AMAZING PEOPLE ARE MAKING DESIGNS IN A FUN ENVIRONMENT
When you work with us, you are part of a team of committed collaborators. All aspects of our software
development process are integrated, interactive and agile.
Our approach unifies design, development
and
product management to create exceptional products.We start each project by understanding your business
goals, the user's need and the product requirements.
Then we help translate them into effective and
ellegant
solutions.
Before we sign a contract or write a line of code our team will spend the necessary time needed to
understand our product vision.
The outcome of this scoping session is a proposal or high-level
statement of work. Based on your requirements we provide a complete requirement or just the pieces you
need.
Our design practice offers a full range of services including brands strategy,interaction and
visual
design and user experience testing.
Throughout your project, our designers create and implement visual design and workflows,social
user
feedback and work with you to make sure what gets built is what is needed
All engineers are fluent in the enterprise, mobile and web development technologies.
They collaborate with your team to write and improve code on a daily basis, using proven
practices
such
as test-driven development and pair programming.
Planning and development is interactive, Because we are constantly coding and testing, the product we build are always ready to go live. This iteractive process allows for changes as business requirements evolve.
Black Panther
Ontaria Security
Mustard Passion
Jim Carrey
Good Day
Form Filling
Burned Mock-up
Giraffe Ark
DELANI STUDIO
2019


On the morning of February 22, 1884, John Heath was taken from the Court House and lynched on a telegraph pole on the corner of Second and Toughnut streets and the story of his death is dramatically covered during the Ghost Tour. After he died there was a coroner's inquest, but the jury wanted to protect the mob of men who had hanged Heath, so they concluded: "We the undersigned, a jury of inquest, find that John Heath came to his death from emphysema of the lungs – a disease common in high altitudes – which might have been caused by strangulation, self-inflicted or otherwise." Since he was found at a "high altitude" (the top of a telegraph pole) and may have suffered emphysema, and, further, his death may have been "self-inflicted," no one was ever charged with his death. I cannot prove this, but it is said that John Heath's lawyer lived across the street from where he was lynched and the mob chose that corner intentionally to "send a message" to that lawyer.
Because he had died so violently and no one was ever held responsible for the murder, it is believed that his spirit frequently returns to this corner to seek justice. Guests report hearing him screaming on a pole that is no longer here. There are also numerous photos that show paranormal activity on this corner, like the two included on this page: they were taken by the same guest a few seconds apart, the top one has no paranormal activity and the other has a vortex.
Tour Information: The corner where John Heath was lynched is on our tour route and I share some of the history and paranormal activity witnessed here as we pass this location.


{% extends "_layout/base.html" %} {% block content %}
{{ collection.description }}
This drawer is empty
Add collection


What is a required item on a page?
- A required item is a virtual goods designed by a creator for his/her book
- It has a name, a small picture, a price and a short description
- Creators design these items on the book edit page
- It can be placed on a particular page in the Required Items section
- A required item means that the reader must have it before reading that page
- If the reader does not have it in her inventory, she has to buy it
- A required item usually cannot be placed in the reader's gallery
- There can be multiple items required on a page, but only one is necessary
- A reader can choose to buy at least one of the items to go on reading
What is a reward on a page?
- A reward is a virtual goods designed by a creator for his/her book
- It has a name, a small picture, a price and a short description
- Creators design these items on the book edit page
- Add a virtual goods to a particular page as reward for all readers
- A reward item can be collected by a reader only once
- And it can be placed in readers' galleries if the creator so defines
- A reward item is displayed in a way determined by a dynamic control
- It can show up automatically soon after the reader turns to the page
- Or it can be displayed as a button or image for readers to click and pick
What is a quest and how to create it?
A quest is a task for the reader to complete. It is designed by the creator for the background story. It consists of a big image as the scene and a list of items placed on it. Originally the reader has none of these items, and while the reader reads the book, he/she can collect the items one by one, and eventually collect all items to complete the quest. When a reader completes a quest, he/she may get a prize.
- Create an image of size 740x500 pixels and save as JPG file
- Upload it as a virtual goods with display=false, price=0
- Create a number of small transparent images as PNG files
- Stick these small images onto the big one to find out top-left coordinates
- Upload these stickers as virtual goods and set a price if for sale
- Create a quest on the book edit page
- Each quest has a name, a background image and up to five stickers
- Give each sticker an x and y coordinate, scale of 0.1 to 1.0
- And assign another virtual goods as a reward for completing the quest
- The reward and stickers can be any virtual goods either bought or collected
Creat a quest script
A quest script is in JSON format. If you're not sure what JSON is, you could either contact me for help with your quests, or wait until our visual quest editor is out.
- A book can have multiple (10) quests, as a list of objects: [{...},{...},...]
- Each quest has a "qid" which is an image ID, "qname" as the name of the quest, and a list of "items", a prize and an introduction.
- Items are the shadow objects in the quest picture, these are virtual goods placed throughout the book pages.
- An item has a "vgid" ID, x and y coordinates, "sc" as a scaling factor, and possible filters (to be available).
- A prize is also a virtual goods image with an ID given here. If no prize, set the ID as zero
- You can find the image IDs from the virtual goods table on the Creators page.
- The quest image, the background picture, must be of 740x500 pixels in size, objects should be smaller.
- Object images should have a transparent background saved as PNG files.
- It's better to avoid objects overlapping each other.
- It's recommended to design the objects in different layers, locate the positions and design the script.
- Quest script is validated upon submission and will be rejected if invalid.
- You can validate any JSON text on http://www.jsonlint.com/ website.
How to create a dynamic control?
A dynamic control is a piece of code that defines how to present various types of rewards, collectibles and to-buy items placed on a page. A visual control editor is being developed, but at the moment please contact me (http://facebook.com/tedwen) for help.
- To add a donation link to an external website (replace image ID 109001 with yours):
- A popup reward a second after the reader turns to the page (Remember to add this item (96001 in this example) to Rewards section):
- An clickable image on a page to collect a reward (Remember to add this item to the Rewards section):
- A button to show a popup (if no image, use a button).
- To add a reward or collectible, remember to add the item to the Rewards section in addition to the dynamic control code.
- A donate to the creator button is not necessary because there is already a Like button on every page.


Notes on the Fretboard
Clear highlights
C-G 2nd. Position
C-G 4th. Position
C-G 7th. Position
All C
All D
All E
All F
All G
All A
All B


There are 6 alkaline earth metals, they are beryllium (Be), magnesium (Mg), calcium (Ca), strontium (Sr), barium (Ba), and radium (Ra). They have very similar properties: they are all shiny, silvery-white, somewhat reactive metals at standard temperature and pressure.
Halogens consist of 5 chemically related elements: fluorine (F), chlorine (Cl), bromine (Br), iodine (I), and astatine (At). The artificially created element 117 (ununseptium) may also be a halogen.
Alaki metals consist of the chemical elements lithium (Li), sodium (Na), potassium (K), rubidium (Rb), caesium (Cs), and francium (Fr).
There are 6 Alkaline Earth Metals, they are beryllium (Be), magnesium (Mg), calcium (Ca), strontium (Sr), barium (Ba), and radium (Ra). They have very similar properties: they are all shiny, silvery-white, somewhat reactive metals at standard temperature and pressure.
The 6 Noble Gases that occur naturally are helium (He), neon (Ne), argon (Ar), krypton (Kr), xenon (Xe), and the radioactive radon (Rn). Ununoctium (Uuo), which was recognised as an element in December 2015, is predicted to be a noble gas as well but this fact is not confirmed.


