76 Greet is deﬁned in the Dog sprite. If Fido is a particular cocker spaniel, and you ask Fido to greet someone, Fido inherits the greet method from Dog, but Dog itself couldn’t actually run that method, because Dog doesn’t have greet as friend or greet as enemy. And perhaps only individual dogs such as Fido have friend? methods. Even though the greet method is deﬁned in the Dog sprite, when it’s running it remembers what speciﬁc dog sprite called it, so it knows which greet as friend to use. Dog’s greet block is called a polymorphic method, because it means different things to different dogs, even though they all share the same script. D. Local State in Sprites: Variables and Attributes A sprite’s memory of its own past history takes two main forms. It has variables, created explicitly by the user with the “Make a variable” button; it also has attributes, the qualities every sprite has automatically, such as position, direction, and pen color. Each variable can be examined using its own orange oval block; there is one set block to modify all variables. Attributes, however, have a less uniform programming interface in Scratch: ● A sprite’s direction can be examined with the direction block, and modiﬁed with the point in direction block. It can also be modiﬁed less directly using the blocks turn, point towards, and if on edge, bounce. ● There is no way for a script to examine a sprite’s pen color, but there are blocks set pen color to <color>, set pen color to <number>, and change pen color to modify it. ● A sprite’s name can be neither examined nor modiﬁed by scripts; it can be modiﬁed by typing a new name directly into the box that displays the name, above the scripting area. The block, if any, that examines a variable or attribute is called its getter; a block (there may be more than one, as in the direction example above) that modiﬁes a variable or attribute is called a setter. In Snap ! we allow virtually all attributes to be examined. But instead of adding dozens of reporters, we use a more uniform interface for attributes: The my block’s menu (in Sensing; see page 78) includes many of the attributes of a sprite. It serves as a general getter for those attributes, e.g., my [anchor] to ﬁnd the sprite, if any, to which this sprite is attached in a nesting arrangement (see page 10). Similarly, the same set block used to set variable values allows setting some sprite attributes. E. Prototyping: Parents and Children Most current OOP languages use a class/instance approach to creating objects. A class is a particular kind of object, and an instance is an actual object of that type. For example, there might be a Dog class, and several instances Fido, Spot, and Runt. The class typically speciﬁes the methods shared by all dogs (RollOver, SitUpAndBeg, Fetch, and so on), and the instances contain data such as species, color, and friendliness. Snap ! uses a different approach called prototyping, in which there is no distinction between classes and instances. Prototyping is better suited to an experimental, tinkering style of work: You make a single dog sprite, with both methods (blocks) and data (variables); you can actually watch it and interact with it on the stage; and when you like it, you use it as the prototype from which to clone other dogs. If you later discover a bug in the behavior of dogs, you can edit a method in the parent, and all of the children will automatically share the new version of the method block. Experienced class/instance programmers may ﬁnd prototyping strange at ﬁrst, but it is actually a more expressive system, because you can easily simulate a class/instance hierarchy by hiding the prototype sprite!


