PM
No outside connections? No outside hash
jl777c24-Jul-19 01:02 PM
they probably have only trusted nodes since no corporate node would ever be compromised
it is just like govt nodes, 100% immune from being compromised
just trust them
they care about women and children
1
Hollowman900024-Jul-19 01:03 PM
If this works why would they utilize antara vs forking ETH again


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Function Documentation
Man pages for all functions exported by celda
|
|
available_models
|
available models |
|
calc.perplexity
|
calc.perplexity |
|
calc.perplexity2
|
calc.perplexity2 |
|
calculateLoglikFromVariables.celda_C()
|
Calculate the celda_C log likelihood for user-provided cluster assignments |
|
calculateLoglikFromVariables.celda_CG()
|
Calculate log lileklihood for the celda Cell and Gene clustering model, given a set of cell / gene cluster assignments |
|
calculateLoglikFromVariables.celda_G()
|
Calculate the celda_G log likelihood for user-provided cluster assignments |
|
calculateLoglikFromVariables()
|
Calculate a log-likelihood for a user-provided cluster assignment and count matrix, per the desired celda model. |
|
calculatePerplexity(<celda_C>)
|
Calculate the perplexity from a single celda model |
|
calculatePerplexity(<celda_CG>)
|
Calculate the perplexity from a single celda model |
|
calculatePerplexity(<celda_G>)
|
Calculate the perplexity from a single celda model |
|
calculatePerplexity()
|
Calculate the perplexity from a single celda model |
|
calculatePerplexityWithResampling()
|
Calculate and visualize perplexity of all models in a celda_list, with count resampling |
|
calculateTsne()
|
Run the t-SNE algorithm for dimensionality reduction |
|
cC.decomposeCounts()
|
Takes raw counts matrix and converts it to a series of matrices needed for log likelihood calculation |
|
cCG.decomposeCounts()
|
Takes raw counts matrix and converts it to a series of matrices needed for log likelihood calculation |
|
celda()
|
Deprecation warning for old grid search function |
|
celdaGridSearch()
|
Run the celda Bayesian hierarchical model on a matrix of counts. |
|
celdaHeatmap(<celda_C>)
|
celdaHeatmap for celda Cell clustering function |
|
celdaHeatmap(<celda_CG>)
|
celdaHeatmap for celda Cell and Gene clustering model. |
|
celdaHeatmap(<celda_G>)
|
celdaHeatmap for celda Gene clustering model |
|
celdaHeatmap()
|
Render a stylable heatmap of count data based on celda clustering results. |
|
celdaProbabilityMap(<celda_C>)
|
Renders probability and relative expression heatmaps to visualize the relationship between feature modules and cell populations. |
|
celdaProbabilityMap(<celda_CG>)
|
Renders probability and relative expression heatmaps to visualize the relationship between feature modules and cell populations. |
|
celdaProbabilityMap()
|
Renders probability and relative expression heatmaps to visualize the relationship between feature modules and cell populations. |
|
celdaTsne(<celda_C>)
|
Embeds cells in two dimensions using tSNE based on celda_C results. |
|
celdaTsne(<celda_CG>)
|
Embeds cells in two dimensions using tSNE based on celda_CG results. |
|
celdaTsne(<celda_G>)
|
Embeds cells in two dimensions using tSNE based on celda_CG results. |
|
celdaTsne()
|
Embeds cells in two dimensions using tSNE based on celda_CG results. |
|
celda_C()
|
celda Cell Clustering Model |
|
celda_CG()
|
celda Cell and Gene Clustering Model |
|
celda_G()
|
celda Gene Clustering Model |
|
cG.decomposeCounts()
|
Takes raw counts matrix and converts it to a series of matrices needed for log likelihood calculation |
|
clusterProbability(<celda_C>)
|
Calculates the conditional probability of each cell belong to each cluster given all other cluster assignments |
|
clusterProbability(<celda_CG>)
|
Calculates the conditional probability of each cell belong to each cluster given all other cluster assignments |
|
clusterProbability(<celda_G>)
|