Copyright © 2009 Marc Worrell
Behaviours: gen_model.
Authors: Marc Worrell (marc@worrell.nl).
| delete/2 | Delete a resource. |
| duplicate/3 | Duplicate a resource. |
| exists/2 | |
| get/2 | Read a whole resource. |
| get_acl_props/2 | Get the ACL fields for the resource with the id. |
| get_raw/2 | Get the resource from the database, do not fetch the pivot fields. |
| insert/2 | Insert a new resource. |
| is_a/2 | Return the categories and the inherited categories of the resource. |
| is_a/3 | Check if the resource is in a categorie. |
| is_a_id/2 | Return the categories and the inherited categories of the resource. |
| is_cat/3 | Check if the resource is exactly the category. |
| is_deletable/2 | |
| is_editable/2 | |
| is_me/2 | |
| is_visible/2 | |
| m_find_value/3 | Fetch the value for the key from a model source. |
| m_to_list/2 | Transform a m_config value to a list, used for template loops. |
| m_value/2 | Transform a model value so that it can be formatted or piped through filters. |
| media/2 | |
| name_lookup/2 | Return the id of the resource with a certain unique name. |
| name_to_id/2 | Return the id of the resource with the name. |
| name_to_id_cat/3 | |
| name_to_id_cat_check/3 | |
| name_to_id_check/2 | |
| o/2 | |
| o/3 | |
| o/4 | |
| op/2 | |
| p/3 | Fetch a property from a resource. |
| p/4 | |
| p_no_acl/3 | Fetch a property from a resource, no ACL check is done. |
| page_path_to_id/2 | |
| page_url/2 | |
| rid/2 | Fetch a resource id from any input. |
| s/2 | |
| s/3 | |
| s/4 | |
| sp/2 | |
| touch/2 | "Touch" the rsc, incrementing the version nr and the modification date/ modifier_id. |
| update/3 | Update a resource. |
| uri_lookup/2 | Return the id of the resource with a certain uri. |
delete(Id::Props, Context) -> ok | {error, Reason}
Delete a resource
duplicate(Id, Props, Context) -> {ok, NewId} | {error, Reason}
Duplicate a resource.
exists(Name, Context) -> any()
get(Id, Context) -> PropList | undefined
Read a whole resource
get_acl_props(Id, Context::#context{}) -> #acl_props{}
Get the ACL fields for the resource with the id. The id must be an integer
get_raw(Id, Context) -> any()
Get the resource from the database, do not fetch the pivot fields.
insert(Props, Context) -> {ok, Id} | {error, Reason}
Insert a new resource
is_a(Id::int(), Context) -> list()
Return the categories and the inherited categories of the resource. Returns a list with category atoms
is_a(Id::int(), Cat::atom(), Context) -> bool()
Check if the resource is in a categorie.
is_a_id(Id::int(), Context) -> list()
Return the categories and the inherited categories of the resource. Returns a list with category ids
is_cat(Id, Cat, Context) -> any()
Check if the resource is exactly the category
is_deletable(Id, Context) -> any()
is_editable(Id, Context) -> any()
is_me(Id, Context) -> any()
is_visible(Id, Context) -> any()
m_find_value(Id::Key, M::Source, Context) -> term()
Fetch the value for the key from a model source
m_to_list(M::Source, Context) -> List
Transform a m_config value to a list, used for template loops
m_value(M::Source, Context) -> term()
Transform a model value so that it can be formatted or piped through filters
media(Id, Context) -> any()
name_lookup(Name, Context) -> any()
Return the id of the resource with a certain unique name. name_lookup(Name, Context) -> int() | undefined
name_to_id(Name::NameString, Context) -> {ok, int()} | {error, Reason}
Return the id of the resource with the name
name_to_id_cat(Name, Cat, Context) -> any()
name_to_id_cat_check(Name, Cat, Context) -> any()
name_to_id_check(Name, Context) -> any()
o(Id, Context) -> any()
o(Id, Predicate, Context) -> any()
o(Id, Predicate, N, Context) -> any()
op(Id, Context) -> any()
p(Id, Property, Context) -> any()
Fetch a property from a resource. When the rsc does not exist, the property does not exist or the user does not have access rights to the property then return 'undefined'. p(ResourceId, atom(), Context) -> term() | undefined
p(Id, Property, DefaultValue, Context) -> any()
p_no_acl(Id, Predicate, Context) -> any()
Fetch a property from a resource, no ACL check is done.
page_path_to_id(Path, Context) -> any()
page_url(Id, Context) -> any()
rid(Id, Context) -> any()
Fetch a resource id from any input
s(Id, Context) -> any()
s(Id, Predicate, Context) -> any()
s(Id, Predicate, N, Context) -> any()
sp(Id, Context) -> any()
touch(Id, Context) -> {ok, Id} | {error, Reason}
"Touch" the rsc, incrementing the version nr and the modification date/ modifier_id. This should be called as part of another update or transaction and does not resync the caches, and does not check the ACL. After "touching" the resource will be re-pivoted.
update(Id, Props, Context) -> {ok, Id} | {error, Reason}
Update a resource
uri_lookup(Uri, Context) -> any()
Return the id of the resource with a certain uri. uri_lookup(string(), Context) -> int() | undefined
Generated by EDoc, Apr 11 2012, 20:41:29.


cv
General Information
| Full Name | Lei Xiong |
| Contact | jsxlei [at] gmail [dot] com |
Research Areas
- My PhD research focuses on developing artificial intelligence tool for analysis of single-cell RNA-seq and ATAC-seq data to uncover gene regulation in cell heterogeneity. In my PhD dissertation, “Artificial intelligence method for single-cell ATAC-seq data via feature extraction”, I studied analysis methods for uncovering the regulatory variations across various cell types observed from high-dimensional and sparse single-cell ATAC-seq data.
- I am also passionate about applying network analysis methods. Previously, I did some research in structural-protein-protein interaction (PPI) network to find out structural modules enriching cancer mutations and drug targets.
Education
-
2015 - 2020
Tsinghua University – Ph.D. in Computational Biology
- Advised by Prof. Qiangfeng Cliff Zhang
- Thesis: Artificial intelligence method for single-cell ATAC-seq data via feature extraction
-
2011 - 2015
University of Science and Technology of China – B.S. in Biology
- Advised by Prof. Nieng Yan
- Thesis: Structure basis and transport mechanism of membrane protein GLUT3
Other Research Experience
-
2013 - 2014
Research for Undergraduates Program
- University of Science and Technology of China
- Advised by Prof. Jianye Zang
- Structure and function research of ubiquitin ligase TRAIP (TRAF-interacting protein) including molecular clone, protein expression and purification, pull down, inclusion body denaturation and renaturation experiments.
-
2012 - 2013
2013 USTC-China iGEM Team
- University of Science and Technology of China
- In Situ Transdermal Vaccine. We constructed a macromolecule transdermal delivery system with the support of TD-1 polypeptide in Bacillus subtilis. We also designed a reporter system and a kill switch for usability and biosafety.
Honors and Awards
-
Scholarships
- Student Scholarship (2011-2013)
-
Awards
- Top Ten Advances in Bioinformatics in China in 2019 (2020)
- Top Ten Algorithms and Tools for Bioinformatics in China in 2019 (2020)
- International Genetically Engineered Machine Competition (iGEM): Gold Medal (2013)
Skills
- ○ Designing artificial intelligence algorithm for biological problems
- ○ Biological data mining
- ○ Single-cell analysis
- ○ Network analysis
- ○ Frameworks: Pytorch, Tensorflow, NumPy, SciPy, Pandas.


Survey Portal is unique product developed by SP Infotech using which any company can
conduct the survey for anything weather it is product , service or any topic.
Survey portal is easy to
use application which helps any organization to conduct survey using a database
hosted by SP Infotech.
Survey portal is customizable and extendable as per the requirement of the client.
Contacts:
Thank you for visiting.


RealStorm Pixel-Tool
Hold down alt and click to select color under cursor in a pixel cell
Width:
Height:
Create New Image
Create Object
Create Object from Image
Name:
SaveObject
LoadObject
Selected Color:
Selected Background Color:
Set Script
player = RealStormEngine.my_gameObjects[0]; projectile = RealStormEngine.my_gameObjects[1];
RealStorm Editor
Scene Name:
SaveScene
LoadScene
Play
Pause
RealStorm Editor List


How to Make Espresso Without an Espresso Machine
Here are the ways to make espresso at home:
How to Make Espresso with an Aeropress
We love the Aeropress at Roasty. It’s the perfect way to make coffee on the road and, as we discovered, a great way to make a shot of espresso without an espresso machine. Here’s the step-by-step process for brewing a shot of espresso with the Aeropress. This recipe is for a double shot of espresso. For a single shot, just cut the amount of water and coffee in half.
What you’ll need:
- Aeropress
- Freshly roasted coffee beans
- Automatic Grinder
- Scale for coffee measuring (or a tablespoon)
- Electric Gooseneck Kettle or Stovetop Kettle
Heat up the water. Heat some water to 185 degrees F (85 C), removing for 30 seconds after for roughly the ideal temperature. You’ll want to heat up more water than what’s called for in the recipe since you’ll end up measuring out 4 fl oz (or 1/2 cup/120 ml) of the heated water in a later step. Note: the perfect water temperature for brewing coffee is 205 degrees F (96 C), or almost boiling. However, with an Aeropress, you can play with any temperature between 185 – 205 degrees F (85 – 96 C).
Grind your coffee beans. While the water’s heating, grind your beans to a fine consistency, until you have 2 tablespoons or 30 grams. An Automatic Burr Grinder is the most precise way to get fine, espresso ground coffee, but you can always have your beans ground at the coffee shop or store in advance (it just won’t be as fresh).
Set up the Aeropress. Put a filter in the drain cap and rinse with hot water. Then put the drain cap on the AeroPress and place it directly onto a coffee mug or tumbler. Make sure it’s a sturdy container, because you’ll be putting pressure on it.
Fill the Aeropress with the coffee grounds. Fill the Aeropress with your coffee and tamp down the grounds to make sure the grounds are nice and compact (you can use any cylinder shaped item to tamp down – whatever will fit in the Aeropress).
Stir and steep. Measure out 4 fl oz (or 1/2 cup/120 ml) of the heated water, pour the water into the Aeropress and give it a quick stir.
Plunge. Wait 30 seconds after stirring, then plunge with just the weight of your hand. You’ll feel a lot of resistance but keep pushing steadily! Once the plunger is fully depressed, remove the press.
Serve your espresso. Transfer your drink to a fancy espresso cup and enjoy! For clean up, simply remove the drain cap and depress the plunger over your trash or compost to pop out the grounds. Rinse everything with hot water.
If you don’t already own the Aeropress, it’s a convenient and powerful tool to have in your coffee arsenal and a surprisingly effective way to make espresso without an espresso machine.


Crawlers Identification
So how do web servers identify web crawlers?
Crawlers typically identify themselves to a web server by using the User-agent field of an HTTP request
Usually the User-agent field contains a URL where the web administrator can find more information about the crawler.
This is different from Spambots or malicious web crawlers as they are unlikely to place identifying information in the user agent.
It is important to have crawlers to identify themseles so the web administrator can contact the owner of the crawler.