WITNESS LIST State Affairs Committee April 11, 2005 - 9:00A SB 15 FOR: Aitlen, Darlene (Holes Incorporated), Tomball, TX Andrews, Kay (Texas Civil Justice League), Austin, TX Hough Jr., William T (Chip) (Basic Industries of South Texas Ltd.), Corpus Christi, TX Mulholland, Daniel J. (Owens Illinois), Jackson, MS Ratliff, Bill (Texas Civil Justice League), Mt. Pleasant, TX Ratliff, Shannon (Texas Civil Justice League), Austin, TX Trabulsi, Jr., Richard J. (Texans For Lawsuit Reform), Austin, TX Waldrop, Alan (Texans for Lawsuit Reform), Austin, TX Weekley, Richard (Texans for Lawsuit Reform), Houston, TX AGAINST: Arabie, Joseph R. (Texas AFL-CIO), Austin, TX Blevins, Bryan (TTLA Opposition Panel), Beaumont, TX Cunningham, Michael (Insulators and Asbestos Workers Local Union 22 and Families), Pasadena, TX Kronenberg, Richard Dr. (TTLA Opposition Panel), Tyler, TX Levy, Rick (Texas AFL-CIO), Austin, TX Martin, Mike (Rafeal Martinez), Houston, TX Martinez, Rafeal (Self), Houston, TX Tilley, Danny (Texas Building Trades Council), Austin, TX Welch, Laura MD (AFL-CIO), Takoma, MD Wendell, Ware (Texas Watch), Austin, TX ON: Dickey, Burton F. MD (Texans for Lawsuit Reform), Houston, TX Registering, but not testifying: For: Allen, George B. (Texas Apartment Association), Austin, TX Brown, Jay (Valero), Austin, TX Cawood, Jack (Self), McAllen, TX Chatron, Michael (Texas Building Branch AGC), Austin, TX Christian, George Scott (Texas Civil Justice League), Austin, TX Cox, Jayme (Shell Oil), Austin, TX Dipprey, Ron (Association of Chemical Industry of Texas), Austin, TX Dipprey, Ron (Texas Chemical Council), Austin, TX Ellis, Cissy (Alamo Cement Company), Austin, TX Ferrara, Jerry (Celanese), Southlake, TX Gonzales, Victor (Kimberly Clark), Austin, TX Gonzalez, Daniel (Texas Association of Realtors), Austin, TX Guidry Jr., George H. (Georgia Pacific Corporation), Baton Rouge, LA Gutierrez, Hugo (Marathon Oil Company), Houston, TX Hamilton, Jonna Kay (Nationwide Insurance), Austin, TX Hazlewood, Steve (The Dow Chemical Company), Austin, TX Hinkle, Ron (TCJL - Asbestos Coalition), Austin, TX Hollingsworth, Susan (Holes Incorporated), Willis, TX Howden, Robert (Texas Asbestos Consumers Coalition and TCJL), Austin, TX Jack, Benjamin (CenterPoint Energy), Austin, TX Kearns, Dennis (BNSF Railway), Austin, TX Kessler, Ron (Ron Kessler Group), Austin, TX Koenia, Linda (Chevron Phillips Chemical), The Woodlands, TX Marlow, John (American Insurance Association), Austin, TX McBride, Richard (Civil Justice League of Texas), Austin, TX McCauley, Cindy (Lyondell Chemical), Houston, TX Meroney, Mike (Huntsmen), Austin, TX Moore, Julie W. (Occidental Petroleum Corporation), Austin, TX Morgan, Todd (Temple - Inland), Austin, TX Norman, Scott (Texas Association of Builders), Austin, TX Olson, Kym (El Paso Corporation), Houston, TX Parker, Richard (National Safety Equipment Association), Austin, TX Perry, Steve (Chevron Texaco), Austin, TX Phelps, Bill (Total Petrochemical), Dallas, TX Pickle, G. Edward (Shell Oil Company), Kingwood, TX Rice, Jr., Chuck (International Safety Equipment Association), Austin, TX Saxon, Lucinda Dean (Texas Association of Business), Austin, TX Schulle, Jr., Gerhardt (Texas Society of Professional Engineers), San Marcos, TX Sebree, Ben (Texas Oil and Gas Association), Austin, TX Sellers, Tom (Conoco Phillips), Austin, TX Shilling, Mark (Automotive Parts and Services Association), Austin, TX Sickels, Linda (Trinity Independant Incorporation), Austin, TX Stewart, Michael (Texas Aggregates and Concrete Association), Austin, TX Strauser, Robert (Owens Illinois), Austin, TX Summers, Bill (Rio Grande Valley Partnership and Chamber of Commerce), Weslaco, TX Tays, Sara K. (Exxon Mobil Corporation), Austin, TX Voinis, Kirsten (Citizens Against Lawsuit Abuse of Central Texas), Austin, TX Williams, Joe (Texas Retailers Association), Austin, TX Woodard, Shayne (Duke Energy), Austin, TX Woods, Joe (Property Casualty Insurers Association of America), Austin, TX Against: Bailey, Homer (Self), Texas City, TX Clay, Arthur D. (Sheet Metal Local #67 Retirees), San Antonio, TX Clay, Travis (Sheet Metal Workerse Local 67), San Antonio, TX Crafts, Sarah (Texas Building Trades Council), Austin, TX Denson, Ear; E. (Sheet Metal Workers Local 67), Liberty Hill, TX Dobernecki, Jeff T. (International Union of Painters and Allied Trades), Georgetown, TX Espinoza, Daniel R. (District San Antonio), San Antonio, TX Flores, Alisandro L. (Local 67 s/m Workers), Bulverde, TX Fontenot, Gilton (Self), Texas City, TX Galvan, Antonio (Central Building Trades), Pflugerville, TX Gillis, Tracy (Asbestos Workers Local 87), Dripping Springs, TX Greenlaw, Willard D. (Self), Dickinson, TX Keller, Wilfred F. (Sheet Metal Worker C.O. #67), San Antonio, TX Martinez, Daniel (Self), Dilly, TX Martinez, George A. (District 8 Local 67), San Antonio, TX McGrath, Tim (District 19), San Antonio, TX Meyer, Clinton O. (District 26), San Antonio, TX Meyer, Freddy (District 19), Castroville, TX Quintero, Richard (Sheet Metal Workers #67), Austin, TX Richardson, John R. (Self), Bacliff, TX Salvatore, Robert (San Antonio Building Trades Council), San Antonio, TX Shelton, Russell C. (Texas Pipe Trades Association), Austin, TX SB 442 Registering, but not testifying: For: Williams, Joe (Texas Retailers Association), Austin, TX SB 449 FOR: Thompson, Jay (Texas Association of Life Insurers, GPM Life), Austin, TX Registering, but not testifying: On: Brady, Kevin (Texas Department of Insurance), Austin, TX SB 728 Registering, but not testifying: Against: Harvey, Jay (Texas Trial Lawyers Association), Austin, TX Harvey, Jay (Texas Trial Lawyers Association), Austin, TX SB 805 Registering, but not testifying: For: Fowler, Jenny (Humana, Inc.), Austin, TX Green, Shelton (Texas Association of Business), Austin, TX Oates, John (Aetna, Inc.), Austin, TX Springer, Stacy (Texas Small Business Alliance in Austin), Austin, TX On: Tredway, Ryan (TDI), Austin, TX SB 809 FOR: McCandless, Pati (Unicare), Austin, TX AGAINST: Fix, Jennifer (Self), Burleson, TX Francis, Sam (Texas Professional Benefit Administrators Association), Austin, TX Hall, Jennifer (Texas Association of Counties Health and Employee Benefits Pool), Austin, TX Hambrick, Dan (City of Grapevine, TX), Grapevine, TX Ritchie, Jay (HCC Life Insurance Company), Houston, TX ON: Bingham, Bill (Texas Department of Insurance), Austin, TX Browning, Steve (Texas Health Insurance Risk Pool), Austin, TX Registering, but not testifying: For: Angel, Brain (AFLAC), Columbus, GA Dennis, Hilary (Texas Medical Association), Austin, TX Fowler, Jenny (Humana, Inc), Austin, TX Manross, Lee (Texas Association of Health Underwriters), Austin, TX Oates, John (Aetna), Austin, TX Rummel, Leah (Texas Association of Health Plans), Austin, TX Springer, Stacy (Texas Small Business Alliance in Austin), Austin, TX Against: Kamm, Bob (TML Intergovernmental Risk Pool), Austin, TX Wollgemuth, Cristen (Texas Pharmacy Association, Academy of Independent Pharmacists), Austin, TX Zamrazil, Kristie (Texas Pharmacy Association), Austin, TX Providing written testimony: Against: Bosserman, Barbara (Great West Life and Annuity Insurance Comany), Austin, TX SB 1052 Registering, but not testifying: For: Powell, Leilah (Bexar County Commissioners Court), San Antonio, TX SB 1323 FOR: Scott, Caroline (Gardere Wynne Sewell), Austin, TX AGAINST: Davis, Will (American National Insurance Company), Austin, TX Doughty, Ron Vice President (Prudential Financial), Autin, TX Nation, Brenda (American Council of Life Insurers), Austin, TX Thompson, Jay (TALHI, Prudential), Austin, TX ON: Bingham, Bill (Texas Department of Insurance), Austin, TX Crawford, Denise Voigt Securities Commissioner (State Securities Board), Austin, TX Peden, R. Scott (Life Partners, Inc.), Waco, TX Registering, but not testifying: For: Vane, Mark (Gardere Wynne Sewell), Austin, TX


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1. Include the visualization div where the visualization will be load up
2. Refer to the div included earlier
//... var svg = d3.select("#container").append("svg"); //...


Mo Farah to run 10 000m at Tokyo Olympics
London - Mo Farah announced on Friday he was targeting a return to the track to compete in the 10 000m at next year's Olympic Games.
The Briton has won double gold at each of the last two Olympics, taking the 5 000m and 10 000m crowns in London and Rio, but switched to the marathon in 2017.
Farah, 36, won the Chicago Marathon in 2018 but has decided to return to the track, admitting the marathon had been "a learning curve".
"The big news is I'm back on the track in the 10 000m in Tokyo next year," he said on his YouTube channel.
"I hope I haven't lost my speed but I'll train hard for it and see what I can do."
Farah slashed 37 seconds off the European record with a time of 2:05:11 when he won in Chicago but with Kenya's Eliud Kipchoge ruling the marathon world, the Briton was never as dominant at the longer distance as he had been on the track.
"To win the Chicago Marathon was nice, to finish third in London was OK, it was good. It's been a learning curve for me," he continued.
"(But) next year I've decided, Tokyo 2020, I'm gonna be back on the track. I'm really excited to be competing back on the track and give it a go in the 10 000m."
Farah has come under intense scrutiny for his cooperation with US coach Alberto Salazar, who has been banned for four years by the US anti-doping authorities.
Farah broke off cooperation with Salazar in 2017.
UK Athletics on Thursday announced a review into its dealings with the coach and his Nike Oregon Project.
The federation's performance director Neil Black, who once described Salazar as a genius, left his role in October.
Before running in this year's Chicago Marathon, Farah responded angrily to media questions about his time with Salazar, saying "I was never given anything".


You will not hear the sound of the start signal if you minimize your browser window, so keep it open in the background! Do not click on the little "_" icon of the browser window's upper right corner!
Also, this feature has only been tested in Chrome, Firefox, Opera, Safari and Chromium browsers. Use at your own risk in other browsers like Internet Explorer etc.
If you use this tool on a phone, you will probably not hear any sound, because phones don't usually allow sound to be played without direct interaction of a user. But you will see the screen flash green, so that should hopefully be enough notice for you.