Calculates the conditional probability of each cell belong to each cluster given all other cluster assignments |
|
clusterProbability()
|
Get the probability of the cluster assignments generated during a celda run. |
|
compareCountMatrix()
|
Check whether a count matrix was the one used in a given celda run |
|
completeLogLikelihood()
|
Get the complete log likelihood for a given celda model. |
|
differentialExpression()
|
Gene expression markers for cell clusters using MAST |
|
distinct_colors()
|
Generate a distinct palette for coloring different clusters |
|
eigenMatMultInt()
|
Fast matrix multiplication for double x int |
|
factorizeMatrix(<celda_C>)
|
Generate factorized matrices showing each feature's influence on the celda_C model clustering |
|
factorizeMatrix(<celda_CG>)
|
Generate factorized matrices showing each feature's influence on the celda_CG model clustering |
|
factorizeMatrix(<celda_G>)
|
Generate factorized matrices showing each feature's influence on the celda_G model clustering |
|
factorizeMatrix()
|
Generate factorized matrices showing each feature's influence on cell / gene clustering |
|
fastNormProp()
|
Fast normalization for numeric matrix |
|
fastNormPropLog()
|
Fast normalization for numeric matrix |
|
fastNormPropSqrt()
|
Fast normalization for numeric matrix |
|
featureModuleLookup(<celda_C>)
|
Obtain the gene module of a gene of interest |
|
featureModuleLookup(<celda_CG>)
|
Obtain the feature module of a feature of interest |
|
featureModuleLookup(<celda_G>)
|
Obtain the gene module of a gene of interest |
|
featureModuleLookup()
|
Obtain the gene module of a gene of interest |
|
finalLogLikelihood()
|
Get the log likelihood from the final iteration of Gibbs sampling
for a given celda model. |
|
moduleHeatmap()
|
Transcriptional state heatmap |
|
normalizeCounts()
|
Performs normalization, transformation, and/or scaling on a counts matrix |
|
pbmc_res
|
pbmc_res |
|
pbmc_select
|
pbmc_select |
|
plotDimReduceCluster()
|
Create a scatterplot based on celda cluster labels. |
|
plotDimReduceGene()
|
Create a scatterplot for each row of a normalized gene expression matrix where x and y axis are from a data dimensionality reduction tool. |
|
plotDimReduceGrid()
|
Create a scatterplot given two dimensions from a data dimensionality reduction tool (e.g tSNE) |
|
plotDimReduceState()
|
Create a scatterplot based off of a matrix containing the celda state probabilities per cell. |
|
plotGridSearchPerplexity(<celda_C>)
|
Plot perplexity as a function of K from celda_C models |
|
plotGridSearchPerplexity(<celda_CG>)
|
Plot perplexity as a function of K and L from celda_CG models |
|
plotGridSearchPerplexity(<celda_G>)
|
Plot perplexity as a function of L from a celda_G model |
|
plotGridSearchPerplexity()
|
Visualize perplexity of every model in a celda_list, by unique K/L combinations |
|
plotHeatmap()
|
Renders a heatmap based on a matrix of counts where rows are features and columns are cells. |
|
recodeClusterY()
|
Re-code gene cluster labels by provided mapping scheme |
|
recodeClusterZ()
|
Re-code cell cluster labels by provided mapping scheme |
|
runParams()
|
Get run parameters for a celda run. |
|
sample.cells
|
sample.cells |
|
selectBestModel()
|
Select models with the best log likelihood from a 'celda_list' object gererated by celdaGridSearch. |
|
semi_pheatmap()
|
A function to draw clustered heatmaps. |
|
simulateCells(<celda_C>)
|
Simulate cells from the cell clustering generative model |
|
simulateCells(<celda_CG>)
|
Simulate cells from the cell/feature bi-clustering generative model |
|
simulateCells(<celda_G>)
|
Simulate cells from the feature clustering generative model |
|
simulateCells()
|
Simulate count data from the celda generative models. |
|
subsetCeldaList()
|
Select a subset of models from a 'celda_list' object generated by celdaGridSearch. |
|
topRank()
|
Identify features with the highest influence on clustering. |


