Event Location:
Tallinna Polütehnikum
Pärnu mnt. 57
Tallinn, Harju maakond, Estonia
Abstract:
This session covers the aspects to be considered to deploy a Self Service BI Solution, also will be made a comparison between two Analysis Services modes, Tabular and Multidimensional, exploring advantages, features and capabilites, explaining when and why use each model.
Speaker(s): LEONEL ABREU,
Abstract:
If you flirt with SSAS Tabular or you are forced by circumstances to start you journey as SSAS Tabular developer. If you work with Power BI and wand to understand the engine better. Or if you just want to understand basics of SSAS Tabular technology and its traps. Join me for a short ride around SSAS Tabular galaxy. We will look into basic concepts, architecture and development process of SSAS Tabular.
Speaker(s): Roman Lansky,
Abstract:
In this session, you will learn how to detect and avoid common problems related to database corruption. There will be a description of the data structures in SQL Server and how it can affect the availability of SQL Server during corruption. On the session, there will be not only theoretical knowledge but many real life examples with demos.
Speaker(s): Michal Sadowski,
Abstract:
Autonomous means capable of existing independently. Think maintenance free, self-healing, auto-tuning databases able to detect and automatically mitigate security risks. Sounds like impossible dream, but here is the best part - Azure is gradually making that next level of automation possible. Cloud economy of scale combined with unique features of Azure Sql PaaS resources transform and enhance the way we collect, store and process data. And that also affects our professional lives. Find out where we are on The Road to The Autonomous Databases, what are main building blocks, what kind of obstacles we still have to overcome and how to turn these challenges into opportunities.
Speaker(s): Janusz Rokicki,
Abstract:
Azure Data Factory v1 has not been considered a full blown ETL solution on the market. Data Factory v2 is now introduced to fill in the gaps. We will deliver the understanding of the different features functionalities of both versions as well as a summary comparison between the two. We will also demonstrate the latest and greatest capabilities by orchestrating an end-to-end pipeline with Data Factory v2 including machine learning workloads with Azure DataBricks.
Speaker(s): Oskari Heikkinen,
Abstract:
SQL Server Management Studio is still at the heart of most DBA and developer’s day. We take it for granted but have you ever taken a deeper look at the features and shortcuts inside SSMS that can make your day to day work much easier and more productive? Join me in this demo intensive session, where I will show you many of the tricks and handy shortcuts, including some new features in SSMS 2017, that I have learnt over the years that have helped me to make my day easier and more productive At the end of this session you will have learnt many new tricks that you can use to make SSMS more useful and fun to use.
Speaker(s): David Postlethwaite,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
 System Engineer degree since 2007, formerly Data Warehousing teacher at university levels from 2008 to 2009. Microsoft Student Partner during the years 2006 - 2007, Second Place in Microsoft Andean Imagine Cup (2006), MCTS, MCITP and MCP with over a decade of experience with SQL Server, working in BI, DBA, Database Development, .net Development, SharePoint and other several areas. Since 2014 based in Barcelona, Spain. Learning enthusiast and always with passion for new technologies and new challenges.
LinkedIn: https://www.linkedin.com/in/oskariheikkinen/
I have several years of experience in using Azure for data analytics at large scale. I have been implementing numerous projects for some of the largest companies in the Nordics. I am a Microsoft MVP, and I currently hold the MCSE: Data Management and Analytics certification. As a Microsoft Certified Trainer (MCT), I hold classes for subject-matter experts and consultants internally as well as give external customer training sessions. I am also an active speaker on community events.
LinkedIn: https://pl.linkedin.com/pub/michal-sadowski/4/b33/717
Contact: https://sqlplayer.net/michal-sadowski
Senior Database Platform Engineer, leader of Krakow chapter of Data Community Krakow (former Polish SQL Server User Group) - PASS Chapter. With more than 10 years of experience in Microsoft technology, helps in advisory for performance optimization on database and application levels. Active speaker on SQL Server related events like SQLSaturday, SQLDay, MCT Global Summit etc. Holds many Microsoft certificates including MCSE: Data Platform, MCSA: SQL Server 2012/2014/2016, MCSA: Windows Server 2008/2012/2016 and MCT.
LinkedIn: https://www.linkedin.com/in/jrokicki/
Contact: https://www.datasic.com/
Microsoft Data Platform Expert and certified Azure Solutions Architect with over 12 years’ experience working in the City of London in the Financial Services Industry (retail and investment banking, multi-asset class trading systems). He has passion for secure, high-performance, maintainable data-driven applications that can be deployed with confidence on-premises and in-cloud. With educational background in intelligent sensor networks and machine learning he is always in search of automation and optimisation opportunities. Has presented at SqlBits, PASS Summit, SqlDay and multiple Sql Saturday events (Tel-Aviv, Kiev, Paris, Tallinn, Bonn, Bratislava, Goteborg, Kharkiv, Bucharest, Sofia, Ljubljana) and user groups.
LinkedIn: https://www.linkedin.com/in/romanlansky/
Contact: http://joyfulcraftsmen.com/blog/
Roman Lansky is MSBI consultant with over a decade of experience with SQL Server, SSAS, SSIS and data warehouse design and architecture. He likes to understand how thinks works and is passionate in performance tuning. He is author of the SSAS Tabular performance blogs and works in MSBI consultation focused company - Joyful Craftsmen.
LinkedIn: http://www.linkedin.com/in/davidpostlethwaite
Contact: http://www.gethynellis.com/
David Postlethwaite has been a DBA for Liverpool Victoria in Bournemouth, England since 2008 He supports both Oracle and SQL Server from 2000 to 2017, DBMS, SSIS, SSAS and Reporting Services. In 2015 David built and deployed the company's first cloud solution using Microsoft Azure SQL Database and web services Before becoming a DBA David was a .NET developer and way back in history a Windows and Netware administrator. He is an occasional blogger on www.gethynellis.com David is a regular speaker for SQL Saturday. Most of his presentations can be found on his YouTube channel www.youtube.com/c/DavidPostlethwaiteSQL


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FCC Tribute Page
This is a tribute page
Bulma start
project.
It includes everything you need to
build your own website
with Bulma.