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LAKE PLACID A film review by Christopher Null Copyright 1999 Christopher Null filmcritic.com
It's crocodile season, opening the hunting period on an animal that has been woefully underused in the horror movie litany to date. But unlike most horror/thriller pics, this one features a script by David E. Kelley, best known as the creator of TV's `Ally McBeal.'
So there's some promise here.  A little. It's better than ANACONDA, anyway.
High concept plot: Giantest crocodile on earth stalks Black Lake (*not* Lake Placid) in Maine. When a guy gets eaten, local fish and game warden (Pullman) and New York paleontologist (Fonda) come to investigate. Croc chase ensues, with help from `eccentric millionaire mythology professor' Platt and local yokel sheriff Gleeson.
About the only thing that rescues LAKE PLACID from drecksville is Kelley's flair for sarcastic dialogue, which punctuates just about every line from the mouth of every character – and is most notably funny coming from Betty White (local eccentric lake dweller). But witty repartee a five-star movie does not make.
Coming in at about 80 minutes running time, witty repartee does make for a passable way to spend an hour. Too bad they couldn't have just worked this plot into an episode of `Ally McBeal.'
RATING: ***
|------------------------------| \ ***** Perfection \ \ **** Good, memorable film \ \ *** Average, hits and misses \ \ ** Sub-par on many levels \ \ * Unquestionably awful \ |------------------------------|
MPAA Rating: R
Director: Steve Miner Producer: David E. Kelley, Michael Pressman Writer: David E. Kelley Starring: Bridget Fonda, Bill Pullman, Oliver Platt, Brendan Gleeson, Betty White
http://www.foxmovies.com/lakeplacid/
Christopher Null -<EMAIL_ADDRESS>- http://www.filmcritic.com Author - Network Administrator's Reference, 1999, ISBN 0-07-882588-1 http://www.amazon.com/exec/obidos/ASIN/0078825881/filmcriticcom
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


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Train Tracks Express
$$channelname$$ hype train
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(requirements to get to next level)
Tap for more info
(latest contribution)
(bits conductor)
(subs conductor)
Select channel:
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The spines are so slim.
How can you read the titles?
Why are they so low to the ground?
You glance at the books on display on top of the shelves. You see a book called How to be on the Moon by Viviane Schwarz, looks cute. Maybe I'll hold on to it and check it out later.
| Go North | ||
| Go West | Go East | |
| Go South |


REAL AI LIMITED
Official Home Page
Real AI is an artificial intelligence research company based in Hong Kong.
Real AI Limited
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822 Lai Chi Kok Road
SHAM SHUI PO
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If you have any comments about our website, you can email us at<EMAIL_ADDRESS>However, we are unable to provide a direct response.
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DISCLAIMER: This website, including all information and materials contained in this website, is provided on an "as is" basis at your sole risk, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, title, non-infringement, completeness, reliability, timeliness, availability, and fitness for a particular purpose or use. In no event will Real AI Limited be liable for any claim, damages, or other liability, arising from, out of, or in connection with your using this website.


{% load i18n %} {% block head %} {{ wizard.form.media }}
Enter your research topic below. Try to limit the topic to one sentence that fully describes your research. Here are a few examples:


Microsoft's Office 365 down worldwide
It doesn't look like Office 365 will be up for all 365 days of this particular year.
The cloud version of Microsoft Office is offline for many users in what seems like a worldwide issue.
At 2.13pm NZT, Microsoft posted a bulletin saying, "We've identified a problem that may be preventing customers from accessing the Microsoft 365 admin center and multiple services. We're working to restore access to the admin center, and we'll provide more information when we've isolated the root cause."
Administrative functions are inaccessible for many users in the US, Australia, NZ and elsewhere and functions including Teams, SharePoint, OneDrive, Skype, Yammer and Outlook 365 are offline.
Although it was not immediately clear how many customers were affected. Some were quick to make hay on Twitter:
Microsoft CEO Satya Nadella is due in the country shortly to speak at a company event at Eden Park this Friday.


Copies a file from an NTFS partitioned volume by reading the raw volume and parsing the NTFS structures
Needs Admin
https://github.com/mattifestation/PowerSploit/blob/master/Exfiltration/Invoke-NinjaCopy.ps1
https://clymb3r.wordpress.com/2013/06/13/using-powershell-to-copy-ntds-dit-registry-hives-bypass-sacls-dacls-file-locks/
moduleSource = self.mainMenu.installPath + "/data/module_source/collection/Invoke-NinjaCopy.ps1


Get the UK National Biodiversity Network ID from taxonomic names.
get_nbnid(name, ask = TRUE, verbose = TRUE, rec_only = FALSE, rank = NULL, rows = NA, ...) as.nbnid(x, check = TRUE) # S3 method for nbnid as.nbnid(x, check = TRUE) # S3 method for character as.nbnid(x, check = TRUE) # S3 method for list as.nbnid(x, check = TRUE) # S3 method for data.frame as.nbnid(x, check = TRUE) # S3 method for nbnid as.data.frame(x, ...) get_nbnid_(name, verbose = TRUE, rec_only = FALSE, rank = NULL, rows = NA, ...)
TRUE
ids of recommended names are returned (i.e.
synonyms are removed). Defaults to FALSE
. Remember, the id of a synonym is a
taxa with 'recommended' name status.get_nbnid_
to get back all, or a subset, of the raw data that you are
presented during the ask process.nbn_search
as.nbnid
as.nbnid
A vector of taxonomic identifiers as an S3 class.
If a taxon is not found an NA
is given. If more than one identifier is found the
function asks for user input if ask = TRUE
, otherwise returns NA
.
If ask=FALSE
and rows
does not equal NA
, then a data.frame is
given back, but not of the uid class, which you can't pass on to other functions
as you normally can.
Comes with the following attributes:
ask = FALSE
then 'NA due to ask=FALSE')
TRUE
, even if you get 1 name back because we try
to pattern match the name to see if there's any direct matches. So sometimes
this attribute is TRUE
, as well as pattern_match
, which then returns 1
resulting name without user prompt.
TRUE
then multiple_matches
must be TRUE
, and we found a perfect match to your
name, ignoring case. If FALSE
Other taxonomic-ids: get_boldid
,
get_colid
, get_eolid
,
get_gbifid
, get_ids
,
get_natservid
, get_tolid
,
get_tpsid
, get_tsn
,
get_uid
, get_wormsid
## Not run: ------------------------------------ # get_nbnid(name='Poa annua') # get_nbnid(name='Poa annua', rec_only=TRUE) # get_nbnid(name='Poa annua', rank='Species') # get_nbnid(name='Poa annua', rec_only=TRUE, rank='Species') # get_nbnid(name='Pinus contorta') # # # The NBN service handles common names too # get_nbnid(name='red-winged blackbird') # # # specify rows to limit choices available # get_nbnid('Poa annua') # get_nbnid('Poa annua', rows=1) # get_nbnid('Poa annua', rows=25) # get_nbnid('Poa annua', rows=1:2) # # # When not found # get_nbnid(name="uaudnadndj") # get_nbnid(c("Zootoca vivipara", "uaudnadndj")) # get_nbnid(c("Zootoca vivipara","Chironomus riparius", "uaudnadndj")) # # # Convert an nbnid without class information to a nbnid class # as.nbnid(get_nbnid("Zootoca vivipara")) # already a nbnid, returns the same # as.nbnid(get_nbnid(c("Zootoca vivipara","Pinus contorta"))) # same # as.nbnid('NHMSYS0001706186') # character # as.nbnid(c("NHMSYS0001706186","NHMSYS0000494848","NBNSYS0000010867")) # character vector, length > 1 # as.nbnid(list("NHMSYS0001706186","NHMSYS0000494848","NBNSYS0000010867")) # list # ## dont check, much faster # as.nbnid('NHMSYS0001706186', check=FALSE) # as.nbnid(list("NHMSYS0001706186","NHMSYS0000494848","NBNSYS0000010867"), check=FALSE) # # (out <- as.nbnid(c("NHMSYS0001706186","NHMSYS0000494848","NBNSYS0000010867"))) # data.frame(out) # as.nbnid( data.frame(out) ) # # # Get all data back # get_nbnid_("Zootoca vivipara") # get_nbnid_("Poa annua", rows=2) # get_nbnid_("Poa annua", rows=1:2) # get_nbnid_(c("asdfadfasd","Pinus contorta"), rows=1:5) # # # use curl options # library("httr") # get_nbnid("Quercus douglasii", config=verbose()) # bb <- get_nbnid("Quercus douglasii", config=progress()) ## ---------------------------------------------