Mirai Bot 4.14 - Giveaways, Music, and More
Mirai Bot 4.14 brings some long-needed changes to Mirai. First, there's the new giveaway system that gives you the ability to run easily configurable giveaways in your server. Along with that, the music system has been completely re-written to be more powerful than ever, bringing us much closer to the full release. Even if you don't use those features, this update still includes a ton of improvements to Mirai.
Sat, 13 Oct 2018 05:00:00 UTC — Tags: mirai, discord, changelog, website
Mirai Bot 4.13 and a New Website
Mirai Bot 4.13 includes tons of fixes and improvements, but most importantly it includes support for the new website. I've spent a lot of time rewriting the website from scratch to build a better version. See what's changed in this release.
Fri, 13 Jul 2018 05:00:00 UTC — Tags: mirai, discord, changelog, website
Mirai Bot 4.12 Changelog
What's new in Mirai Bot v4.12
Fri, 02 Mar 2018 06:00:00 UTC — Tags: mirai, discord, changelog


Main / About us
About us
EnergyMax is an international, dynamically developing company with its production.
This community of people, who knows, how to achieve maximum health outcomes, wealth and success. Besides, every day we become more and more!
Products of our company effectively strengthen health, give energy, and improve the quality of human life.
The company does not stand still – cooperating with scientists and the use of advanced technologies that allow us to constantly bringing innovative products to market.
Currently, the company not only provides access to revolutionary products but also allows you to create a profitable reliable, business on its basis.
Why do we recommend EnergyMax?
Own product
Patented product
Step-by-step training system from the best business coaches
The company has its production, which means 100% responsible for the quality of products. The company does not overpay intermediaries, which allows you to make maximum payments to partners.
A patented product, there are no analogues in the world.
Step-by-step training system from the best business coaches. Experts are sharing with all the most trend approaches for making business, and
with such training, success is inevitable.
Automated system for attracting customers and partners
Employees are an expert
in their field
The peculiarity of the company is an automated system for attracting customers and partners, which allows you to receive incoming applications from people already interested in the business and simplifies the work as much as possible.
Employees are an expert in their field, who invests the maximum of his knowledge and skills in its development
Quickly come up with consistently high earnings helps our partner all of this. You will get into the business environment with modern business processes, start learning from the leaders of the company and get the tools to build your high-yield business, becoming a part of our team.
Write your success story with us!
© 2020. nrg-max.com


|
GLAC
1.0
|
Go to the source code of this file.
Functions | |
| complex | SU2Determinant (SU2 H) |
| File for holding various functions. More... | |
| complex | SU3Determinant (SU3 H) |
| double | traceRealMultiplication (SU3 A, SU3 B) |
| traceRealMultiplication More... | |
| double | traceImagMultiplication (SU3 A, SU3 B) |
| traceImagMultiplication More... | |
File for holding various functions.
traceImagMultiplication
| A | |
| B |


National Convention 2016 at Nashville
In the year of 2016, the MTHS TSA only sent two students to the national competition, being sophomores Reiden Chea and Trevor Doan. However, despite the low numbers from MTHS, the duo established deep connections with the BTMS TSA who sent nine students. Nearly all of those students have since come to MTHS, and are heavily involved and committed in our TSA chapter. The dynamic duo brought home a fourth-place finish in Structural Design and Engineering, losing out on the third-place trophy by the narrowest of margins: 0.089 points. This loss would only light a fire of ambition for the MTHS Structural Design and Engineering team to win next year’s national competition. In terms of BTMS, Cole Johnston took home a second-place finish in Career Preparation, the Junior Solar Sprint Team claimed eighth, and the Leadership Strategies team finished in fifth. All of these students have since migrated to MTHS, where they continue to work hard to maintain their reputation.


Installing IIS on Windows Server 2008
Published Monday 09 August 2010
It's in Administrative Tools/ Server Manager / scroll to Roles then Add Roles.
In the roles wizard is a checkbox for Web Server (IIS)
There are various sub options including Common HTTP Features (static content for your images, css), ASP.Net, Security (Windows authentication), Performance (compression), Management Tools (IIS manager)
Here's the details
Previously: Code style (01 Aug 2010)


Leaked UN Report Shows Failed Investigation on Sexual Abuse
by ASSOCIATED PRESSDAKAR, Senegal (AP) — The United Nations botched its investigation into accusations of sexual abuse in Central African Republic, letting down victims, according to a draft report.
The report, written in 2017 but not yet made public, was leaked to The New Humanitarian and seen by The Associated Press.
A senior U.N. official disputed the findings in the draft report, which the U.N. said were not included in the final report.
An Associated Press investigative series in 2017 uncovered roughly 2,000 allegations of sexual abuse and exploitation by U.N. peacekeepers around the world over a 12-year period.
The roughly 11,000 peacekeepers in Central African Republic had the most sexual misconduct allegations – 52- of any U.N. peacekeeping mission in 2016.
“The leaked review … gives a rare behind-the-scenes glimpse at how the U.N. system investigates claims of sexual abuse and exploitation by its own peacekeepers – and shows why it often fails the victims it is supposed to serve,” according to the New Humanitarian.
The failed investigation into the allegations in the Central African Republic cost the U.N. more than $480,000.
Inadequate storage ruined DNA samples that had been collected to connect victims to their alleged perpetrators, according to the report.
“Most were already rotten. It is therefore hardly surprising that positive results could not (be) obtained,” the report said of the DNA samples. Many of the samples were taken from March to May 2016, and then they were stored in Bangui for months and were not delivered to the Nairobi office for the investigation until April 2017.
The report noted the importance of the role of DNA evidence in linking a possible perpetrator to a victim. “It was noted that none of the DNA samples collected was deemed usable by labs retained for that purpose,” said the report.
The lack of action on the investigation left victims feeling abandoned and without any recourse for the sexual exploitation they say they experienced at the hands of the Burundi and Gabonese troops, according to the New Humanitarian who spoke with victims.
But Ben Swanson, the director of the U.N. investigations division in the Office of Internal Oversight Services, the U.N.’s internal watchdog known as OIOS, said OIOS “did all of the DNA swabbing in Dekoa, when and where it was relevant, and we also followed up with missions to Gabon and Burundi to swab soldiers identified as fathers.”
In December 2016, the U.N. announced that OIOS had completed an internal investigation into allegations of sexual abuse against Burundian and Gabonese peacekeepers deployed in Dekoa in Kemo prefecture, Central African Republic.
OIOS interviewed 139 people, investigated their accounts and identified 16 possible perpetrators from Gabon and 25 from Burundi through photos and corroborating evidence, the U.N. said. Of the 139 victims, 25 were minors who asserted that they were sexually assaulted and eight paternity claims were filed, the U.N. said.
“We took swabs from around 20 victims and their children,” Swanson said, and the laboratory used to do the DNA testing was unable to extract any DNA samples from two or three of the swabs which may have been the result of operator error, poor storage techniques or the laboratory.
“Because the victims were adamant as to the identity of the fathers and we didn’t want to miss any evidential opportunities we repeated the entire exercise,” Swanson said.
“I can tell you that the lab was able to say ‘with a high degree of confidence’ that the soldiers identified were not the fathers of the children they were alleged to be,” he said.
The U.N. relies on the country contributing peacekeepers to deal with allegations of misconduct and to determine possible punishments. According to the report, Burundi investigators who went to conduct interviews in 2016 did not have the necessary skills and experience. The interviews seemed to look to discredit witnesses, it said, and interpreters also lacked the needed skills.
The U.N. has vowed to end impunity for sexual misconduct and to work with countries supplying peacekeepers to do more to combat the abuses.

The U.N. received 259 allegations of sexual exploitation and abuse last year, according to The New Humanitarian, a major increase from the two previous years.
___
By CARLEY PETESCH Associated Press
Associated Press journalist Edith M. Lederer contributed from the U.N.
Copyright © 2019 courthousenews.com


I've got 99 problems and our old code is all of them
About me
- Asha Ramji
- Web Developer at Digital Echidna
- 1.5 years of Drupal experience
- Studying computer science
- Harry Potter enthusiast
A bit of backstory
- Why an audit checklist?
- Certification made up of entityforms
- Admin interface to view results
- Additions added were never originally in scope, structure is now highly customized
The requirements
- Compare questions to the answers
- Only audit certain questions
- Include step numbers beside questions
- Form to track audit results
- Display each user's results in the admin interface
The problem
- Form questions change annually
- Auditted questions change annually
- Conditional fields & field collections
- Step numbers beside questions
Let me explain using Harry Potter
Can we use a view?
- Is it dynamic enough?
- Don't want to rebuild each year
- Need multiple question responses grouped together
- Questions have letters associated with them
Using a view
Without aggregation vs. with aggregation
Nope, can't use a view
- Aggregation settings don't group together properly
- Still need to customize to include step numbers
The solution
- Customize it the way we want and make it reusable
- Include file with questions to be auditted
- Include file containing associated letters
- Strictly structured to account for different field types
How are they structured?
- Field groups
- Fields with one response
- Fields with multiple responses (conditional fields)
- Field collections
checklist.questions.php
$questions = array(
// Harry Potter Certification
'harry_potter_certification' => array( // Form
'field_have_you_seen_all_8_harry_' => array( // Multiple Answers
'field_select_the_movies_you_ve_s',
),
'field_have_you_read_any_of_the_7' => array(
'field_select_the_books_you_ve_re',
),
),
// Harry Potter Certification Pt 2
'harry_potter_certification_pt_2' => array(
'field_who_is_your_favourite_char' => array(
'field_include_an_image',
),
'field_who_is_your_least_favourit' => array(
'field_include_another_image',
),
'field_name_as_many_characters_as' => array( // Field Collection
'field_character_name',
),
'field_do_you_love_harry_potter_', // Single Answer
),
);
letters.questions.php
$letters = array(
// Harry Potter Certification
'harry_potter_certification' => array(
'field_have_you_seen_all_8_harry_' => 'a',
'field_have_you_read_any_of_the_7' => 'b',
),
// Harry Potter Certification Pt 2
'harry_potter_certification_pt_2' => array(
'field_who_is_your_favourite_char' => 'a',
'field_who_is_your_least_favourit' => 'b',
'field_name_as_many_characters_as' => array(
'field_character_name' => 'c',
),
'field_do_you_love_harry_potter_' => 'd',
),
);
So what?
here's how this is gonna go
- Create empty arrays to hold questions, answers and steps
- Set step number associated with each form
- Pull question label and answer from each field, store in arrays
- Pass arrays to template to render table
Fields groups
if (array_key_exists($group_field_name, $questions[$step][$group->group_name])
&& !empty($entityform->{$group_field_name}[LANGUAGE_NONE][0]['value'])) {
_hp_audit_checklist_group_multiple_answers($entityform, $step, $group, $group_field_name, $questions, $answers);
}
- Checks if parent field contains multiple answers (ie. select list) or just one
- Combines into one row per field group
Fields collections
if (isset($questions[$step][$field_name])
&& is_array($questions[$step][$field_name])
&& isset($field[LANGUAGE_NONE][0]['revision_id'])) {
_hp_audit_checklist_field_collection_answers($step, $field, $field_name, $num, $questions, $answers);
}
- Loads all field collection items
- Each item rendered into its own row
- Items with conditional fields are grouped together
Fields with multiple answers
if (isset($questions[$step][$field_name])
&& is_array($questions[$step][$field_name])
&& !empty($field[LANGUAGE_NONE][0]['value'])) {
_hp_audit_checklist_multiple_answers($entityform, $step, $field_name, $field, $questions, $answers);
}
- Load response to parent field's question
- Checks if child field contains multiple answers (ie. select list) or just one
- Combine answers together in one row
Fields with one answer
if (sizeof($field[LANGUAGE_NONE]) > 1) {
foreach($field[LANGUAGE_NONE] as $key => $answer) {
// Create array item
$answers[$step][$field_name] = '';
if ($key == (sizeof($field[LANGUAGE_NONE])-1)) {
$answers[$step][$field_name] .= $answer['value'];
} else {
$answers[$step][$field_name] .= $answer['value'] . ', ';
}
}
} else {
// single answer
$answers[$step][$field_name] = $field[LANGUAGE_NONE][0]['value'];
}
- Check if response can be multiple values (ie. select list)
- Concatenate with commas if applicable
Making the table
- Questions and answers array use field names as keys
- Creates one row per key
- Include extra row when a step number changes
- Custom template embeds audit results form below table
Tada!
The result
- Table view checklist containing required questions and answers
- Logic is generally enough to allow it to be dynamic
- Form embedded to track results
- Replace field names and letters next year to reuse
Final Thoughts
- Met client's needs and requirements
- Will take less time to change out questions next year
- Put questions and letters into form for the client to fill
The end
Thanks for listening!
Asha Ramji - aramji@echidna.ca