Before Change
def test_networkml_train_sos():
tempdir = tempfile.TemporaryDirectory()
savew = os.path.join(tempdir.name, "intermediate.model")
netml = run_networkml(
["-p", "tests/", "-o", "train", "-a", "sos"],
model="networkml/trained_models/sos/SoSmodel",
savew=savew)
assert not netml.model.feature_list
// TODO: this was test_networkml_test_onelayer(); however it depends
// upon the mode generated by test_networkml_train_sos()
run_networkml(
After Change
def test_networkml_train_sos():
with tempfile.TemporaryDirectory() as tempdir:
savew = os.path.join(tempdir, "intermediate.model")
netml = run_networkml(
["-p", "tests/", "-o", "train", "-a", "sos"],
model="networkml/trained_models/sos/SoSmodel",
savew=savew)
assert not netml.model.feature_list
// TODO: this was test_networkml_test_onelayer(); however it depends
// upon the mode generated by test_networkml_train_sos()
run_networkml(
["-p", "tests/", "-o", "test"],
model=savew, expected_code=1, savew=savew)
def test_networkml_test_randomforest():
os.environ["POSEIDON_PUBLIC_SESSIONS"] = ""
with tempfile.TemporaryDirectory() as tempdir:
savew = os.path.join(tempdir, "not.used")


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Comments
-
Greg Hurrell
Thanks for submitting the request. A lot of what you ask for is already duplicated elsewhere in the database; for example, see feature request #3 or feature request #28, or do a search for the word "floater" (you'll get *lots* of results).
But for clarity I am going to reword your summary so that it reflects only one of the aspects you mention, and I am going to split the other aspects off into different requests. So now we have:
(1). Background box color (black, green, red, whatever) (feature request #28)
(2). Background box shape (square corners, rounded, etc) (feature request #345)
(3). Font face, color and size for the different text elements (feature request #346)
(4). Location of text elements and (5). Boundary padding between text and border (feature request #347)
(6). Remaining time preference (feature request #28)
-
Greg Hurrell
(In reply to comment #1)
(6). Remaining time preference (feature request #28)
Oops. That should be:
(6). Remaining time preference (feature request #3)
-
Greg Hurrell
Changing assignment to reflect my new email address.
https://wincent.com/a/news/archives/2006/05/change_of_email.php
Add a comment
Comments are now closed for this issue.


Severe storm
What to do during a severe storm
- If you are indoors, stay away from windows, doors and fireplaces.
- You may want to go to the sheltered area that you and your family chose for your emergency plan.
- If you are advised by officials to evacuate, do so.
- You can use a cellular telephone during a severe storm, but it's not safe to use a land-line telephone.
- Never go out in a boat during a storm. If you are on the water and you see bad weather approaching, head for shore immediately. Always check the marine forecast before leaving for a day of boating and listen to weather reports during your cruise.
- If you are in a car, stop the car away from trees or power lines that might fall on you). Stay there.
What to do during:
Blizzards
- When a winter storm hits, stay indoors. If you must go outside, dress for the weather. Outer clothing should be tightly woven and water-repellent. The jacket should have a hood. Wear mittens - they are warmer than gloves - and a hat, as large portion of body heat is lost through the head.
- In wide-open areas, visibility can be virtually zero during heavy blowing snow or a blizzard. You can easily lose your way. If a blizzard strikes, do not try to walk to another building unless there is a rope to guide you or something you can follow.
- If you must travel during a winter storm, do so during the day and let someone know your route and arrival time.
- If you start moving downward with the avalanche, stay on the surface using a swimming motion.
- If your car gets stuck in a blizzard or snowstorm, remain calm and stay in your car. Allow fresh air in your car by opening the window slightly on the sheltered side - away from the wind. You can run the car engine about 10 minutes every half-hour if the exhaust system is working well. Beware of exhaust fumes and check the exhaust pipe periodically to make sure it is not blocked with snow.
- To keep your hands and feet warm, exercise them periodically. In general, it is a good idea to keep moving to avoid falling asleep. If you do try to shovel the snow from around your car, avoid overexerting yourself.
- Overexertion in the bitter cold can cause death as a result of sweating or a heart attack.
- Keep watch for traffic or searchers.
Hail
- Take cover when hail begins to fall. Do not go out to cover plants, cars or garden furniture or to rescue animals. Hail comes down at great speed, especially when accompanied by high winds. Although no one in Canada has ever been killed by hail, people have been seriously injured by it.
- When a hailstorm hits, stay indoors, and keep yourself and your pets away from windows, glass doors and skylights which can shatter if hit by hailstones. Avoid using the telephone during a storm, and do not touch metal objects like stoves, radiators, metal pipes, and sinks.
- When a hailstorm hits, find shelter and avoid underpasses or any low lying areas that may flood.
Ice storms
- Ice from freezing rain accumulates on branches, power lines and buildings. If you must go outside when a significant amount of ice has accumulated, pay attention to branches or wires that could break due to the weight of the ice and fall on you. Ice sheets could also do the same.
- Never touch power lines. A hanging power line could be charged (live) and you would run the risk of electrocution. Remember also that ice, branches or power lines can continue to break and fall for several hours after the end of the precipitation.
- When freezing rain is forecast, avoid driving. Even a small amount of freezing rain can make roads extremely slippery.
- Rapid onsets of freezing rain combined with the risks of blizzards increase the chances for extreme hypothermia. If you live on a farm, move livestock promptly to shelter where feed is available. Forage is often temporarily inaccessible during and immediately after ice storms. Animal reactions to ice storms are similar to that of blizzards.
Lightning
- Always take shelter during a lightning storm.
- There is no safe place outside during a thunderstorm. Safe shelter can be found either in an enclosed building or a hard-topped vehicle.
- If you can see lightning or hear thunder, you are in danger of being hit. Seek shelter immediately.
- Wait 30 minutes after the last lightning strike in a severe storm before venturing outside again.
- Do not ride bicycles, motorcycles, tractors, or golf carts. These will not protect you from a lightning strike.
Thunderstorms
- During thunderstorms, you should also stay away from items that conduct electricity, such as corded telephones, appliances, sinks, bathtubs, radiators and metal pipes.