Incoming order event can be from various devices, hand held scanners, automated carousal belt, etc. This creates an Order entity in the system and waits for order item events to come in. Optionally set a timeout to wait for the order items.
Incoming order item events associated to the parent order. Availibility check the item against the inventory (static storage system).
Issue shipping once all order items are received. Optionally set a order shipping timeout, to complete the order in the a given time frame.
On shipping confirmation event, the Order is moved to invoicing.
Based on the customer level (gold/silver/bronze), appropriate % discount is applied to the invoice
All timedout orders are cancelled
Sending Events
Create a new Order with the below details,
Create Item for a pre-created Order with the below details,
Send a shipping completed event. To move the order from shipping to invoice generation


2015Layer7
Score: 1
Captain: nero
Challenges solved
| category & points | name | solved at |
|---|---|---|
| Reverse Engineering 1 | access control | 2015-05-17 15:02:00 UTC |
Achievements
| name | description | condition | unlocked at | unlocked by |
|---|
Members
- wbbhe
- marcia
- nero
- test12
- 2ngyoe
- Roxer
- revsic
- r3tina
- hyunhoo
- skyoun97
- Almond
- tonix
- harin
- jmj
- soomin
- StealStick
- youben
- AkDoong
- Nar


We are scientists interested in coastal buildings.
Check out our
GitHub organization to see some of our ongoing work
Members:
Projects:
- Our Code:
- Site Selector (Work In Progress):...
- Damage detection (Work In Progress):......
- Other useful code (not ours):
- Heris et al Rasterizer:...
- Data Sources:
- MS Buildings:...
- OpenCityModel:...
- Open Street Map:...
- Design Safe repositories:...
- HIFLD:...
- NOAA ERI:...
Funding for this projects will come from:


{% extends "base.html" %} {% load humanize %} {% load recipient_utils %} {% block title %} {{ recipient.name | format_list }} | {{ country.name }} {% endblock %} {% block page_title %}
{{ recipient.name | list_first }} is a recipient of farm subsidies from {{ country.name }}.
{{ recipient.name | format_list }} has received (at least) {{ recipient_total|intcomma_floatformat }} € in payments of farm subsidies from the European Union under the CAP.
| Year | Scheme | Amount |
|---|---|---|
| {{ payment.year.year }} | {{ payment.scheme }} | {{ payment.amount | intcomma_floatformat }} € |
| Total: | {{ recipient_total | intcomma_floatformat }} € |


Leaflet Easymap is a Leaflet based library for
integrating maps in webpages without programming.
You just include the library in your web page and every html element with
class leasymap will be turned into a Leaflet map
(see examples below).
Important: the element must have a height set
(otherwise you'll see nothing).


Preparation time
over 2 hours
Cooking time
less than 10 mins
Serves
Serves 6
Lightly caramelised watermelon is a revelation as an accompaniment to vanilla-rich panna cotta and melon soup in this showy dinner party dessert recipe.
For the vanilla panna cotta, put the cream and sugar into a saucepan.
Split the vanilla pods, using a sharp knife scrape the seeds into the cream, and add the pods. Bring to the boil.
Squeeze any excess water from the soaked gelatin. Add it to the hot cream and whisk to dissolve. Pour the mixture into six individual dariole moulds.
Place into the fridge and leave to set.
For the ‘roasted’ watermelon, fry the watermelon in a pan until coloured all over.
For the soup, blend all the excess watermelon in a blender or food processor.
In a pan, heat the sugar and 100ml/3½fl oz water until the sugar dissolves. Leave to cool down. Stir in the mint, ground ginger and blended watermelon and put in the fridge to chill.
Once the panna cotta has set, turn each one out onto a plate.
Serve with the chilled soup and caramelised watermelon chunks.


Let’s Pretend
What’s the Difference?
- Better design?
- Professional speakers?
Nope.
The keys to a great presentation
- Don’t get lost in the details.
- Focus on the benefits to the audience.
- Don’t rely too much on your slides.
- Learn to pretend as if you know what you’re doing.
- ...and then there’s The Ultimate Presenters’ Tool™.
What’s The Ultimate Presenters’ Tool™, you ask?
Tip #1
Don’t Get Lost in the Details
This Is My Sister
Her stories are famously terrible.
Only Include Essential Details
“But all of these details are absolutely essential to my presentation!”
“For sale: baby shoes, never worn.”
– Ernest Hemingway (probably apocryphal)
Tip #2
Focus on the
Benefits
People are selfish
Use That
Focus on Benefits in
3 Easy Steps
Step 1:
Find the Thing That Hurts
Step 2:
Poke It
Step 3:
Offer a Better Solution
Tip #3
Don’t Rely Too Much on Your Slides
Slides are not a written report.
Slides are not notes for the speaker.
Slides are not a script.
Slides are not a collection of your favorite GIFs.
This one is my favorite
Tip #4
Pretend You Know What You’re Doing
Practice Don’t memorize
Run Through ItOut Loud
“Defrosting” a talk can be harder than writing the thing in the first place.
– Lorna Mitchell
Now It’s Time for The Ultimate Presenters’ Tool™
People Respond to Emotions
“But what if my topic is boring?”
A report on nominative determinism and its negative impact on
socioeconomic status and mobility.
The story of four strangers who take a road trip to ask a politician for
things they already have.
A detailed account of a grumpy alcoholic's poor life choices.
Good stories follow an arc
Air Your DirtyLaundry
How to make your next talk great:
- Don’t get lost in the details.
- Focus on the benefit to the audience.
- Don’t rely too much on slides.
- Pretend you know what you’re doing.
- Tell stories.


{% extends "blogs/blobon.html" %}
{% load thumbnail i18n compress calculate_age %}
{% load url from future %}
{% block indication %}
{% trans "Blobon.com" %}
{% endblock %}
{% block breadcrumb %}
{% blocktrans %}Blobon is a young innovative digital company, made in Canada. Our mission is to develop lightweight, simple and effective interfaces for both small and large businesses and individuals. Our vision is to connect all these entities together as a large social network.
Blobon is an open and flexible technology that adapts to you and to all types of devices. We are mobile, such as your public.
Blobon is also a one-stop turnkey solutions for all your digital needs: architecture, design, content, strategy, branding. We offer customized and effective solutions that will simplify your work.
Because your audiences are overstretched and increasingly difficult to satisfy, give them Blobon, a young, social, engaging, easy to use, dynamic and friendly platform.
Finally, Blobon is a straight to the point, friendly and entertaining network - because life is too short to be bored.
You've already spent too long Blobon:{% endblocktrans %}{% trans " contact us now!"%}


À Deriva
À Deriva is a movie starring Laura Neiva, Vincent Cassel, and Camilla Belle. A 1980s-set drama about a teenage girl undergoing her sexual awakening when she learns about her father's infidelities.