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Fax: (00355 4) 23 34 97
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Tirana, Albania
Phone: +355 4275 900
Fax: +355 4250 921
Web Site:Italian Embassy in Tirana, Albania
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Pilar Gomez Manso, Embajada de Espaba
Rruga Skenderbej, 43
Tirana, Albania
Phone: 4 274 960 or 4 274 961
From Spain: 00355 4 274 960/ 00355 4 274 961
Fax: 00355 4 225 383
Email<EMAIL_ADDRESS>French Embassy in Tirana, Albania
14 rue Skenderbeg
Tirana, Albania
Phone: [355] (4) 223 37 50
Fax: [355] (4) 234 442
Web Site: French Embassy in Tirana, Albania
Email<EMAIL_ADDRESS>FOR FURTHER EMBASSIES IN ALBANIA PLEASE SEE LINK
Foreign Embassies in Albania
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Emergency numbers Police 19, Ambulance 17 and Fire 18.
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Adriatic Pilot - Croatia,Slovenia, Montenegro, East Coast of Italy, Albania
By Trevor and Dinah Thompson
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ISBN<PHONE_NUMBER>333
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### Causal inference and the problem of batch effects ![:scale 40%](images/neurodata_blue.png) Eric W. Bridgeford | {Biostatistics, BME, CIS}
[ericwb95@gmail.com](mailto:ericwb95 at gmail dot com) | [ericwb.me](https://ericwb.me) --- name:talk ### Outline - [Motivation and background](#back) - [Notation](#theory) - [Batch effect detection](#detect) - [Batch effect removal](#removal) - [Results](#results) - [Discussion](#disc) ### [Additional Content](#extra) --- name:back ### Outline - Motivation and background - [Notation](#theory) - [Batch effect detection](#detect) - [Batch effect removal](#removal) - [Results](#results) - [Discussion](#disc) ### [Additional Content](#extra) --- ### Typical problem in causal inference
![:scale 60%](images/defense/causal1.png)
- goal: estimate impact of an exposure (smoking) on measurements (cancer), given a set of covariates (ages) --- ### Problem 1: Unmeasured variables
![:scale 60%](images/defense/causal2.png)
- what if we didn't measure genetics, but genetics cause cancer and influence smoking? --- ### Solution 1
![:scale 60%](images/defense/causal3.png)
- Assume unmeasured variables are minimally impactful --- ### Problem 2
![:scale 60%](images/causal/causal_ex.png)
- Measured: old smokers, and young non-smokers - did the smoking cause the cancer, or is cancer just a function of aging? - Technical term: .ye[Confounding] --- ### Connectome data - connectome: brain graph, where the data structure indicates the level of functional similarity or structural connectivity between areas of the brain - relatively expensive, and very high-dimensional - goal: identify properties of these graphs that allow researchers to derive insights about people - expense $\Rightarrow$ collect data and pool --- ### The problem of batch effects - .ye[batch effect]: the impact of the data collection procedure (measurement device, measurement protocol, season, etc.) on the data collected - .ye[demographic effect]: impact on the data of scientifically "interesting" characteristics of the object under study - When datasets are demographically diverse (high confounding) and measurements are large (high number of features), difficult to
mitigate batch effects
while
preserving demographic effects
--- ### DAG for a neuroimaging study
![:scale 100%](images/causal/CoRR_DAG.png)
--- ### Existing work (detection) - classical causal inference focuses on this problem for univariate measurements (e.g., smoking) - some work (causal structure learning, conditional independence testing) attempts to do this in multivariate regimes --- ### Existing work (detection) ##### Conditional Independence (CI) testing - focus on the problem of determining whether two variables are not (causally) related more than the problem of determing whether two variables are (causally) related - establishing causality is difficult (need more assumptions) --- ### Existing work (correction) - ComBat (Johnson 2007) - fit a linear model, where individual "batches" are a linear transformation of one another - optional: "adjust" with terms for covariates (e.g., age, sex) -- - Surrogate Variable Analysis (SVA, Leek 2007/2014) - use latent variable models to capture sources of batch heterogeneity -- - NeuroHarmonize (Pomponio 2020) - estimate covariate relationships with Generalized Additive Models (GAMs) --- ### How do we propose to address batch effects? - skip to latent variable modelling (e.g., SVA)? - focus on estimation with linear/non-linear models (e.g., ComBat, NeuroHarmonize)? Proposal: leverage techniques from .ye[causal inference] to yield strategies which are both theoretically and empirically sensible for batch effect analyses - develop suitable language and estimands which allow us to be .ye[precise] when referring to batch effects --- ### Motivating questions - how do we formulate batch effects as a problem in causal inference? - how do we detect causal effects for more than two exposures? - under what assumptions are existing techniques to estimate batch effects reasonable? - how robust are these techniques to confounding? - what happens if the relationships are .ye[complicated] and there is confounding? --- name:theory ### Outline - [Motivation and background](#back) - Notation - [Batch effect detection](#detect) - [Batch effect removal](#removal) - [Results](#results) - [Discussion](#disc) ### [Additional Content](#extra) --- ### General notation | Symbol | Interpretation | | --- | --- | | $U$ | random variable | | $u$ | realization of random variable $U$ | | $F\_{U \vert v}$ | distribution of $U$ conditional on $V = v$ | | $f\_{U \vert v}(u)$ | density of $u$ conditional on $V = v$ | | $P\_{U \vert v}(u)$ | probability of $U = u$ conditional on $V = v$ | --- ### Specific notation | Symbol | Interpretation | | --- | --- | | $Y_i$ | random variable representing an outcome | | $T_i$ | random nominal ($1$ of $K$) exposure | | $X_i$ | random variable representing measured covariates | | $Z_i$ | random variable representing unmeasured covariates | | $Y_i(t)$ | potential outcome under exposure $t$ | --- name:detect ### Outline - [Motivation and background](#back) - [Notation](#theory) - Batch effect detection - [Batch effect removal](#removal) - [Results](#results) - [Discussion](#disc) ### [Additional Content](#extra) --- ### Causal discrepancy Let $ F\_{Y\_i\(t\)| x, z}$ denote the potential outcome distribution, conditional on the measured .ye[and] unmeasured covariates - distribution we would expect in exposure $t$ for an individual $i$ with covariates $(x, z)$ $$\forall t \in [K]:\;\;\;\;f\_{Y\_i(t)}(y) = \int\_{\mathcal X, \mathcal Z} f\_{Y\_i\(t\)| x, z}(y) f(x,z) d(x,z)$$ for two exposures $k$ and $l$, there is a .ye[causal discrepancy] if: $$F\_{Y\_i(k)} \neq F\_{Y\_i(l)}$$ -- - idea: potential outcomes (e.g., connectomes) for an individual differ in exposures (e.g., batches) $k$ and $l$ - "batch effect" --- ### Observational studies and causality - .ye[observational study]: exposure is not controlled directly by the experimenters - $Y_i$ and $Y_i(t)$ do not, in general, have similar conditional/unconditional distributions -- - problem: sensible definition of a batch effect relies on potential outcomes $Y_i(t)$ -- - what other estimands "might" people discuss? --- ### Associational effects - observe $(y_i, t_i)$ for $i \in [n]$ Associational effect exists between exposures $k$ and $l$ if: $$F\_{Y\_i \vert k} \neq F_{Y\_i \vert l}$$ -- - Causal if measured and unmeasured covariates ($X$ and $Z$) are .ye[non-confounding] (unconditional ignorability and overlap) - difficult to argue in observational studies - if exposures (e.g., batches) fundamentally differ at all then not causal --- ### Problem with associational effects
![:scale 60%](images/causal/ass_fail.png)
- Propensity score overlap: $P\_{T\_i | x}(t) > 0$ for all $x, t$ - "covariates cannot fully determine who is in which exposure" --- ### Conditional Effect - Observe $\left(y_i, t_i, x_i\right)$ for all $i \in [n]$ - $F_{Y\_i \vert t, x}$ is the .ye[observed] outcome distribution, conditional on the exposure (e.g., batch) and covariates $x$ Conditional effect exists between exposures $k$ and $l$ if: $$F\_{Y\_i \vert k, x} \neq F\_{Y\_i \vert l, x}$$ - Causal if .ye[no unmeasured confounding] (ignorability and overlap) - most common estimands from observational data --- ### Causal Crossover Effect - Observe $\left(y_i(t), x_i(t)\right)$ for all ${\color{yellow}{t \in [K]}}, i \in [n]$ - .ye[directly observe] outcomes for all exposures (batches) $$F\_{Y\_i(k) | x(k)} \neq F\_{Y\_i(l) | x(l)}$$ - covariates $x(t)=$ traits/histories + States -- - Traits/histories are constant to individuals, and therefore perfectly balanced across exposures by design -- - States may be different, as they are functions of location & time - $x(k)$ may be unequal to $x(l)$ --- ### Detecting batch effects - detection tends to be an easier problem than estimation - Universally consistent tests readily exist for natural tests for non-causal effects in general settings (bounded variance) .ye[implication]: don't need to limit the scope of distributions under consideration, nor the types of "differences" that can exist between exposures --- ### Detecting batch effects ##### Crossover - states observed (or not) and constant/randomized: $\texttt{DCorr}$ - states observed and not constant/non-randomized: $\texttt{cDCorr}$ - .ye[usually not possible] -- ##### Associational - $\texttt{DCorr}$ - requires extremely strong assumptions (almost always violated) -- ##### Conditional - $\texttt{cDCorr}$ - no unmeasured confounding (possibly violated) - overlap (usually violated in observational data) --- ### Overlap and batch effects (conditional)
![:scale 60%](images/causal/ass_fail.png)
- Overlap is a pre-hoc criterion: $P\_{T\_i | x}(t) > 0$ for all $x, t$ - "Approximate" overlap? Propensity trimming --- ### Propensity Trimming - use generalized propensity score to "trim" individuals with extremely low (or high) propensities for particular exposures - generalizes well to $K \geq 2$ exposures
![:scale 100%](images/causal/vm.png)
- Causal $\texttt{cDCorr}$: prepend VM to $\texttt{cDCorr}$ --- ### Simulations ##### Settings - $n = 100$ samples in $ \geq 2$ batches - $d=10$ (low dimensional) or $d=101$ (high dimensional) - Settings: non-linear, non-monotone, second order, and multi-class
![:scale 100%](images/causal/Sigmoidal.png)
--- ### Simulations ##### Key Aspects - Balance: how well do the covariates overlap? - Effect size $\Delta$: how different is the data between batches? - $H_0 :$ no difference in batch distributions - $H_A :$ difference in batch distributions
![:scale 100%](images/causal/Sigmoidal.png)
--- ### Question 1: validity - Fix $\Delta = 0$ ($H_0$ is true) - for all tests, varying balance from $0.2$ (low balance) to $1.0$ (complete balance): - estimate type I error rate over $r=100$ trials -- - Valid test: Type I error rate $\leq \alpha = 0.05$ - total of 80 possible settings (10 levels of "covariate balance" $\times$ 8 simulation settings) - cMANOVA cannot be used in high dimensional regimes (40 total settings) - KernelCDTest cannot be used in $>2$ exposure settings (20 total settings) - "Valid": type I error rate within 1 SE of $.05$ --- ### Validity testing
![:scale 100%](images/causal/validity.png)
- Causal cDCorr and KernelCDTest are only valid strategies - Causal cDCorr only test which works on $>2$ exposure settings --- ### Question 2: power - $H_A$ is true for $\Delta > 0$ (there is an effect) - for all tests, vary $\Delta$ from $0$ to $1.0$ (outcome/covariate distributions very different across exposures): - estimate statistical power over $r=100$ trials -- - Powerful test: statistical power increasing (to $\approx 1$) as $\Delta$ increases --- ### Power testing
![:scale 95%](images/causal/powers.png)
- Causal $\texttt{cDCorr}$ is only approach powerful in all contexts (and at or near highest in any one context) --- name:removal ### Outline - [Motivation and background](#back) - [Notation](#theory) - [Batch effect detection](#detect) - Batch effect removal - [Results](#results) - [Discussion](#disc) ### [Additional Content](#extra) --- ### Causal effect removal A function $g$ removes the causal discrepancy between exposures $k$ and $l$ if: $$F\_{Y\_i(k)} \neq F\_{Y\_i(l)}$$ and: $$F\_{g(Y\_i(k), k)} = F\_{g(Y\_i(l), l)}$$ -- - idea: $g$ is a (usually unknown) function which "corrects" the disparity between potential outcomes -- - "batch effect correction": estimating $g$ via $\hat g$ --- ### Removing batch effects ##### Associational setting - estimate $\hat g$ using $\left(y_i, t_i\right)$ for all $i \in [n]$ ##### Conditional setting - estimate $\hat g$ using $\left(y_i, t_i, x_i\right)$ for all $i \in [n]$ ##### Crossover setting - estimate $\hat g$ using $\left(y_i(t), x_i(t)\right)$ for all ${\color{yellow}{t \in [K]}}, i \in [n]$ - if states are changing, use conditional technique, else, associational --- ### Limitations of $\texttt{ComBat}$ $\texttt{cComBat}$: model "batch" as a linear transform of an "underlying" signal $$y\_i = \alpha\_{t\_i} \left(f(x\_i) + \beta\_{t\_i}\right)$$ where $f$ is known -- - estimate $\alpha\_{t\_i}$ and $\beta\_{t\_i}$ via regression approaches - How critical is "knowing" $f$? --- ### Simulation Let $\mathbb E[Y\_i(t) | x] = \beta_x f(x) + \beta_0\mathbb I(t = 2)$ be the "expected signal" for a covariate level $x$ in batch $t$