This form is use to collect information on how students feel about microverse
Name:
Email:
Number:
Select list one two three four


I will never ever take ambien again. Since highschool I have had a problem with depression and one of the problems I was having was that I could not get to sleep easily, or it would be hard to stay asleep as well. I took my nightly handful of pills (effexor, zyprexa) and my ambien and I sat down to start playing some video games before I got tired. My memory of that night ends here, the next memory I have is of me in the ICU at steven's hospital.  This is just what other people have told me about what happened that night, I have no memory of it at all.
So if you are going to take ambien, be very careful, I don't even know what triggered me to do this because I have never attempted suicide before. I don't even remember how bad of shape I was in when I entered the hospital, but from what everyone told me I almost died, and had it not been for my friend's mom rushing me to the hospital as fast as she did, I would be dead instead of writing this.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Methods
- fixtures
- require_fixture_classes
- set_fixture_class
- setup_fixture_accessors
- try_to_load_dependency
- uses_transaction
- uses_transaction?
Public Instance methods
[ show source ]
# File vendor/rails/activerecord/lib/active_record/fixtures.rb, line 837 837: def fixtures(*table_names) 838: if table_names.first == :all 839: table_names = Dir["#{fixture_path}/*.yml"] + Dir["#{fixture_path}/*.csv"] 840: table_names.map! { |f| File.basename(f).split('.')[0..-2].join('.') } 841: else 842: table_names = table_names.flatten.map { |n| n.to_s } 843: end 844: 845: self.fixture_table_names |= table_names 846: require_fixture_classes(table_names) 847: setup_fixture_accessors(table_names) 848: end
[ show source ]
# File vendor/rails/activerecord/lib/active_record/fixtures.rb, line 862 862: def require_fixture_classes(table_names = nil) 863: (table_names || fixture_table_names).each do |table_name| 864: file_name = table_name.to_s 865: file_name = file_name.singularize if ActiveRecord::Base.pluralize_table_names 866: try_to_load_dependency(file_name) 867: end 868: end
[ show source ]
# File vendor/rails/activerecord/lib/active_record/fixtures.rb, line 833 833: def set_fixture_class(class_names = {}) 834: self.fixture_class_names = self.fixture_class_names.merge(class_names) 835: end
[ show source ]
# File vendor/rails/activerecord/lib/active_record/fixtures.rb, line 870 870: def setup_fixture_accessors(table_names = nil) 871: table_names = [table_names] if table_names && !table_names.respond_to?(:each) 872: (table_names || fixture_table_names).each do |table_name| 873: table_name = table_name.to_s.tr('.', '_') 874: 875: define_method(table_name) do |*fixtures| 876: force_reload = fixtures.pop if fixtures.last == true || fixtures.last == :reload 877: 878: @fixture_cache[table_name] ||= {} 879: 880: instances = fixtures.map do |fixture| 881: @fixture_cache[table_name].delete(fixture) if force_reload 882: 883: if @loaded_fixtures[table_name][fixture.to_s] 884: @fixture_cache[table_name][fixture] ||= @loaded_fixtures[table_name][fixture.to_s].find 885: else 886: raise StandardError, "No fixture with name '#{fixture}' found for table '#{table_name}'" 887: end 888: end 889: 890: instances.size == 1 ? instances.first : instances 891: end 892: end 893: end
[ show source ]
# File vendor/rails/activerecord/lib/active_record/fixtures.rb, line 850 850: def try_to_load_dependency(file_name) 851: require_dependency file_name 852: rescue LoadError => e 853: # Let's hope the developer has included it himself 854: 855: # Let's warn in case this is a subdependency, otherwise 856: # subdependency error messages are totally cryptic 857: if ActiveRecord::Base.logger 858: ActiveRecord::Base.logger.warn("Unable to load #{file_name}, underlying cause #{e.message} \n\n #{e.backtrace.join("\n")}") 859: end 860: end
[ show source ]
# File vendor/rails/activerecord/lib/active_record/fixtures.rb, line 895 895: def uses_transaction(*methods) 896: @uses_transaction = [] unless defined?(@uses_transaction) 897: @uses_transaction.concat methods.map(&:to_s) 898: end
[ show source ]
# File vendor/rails/activerecord/lib/active_record/fixtures.rb, line 900 900: def uses_transaction?(method) 901: @uses_transaction = [] unless defined?(@uses_transaction) 902: @uses_transaction.include?(method.to_s) 903: end


hi!
ghost
boo!
not here
Make noDisplay visible in 500ms
transparent
high
low
a
b
c
Bones are great!
Do you like bones, too?