CCNBridge :
Members
-
<inner> _events
-
List of _events [Item] --> Event name : _listener-list name
-
<inner> _listeners
-
List of callbacks / _listeners to execute each time an event occur
Methods
-
<inner> _getEvents()
-
Get _events
Returns:
array -
<inner> _getListeners()
-
Get _listeners
Returns:
array -
<inner> _off(eventType, callback, scope)
-
Unregister _one or several previously registred callbacks warning: This should not work for anonymous callbacks
Parameters:
Name Type Description eventType
string The event type to unregister callback
function The callback to unregister scope
object The callback's scope (default: window) Note : if no callback is provided, this method will unregister every callbacks for the given eventType Note : if no eventType is provided, this method will unregister every callbacks for every _events -
<inner> _offClick(callback, scope)
-
Alias for "off" method with click event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offDoubleClick(callback, scope)
-
Alias for "off" method with double-click event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offDrag(callback, scope)
-
Alias for "off" method with drag event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offDrop(callback, scope)
-
Alias for "off" method with drop event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offHandle(callback, scope)
-
Alias for "off" method with handle event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offMessage(callback, scope)
-
Cancel a callback when the system triggers a message
Parameters:
Name Type Description callback
function The callback to execute. scope
object The optionnal callback's scope. -
<inner> _offMove(callback, scope)
-
Alias for "off" method with move event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offRelease(callback, scope)
-
Alias for "off" method with release event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offRotate(callback, scope)
-
Alias for "off" method with rotate event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offRotateEnd(callback, scope)
-
Alias for "off" method with rotateend event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offRotateStart(callback, scope)
-
Alias for "off" method with rotatestart event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offScroll(callback, scope)
-
Alias for "off" method with scroll event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _offZoom(callback, scope)
-
Alias for "off" method with zoom event-type
Parameters:
Name Type Description callback
function The callback to cancel scope
object The optionnal callback's scope -
<inner> _on()
-
Register a callback for an event
Throws:
Will throw an error if the eventType is unknown -
<inner> _onClick(callback, scope)
-
Alias for "on" method with click event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onDoubleClick(callback, scope)
-
Alias for "on" method with double-click event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onDrag(callback, scope)
-
Alias for "on" method with drag event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onDragEnd(callback, scope)
-
Alias for "on" method with dragend event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onDragEnter(callback, scope)
-
Alias for "on" method with dragenter event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onDragLeave(callback, scope)
-
Alias for "on" method with dragleave event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onDragOver(callback, scope)
-
Alias for "on" method with dragover event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onDragStart(callback, scope)
-
Alias for "on" method with dragstart event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onDrop(callback, scope)
-
Alias for "on" method with drop event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onHandle(callback, scope)
-
Alias for "on" method with handle event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onMessage(callback, scope)
-
Register a callback to be executed when the system triggers a message
Parameters:
Name Type Description callback
function The callback to execute. scope
object The optionnal callback's scope. -
<inner> _onMove(callback, scope)
-
Alias for "on" method with move event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onRelease(callback, scope)
-
Alias for "on" method with release event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onRotate(callback, scope)
-
Alias for "on" method with rotate event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onRotateEnd(callback, scope)
-
Alias for "on" method with rotateend event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onRotateStart(callback, scope)
-
Alias for "on" method with rotatestart event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onScroll(callback, scope)
-
Alias for "on" method with scroll event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _onZoom(callback, scope)
-
Alias for "on" method with zoom event-type
Parameters:
Name Type Description callback
function The callback to execute scope
object The optionnal callback's scope -
<inner> _removeAllListeners(eventType)
-
Alias for "off" method with optionnal eventType
Parameters:
Name Type Description eventType
string The optionnal eventType for which all callbacks will be canceled -
<inner> _send(message)
-
Send a message to the system
Parameters:
Name Type Description message
function The message to send


mb.tictactoe.data
Classes
Board
CellData
CellState
ChatMsg
CrossOrNought
EmptyOrCrossOrNought
Game
Participant
PlayerSide
ServerData
SystemMsg


This is a demo showing how to open YouTube videos with Fancybox.
Click an image below to expand the video over this screen.
Check out the tutorial and download here.
This is a demo showing how to open YouTube videos with Fancybox.
Click an image below to expand the video over this screen.
Check out the tutorial and download here.


--- title: Mission heading: CloudFx Mission subtitle: CloudFx Mission layout: short permalink: /mission/ ---
Connect
Research
Sharing


{% extends 'admin/master.html' %} {% import 'admin/lib.html' as lib with context %} {% import 'admin/static.html' as admin_static with context%} {% import 'admin/model/layout.html' as model_layout with context %} {% import 'admin/actions.html' as actionlib with context %} {% import 'admin/model/row_actions.html' as row_actions with context %} {% block head %} {{ super() }} {{ lib.form_css() }} {% endblock %} {% block breadcrumb %}
{% endblock %} {% block body %}


IRIS A film review by Steve Rhodes Copyright 2001 Steve Rhodes RATING (0 TO ****): ***
Aurelio Grimaldi's IRIS is a charming little story about a day in the life of a first grade girl named Maria. Maria is played by the winsome Arancia Cecilia Grimaldi, a girl with a big smile and adult front teeth just peaking through. On a sunny day on Ustica, a beautiful Italian island, Maria has her heart set on getting a bunch of "those blue flowers" for her Mama for her birthday.
A girl on a mission but with only enough money to buy one Iris, she goes on a long walk to where her father, a bricklayer, is working. The florist told her to get the money from her father, whom Maria, incorrectly, assumes has "lots." Along the way in this sweet story, she encounters everything from a barking dog to a topless swimmer. One briefly creepy scene amply demonstrates the downside of letting young kids roam on their own. IRIS is quite a simple story but a heart-warming one.
IRIS runs a fast 1:16. The film is in Italian with English subtitles. It is not rated but would probably be stuck with an R because of the topless swimmer scene, something common in Europe but not the U.S. The film would be acceptable for kids around 11 and up.
Web: http://www.InternetReviews.com Email<EMAIL_ADDRESS>Want free reviews and weekly movie and video recommendations via Email? Just send me a letter with the word "subscribe" in the subject line.
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


Pokemon#
Type:
Weight
kg
Height
m
Abilities:
Normal Abilities:
Hidden Ability:
First Appearance:
Generation
Generate New Card


[% page.site = "Combust" %]
[% USE JavaScript %]
Combust is not configured for the hostname [% r.hostname %].
Make sure that you have:
- added a [$sitename] section and
- the $sitename to the sites configuration parameter in combust.conf and
- created a apache/conf/sites/$sitename.tmpl apache configuration template file.


Home
Signup
Username:
Password:
Change Password....
Click here........
If you Wants to deactivate your account from our site :(