![:scale 60%](images/causal/sim_simple.png)
--- ### Simulation Sample $(t\_i, x\_i, \epsilon\_i)$ and let $y\_i = \mathbb E[Y(t\_i) | x\_i] + \epsilon_i$ for $n=200$ points
![:scale 60%](images/causal/sim_simple2.png)
--- ### Simulation Fit linear model, obtain estimate of $\beta_0$
![:scale 60%](images/causal/sim_simple4.png)
--- ### Simulation Let $\hat g(y, t) = y - \hat \beta_0 \mathbb I(t = 2)$ - Success: $\hat g\left(\mathbb E[Y\_i(2) | x\_i], 2\right) \approx \hat g\left(\mathbb E[Y\_i(1) | x\_i], 1\right)$
![:scale 60%](images/causal/sim_simple5.png)
--- ### When we "guess" right, balance doesn't matter
![:scale 90%](images/causal/correct_spec.png)
- "Extrapolation" works just fine --- ### When we "guess" wrong, disaster
![:scale 90%](images/causal/incorrect_spec.png)
- $\texttt{ComBat}$ will (willingly and ablely) give us arbitrarily wrong answers - "Extrapolation" works very not fine: batch effects are worse! --- ### When we "guess" wrong, disaster - "Extrapolation" requires extreme confidence in our model - when the number of outcome dimensions are high, how can we possibly expect to "guess" appropriate $f$ (for every dimension)? -- ##### (Possible) solutions - nonparametric estimation of $f$ ($\texttt{NeuroHarm}$) - number of samples can be low; unclear how robust techniques (e.g., GAMs) are to HDLSS (high dimensionality; low sample size) problem -- - Matching? --- ### Matching - .ye[Goal]: equate the empirical propensity distributions between the two batches - .ye[Implication]: improves robustness of estimates to model misspecification (Warnbaum 2012, VDV 1998)
![:scale 90%](images/causal/matching.png)
--- ### When we "guess" wrong, stable
![:scale 90%](images/causal/incorrect_spec_causal.png)
- prepending "matching" yields stable estimates of batch effects - limited overlap: no information to do .ye[anything] --- name:results ### Outline - [Motivation and background](#back) - [Notation](#theory) - [Batch effect detection](#detect) - [Batch effect removal](#removal) - Results - [Discussion](#disc) ### [Additional Content](#extra) --- ### CoRR Demographics
![:scale 100%](images/causal/demographic.png)
--- ### Associational/conditional effects can almost always be estimated
![:scale 70%](images/causal/ass_eff.png)
--- ### Effects which respect confounding biases cannot
![:scale 70%](images/causal/caus_eff.png)
--- ### Procedure for real data analysis .ye[Motivation]: does the manner in which we include/exclude samples for batch effect "correction" and subsequent inference matter? - If confounding is irrelevant, smaller samples = similar inference, but (in general) higher $p$-values - If adjustment functions are reasonable "within covariate overlap", should get similar inference with approximate propensity trimming and fully matched
![:scale 60%](images/defense/eff_exp.png)
--- ### Edge-wise analysis - Effect of sex on connectivity (per edge), conditional on age
![:scale 100%](images/causal/effect_size.png)
- (approximate) propensity trimming and fully matching yield widely different inference (much smaller $p$-values with fully matching) --- ### Different assumptions produce radically different inference
![:scale 100%](images/causal/edges.png)
- compare strategies using full data to strategies using matching: almost orthogonal inference - upstream assumptions for batch effect correction play enormous role downstream - is batch effect correction adding artifacts? - are we missing crucial variables somewhere along the way? --- name:disc ### Outline - [Motivation and background](#back) - [Notation](#theory) - [Batch effect detection](#detect) - [Batch effect removal](#removal) - [Results](#results) - Discussion ### [Additional Content](#extra) --- ### Accomplishments (detection) - developed new causal language and machinery for detection of causal effects in high-dimensional regimes - demonstrated valid and powerful performance, unlike existing approaches (which often were neither) ##### Implications - causal structure learning? - causal discovery? - large number of covariates? - continuous exposures? - $\texttt{VM}$ with ordinal exposures? --- ### Accomplishments (estimation) - revised language for batch effects to be more precise and specific - provides theoretical foundation for past investigations of batch effects (e.g., Fortin 2018) - simulations reveal substantial limitations of $\texttt{ComBat}$ - uncover the benefits of causal perspective - discussion of paper focuses on implications of these results with internal and external validity (not discussed) - .ye[internal validity]: "unbiased estimand" (causal techniques prioritize this) - .ye[external validity]: achieve results which apply widely (non-causal techniques might achieve this, if causality doesn't matter) - internal validity is a pre-req for external validity, so unclear the implications of non-causal protocols --- ### Limitations/Future work - limited studies with high sample size and demographic overlap between batches - ABCD study (2021) "playground" ##### Strategies to investigate batch effects pre-hoc - possible that there exist pre-processing strategies that reduce or worsen batch effects - Pre-process ABCD study using large number of pre-processing pipelines (ChildMind Institute) - use mechanisms for quantifying batch effects previously developed (Causal $\texttt{cDCorr}$) to quantify the "batch effect" - directly investigate the impact of pre-processing choices on batch effects - identify strategies which "reduce" batch effects downstream, and strategies which "worsen" batch effects downstream --- ### Limitations/Future work - limited studies with high sample size and demographic overlap between batches - ABCD study (2021) "playground" ##### Investigate essential covariates for batch effect correction - seems (fairly) clear that if the covariate/outcome model is wrong, the result is disasterous - likely that age and sex are woefully insufficient to characterize the full covariate/outcome relationship - perform a sensitivity analysis of batch effect adjustment techniques to upstream different covariate/outcome model specifications - identify covariates whose inclusion is critical to properly adjusting for batch effects, and covariates which are fairly irrelevant - investigate stability of estimates in a richer covariate space than just age and sex --- ### Limitations/Future work - limited studies with high sample size and demographic overlap between batches - ABCD study (2021) "playground" ##### Developed principled connectome-specific methods for batch effect correction (embedding) - connectome: brain graph - use spectral embeddings or graph neural networks to "embed" connectomes into a latent space which is lower dimensionality - estimation via GAMs is far simpler in less complicated spaces - investigate the utility of non-linear strategies in a regime where they can excel --- ### Limitations/Future work - limited studies with high sample size and demographic overlap between batches - ABCD study (2021) "playground" ##### Developed principled connectome-specific methods for batch effect correction (GNN) - use graph neural networks to "post-process" connectomes which obtain maximally similar representations to the connectomes, but are maximally .ye[uninformative] for batch effect detection (Seedat 2022) --- ### Acknowledgements
Josh Vogelstein
Mike Powell
Ting Xu
Greg Kiar
Stephanie Noble
Brian Caffo
Michael Milham
J1 Chung
Sambit Panda
Ross Lawrence
Brian Gilbert
Cencheng Shen
Alexandra Badea
- [Batch Effects (Bioarxiv)](https://www.biorxiv.org/content/10.1101/802629v8) - [Causal effect detection (draft)](https://www.overleaf.com/read/vrmfvgnzyqzk) - Code for [causal effect detection](https://github.com/ebridge2/cdcorr) and [batch effects paper](https://github.com/ebridge2/batch_effects) --- name:extra ### [Outline](#talk) ### Additional Content - [Theory](#deeptheory) - [Extensions to unconditional effects](#uncond) - [Simulations](#sims) - [](#) --- name:deeptheory ### [Outline](#talk) ### Additional Content - Theory - [Extensions to unconditional effects](#uncond) - [Simulations](#sims) - [](#) --- ### The average treatment effect $\gamma \triangleq ATE = \mathbb E[Y_i(2) - Y_i(1)]$ $H_0 : \gamma = 0$ against $H_A : \gamma \neq 0$ - .ye[Fundamental problem of causal inference]: we observe $Y_i$, not $Y_i(t)$ --- ### "Standard causal assumptions" - Consistency: $Y_i = \\sum_t Y_i(t) \\mathbb I\\{T_i = t\\}$ - we only observe one potential outcome (the rest are "missing") -- - Positivity: $P(T_i = t | X_i = x) > 0$ for all $x$ - covariate distributions "overlap" -- - Ignorability: $\left(Y_i(1), ..., Y_i(K)\right) \perp T_i | X_i$ - missing data does not depend on missing data - Did we "observe" all impactful variables? -- - No interference: treatments of units do not impact potential outcomes of other units --- ### Causal assumptions allow us to describe potential outcomes Under standard causal assumptions, $\mathbb E[Y_i(t)] = \mathbb E\left[\mathbb E[Y_i | T_i = t, X_i]\right]$ - uses: .ye[$G$-computation formula] - convenience: $\mathbb E[Y_i | T_i = 1, X_i]$ can be estimated -- $\gamma \triangleq ATE = \mathbb E[Y_i(2) - Y_i(1)]$ $\Rightarrow \gamma = \mathbb E[\mathbb E[Y_i | T_i = 1, X_i] - \mathbb E[Y_i | T_i = 1, X_i]]$ --- ### Simulated example
![:scale 80%](images/causal/task.png)
- assumption: age and malnutrition are the only factors that impact height --- ### Simulated example
![:scale 80%](images/causal/data.png)
- Causal assumptions? --- ### Simulated example
![:scale 80%](images/causal/ate_ex_pos.png)
- Run linear regression, look for offset - Offset "is" $\hat \gamma$ --- ### "Unequal" impact across covariates?
![:scale 80%](images/causal/cate_mot.png)
--- ### Conditional ATE $\gamma_x \triangleq CATE(x) = \mathbb E[Y_i(2) - Y_i(1) | X = x]$ $H_0 : \gamma_x = 0$ against $H_A : \gamma_x \neq 0$ - Standard assumptions: $\mathbb E[Y_i(t) | X = x] = \mathbb E[Y_i | T_i = t, X = x]$ - as before, can be estimated (and tested) --- ### "Unequal" impact across covariates?
![:scale 80%](images/causal/cate_task.png)
- problem: higher order moments? --- ### Causal conditional discrepancy - .ye[Causal conditional discrepancy]: $F\_{Y\_i(k) | x} \neq F\_{Y\_i(l) | x}$ - for two "exposures" $k$ and $l$, there is a "discrepancy" in the potential outcome distributions for some covariate level $x$ -- - problem: cannot determine from data (some potential outcomes $Y_i(t)$ are typically missing) --- ### Conditional discrepancy .ye[Conditional discrepancy]: $F\_{Y\_i | k, x} \neq F\_{Y\_i | l, x}$ - can determine from observed data (statements about observed $Y_i$) -- ##### Lemma Under standard causal assumptions, the following two tests are equivalent: 1. $H\_0 : F\_{Y\_i(k) | x} = F\_{Y\_i(l) | x}$ against $H\_A: F\_{Y\_i(k) | x} \neq F\_{Y\_i(l) | x}$ 2. $H\_0 : F\_{Y\_i | k, x} = F\_{Y\_i | l, x}$ against $H\_A: F\_{Y\_i | k, x} \neq F\_{Y\_i | l, x}$ -- - Tests for causal conditional conditional discrepancies are equivalent to conditional discrepancies --- ### Testing for causal discrepancies Related: Conditional independence (CI) testing $H\_0 : F\_{Y\_i, V\_i | x} = F\_{Y\_i | x}F\_{V\_i | x}$ against $H\_A : F\_{Y\_i, V\_i | x} \neq F\_{Y\_i | x}F\_{V\_i | x}$ - if $V\_i$ is nominal, this is a conditional discrepancy test ##### Remark (known) If $V\_i$ is nominal, a CI test is equivalent to a conditional discrepancy test. ##### Corollary Adding causal assumptions, a CI test is equivalent to a causal conditional discrepancy test. --- ### Causal assumptions in observational studies - .ye[observational study]: variable under study is not controlled directly by the experimenters - e.g., who is malnourished vs adequately nourished -- - consistency: is there only one exposure level? -- - interference: domain expertise --- ### Ignorability in observational studies - if we didn't measure all the variables, are they unimportant things like number of guitars owned...
![:scale 60%](images/defense/causal3.png)
--- ### Ignorability in observational studies - if we didn't measure all the variables, are they things like or important things like BMI?
![:scale 60%](images/defense/causal5.png)
- Down to domain expertise --- ### Positivity in observational studies - Positivity/"overlap": are there areas of the covariate distribution that are .ye[impossible] to yield for some exposures?
![:scale 60%](images/causal/positivity_viol.png)
- Intuition: if we do not "see" outcomes for different exposure levels, it can only "hurt" us inferentially --- ### Positivity in observational studies - Impossible to verify: condition about $P(T_i = t | X_i = x) > 0$ - we can modify the data to .ye[approximate] positivity - Vector matching: exclude covariate levels that are "impossible" - .ye[propensity trimming] to $K \geq 2$ exposures
![:scale 100%](images/causal/vm.png)
--- ### Proposed causal discrepancy test - Prepend vector matching (VM) to conditional independence test (e.g., Conditional Distance Correlation) to yield a causal conditional discrepancy test -- ##### Known 1. Applies readily to high-dimensional data 2. Applies to multiple exposures (lemma) 3. (Asymptotically) consistent test for causal conditional discrepancy testing (corollary) - works with .ye[enough] data -- ##### Unknown - Finite sample performance? - Impact of group imbalance on inference? ---