{{#if selectedGeohash }}
Zoom and click to find your geohash area or click on an existing one.
{{profile.geohash}} - {{profile.location}} {{ profile.blog }}
There are no user in this area, wanna be the first?


Only in New York The Out of Towners
By Ross Anthony
Road trip movies have one thing in common -- the concept of getting there. Perhaps that's why I get antsy; awaiting the delayed arrival, most of the movie seems ... well, pending. You might not expect a movie named "Out of Towners" to spend a lot of time on the road, but it does.
With their son off to Europe, Henry (Martin) and Nancy (Hawn) can no longer ignore the creaky sounds of their crusty rutty marriage. (Witnessing Goldie and Steve bicker like your parents might make you feel prematurely gray.) However, a 10 AM job interview for Henry, sends this average Ohio couple New York bound. A painful array of mishaps prevents them from making the trip in a straight line. Somewhere among the obstacles, they rediscover their marriage. But it's the flea circus of calamity, that brings us from the beginning of the film to the end and balances us like a spinning plate on the pointed edge of tedium.
That said, Steve and Goldie are brilliant and hilarious, despite the working conditions. This is the more wild and woolly Steve of "Planes, Trains and Automobiles" from which "Out of Towners" borrows the infamous car rental scene. A big Steve fan myself, I enjoyed his performance in this picture. Goldie was rock solid as well, and John Cleese is 100% stand-up British pomp.
It's the star power that gravitates this rather aggravating plot from the depths it would surely have found without them. Seldom mediocre, "Out of Towners" toggles between the tedious and the hilarious -- fortunately capturing the latter more often than the former.
Starring Goldie Hawn, Steve Martin and John Cleese. Directed by Sam Weisman. Based upon the screenplay by Neil Simon. Rated PG-13 Produced by Robert Cort and David Madden, Robert Evans and Teri Schwartz at Paramount/Robert Evans/Cherry Alley and Cort/Madden.
Grade..........................B
-- Copyright © 1999 Ross Anthony, currently based in Los Angeles, has scripted and shot documentaries, music videos, and shorts in 35 countries across North America, Europe, Africa and Asia. For more reviews visit: http://RossAnthony.com
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


Do your skills match this career?
You will be asked 10 questions to see if your current skills are matched to this career and will take you roughly 10 to 15 minutes to complete.
These question are here to help you understand what it's like to be in this career and will give you a good understanding if this career is suited to you.
8 out of 10 people who completed this questionnaire find the explanation helpful to plan their future career path and goals.


Product Description:
Price Per Unit: $
Quantity Available:
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Terms and Conditions: Before making a commitment please visit the link above to ensure the posting's legitimacy.  GPA will not be liable if there is a delay, and we will not accept your order if payment is not authorized.
How many units would you like to purchase?


---
layout: page
header-img: "img/mcgill_univ_winter.gif"
---
Lijun Sun
Ph.D. / Director / Assistant Professor
Github
Google Scholar
Website
Lijun Sun is currently an Assistant Professor in the Department of Civil Engineering at McGill University. He received his PhD degree in Civil Engineering (Transportation) from National University of Singapore, and earned a Bachelor degree in Civil Engineering from Tsinghua University. During his PhD, he worked at Mobility and Transport Planning module at the Future Cities Laboratory, Singapore-ETH center. Prior to joining McGill, he was a Postdoctoral Associate at MIT Media Lab.
Research Interests
- Urban computing & smart cities
- Machine learning for mobility modeling
- Intelligent transportation systems
- Spatio-temporal traffic state modeling/predication
- Infrastructure resilience
- Data-driven urban/transportation systems modeling
- Human mobility and travel behavior
- Agent-based modeling and simulation
- Public transportation operation & planning
Selected Publications
- Wang J, Sun L (2020) Dynamic holding control to avoid bus bunching: A multi-agent deep reinforcement learning framework. Transportation Research Part C: Emerging Technologies 116: 102661. (doi:10.1016/j.trc.2020.102661)
- Liu L, Sun L, Chen Y, Ma X (2019) Optimizing fleet size and scheduling of feeder transit services considering the influence of bike-sharing systems. Journal of Cleaner Production 236: 117550. doi:10.1016/j.jclepro.2019.07.025)
- Wang J, Kong X, Xia F, Sun L (2019) Urban human mobility: data-driven modeling and prediction. ACM SIGKDD Explorations 21(1): 1-19. (doi:10.1145/3331651.3331653)
- Gao J, Sun L, Cai M (2019) Quantifying privacy vulnerability of individual mobility traces: A case study of license plate recognition data. Transportation Research Part C: Emerging Technologies 104:78-94. (doi:10.1016/j.trc.2019.04.022)
- Zhang M, Zhou X, Zhang Y, Sun L, Dun M, Du W, Cao X (2019) Propagation index on airport delays. Transportation Research Record 2673(8): 536-543. (doi:10.1177/0361198119844240)
- Chen X, He Z, Sun L (2019) A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies 98:73-84. (doi:10.1016/j.trc.2018.11.003)
- Alabdulkareem A, Frank MR, Sun L, AlShebli B, Hidalgo C, Rahwan I (2018) Unpacking the polarization of workplace skills. Science Advances 4(7):eaao6030. (doi:10.1126/sciadv.aao6030)
- Sun L, Erath A, Cai M (2018) A hierarchical mixture modeling framework for population synthesis. Transportation Research Part B: Methodological 114:199-212. (doi:10.1016/j.trb.2018.06.002) [Code]