Generally we use IPv4 in all our networking devices/environment/test bed. But Some special cases requires IPv6 address. There are plenty of resources available in the internet for Mininet with IPv4.
In this blog, we discuss about using the IPv6 addresses in the Mininet Topology. We also discuss about, How to do Ping and TCP, UDP Traffic Tests (IPerf) with IPv6 addresses.
Some basic concepts of IPv6 addresses
Link-local address is a IPv6 unicast address that is valid only for communications within the network segment (link) or the broadcast domain. Link-local addresses are autoconfigured. Link local address prefix is FE80::/10
.
Example Link local addresses are fe80::200:ff:fe00:1/64
,fe80::200:ff:fe00:1/64
.
The global unicast address is globally unique in the Internet. Examples: 2001::1/64
, 2001::2/64
Objective Lets use the RYU Controller with Mininet Single Topology(4 switchs).
Start the Ryu Controller with Simple Openflow 1.3 Switch application as below,
ryu-manager ryu.app.simple_switch_13
sudo mn --controller=remote,ip=<IP_ADDRESS> --mac -i <IP_ADDRESS>/24 --switch=ovsk,protocols=OpenFlow13 --topo=single,4
In the mininet terminal, issue the below command,
h1 ifconfig h1-eth0
h2 ifconfig h2-eth0
h3 ifconfig h3-eth0
h4 ifconfig h4-eth0
ping6 utility is used for IPv6 Ping
Syntax :
ping6 -I [Link local address | Unicast address]
Example:
h1 ping6 -I h1-eth0 fe80::200:ff:fe00:2
In IPERF “-V” option is used for enabling IPv6.
To run the TCP server:
Run this IPerf Server in Host2(h2)
Note: open xterm for hosts, to run the iperf tests.
iperf -V -s
To run the TCP Client:
Global Unicast Address
iperf -c -V
Example:
iperf -c 2001:2 -V
LinkLocal Address
iperf -c <link local address%interface name> -V Example: iperf -c fe80::200:ff:fe00:2%h1-eth0 -V
In our case, we are using the Link Local address, hence we using this below command,
Run this IPerf Client in Host1(h1), connecting to h2.
iperf -c fe80::200:ff:fe00:2%h1-eth0 -V
Note: we need to specify IPv6 link local address in this “IPv6-address%interface” format.
UDP Test is similar to above.
Assign the IPv6 global unicast address to hosts
h1 ifconfig h1-eth0 inet6 add 2001::1/64
h2 ifconfig h2-eth0 inet6 add 2001::2/64
Ping Test
h1 ping6 -I h1-eth0 2001::2
h2 ping6 -I h2-eth0 2001::1
TCP Test
Start the Tcp Server in h2
iperf -V -s
Start the Tcp Client in h1
iperf -c 2001::2 -V
To check the IPv6 routes
ip -6 route


Over at The Dabbler today I have exhumed, and slightly tweaked, a very long sentence which first appeared as part of the text of Crop Circles : The Crunlop Experiment, an out of print pamphlet from the last century. In the comments, Brit suggests that “Ikea would reduce all that to three simple yet bewildering black-and-white pictures”, which reminded me of the diagram accompanying another Hooting Yard craft project from 2004, to wit:


--- title: Prepare your dataset weight: 29 ---
By the end of this section you should be able to:
{{% notice info %}}
* use the dplyr
package to clean and recode your dataset.
{{% /notice %}}
For this section we will be using data from the ‘British Election Study’ survey to investigate the effect of perceptions about the economy on party choice. This is part of a broader research examining the effect of political issues on party choice and especialy on the effect of the economy on the party in government.
load("bes.RData")
We will analyse data from the 2017 general election, and we will test whether citizens’ who feel that the economy is doing better will support the party in government.
We can download the full dataset from the website below:
We should download the post-election study entitled: 2017 Face-to-face Post-election Survey Version 1.3
We will use the following variables from the dataset:
| Name | Description (name in the dataset in parenthesis) |
|---|---|
| incumbent | (1) The respondent will vote for the party in government (0) will vote for another party (b02) |
| left-right | position on the left- right spectrum (e01) |
| egocentric_retro | retrospective evaluation of personal finances (l01) |
| egocentric_pros | prospective evaluation of personal finances (l02) |
| sociotropic_retro | retrospective evaluation of UK’s economy (l04) |
| sociotropic_pros | prospective evaluation of UK’s economy (l03) |
Our next step is to create a sub-set including only the variables we will include in our analysis
library(dplyr)
eco_voting<-bes %>%select(b02,Age,edlevel,e01,l01,l02,l03,l04,p01)
View(eco_voting)
Note: If you use RStudio, you can type the pipe (\(%>%\)) with Ctrl + Shift + M if you have a PC or Cmd + Shift + M if you have a Mac.
Our next step is to replace the negative values (-1, -2) to NAs
eco_voting[eco_voting <=-1] <- NA
eco_voting[eco_voting <=-2] <- NA
We will use the rename()
function which is part dplyr
:
eco_voting<-eco_voting %>% rename(left_right=e01, egocentric_retro=l01,egocentric_pros=l02,sociotropic_pros=l03,sociotropic_retro=l04,brexit=p01)
View(eco_voting)
{{% notice info %}}
* The dplyr package offers a number of functions to clean and reshape your dataset including; rename()
and select()
.
{{% /notice %}}


Useful Links
This page consists of a growing collection of links to useful reading material related to software engineering.
The links on this page are intended to be long-lived resources related to problem solving and general understanding of how stuff works.
Networking
- OpenSSH Cookbook: Proxies and Jump Hosts
- OpenSSH Cookbook: Tunnels
- CIDR.xyz interactive IP address and CIDR range visualizer
Cloud
Design Patterns
Miscellaneous
SOLID
Infosec
Information Retrieval
Indexing
Searching
Ranking
Case Studies
- The Architecture of Algolia's Distributed Search Network
- The Anatomy of a Large-Scale Hypertextual Web Search Engine [Page and Brin]
Other
Algorithms and Data Structures
General Reference
- Steven Skiena's audio lectures [Stony Brook]
- Problem Solving with Algorithms and Data Structures using Python


//
// Copyright (c) Microsoft and contributors. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//
// See the License for the specific language governing permissions and
// limitations under the License.
//
// Expose 'AccessCondition'.
/**
* Defines constants, enums, and utility functions for use with storage access condition.
* @namespace
*/
'use strict';
var constants = require('./constants');
exports = module.exports;
/**
* Constructs an empty access condition.
*
* @return {object} An empty AccessCondition object
*/
exports.generateEmptyCondition = function () {
return {};
};
/**
* Constructs an access condition such that an operation will be performed only if the resource does not exist on the service
*
* Setting this access condition modifies the request to include the HTTP If-None-Match conditional header
* @return {object} An AccessCondition object that represents a condition that checks for nonexistence
*/
exports.generateIfNotExistsCondition = function () {
var accessCondition = {};
accessCondition[constants.HeaderConstants.IF_NONE_MATCH] = '*';
return accessCondition;
};
/**
* Constructs an access condition such that an operation will be performed only if the resource exists on the service
*
* Setting this access condition modifies the request to include the HTTP If-Match conditional header
* @return {object} An AccessCondition object that represents a condition that checks for existence
*/
exports.generateIfExistsCondition = function () {
var accessCondition = {};
accessCondition[constants.HeaderConstants.IF_MATCH] = '*';
return accessCondition;
};
/**
* Constructs an access condition such that an operation will be performed only if the resource's ETag value
* does not match the specified ETag value
*
* Setting this access condition modifies the request to include the HTTP If-None-Match conditional header
*
* @param {string} etag The ETag value to check against the resource's ETag
* @return {object} An AccessCondition object that represents the If-None-Match condition
*/
exports.generateIfNoneMatchCondition = function (etag) {
var accessCondition = {};
accessCondition[constants.HeaderConstants.IF_NONE_MATCH] = etag;
return accessCondition;
};
/**
* Constructs an access condition such that an operation will be performed only if the resource's ETag value
* matches the specified ETag value
*
* Setting this access condition modifies the request to include the HTTP If-Match conditional header
*
* @param {string} etag The ETag value to check against the resource's ETag
* @return {object} An AccessCondition object that represents the If-Match condition
*/
exports.generateIfMatchCondition = function (etag) {
var accessCondition = {};
accessCondition[constants.HeaderConstants.IF_MATCH] = etag;
return accessCondition;
};
/**
* Constructs an access condition such that an operation will be performed only if the resource has been
* modified since the specified time
*
* Setting this access condition modifies the request to include the HTTP If-Modified-Since conditional header
*
* @param {Date|string} time A date object specifying the time since which the resource must have been modified
* @return {object} An AccessCondition object that represents the If-Modified-Since condition
*/
exports.generateIfModifiedSinceCondition = function (time) {
var accessCondition = {};
accessCondition[constants.HeaderConstants.IF_MODIFIED_SINCE] = time instanceof Date ? time.toUTCString() : time;
return accessCondition;
};
/**
* Constructs an access condition such that an operation will be performed only if the resource has not been
* modified since the specified time
*
* Setting this access condition modifies the request to include the HTTP If-Unmodified-Since conditional header
*
* @param {Date|string} time A date object specifying the time since which the resource must have not been modified
* @return {object} An AccessCondition object that represents the If-Unmodified-Since condition
*/
exports.generateIfNotModifiedSinceCondition = function (time) {
var accessCondition = {};
accessCondition[constants.HeaderConstants.IF_UNMODIFIED_SINCE] = time instanceof Date ? time.toUTCString() : time;
return accessCondition;
}; |