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bytestring-lexing benchmarks
This page is a table of contents for browsing some of the benchmarks for the bytestring-lexing package. First, a description of the machines these benchmarks have been run on. If you would like to add yourself to the list below, just send me your Criterion report and the specs for your system.
- ereshkigal
- OS X 10.10.3, Intel Core i7 2.5GHz, GHC 7.10.1 (64-bit)
- semiramis
- OS X 10.5.8, Intel Core 2 Duo 2.8GHz, GHC 6.12.1 (32-bit)
- megadodo
- Linux 64-bit, AMD Phenom x6 3.6GHz, GHC 7.0.4
- bistromath
- Linux 64-bit, Intel Core 2 E-8500 3.16GHz, GHC 7.0.4
- bistromath-win32
- Windows XP 32-bit, Intel Core 2 E-8500 3.16GHz, GHC 7.0.4
BenchReadDecimal.hs
N.B., the naming convention for the candidate implementations changed significantly between version 0.3 and 0.4. The old benchmarks are retained primarily to show the differences between different architectures running 64-bit Linux, as well as between the same architecture running under different operating systems. Nothing changed in version 0.5, the results are just provided for reference.
- bytestring-lexing-0.3.0: [semiramis] [megadodo] [bistromath] [bistromath-win32]
- bytestring-lexing-0.4.0: [semiramis] [megadodo]
- bytestring-lexing-0.5.0: [ereshkigal]
BenchReadExponential.hs
These files only show (1) the old implementation from bytestring-lexing-<IP_ADDRESS>, (2) the new/current bytestring-lexing implementation, and (3) various competitor libraries (of which bytestring-read is the main competitor). To see the non-winning candidate implementations, uncomment them in the benchmarking file. This benchmark is the same as bytestring-read's benchmark.
- bytestring-lexing-0.5.0: [ereshkigal]