Cabaral, Glory Ann R.
277 Manalo Ext. Puerto Princesa City
Contact No.<PHONE_NUMBER>5
<EMAIL_ADDRESS>Objective:
To get a Job that fits to my knowledge and skills.
Personal Background:
- Date of Birth: Aprill 22, 2000
- Gender: Female
- Civil Status: Single
- Citezenship: Filipino
- Hieght: 6'3
- Weight: 64 kg.
Educational Background:
- Elementary: Funda Elementary School
Brgy. Funda, Cuyo, Palawan
- Secondary: Gaudencio Abordo Memorial National High School-Bisucay
Brgy. Balading, Cuyo, Palawan
- Senior High School: Palawan Polythecnic College Inc.
Brgy. Milagrosa, Puerto Princesa City, Palawan
- College: Palawan State University:
Brgy. Tiniguiban, Puerto Princesa City, Palawan
Skills:
- Can do Coding like Java, Mysql, and etc.
- Can Sing
- Volleyball Player since Elementary to College
- Can Play instruments like Ukulele and Piano
You can check my Hobbies and Interest by clicking this link
My Interest and Hobby


{% extends "im/account_base.html" %} {% load astakos_tags i18n %} {% block page.title %}Contact{% endblock %} {% block body %}
If you would like to contact us for any reason, please feel free to
write
your message in the following form. Feedback and suggestions are
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X-13-Data Version 2.0
Nikki Czaplicki
U.S. Census Bureau
August 26, 2014
Table of Contents
- Introduction
- Requirements
- Getting Started
- Convert from Excel to X-13
- Convert X-13 Output to an Excel file
- Data Formats
- Contact Information
- References
Input and output files from X-13ARIMA-SEATS(X-13A-S) (U.S. Census Bureau, 2015) must be text files; however,
many users store their data in Microsoft Excel files and would like their output in Microsoft Excel format.
X-13-Data is a tool that can:
- Convert data from Microsoft Excel to a text file in
datevalue
format.
- Convert X-13ARIMA-SEATS output files in
x13save
format to a Microsoft Excel file.
Return to contents.
X-13-Data has been tested on Windows XP, Windows 7, and Windows 8.
To run the program, Microsoft.NET Framework 3.5 must be installed
on your computer. It can be downloaded here.
X-13-Data also requires Microsoft Excel installed on your computer and has been tested with
Microsoft Excel 2007 and Microsoft Excel 2010.
Return to contents.
- Download
X13Data.zip
.
- Unzip
X13Data.zip
.
- Double click on
X13Data.exe
to launch. The X-13-Data main screen will display.
Keyboard Shortcuts:
Go=Alt+g
Cancel=Alt+c
- From here, you can choose to convert your Excel data into a file that can be read by X-13ARIMA-SEATS
(see Section 4), or convert your X-13 output files to Excel files
(see Section 5).
Return to contents.
X-13-Data will read data in a Microsoft Excel file in a variety of formats (all described in Section 6)
and convert them to an X-13 data file in datevalue
format. (See the X-13ARIMA-SEATS Reference Manual (2015) for
more information about datevalue
and other accepted formats.)
- Select Excel to X-13 file from the X-13-Data main page and press Go. The X-13-Data Excel
Reader window will appear.
- Select a Microsoft Excel file by either typing the complete file name in the Excel File
text box or navigating to the file using the Browse button. Once an existing Excel file has been
selected, the other controls will become available; use them to select the output file, define the data
format, and locate the data within the spreadsheet, as described below.
Keyboard Shortcuts:
Browse=Alt+b
Go=Alt+g
Cancel=Alt+c
- Select the Excel worksheet that contains the data using the Worksheet Number box.
- Select the format that your data are in using the Data Format box.
See Data Formats below for more information on supported formats.
- If you are reading a single series with Vertical or Horizontal format, use the Save File
box to select a name for the data file that will be created.
- The file must have a .dat extension. X-13-Data will change or add the .dat extension, if necessary.
- If a file with the selected name already exists, a box will display asking if you would like to
overwrite or rename the file.
- If you are reading a file containing multiple series in Vertical-Multiple, Horizontal-Multiple or MRTS
format, each series will be saved to its own data file. These data files will be saved in the same directory
as the input Excel file.
- For Vertical-Multiple and Horizontal-Multiple formats, the data file names will be the series
names whose locations are specified using the Series Names Row/Column box.
- For MRTS format, the location of the series names does not need to be specified.
- For all of these formats, if a file with the same name already exists, the new file name will have
a numeric suffix. For example, if books.dat already exists in the same directory as the input
file, the new data file will be named books(2).dat.
- Check the Date in more than one column box if the dates are organized such that the year is in
the first date column and the period (months or quarters) is in the next column.
- Use the following controls to describe the location of the data within Excel file:
- Date Column: The column in which the dates begin.
- Data Start Column: The column in which the numeric data begin.
- Data Start Row: The row in which the numeric data begin.
- Series Names Row: The row containing the series names.
Only available for Vertical-Multiple format.
- Series Names Column: The column containing the series names. Only available for
Horizontal-Multiple format.
- Data End Column: The column where the user would like the data to end. Only available
for Regression Matrix format.
- Data End Row: The row where the user would like the data to end. Only available for
Regression Matrix format.
- Alternatively, you can specify the starting date manually by checking the Manually Enter Dates
box and then selecting the starting year using the Start Year box and the starting period using
the Start Period box. NOTE: Manually Enter Dates is not available for MRTS format.
- Use the Period box to select the frequency of the data: 12 for monthly data, 6 for bimonthly data,