Plotting areas from different brain atlases ggplot2 is a handy way of displaying results or grouping of aparc data.
The function ggseg()
, is based in the ggplot2 format, it is recommended to get a little familiarized with with plotting data with ggplot2.
Out-of-the-box, ggseg() works without supplying any extra information. It will create a base plot of the aparc brain segmentations.
##
## Attaching package: 'magrittr'
## The following objects are masked from 'package:testthat':
##
## equals, is_less_than, not
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:testthat':
##
## matches
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
The plot is kept as simple as possible, with as little extra information to ggplot as possible. This is done for it to be easy to extend the plot with the many ggplot options available. By default it plots data(dkt)
. For instance, you might want to only see the brain and it’s segments, without any grid or axis information. You can add ggplot2’s theme_void
to achieve this.
You can supply some extra options to ggseg, to alter the plot as you wish.
You can stack the hemispheres if you want
Maybe you want each area to have a different colour. ggseg has a built column called area
wich you can use as fill to achieve this. Note: unlike other ggplots, before using aes
you must indicate mapping=aes
.
Maybe you also want the contour lines to be black rather than white, and also a little thicker?
You can also choose to only view one of the hespheres by adding the hemisphere
option
If you want to plot the entire brain, but hightlight specific areas with colour, you will need to provide the function with some data. The data must include a column that overlaps with the columns in the atlas data. Here we create some data with 4 rows, and an area
and p
column. We provide the p
column as the column that dictates the colour of the area. The area
column matches a column in the dkt atlas.
someData = data.frame(
area = c("transverse temporal", "insula",
"pre central","superior parietal"),
p = sample(seq(0,.5,.001), 4),
stringsAsFactors = FALSE)
ggseg(.data=someData, mapping=aes(fill=p))
We can also change how this looks, by various ggplot functions
ggseg(.data=someData, colour="black", mapping=aes(fill=p)) +
theme_void() +
scale_fill_gradient(low="firebrick",high="goldenrod") +
labs(title="Aparc plots rule", fill="p-value")
If the results are only in one hemisphere, but you still want to plot both of them, make sure your data.fame includes the column hemi
with either “rh” or “lh” for this to happen.
someData$hemi = "right"
ggseg(.data=someData, colour="white",mapping=aes(fill=p)) +
theme_void() +
scale_fill_gradient(low="firebrick",high="goldenrod") +
labs(title="Aparc plots rule")
If your data has different statistics for different groups, and you want to plot them all, we can use ggplot’s facet_wrap
or facet_grid
for that. The data needs to be in long format for this to work, like ggplot likes it. Also, you’ll need to merge the data outside of the plotting function for it to work nicely. This is because the atlas data must be completely duplicated for plotting to look nice.
someData = data.frame(
area = rep(c("transverse temporal", "insula",
"pre central","superior parietal"),2),
p = sample(seq(0,.5,.001), 8),
AgeG = c(rep("Young",4), rep("Old",4)),
stringsAsFactors = FALSE)
ggseg(.data=someData, colour="white", mapping=aes(fill=p)) +
facet_wrap(~AgeG, ncol=1) +
theme(legend.position = "bottom")
as you can see. The result of this facetting is not what we are after. This is because the the function requires full data to be able to plot, and the AgeG
column is NA for everything outside the significant results.
To fix this, you must duplicate the atlas data, and supply your data through the atlas
option. This is maybe best achieved by making your data into a list, and then list applying lapply
over them, and binding them together again.
someData = someData %>%
group_by(AgeG)
ggseg(.data = someData, atlas=dkt, colour="white", mapping=aes(fill=p)) +
facet_wrap(~AgeG, ncol=1) +
theme(legend.position = "bottom")
You can read more about more complex functions like this in the vignette for external data
## starting httpd help server ... done
We can change how this looks quite a bit. Maybe you want it on a dark background, and the areas without any data to be transparent?
ggseg(.data = someData, atlas=dkt, colour="white", mapping=aes(fill=p)) +
facet_wrap(~AgeG, ncol=1) +
theme_dark() +
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.title = element_blank()
) +
scale_fill_continuous(na.value="transparent")
Depending on what type of column is used for fill (continuous or discrete), you can also use the differenct scales
to alter the colours.
ggseg(.data = someData, atlas=dkt, colour="white", mapping=aes(fill=p)) +
facet_wrap(~AgeG, ncol=1) +
theme_dark() +
theme(legend.position = "bottom",
axis.text = element_blank(),
axis.title = element_blank()
) +
scale_fill_gradient2(high="goldenrod",low="firebrick",na.value="transparent")
You can try other atlases too.


This is the NoteThat Editor. Work, Study, make it read or anything. Work on and download, Distraction free. Background music for some upbeat.
Download Your Work


Textures. On the left is a texture loaded from a canvas element, showing an orange circle with the word "textures!" over it in white. On the right is a texture loaded from raw data, with the red channel increasing to the right, and the green channel increasing to the top.


Test Paragraph 1
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loading data...


Grids are key to organisation on the webpage. They allow designers to easily create patterns and symmetry in a way
that is pleasing to the user and simple to represent on a webpage.
Grids also provide a conceptual way to scale up and down page view. Responsive webpage layouts are booming as the habits of
consumers move more towards consuming digital media on mobile devices, rather than traditional computers. Needing to represent
webpages in both these fashions means needing to adopt a shared design language and practice. Grids, with their ability to scale
up and down at will, are a good fit for this.
Semantic tags in HTML are the tags that give the reader and the browser deeper knowledge about the actual purpose of the content being displayed and the structure is how this is all organised in terms of parent/child relationships. Semantic tags have come about so that browers are able to represent information in a more meaningful way. When using generic tags like div, there is no context around what the content may be or relate to. To add context a semantic tag can be used; for example h1 indicates that what is written is a header for the whole page, or time denotes a date/time. This information is of particular value to search engines that want to represent meaningful information to their users.


This is a very special function. When there are strange combinations of settings
it could happen that although fileAPI-Support is disabled that we have a multi-upload.
This is only possible in modern browsers if we force the fileAPI to not work.
In this case a form with multiple-attribute enabled would upload multiple files at once.
But only one upload-id would be provided per upload and with $n uploadfiles we would create
$n FileParcels which ALL would trigger the form for themselves. That is why we need one main parcel
and link all the others then.
This is a very special function. When there are strange combinations of settings it could happen that although fileAPI-Support is disabled that we have a multi-upload. This is only possible in modern browsers if we force the fileAPI to not work. In this case a form with multiple-attribute enabled would upload multiple files at once. But only one upload-id would be provided per upload and with $n uploadfiles we would create $n FileParcels which ALL would trigger the form for themselves. That is why we need one main parcel and link all the others then.