graph Class
The api module to perform graph related operations on ArangoDB.
The underlying collections of the graph are still accessible using the standard methods for collections. However the graph module adds an additional layer on top of these collections giving you the following guarantees:
- All modifications are executed transactional
- If you delete a vertex all edges will be deleted, you will never have loose ends
- If you insert an edge it is checked if the edge matches the definition, your edge collections will only contain valid edges These guarantees are lost if you access the collections in any other way than the graph module or AQL.
Item Index
Methods
create
-
graph
-
edgeDefinitions
-
vertexCollections
-
waitForSync
creates a Graph.
Parameters:
-
graph
String- the name of the graph
-
edgeDefinitions
String- (optional) the definitions for edges. Compatibility: the name of the vertex collection.
-
vertexCollections
String- (optional) list of additional vertex collections Compatibility: the name of the edge collection
-
waitForSync
Boolean- if true wait until document has been synced to disk.
Returns:
delete
-
graph
-
[waitForSync=false]
Deletes a graph
Parameters:
-
graph
String- the name of the graph
-
[waitForSync=false]
Boolean optional- wait until document has been sync to disk.
Returns:
edge.create
-
graph
-
edgeData
-
from
-
to
-
label
-
collection
-
[waitForSync=false]
creates an edge within a Graph
Parameters:
-
graph
String- the name of the graph
-
edgeData
Object- the vertex object as JSON. It is possible to set the vertex key by providing the _key attribute.
-
from
String- the start vertex of this edge
-
to
String- the end vertex of this edge
-
label
String- the edges label
-
collection
String- the edge collection
-
[waitForSync=false]
Boolean optional- wait until document has been sync to disk.
Returns:
edge.delete
-
graph
-
id
-
[options.match]
-
[options.rev]
-
[options.forceUpdate]
-
[options.waitForSync=false]
Deletes an edge
Parameters:
-
graph
String- the name of the graph
-
id
String- the edge-handle
-
[options.match]
Boolean optional- defining if the given revision should match the found document or not.
-
[options.rev]
Number optional- The revision, used by the "match" attribute.
-
[options.forceUpdate]
Boolean optional- If set an update is performed even when the given revision does not match.
-
[options.waitForSync=false]
Boolean optional- Wait until document has been synced to disk.
Returns:
edge.get
-
graph
-
id
-
[options]
retrieves an edge from a graph
Parameters:
-
graph
String- the name of the graph
-
id
String- the edge-handle
-
[options]
Object optional- an object with the following optional parameters:
-
[match]
Boolean optional- defining if the given revision should match the found document or not.
-
[rev]
Number optional- The revision, used by the "match" attribute.
Returns:
edge.patch
-
graph
-
id
-
data
-
[options]
patches an edge with the data given in data
Parameters:
-
graph
String- the name of the graph
-
id
String- the edge-handle
-
data
Object- the data of the edge as JSON object
-
[options]
Object optional- an object with the following optional parameters:
-
[match]
Boolean optional- defining if the given revision should match the found document or not.
-
[rev]
Number optional- The revision, used by the "match" attribute.
-
[forceUpdate]
Boolean optional- If set an update is performed even when the given revision does not match.
-
[waitForSync=false]
Boolean optional- Wait until document has been synced to disk.
-
[keepNull=true]
Boolean optional- if set to false a patch request will delete every null value
Returns:
edge.put
-
graph
-
id
-
data
-
[options]
replaces an edge with the data given in data.
Parameters:
-
graph
String- the name of the graph
-
id
String- the vertex-handle
-
data
Object- the data of the edge as JSON object
-
[options]
Object optional- an object with the following optional parameters:
-
[match]
Boolean optional- defining if the given revision should match the found document or not.
-
[rev]
Number optional- The revision, used by the "match" attribute.
-
[forceUpdate]
Boolean optional- If set an update is performed even when the given revision does not match.
-
[waitForSync=false]
Boolean optional- Wait until document has been synced to disk.
Returns:
edges
-
graph
-
[options]
returns all edges of a graph.
Parameters:
-
graph
String- the name of the graph.
-
[options]
Object optional- the following optional parameters are allowed:
-
[batchSize]
Number optional- the batch size of the returned cursor.
-
[limit]
Number optional- limit the result size.
-
[count=false]
Boolean optional- return the total number of results.
-
[filter]
Object optional- a optional filter, The attributes of filter:
-
[labels]
List optional- filter by an array of edge labels (empty array means no restriction) -
[properties]
Object optional- filter neighbors by an array of edge properties, The attributes of a property filter: -
[key]
String optional- filter the result vertices by a key value pair -
[value]
String optional- the value of the key -
[compare]
String optional- a compare operator
Returns:
get
-
graph
retrieves a graph from the database
Parameters:
-
graph
String- the name of the graph
Returns:
getEdgesForVertex
-
graph
-
vertex
-
[options]
returns all neighbouring edges of the given vertex .
Parameters:
-
graph
String- the name of the graph
-
vertex
String- the vertex
-
[options]
Object optional- the following optional parameters are allowed:
-
[batchSize]
Number optional- the batch size of the returned cursor.
-
[limit]
Number optional- limit the result size.
-
[count=false]
Boolean optional- return the total number of results.
-
[filter]
Object optional- a optional filter, The attributes of filter:
-
[labels]
List optional- filter by an array of edge labels (empty array means no restriction) -
[properties]
Object optional- filter neighbors by an array of edge properties, The attributes of a property filter: -
[key]
String optional- filter the result vertices by a key value pair -
[value]
String optional- the value of the key -
[compare]
String optional- a compare operator
-