2. Using Piperack
Running pipelines with Piperack is performed through a RESTful web services API. The examples that follow use curl, but any tool that supports the ability to GET and POST resources to a web service will work.
Important
When running, Piperack has all of the privileges of the user who started it. Just because you could allow the public to access it, doesn't mean you should.
When started, Piperack listens for requests
on the configured port. In the examples that follow, it is running on
“localhost
” on port “8088
”. If you're running
it on a different machine or port, adjust the examples accordingly.
For most endpoints, content negotiation is used to determine how the results should be returned. XML, HTML, plain text, and JSON are all supported. Endpoints that return the outputs of a pipeline after it has been run always return XML.
Our example pipeline for this section is pipe.xpl
:

It doesn't do anything useful, it simply copies its input to two different output ports.
2.1. /status
The /status
endpoint just returns information about the
status of the server. Only GET is supported.
$
curl -H "Accept: text/plain" http://localhost:8088/status
XML Calabash version 1.0.14a, an XProc processor. Running on Saxon version <IP_ADDRESS>, EE edition. Copyright © 2007-2013 Norman Walsh See docs/notices/NOTICES in the distribution for licensing. See also http://xmlcalabash.com/ for more information.
2.2. /help
(or just /
)
The /help
endpoint returns a summary of the valid endpoints
only GET is supported.
2.3. /pipelines
A new pipeline is added to the server by POSTing it to the
/pipelines
endpoint. The user can suggest an ID with the
name
parameter. (If the requested name is in use,
a different, unique name will be returned.)
$
curl -X POST<EMAIL_ADDRESS>http://localhost:8088/pipelines?name=mypipe
<response xmlns="http://xmlcalabash.com/ns/piperack"> 201 <expires>2013-10-25T15:49:04Z</expires> <uri>http://localhost:8088/pipelines/mypipe</uri> <message>Created http://localhost:8088/pipelines/mypipe</message> </response>
The response also includes the HTTP Location header, pointing to the newly minted URI for the pipeline.
All pipelines get an expiration time when they are created. Periodically, the server will delete any pipelines that are past their expiration time.
The default expiration time can be set in several ways, see
Section 32, “Piperack configuration”. The expires
parameter
can be used to specify a different expiration time for any particular pipeline
when it is created. If -1 is specified, the pipeline will never expire.
Performing a GET on /pipelines
returns a list of the pipelines
available.
$
curl -H "Accept: application/json" http://localhost:8088/pipelines
{"pipelines":["http://localhost:8088/pipelines/mypipe"]}
2.4. /pipelines/{id}
Performing a GET on /pipelines/{id}
returns information
about the pipeline:
$
curl http://localhost:8088/pipelines/mypipe
<pipeline xmlns="http://xmlcalabash.com/ns/piperack"> <uri>http://localhost:8088/pipelines/mypipe</uri> <has-run>false</has-run> <input documents="0">source</input> <input primary="true" documents="0">parameters</input> <option> <name>opt1</name> <value default="true"/> </option> <option> <name xmlns:ex="http://example.com/ns/example">ex:opt2</name> <value default="true"/> </option> </pipeline>
The exact information returned varies depending on the state of the pipeline (before or after it has been run) and on the state of the various inputs, outputs, options, and parameters.
As we shall see, there are individual endpoints for sending documents to the pipeline, setting options, setting parameters, and running the pipeline. However, you can post directly to the pipeline URI.
POSTing multipart/form-data
according to RFC 2388
to /pipelines/{id}
has the following effects:
-
Each uploaded document is delievered to the named input port. It is an error if the pipeline has no such port.
-
Any POSTed data that isn't an uploaded document is treated as the value of a named option or parameter.
POSTing a single document to /pipelines/{id}
has the
following effects:
-
The POSTed document is delivered to the pipeline's primary input port. It is an error if the pipeline does not have a primary input port.
-
Any parameters passed as part of the URI are delivered to the pipeline as the values of named options or parameters.
By default simple name=value
pairs posted
to the pipeline are treated as named options.
To specify a parameter, use a
name of the form -pparameter-name
. By default
the parameter will be written to the primary parameter input port.
To specify a particular port explicitly, use a field name of the form
-pport@parameter-name
.
Use fields of the form
xmlns:
to specify namespace bindings,
if necessary.
prefix
After the POSTed data is processed, the pipeline is run.
If the pipeline runs successfully, the first document on the pipeline's primary output port is returned. If the pipeline has no primary output port, nothing is returned.
For example:
$
curl -X POST -F "source=@doc.xml;type=application/xml" -F "opt1=somevalue" http://localhost:8088/pipelines/mypipe
<doc/>
Or
$
curl -X POST -H "Content-type: application/xml" -d @doc.xml "http://localhost:8088/pipelines/mypipe&opt1=somevalue"
<doc/>
2.5. /pipelines/{id}/inputs/{port}
POSTing to /pipelines/{id}/inputs/{port}
delivers the POSTed
document to the specified port.
2.6. /pipelines/{id}/options
POSTing to /pipelines/{id}/options
delivers the POSTed
parameters (from the application/x-www-form-urlencoded
body or from the URI) to the pipeline as options.
$
curl -X POST "http://localhost:8088/pipelines/mypipe/options?xmlns:x=http://example.com/ns/example&x:opt2=two&opt1=one"
<response xmlns="http://xmlcalabash.com/ns/piperack"> 202 <message>Options added: opt1, x:opt2</message> </response>
The pipeline information endpoint will now show that those values have been specified.
$
curl http://localhost:8088/pipelines/mypipe
<pipeline xmlns="http://xmlcalabash.com/ns/piperack"> <uri>http://localhost:8088/pipelines/mypipe</uri> <has-run>false</has-run> <input primary="true" documents="0">source</input> <input documents="0">parameters</input> <option> <name>opt1</name> <value>one</value> </option> <option> <name xmlns:ex="http://example.com/ns/example">ex:opt2</name> <value>two</value> </option> </pipeline>
2.7. /pipelines/{id}/options/{option}
POSTing to /pipelines/{id}/options/{option}
sets the value
of the option named “{option}
” to the POSTed
content. This option exists to support the “general values” extension; it's
otherwise not necessary.
If the option name is in a namespace, use a parameter of the form
xmlns:
on the URI to specify
the namespace binding.
prefix
2.8. /pipelines/{id}/parameters
POSTing to /pipelines/{id}/parameters
delivers the POSTed
parameters (from the application/x-www-form-urlencoded
body or from the URI) to the pipeline as parameters.
This endpoint uses the primary parameter input port.
2.9. /pipelines/{id}/parameters/{port}
POSTing to /pipelines/{id}/parameters/{port}
delivers the POSTed
parameters (from the application/x-www-form-urlencoded
body or from the URI) to the pipeline as parameters on the
“{port}
” parameter input port.
2.10. /pipelines/{id}/parameters/{port}/{param}
POSTing to /pipelines/{id}/parameters/{port}/{param}
sets the value
of the option named “{param}
” to the POSTed
content. This option exists to support the “general values” extension; it's
otherwise not necessary.
If the parameter name is in a namespace, use a parameter of the form
xmlns:
on the URI to specify
the namespace binding.
prefix
2.11. /pipelines/{id}/run
POSTing to the /pipelines/{id}/run
endpoint runs
the pipeline and returns the first document on the primary output port.
The pipeline information endpoint will now show outputs instead of inputs.
$
curl http://localhost:8088/pipelines/mypipe
<pipeline xmlns="http://xmlcalabash.com/ns/piperack"> <uri>http://localhost:8088/pipelines/mypipe</uri> <has-run>true</has-run> <output primary="true" documents="0">result</output> <output documents="1">result2</output> </pipeline>
2.12. /pipelines/{id}/outputs/{port}
After a pipeline has been run, a GET request to
/pipelines/{id}/outputs/{port}
returns the next document
from the specified port.
$
curl http://localhost:8088/pipelines/mypipe/outputs/result2
<doc/>
2.13. /pipelines/{id}/reset
POSTing to /pipelines/{id}/reset
resets the pipeline: deletes
all inputs, outputs, options, and parameters.
2.14. /stop
POSTing to /stop
stops Piperack.


randomForest Description Classification and regression based on a forest of trees using random inputs.
Biomedr Description The BioMedR package offers an R/Bioconductor package generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions.
boruta Description An all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies.
caret Description Misc functions for training and plotting classification and regression models.
foreign Description Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.
knitr Description Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.
neuralnet Description Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.
nnet Description Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.
install.packages("randomForest")
## Error in contrib.url(repos, type): trying to use CRAN without setting a mirror
install.packages("foreign")
## Error in contrib.url(repos, type): trying to use CRAN without setting a mirror
install.packages("caret")
## Error in contrib.url(repos, type): trying to use CRAN without setting a mirror
library(randomForest)
library(foreign) library(caret)
Algorithm
Algorithm
40 % of data is used as training data
features <- read.arff("boruta_selected_features.arff")
## Warning in file(file, "r"): cannot open file ## 'boruta_selected_features.arff': No such file or directory
## Error in file(file, "r"): cannot open the connection
features <- features[sample(nrow(features)),]
## Error in eval(expr, envir, enclos): object 'features' not found
indexes = sample(1:nrow(features), size=0.4*nrow(features))
## Error in nrow(features): object 'features' not found
test = features[indexes,]
## Error in eval(expr, envir, enclos): object 'features' not found
train = features[-indexes,]
## Error in eval(expr, envir, enclos): object 'features' not found
cat("No of missing values : ",sum(is.na(features)),"\n")
## Error in cat("No of missing values : ", sum(is.na(features)), "\n"): object 'features' not found
cat("No. of variables : ",ncol(features),"\n")
## Error in ncol(features): object 'features' not found
cat("Training cases : ",nrow(train),"\n")
## Training cases :
cat("Test cases : ",nrow(test),"\n")
## Error in nrow(test): object 'test' not found
Algorithm
attach(features)
## Error in attach(features): object 'features' not found
forest <- randomForest(output ~., data = features,ntree=1000, mtry=range[j])
## Error in eval(expr, envir, enclos): object 'features' not found