4 for quarterly data, 2 for biannual data or 1 for annual data.
- X-13-Data will stop reading an Excel file when:
- Two consecutive non-numeric values in a series, e.g. two consecutive missing values. (Note
that in X-13ARIMA-SEATS, by default the value -99999 in a series is considered a missing
value; if your data has missing values, it is best to change them to this value before
attempting to convert the file.)
- An empty row or column is encountered
Return to contents.
The exact format of the Excel file varies depending on the type of file being converted and how many series are being converted at a time.
But the general format will include:
- the series name
- the file type
- labels for the date and data columns
- converted data
To convert X-13 output files to Excel files.
- Select X-13 Output to Excel file from the X-13-Data main page and press Go. The X-13-Data
Excel Writer window will appear.
Keyboard Shortcuts:
Browse=Alt+b
Go=Alt+g
Cancel=Alt+c
- Navigate to your directory using the file explorer tree on the left. This will populate the list of series in the
first list box. By default, series names will be displayed here. You can populate this list with
metafiles (text files containing lists of series) by selecting "Metafile"
from the drop-down box above the list.
- Select at least one item from the list. The second list will be populated with possible file types. If you select by series,
X-13-Data will display the file types that actually exist in the directory for the selected series. If you select by metafile,
X-13-Data will display all file types.
- Select one or more file types from the list on the right. You can select file types by file description or by file extension. Use the drop down menu
above the list on the right to switch between these two options. You can customize the order of the file types by clicking on the
Customize File Type List button. Click here to see an example of the Change File Type Order box.
- Select how the data wil be grouped in the Excel file. You can group data by series or by file type.
- Enter the name for the Microsoft Excel file in the save file box. If you would like to save the file to the currently selected directory, just
enter the file name. Otherwise, include the complete path and filename. This file must have a .xls or .xlsx extension. You can also navigate to the
location where you would like to save the file using the Browse button.
- Press the Go button to convert the files. See below for an example of a converted file.
A note about grouping.
Series can be grouped by series or file type. Each grouping will be on its own worksheet in the Excel file.
X-13ARIMA-SEATS can accommodate series with one of five periods: annual, biannual, quarterly, bimonthly, and monthly. X-13-Data can convert files
of all four periods at once if requested by the user. When organizing the output by series, this doesn't present a problem since all
file types for a single series will have the same period. However, when organizing the output by file types; there is the potential for different
periods for the same file type. In this case, X-13-Data creates a separate worksheet for each combination of file type and period.
In this case, the worksheet name will be the file type followed by the period.
Return to contents.
Vertical
The dates are in one or two columns and the data is in another column
Horizontal
Data is in a table, with each row containing one year of data. Each column contains data for one period.
Vertical-Multiple
Similar to Vertical for single series, each series is in its own column. There is a row containing the
names of each series. These are used to name the data files created by X-13-Data.
Horizontal-Multiple
One column contains the series names. (Here it is column A.) Each series is presented in its entirety before the
next series begins. Each row contains one year of data. Each month (quarter) is in its own column.
MRTS
MRTS refers to the U.S. Census Bureau's Monthly Retail Trade and Food Services report. It is available
at
http://www.census.gov/retail/
X-13-Data will create a data file for each unadjusted series from the MRTS report. You do not need to
specify any parameters when the MRTS format has been selected.
Regression Matrix
This is similar to Vertical Multiple format except that entire matrix will be saved into one data file. This type of file can be read
into X-13ARIMA-SEATS as a user-defined regressor.
Use the controls to specify the start and end rows and columns of the regression matrix. Data Start Column
and Data Start Row are required. Leave Data End Column and Data End Row at their default values to read
the entire matrix. Otherwise, use Data End Column and Data End Row to read a subset of the data in the Excel file.
Return to contents.
For more information or to report bugs, please contact Nikki Czaplicki.
Email<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>
Return to contents.
U.S. Census Bureau (2015).X-13ARIMA-SEATS,Version 1.1
U.S. Census Bureau (2015).
X-13ARIMA-SEATS Reference Manual,Version 1.1
Return to contents.


Favorite Places
Favorite Places in the US
Grand Canyon
NYC
Seattle
Favorite Places in Europe
Berlin Wall
Eiffel Tower
Monte Carlo


Home
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Sci-fi
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Tom and Jerry
Sponge Bob
The Simpsons
Futurama


GEOIP Detect and Redirect
This feature will allow editors to set up the connection between a series of pages, which are localizations of a same subject. "GEOIP Detect and Redirect" or "GDNR" in short, will check user's location and compare with current page's language. If they are not matched, a modal/popup will be shown to ask user if they want to jump to the right page. Once set up, GDNR should be executed automatically, please check console log
if it is not working as expected.
- If user's location is not on code list, the popup will not show.
- If user chooses to cancel the redirection, the popup will not show anymore.