[direction=any]
String optionalfilter for inbound (value "in") or outbound (value "out") neighbors.
Returns:
getNeighbourVertices
-
graph
-
vertex
-
[options]
returns all neighbouring vertices of the given vertex .
Parameters:
-
graph
String- the name of the graph
-
vertex
String- the vertex
-
[options]
Object optional- the following optional parameters are allowed:
-
[batchSize]
Number optional- the batch size of the returned cursor.
-
[limit]
Number optional- limit the result size.
-
[count=false]
Boolean optional- return the total number of results.
-
[filter]
Object optional- a optional filter, The attributes of filter:
-
[labels]
List optional- filter by an array of edge labels (empty array means no restriction) -
[properties]
Object optional- filter neighbors by an array of edge properties, The attributes of a property filter: -
[key]
String optional- filter the result vertices by a key value pair -
[value]
String optional- the value of the key -
[compare]
String optional- a compare operator
-
[direction=any]
String optionalfilter for inbound (value "in") or outbound (value "out") neighbors.
Returns:
keepNull
-
val
Sets the keepNull value for all further requests in the graph module.
Parameters:
-
val
Boolean
Returns:
- The modified instance of the graph module.
list
-
graph
-
collectionName
Remove a edge collection from the graph All vertex collections will still be known to the graph and have to be removed seperately.
Parameters:
-
graph
String- the name of the graph
-
collectionName
String- the name of the collection.
Returns:
list
()
Promise
retrieves a list of graphs from the database
Returns:
list
-
graph
-
collectionName
Add another edge definition
Parameters:
-
graph
String- the name of the graph
-
collectionName
String- the name of the collection.
Returns:
list
-
graph
Lists all vertex collections
Parameters:
-
graph
String- the name of the graph
Returns:
list
-
graph
-
collectionName
Remove a vertex collection from the graph The collection may not be used in an edge definition of this graph.
Parameters:
-
graph
String- the name of the graph
-
collectionName
String- the name of the collection.
Returns:
list
-
graph
-
collectionName
Add another vertex collection
Parameters:
-
graph
String- the name of the graph
-
collectionName
String- the name of the collection.
Returns:
list
-
graph
Lists all edge collections
Parameters:
-
graph
String- the name of the graph
Returns:
vertex.create
-
graph
-
vertexData
-
collection
-
[waitForSync=false]
creates a Vertex within a Graph
Parameters:
-
graph
String- the name of the graph
-
vertexData
Object- the vertex object as JSON. It is possible to set the vertex key by providing the _key attribute.
-
collection
String- vertex collection name
-
[waitForSync=false]
Boolean optional- wait until document has been sync to disk.
Returns:
vertex.delete
-
graph
-
id
-
[options]
Deletes a vertex
Parameters:
-
graph
String- the name of the graph
-
id
String- the vertex-handle
-
[options]
Object optional- an object with the following optional parameters:
-
[match]
Boolean optional- defining if the given revision should match the found document or not.
-
[rev]
Number optional- The revision, used by the "match" attribute.
-
[waitForSync]
Number optional- Boolean, wait until document has been synced to disk.
-
[forceUpdate]
Boolean optional- If set an update is performed even when the given revision does not match.
Returns:
vertex.get
-
graph
-
id
-
[options.match]
-
[options.rev]
retrieves a vertex from a graph
Parameters:
-
graph
String- the name of the graph
-
id
String- the vertex-handle
-
[options.match]
Boolean optional- defining if the given revision should match the found document or not.
-
[options.rev]
Number optional- The revision, used by the "match" attribute.
Returns:
vertex.patch
-
graph
-
id
-
data
-
[options]
patches a vertex with the data given in data
Parameters:
-
graph
String- the name of the graph
-
id
String- the vertex-handle
-
data
Object- the data of the vertex as JSON object
-
[options]
Object optional- an object with the following optional parameters:
-
[match]
Boolean optional- defining if the given revision should match the found document or not.
-
[rev]
Number optional- The revision, used by the "match" attribute.
-
[forceUpdate]
Boolean optional- If set an update is performed even when the given revision does not match.
-
[waitForSync=false]
Boolean optional- Wait until document has been synced to disk.
-
[keepNull=true]
Boolean optional- if set to false a patch request will delete every null value
Returns:
vertex.put
-
graph
-
id
-
data
-
[options]
replaces a vertex with the data given in data.
Parameters:
-
graph
String- the name of the graph
-
id
String- the vertex-handle
-
data
Object- the data of the vertex as JSON object
-
[options]
Object optional- an object with the following optional parameters:
-
[match]
Boolean optional- defining if the given revision should match the found document or not.
-
[rev]
Number optional- The revision, used by the "match" attribute.
-
[forceUpdate]
Boolean optional- If set an update is performed even when the given revision does not match.
-
[waitForSync=false]
Boolean optional- Wait until document has been synced to disk.
Returns:
vertices
-
graph
-
[options]
returns all vertices of a graph.
Parameters:
-
graph
String- the name of the graph.
-
[options]
Object optional- the following optional parameters are allowed:
-
[batchSize]
Number optional- the batch size of the returned cursor.
-
[limit]
Number optional- limit the result size.
-
[count=false]
Boolean optional- return the total number of results.
-
[filter]
Object optional- a optional filter, The attributes of filter:
-
[properties]
Object optional- filter neighbors by an array of edge properties, The attributes of a property filter: -
[key]
String optional- filter the result vertices by a key value pair -
[value]
String optional- the value of the key -
[compare]
String optional- a compare operator
Returns:
waitForSync
-
val
Sets the waitForSync value for all further requests in the graph module.
Parameters:
-
val
Boolean
Returns:
- The modified instance of the graph module.