Period
to
Turbine
Refresh
Main
Exception
Scada Data
Alarm Data
JMR & Breakup
Permanent Met Tower Data
Data Available from:
-
until:
-
Total Turbine
0
Total Data
0
Total Power
0.00
Total Power Lost
0.00
Total Production
0.00
Average Wind Speed
0.00
Hide / Show Columns
Data Available from:
-
until:
-
Total Turbine
0
Total Data
0
Total Production
0.00
Average Wind Speed
0.00
Data Available from:
-
until:
-
Total Data
0
Data Available from:
-
until:
-
Total Data
0
Scada Exception Time Duration
Scada Anomaly
Alarm Overlapping Data
Alarm Anomaly
Data Available from:
-
until:
-
Total Turbine
0
Total Data
0
Total Power
0.00
Total Power Lost
0.00
Total Production
0.00
Average Wind Speed
0.00
Hide / Show Columns
Data Available from:
-
until:
-
Total Turbine
0
Total Data
0
Total Power
0.00
Total Power Lost
0.00
Total Production
0.00
Average Wind Speed
0.00
Hide / Show Columns
Data Available from:
-
until:
-
Total Turbine
0
Total Data
0
Data Available from:
-
until:
-
Total Turbine
0
Total Data
0


Class PauseMenu
Handles Pause Menu behavior.
Inheritance
UnityEngine.Object
UnityEngine.Component
UnityEngine.Behaviour
UnityEngine.MonoBehaviour
PauseMenu
Implements
Namespace: Global
Assembly: TowerDefense.dll
Syntax
public class PauseMenu : MonoBehaviour, IUISystem
Methods
Hide()
Deactivates the PauseMenu object.
Declaration
public void Hide()
OnBrightnessSlider()
Callback for the brightness slider.
Declaration
public void OnBrightnessSlider()
OnQuit()
Callback for the quit button. Brings up the dialog for confirmation and loads the main scene if confirmed.
Declaration
public void OnQuit()
OnResume()
Callback for the resume button. Unpause the game with timeScale and deactivate menu object.
Declaration
public void OnResume()
OnSettings()
Callback for the settings button. Switch the active panel.
Declaration
public void OnSettings()
OnSettingsAccept()
Callback for the accept button in the settings panel. Switches active panels. TODO: Split settings into a different script, maybe have the implementation in a settings object that will save all settings?
Declaration
public void OnSettingsAccept()
Show()
Pauses the game with timeScale and activates the PauseMenu object.
Declaration
public void Show()
Show(GameObject)
To implement the IUISystem. TODO: Change the interface to better handle void functions.
Declaration
public void Show(GameObject obj)
Parameters
| Type | Name | Description |
|---|---|---|
| UnityEngine.GameObject | obj |


{% extends "base2.html" %} {% block content %}
| Feild | Candidate's Detail |
|---|---|
| Name | {{ student.student_name }} |
| {{ student.email }} | |
| Guardian's Name | {{ student.guardian_name }} |
| Institute | {{ student.institute }} |
| Course | {{ student.course }} |
| Starting Date | {{ student.dos }} |
| Address for communication | {{ student.address }} |
| Mobile Number | {{ student.mobile }} |
| Branch | {{ student.branch }} |
| Remarks | {{ student.remarks }} |
| Semester | {{ student.semester }} |
| Year | {{ student.year }} |
| Registration Date | {{ student.created_date }} |
| Approval Status | {{ student.approval_status }} |
| Reason For Rejection | {{ student.reason }} |


Issue: RANGE-OF-START-AND-END-PARAMETERSReferences: COUNT (p257), COUNT-IF (p257), COUNT-IF-NOT (p257),
DELETE (p257), DELETE-DUPLICATES (p254), DELETE-IF (p254),
DELETE-IF-NOT (p254), FILL (p252), FIND (p257), FIND-IF (p257),
FIND-IF-NOT (p257), MAKE-STRING-INPUT-STREAM (p330),
MISMATCH (p257), NSTRING-CAPITALIZE (p304),
NSTRING-DOWNCASE (p304), NSTRING-UPCASE (p304), SUBSTITUTE (p255),
NSUBSTITUTE-IF (p256), NSUBSTITUTE-IF-NOT (p256),
PARSE-INTEGER (p381), PARSE-NAMESTRING (p414), POSITION (p257),
POSITION-IF (p257), POSITION-IF-NOT (p257),
READ-FROM-STRING (p381), REDUCE (p251), REMOVE (p253),
REMOVE-DUPLICATES (p254), REMOVE-IF (p253), REMOVE-IF-NOT (p253),
REPLACE (p252), SEARCH (p258), STRING-CAPITALIZE (p303),
STRING-EQUAL (p301), STRING-DOWNCASE (p303), STRING-GREATERP (p302),
STRING-LESSP (p302), STRING-NOT-EQUAL (p302),
STRING-NOT-GREATERP (p302), STRING-NOT-LESSP (p302),
STRING-UPCASE (p303), STRING/= (p301), STRING< (p301),
STRING<= (p301), STRING= (p300), STRING> (p301), STRING>= (p301),
SUBSEQ (p248), SUBSTITUTE (p255), SUBSTITUTE-IF (p255),
SUBSTITUTE-IF-NOT (p255), WRITE-LINE (p384), WRITE-STRING (p384)
Category: CLARIFICATION
Edit history: 14-Sep-88, Version 1 by Pitman
Problem Description:
CLtL is not always clear about the possible values which the START and END
parameters of built-in Common Lisp can take.
Proposal (RANGE-OF-START-AND-END-PARAMETERS:INTEGER-AND-INTEGER-NIL):
Clarify that for functions permitting a parameter named START, START1,
or START2 which delimits the beginning point in a sequence to be
considered for some operation, that paremeter must be a non-negative
integer. If the argument is optional or key (as is the case for all
functions referenced above except SUBSEQ), the value will default to
0 if not supplied. It is not permissible to pass NIL as this argument.
Clarify that for functions permitting a parameter named END, END1,
or END2 which delimits the end point in a sequence to be considered
for some operation, that paremeter must be a non-negative integer
indicating a termination point or NIL indicating the last element
in the sequence. If the argument is optional or key (as is the case
for all functions referenced above), the value will default to NIL
if not supplied. Supplying NIL is, therefore, equivalent to not
supplying this argument.
Test Case:
(SEARCH "F" "FOO" :START1 NIL) is an error.
(SEARCH "F" "FOO" :START2 0) is permissible and is equivalent to
(SEARCH "F" "FOO")
(SEARCH "F" "FOO" :END2 NIL) is permissible and is equivalent to
(SEARCH "F" "FOO")
Rationale:
To keep data flow between programs from becoming excessively complicated,
it's a good idea to specify what the default values are so that they can
be passed explicitly rather than requiring an alternate calling sequence
for all possible cases where the value might need to default.
Current Practice:
Symbolics Genera implements the proposed behavior.
Cost to Implementors:
Hopefully most implementations already do this. Those that do not will
probably have to make quite a number of small changes to both the code
for these functions and to any associated compiler optimizers.
Cost to Users:
This change is upward compatible with existing user code. Any program
which did not conform to this proposal was already not portable.
Cost of Non-Adoption:
Subtle gratuitous differences in the handling of these arguments would
continue to be a possibility and a barrier to portability.
Benefits:
The language would be more regular and well-defined.
Aesthetics:
If it makes things clearer, it's an improvement.
Discussion:
Pitman supports RANGE-OF-START-AND-END-PARAMETERS:INTEGER-AND-INTEGER-NIL.


Settings
Number of objects per side
Object spacing
Draw circles
Polygon sides
Deformable edges
Starting angle
Background color
Primary color
Bubble color
Dimple color
Deformation size
Place Bubble
Place Dimple
Save stencil
Share
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Deformations
X pos
Y pos
Radius
Strength
Formulas
pending development
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ACTS
Version 1.0.0
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Sample data structure. More...
#include <SystemVariables.hpp>
Public Attributes | |
| long long | microTime |
| short | accelX |
| short | accelY |
| short | accelZ |
| short | gyroX |
| short | gyroY |
| short | gyroZ |
| short | magnetoX |
| short | magnetoY |
| short | magnetoZ |
| int | temp |
| int | pressure |
Sample data structure.
This structure includes all data that will be sampled by the sensortask and will be written by the SDWriterTask. The doublebuffer uses this struct to initialize its buffers.
| short SampleData::accelX |
Accelerometer X axis
| short SampleData::accelY |
Accelerometer Y axis
| short SampleData::accelZ |
Accelerometer Z axis
| short SampleData::gyroX |
Gyroscope X axis
| short SampleData::gyroY |
Gyroscope Y axis
| short SampleData::gyroZ |
Gyroscope Z axis
| short SampleData::magnetoX |
Magnetometer X axis
| short SampleData::magnetoY |
Magnetometer Y axis
| short SampleData::magnetoZ |
Magnetometer Z axis
| long long SampleData::microTime |
Timestamp derived from systick
| int SampleData::pressure |
Air pressure
| int SampleData::temp |
Temperature