Sankey Diagram
How a change lands, +2s
- The change authors are in the leftmost vertical column (Hover over vertical bar to see number of changes).
- The paths widths are proportioned by the number of +2s from the commenter in the second column at so on. (Hover over paths to see number and average time to +2).
- Path colour indicates average length of time between for the right hand party to +2
- Click each path to open specific changes in gerrit. (fails for very large sets)
- Some changes have more than 2 +2s if required by rechecks etc
- Pontential areas to investigate (depending on project) may be indicated by:
- All patches being +2 by a single individual
- Some commenters only being the second +2 c)
- Strong affinity between a pair of developers
- "Fast +2" between certain developers
- Conversely a messy graph with a no clear biases may be a sign of a healthy community.
- See full query in Gerrit:
Author: Daniel Becker
Thanks to Mike Bostock, whose D3 Sankey plugin was used to help build this visualisation.


Streaming 'JSON' ('ndjson') has one 'JSON' record per-line and many modern 'ndjson' files contain large numbers of records. These constructs may not be columnar in nature, but it is often useful to read in these files and "flatten" the structure out to enable working with the data in an R 'data.frame'-like context. Functions are provided that make it possible to read in plain 'ndjson' files or compressed ('gz') 'ndjson' files and either validate the format of the records or create "flat" 'data.table' structures from them.
| Version: | 0.7.0 |
| Depends: | R (≥ 3.2.0) |
| Imports: | Rcpp, data.table, dplyr, dtplyr |
| LinkingTo: | Rcpp |
| Suggests: | testthat |
| Published: | 2018-09-18 |
| Author: | Bob Rudis [aut, cre], Niels Lohmann [aut] (C++ json parser), Deepak Bandyopadhyay [aut] (C++ gzstream), Lutz Kettner [aut] (C++ gzstream)) |
| Maintainer: | Bob Rudis <bob at rud.is> |
| BugReports: | https://gitlab.com/hrbrmstr/ndjson/issues |
| License: | MIT + file LICENSE |
| URL: | http://gitlab.com/hrbrmstr/ndjson |
| NeedsCompilation: | yes |
| SystemRequirements: | zlib, C++14 |
| Materials: | README NEWS INSTALL |
| In views: | WebTechnologies |
| CRAN checks: | ndjson results |
| Reference manual: | ndjson.pdf |
| Package source: | ndjson_0.7.0.tar.gz |
| Windows binaries: | r-devel: ndjson_0.7.0.zip, r-release: ndjson_0.7.0.zip, r-oldrel: ndjson_0.7.0.zip |
| OS X binaries: | r-release: ndjson_0.7.0.tgz, r-oldrel: ndjson_0.7.0.tgz |
| Old sources: | ndjson archive |
| Reverse depends: | streamR |
Please use the canonical form https://CRAN.R-project.org/package=ndjson to link to this page.


Algosigner - Sign Testing
Use Default Tx
Send Transaction
Pay
Group
Multisig
Application
Accept
Transfer
Config
Create
Destroy
Clawback
Freeze