{% include head1.html %} {% include google-analytics.html %}
{% include masthead.html %}
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SSH has the ability to operate as a Certificate Authority for easily issuing and managing SSH keys.
SSH will attempt to authenticate with a public key and/or corresponding cert. CA checks if it's signed/valid, etc.
Part of how it operates is based on AuthorizedPrincipals in the sshd_config.
One was a signed key can be generated is with ssh-keygen where:
-s is the signing key (CA private key)
-n is the Principal name
-I (i) is the identity (can be anything)
$ ssh-keygen -s /home/userca/ca -n 3m3rgencyB4ckd00r -I PleaseSubscribe root
Signed user key root-cert.pub: id "PleaseSubscribe" serial 0 for 3m3rgencyB4ckd00r valid forever
This will create private key root, a public cert root-cert.pub, and a public ssh key root.pub.
Can view information for a cert.pub with:
ssh-keygen -Lf root-cert.pub
Will show type, public key, singing CA, valid period, principals, extensions, etc.
The principal is tied to a specific user, so the principal presented in the cert will be able to authenticate as the user it's tied to.


--- layout: post date: 2011-06-14 time: "09:00:00" authors: ["Greg Wilson"] title: A New Look tags: ["Tooling"] ---
I'm fond of the Software Carpentry logo, but the blue-to-white color fade is difficult to print on coffee mugs, and impossible to embroider on shirts. Thanks to the talented Veronica Wong, we have a new one:
We'll be converting things over piece by piece as we rebuild the website over the summer.


This is a toy language written in c++ following the crafting interpreter book, and ported to WASM . For now is quite limited but I will keep building on it. Feel free to hack around! Check the repository out and my previous language babycpp!
You print values with a print statement.
print 10;
print "hello world";
You can evaluate mathematical expressions like.
print (-1*3.14)+(-13);
remember you need to print to see the result in the output.
you can use variables, TheBinder is dynamically typed and uses var as keyword (not int etc).
var a = 10;
var b = 30;
print a*b;
You can use while and for loops. The compiler only really knows about while loops, normal for loops are just syntax sugar
var a = 0;
while(a < 10){
print a;
a = a + 1;
}
For a for loop do:
for(var a = 0; a < 10;a = a+ 1){
print a;
}
You can use if/else statements
var a = 10;
if(a < 5){
print "less!";
}
else{
print "greater!";
}
If you find a bug please report it by clicking this button


WITNESS A film review by Andrew Hicks Copyright 1997 Andrew Hicks / Fatboy Productions
(1985) *** (out of four)
Aren't we all just a little bit fascinated with the Amish? These are people who have rejected the American government as well as every technological advancement of the last couple centuries in favor of those oppressive suits, beards and brimmed hats -- bonnets for the ladies. Those of us who are slave to electricity and television, not to mention the utter extravagance of running water, wonder what these inbred cults could see in plowing and milking at 4:30 in the morning.
These innate curiosities we all have about Amish people are what elevates WITNESS from being just an average 80's action flick. Harrison Ford, Mr. 80's Action Flick himself, plays a police detective assigned to crack open the murder of a fellow officer. The only witness to the murder is a little Amish boy (Lukas Haas) who happened to be hiding in a bus station bathroom stall at the time of the murder, most likely mesmerized by the flush technology.
Ford drags Haas and his mother (Kelly "Top Gun" McGillis, in her other movie appearance) to the police station, making him root through mug shots and lineups. More importantly, he exposes young Lukas to a world of sin and corruption which McGillis and her bearded counterparts have been trying to shelter him from. That's when Haas sees a picture of one of the force's most respected officers (Danny Glover) and recognizes him as the killer.
Since, beneath its Amish exterior, this is a typical 80's action movie, millions of dollars in drugs are involved and the police corruption runs pretty much throughout the department. So Ford has to find a place to hide himself and his Amish witnesses. What better place than Pennsylvania Amish country? That's when the tables are turned and Ford finds himself a fish out of water, a fish in an oppressive suit and a bigass brimmed hat.
WITNESS could have been a contrived comedy -- those were very popular in the 80's -- but it instead tries to paint a realistic portrait of Amish life. This ends up being a lot funnier than a sitcom approach, because nothing is more laughable than life itself. They bring out every Amish stereotype in the book, even a barn-raising. In this age of political correctness, the Amish are one of the last constituent groups that are still safe to make fun of, probably because they won't ever see or hear about what's being said about them, nor do they care. In their eyes, Billy Graham is a heathen for owning an electric hair dryer.
The middle half of the movie is the actionless assimilation of Ford into the Amish community, which is always interesting if not amusing. Crusty old Eli doesn't like Ford too much, especially when he finds him dancing to rock music with his daughter (McGillis). Amish Kelly doesn't compromise herself with Ford beyond the realm of hungry French kisses, even if she does appear topless in a shower scene. That's where I really wouldn't have minded the Puritan approach; McGillis looks just like my high school Bible teacher. Seeing her make out with Tom Cruise was bad enough.
--
Visit the Movie Critic at LARGE homepage at http://www.missouri.edu/~c667778/movies.html
Serving America For Over 1/33rd of a Century!
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- layout: post title: "New Project" date: 2015-12-25 10:35:00 +0000 tags: image: https://s3.amazonaws.com/hackedu/ed-tech-funding-pigeon.jpg imagecredits: https://www.flickr.com/photos/byneilhall/14264254434 ---
I started paying closer attention to the investment in ed-tech companies in 2014 in a separate site: matrix.hackeducation.com. I wanted to start a new project this year as there were some flaws with how I organized the project and with the data that I collected (or failed to collect).


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To execute Erlang code under the McErlang model checker a translation step is necessary, whereby Erlang code is translated into modified Erlang code suitable for running under the control of the McErlang model checker (which has its proper runtime system for managing processes, communication and nodes). Thereafter, a normal Erlang compilation translates the modified source code into beam (or native) object files.


Boolean
object. Constructing new Boolean
objects is rarely necessary,
and may cause performance problems if done often enough. Also, Boolean
constructors are deprecated since Java 9 and could be removed or made
inaccessible in future Java versions.
Example:
Boolean b1 = new Boolean(true); Boolean b2 = new Boolean(str);
After the quick-fix is applied the result looks like:
Boolean b1 = Boolean.TRUE; Boolean b2 = Boolean.valueOf(str);


Dear {{ name }},
This is a notice to remind you that the book "{{ title }}" is now overdue.
It was due on {{ due_date }}.
Please return it to the CCBC Library as soon as possible.
If you have already returned this book, please disregard this message.
Kind regards,
CCBC Library team


Home | Leaf | Branch | Branch->filledleaf | Branch->filledleaf->leaf | filledleaf | filledleaf/leaf
{{ $currentPage := . }} {{ range .Site.Menus.sectionMenu }} | {{if or ($currentPage.IsMenuCurrent "sectionMenu" .) ($currentPage.HasMenuCurrent "sectionMenu" .) }} ->{{ end }} {{ .Name }}> {{ end }}


{% load staticfiles %}
Choose the places now
{% csrf_token %}
One Way
Round Trip
From Destination
To Destination
Depart On
Return On
Adults
Children
Infants