Past Events
Cluster 2022
Location: Heidelberg, Germany
General Co-chairs:
- Abhinav Bhatele, University of Maryland, USA
- Felix Wolf, Technical University of Darmstadt, Germany
- Trilce Estrada, University of New Mexico, USA
- Torsten Hoefler, ETH Zurich, Switzerland
Cluster 2021
Location: Portland, OR, USA
General Co-chairs:
- Toni Cortes, Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC), Spain
- Kathryn Mohror, Lawrence Livermore National Laboratory, USA
- Ewa Deelman, The University of Southern California (USC), USA
- Lin Gan, Tsinghua University, China
Cluster 2020
Location: Kobe, Japan
General
- Co-chairs:
- Taisuke Boku, University of Tsukuba, Japan
- Pete Beckman, Argonne National Laboratory, USA
- Vice chair: Miwako Tsuji, RIKEN R-CCS, Japan
- Masaaki Kondo, The University of Tokyo, Japan
- Franck Cappello, Argonne National Laboratory, USA
Cluster 2019
Location: Albuquerque, NM, USA
General Chairs:
- Patrick Bridges, University of New Mexico, USA
- Ron Brightwell, Sandia National Laboratories, USA
Program Chairs: Pat McCormick, Los Alamos National Laboratory, USA, Martin Schulz, Technische Universität München, Germany
Webpage
Cluster 2018
Location: Belfast, UK
General Chair:
- Dimitrios Nikolopoulos, Queen’s University, Belfast, UK
- Bronis R de Supinski, Queen’s University, Belfast, UK
Program Chair: Paul Kelly, Imperial College London, UK
Webpage
Cluster 2017
Location: Hawaii, USA
General Chair:
- Naoya Maruyama, Riken, Japan
- Todd Gamblin, Lawrence Livermore National Laboratory, USA
Cluster 2016
Location: Taiwan
General Chair:
- Chung-Ta King, National Tsing Hua University, Taiwan
- Jack Dongarra, University of Tennessee, USA
- Satoshi Matsuoka, Tokyo Institute of Technology, Japan
Cluster 2015
Location: Chicago, IL, USA
General Chair:
- Pavan Balaji, Argonne National Laboratory, IL, USA
- Michela Taufer, University of Delaware, USA
Cluster 2014
Location: Madrid, Spain
General Chair: Maria S. Pérez, Universidad Politécnica de Madrid, Spain
Program Chair:
- Gabriel Antoniu, INRIA, France
- Kate Keahey, Argonne National Laboratory, IL, USA
Cluster 2013
Location: Indianapolis, IN, USA
General Chair: Craig Stewart, Indiana University Pervasive Technology Institute, USA
Technical Program
- Chair: Henry Tufo, University of Colorado at Boulder, USA
- Co-chairs:
- Nicholas Cardo, National Energy Research Scientific Computing Center, USA
- Stephen Simms, Indiana University, USA
Cluster 2012
Location: Beijing, China
General Chair: Guojie Li, Institute of Computing Tech, Chinese Academy of Sciences, China
Technical Program Chairs:
- Xiaodong Zhang, The Ohio State University, USA
- Yuzhong Sun, ICT, Chinese Academy of Sciences, China
- Cho-Li Wang, University of Hong Kong
Cluster 2011
Location: Austin, TX, USA
General Chair: Jay Boisseau, Texas Advanced Computing Center, USA
Technical Program Chairs:
- Toni Cortes, Barcelona Supercomputing Center, Spain
- Kent Milfeld, Texas Advanced Computing Center, USA
Cluster 2010
Location: Heraklion, Greece
General Chair: Dimitrios Nikolopoulos, FORTH-ICS
Technical Program Chairs:
- Angelos Bilas, FORTH-ICS
- Ricardo Bianchini, Rutgers University, USA
Cluster 2009
Location: New Orleans, Louisiana, USA
General Chair: Daniel S. Katz, Louisiana State University, USA
Technical Program Chair: Thomas Sterling, Louisiana State University
Cluster 2008
Location: Tsukuba, Japan
General Chair: Yutaka Ishikawa, University of Tokyo, Japan
Technical Program Chair: Mitsuhisa Sato, University of Tokyo, Japan
Cluster 2007
Location: Austin, TX, USA
General Chair: Karl W. Schulz, Louisiana State University, USA
Technical Program Chair: Kent Milfeld, Texas Advanced Computing Center, USA
Cluster 2006
Location: Barcelona, Spain
General Chairs:
- Toni Cortes, Barcelona Supercomputing Center, Spain
- Ira Pramanick, Sun Microsystems
Technical Program Chair: Bill Gropp, Argonne National Laboratory, USA
Cluster 2005
Location: Boston, MA, USA
General Chairs: Dmiter Avresky, Northeastern University, USA
Technical Program Chair: Thomas Stricker, Google Engineering
Cluster 2004
Location: San Diego, CA, USA
General Chairs:
- Henri Casanova, SDSC, USA
- Daniel Katz, Jet Propulsion Laboratory, USA
Technical Program Chair: Philip Papadapoulos, SDSC, USA
Cluster 2003
Location: Hong Kong, China
General Chairs:
- Jack Dongarra, University of Tennessee, USA
- Lionel Ni, Hong Kong University of Science and Technology, Hong Kong
Technical Program Chair:
- Daniel Katz, Jet Propulsion Laboratory, USA
- Cho-Li Wang, The University of Hong Kong, Hong Kong
Cluster 2002
Location: Chicago, IL, USA
General Chairs:
- Dan Reed, NCSA, USA
- Mark Baker, University of Portsmouth
Technical Program Chair: Bill Gropp, Argonne National Laboratory, USA
Cluster 2001
Location: Newport Beach, CA, USA
General
- Chair: Mark Baker, University of Portsmouth
- Vice Chair:
- Rick Stevens, Argonne National Laboratory, USA
- Nalini Venkatasubramanian, University of California, Irvine, USA
Technical Program Chair: Thomas Sterling, CalTech, USA
Cluster 2000
Location: Chemnitz, Germany
General Chair: Wolfgang Rehm, TU-Chemnitz, Germany
Vice Chair: Thomas Stricker, ETH-Zürich, Switzerland
Technical Program
- Chair: Giovanni Chiola, Università di Genova, Italy
- Vice chairs:
- Toni Cortes, UPC
- Daniel S. Katz, NASA
- Ira Pramanick, Sun
Cluster 1999
Location: Melbourne, Australia
General Chairs:
- Rajkumar Buyya, Monash University
- Mark Baker, University of Portsmouth
Technical Program Chair: Ken Hawick, Adelaide University


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The Justice Tech Catalog is a curated list of data and technology projects affecting the criminal justice system and is hosted by Justice Codes NonProfit. This collection was built as a resource for academics, advocates, computer and data scientists and system stakeholders interested in technology and criminal justice issues. In the collection, users will find open data portals and open source code for dozens of projects across all touchpoints of the criminal justice system. The catalogue is meant to be comprehensive, so inclusion is not an endorsement of the tool or its creators.
This project was born out of work started in 2016 with the John Jay College of Criminal Justice Research and Evaluation Center and Jeffrey Butts. It would not have been possible without the hard work of Lindsey Barrett and funding from Koch Industries.
If you care to contribute, you can email us or take a look at our code on Github.


{% extends "layout.html" %} {% block page_title %} {{ proto.title + ' ' + proto.version }} - Accident - What were you doing {% endblock %} {% block content %} What work related tasks were you doing at the time of the accident? What work related tasks were you doing at the time of the accident? Be specific about what you were doing when the accident occured 0 characters remaining Form Navigation {% endblock %} {% block body_end %} {{ super() }} {% endblock %}
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Creates an array of numbers in the arithmetic progression, starting with the given positive integer and up to the specified limit.
Array.from()
to create an array of the desired length, lim/n
, and a map function to fill it with the desired values in the given range.