Go ahead and checkout a stack of flashcards if the title peeks your interest or create your own stack!


Contrastive Learning Research
The performance of deep convolutional neural networks depends on their capability and the amount of training data. The datasets are becoming larger in every domain and different kinds of network architectures like VGG, GoogLeNet, ResNet, DenseNet, etc., increased network models' capacity.
However, the collection and annotation of large-scale datasets are time-consuming and expensive. Many self-supervised methods were proposed to learn visual features from large-scale unlabeled data without using any human annotations to avoid time-consuming and costly data annotations. Contrastive learning of visual representations has emerged as the front-runner for self-supervision and has demonstrated superior performance on downstream tasks. All contrastive learning frameworks involve maximizing agreement between positive image pairs relative to negative/different images via a contrastive loss function; this pretraining paradigm forces the model to learn good representations. These approaches typically differ in how they generate positive and negative image pairs from unlabeled data and how the data are sampled during pretraining.
Self-supervised approaches such as Momentum Contrast (MoCo) (He et al., 2019, Chen et al.,2020) can leverage unlabeled data to produce pre-trained models for subsequent fine-tuning on labeled data. In addition to MoCo, these include frameworks such as SimCLR (Chen et al., 2020) and PIRL (Misra and Maaten, 2020).
Remote sensing data has become broadly available at the petabyte scale, offering unprecedented visibility into natural and human activity across the Earth. In remote sensing, labeled data is usually scarce and hard to obtain. Due to the success of self-supervised learning methods, we explore their application to large-scale remote sensing datasets.
While most self-supervised image analysis techniques focus on natural imagery, remote sensing differs in several critical ways. Natural imagery often has one subject; remote sensing images contain numerous objects such as buildings, trees, roads, rivers, etc. Additionally, the important content changes unpredictably within just a few pixels or between images at the same location from different times. Multiple satellites capture images of the same locations on earth with a wide variety of resolutions, spectral bands (channels), and revisit rates, such that any specific problem can require a different combination of sensor inputs (Reiche et al., 2018,Rustowicz et al., 2019).
While MoCo and other contrastive learning methods have demonstrated promising results on natural image classification tasks, their application to remote sensing applications has been limited.
Unlike contrastive learning for traditional computer vision images where different views (augmentations) of the same image serve as a positive pair, we propose to use positive pairs from different sensors for the same location.
Method 1 - Sensor-based Geo-alignment Pairs
Method 2 - Geo-alignment Data Fusion
In this work, we demonstrate that pre-training MoCo-v2 on data from multiple sensors lead to improved representations for remote sensing applications.
Many self-supervised learning methods for visual feature learning have been developed without using any human-annotated labels. Compared to supervised learning methods which require a data pair Xi and Yi while Yi is annotated by human labors, self-supervised learning also trained with data Xi along with its pseudo label Pi while Pi is automatically generated for a pre-defined pretext task without involving any human annotation. The pseudo label Pi can be generated by using attributes of images or videos such as the context of images or by traditional hand-designed methods. As long as the pseudo labels P are automatically generated without involving human annotations, then the methods belong to self-supervised learning. Recently, self-supervised learning methods have achieved great progress.
Self-supervised contrastive
learning approaches such as MoCo ,
MoCo-v2, SimCLR, and PIRL have demonstrated
superior performance and have emerged as the fore-runner
on various downstream tasks. The intuition behind these
methods are to learn representations by pulling positive
image pairs from the same instance closer in latent space
while pushing negative pairs from difference instances further
away. These methods, on the other hand, differ in the
type of contrastive loss, generation of positive and negative
pairs, and sampling method.
Contrastive learning of visual representations using MoCo (MoCo-v2 - Chen, et
al., Facebook AI Research, 2020) has emerged as the front-runner for
self-supervision and has demonstrated superior performance on downstream tasks.
There is a performance gap between supervised learning using labels and self-supervised contrastive learning method, MoCo-v2, on remote sensing datasets. For instance, on the Functional Map of the World (fMoW) image classification benchmark, there is an 8% gap in top 1 accuracy between supervised and self-supervised methods. By leveraging spatially aligned images over time to construct temporal positive pairs in contrastive learning and geo-location in the design of pre-text tasks, Geography-Aware Self-supervised Learning (Ayush, et al., Stanford University, 2020) were able to close the gap between self-supervised and supervised learning on image classification, object detection and semantic segmentation on remote sensing and other geo-tagged image datasets.
In this work, we provide an effective approach for improving representation learning using data from different satellite imagery using MoCo-v2.
Does contrastive pre-training with data from multiple sensors lead to improved representations
for remote sensing applications?
Pre-train the contrastive model using unlabelled data from multiple satellites and use that model for
downstream remote sensing tasks.
We want to show that our approach to using images from different satellites for the same location as
naturally augmented images as input to the MoCo-v2 method provides high-quality representations and
transferable initializations for satellite imagery interpretation.
Despite many differences in the data and task properties between natural image classification and
satellite imagery interpretation, we want to show the benefit of MoCo-v2 pretraining across multiple
patches from different satellites for satellite imagery and investigate representation transfer to a
target dataset.
Buildings
Roads
Land cover
Human settlements
| Dataset | Satellites | Number of Images | Image Size | Labels | Notes |
|---|---|---|---|---|---|
| BigEarthNet |
|
590,326 patches
|
20x20 to 120x120 | Multiple, up to 43 |
|
| SEN12MS |
|
541,986 patches
|
256X256 | Single, 17 Full and 10 Simplified | Partial overlapping |
| FMoW |
|
1,047,691 patches
|
Variable Over 2500x2500 | Multiple, up to 63 Bounding Box Annotations |
|
| RESISC45 | NA | 31,500 Images
|
256x256 | Single, 45 scene classes |
|
The BigEarthNet archive was constructed by the Remote Sensing Image Analysis (RSiM) Group and the Database Systems and Information Management (DIMA) Group at the Technische Universität Berlin (TU Berlin). This work is supported by the European Research Council under the ERC Starting Grant BigEarthNet and by the Berlin Institute for the Foundations of Learning and Data (BIFOLD). Before BIFOLD, the Berlin Big Data Center (BBDC) supported the work. BigEarthNet is a benchmark archive, consisting of 590,326 pairs of Sentinel-1 and Sentinel-2 image patches. To construct BigEarthNet with Sentinel-2 image patches (called as BigEarthNet-S2 now, previously BigEarthNet), 125 Sentinel-2 tiles acquired between June 2017 and May 2018 over the 10 countries (Austria, Belgium, Finland, Ireland, Kosovo, Lithuania, Luxembourg, Portugal, Serbia, Switzerland) of Europe were initially selected. All the tiles were atmospherically corrected by the Sentinel-2 Level 2A product generation and formatting tool (sen2cor). Then, they were divided into 590,326 non-overlapping image patches. Each image patch was annotated by the multiple land-cover classes (i.e., multi-labels) that were provided from the CORINE Land Cover database of the year 2018 (CLC 2018). To construct BigEarthNet with Sentinel-1 image patches (called as BigEarthNet-S1), 321 Sentinel-1 scenes acquired between June 2017 and May 2018 that jointly cover the area of all original 125 Sentinel-2 tiles with close temporal proximity were selected and processed. BigEarthNet-S1 consists of 590,326 preprocessed Sentinel-1 image patches - one for each Sentinel-2 patch.
12 BandsThe SEN12MS dataset contains 180,662 patch triplets of corresponding Sentinel-1 dual-pol SAR data, Sentinel-2 multi-spectral images, and MODIS-derived land cover maps. The patches are distributed across the land masses of the Earth and spread over all four meteorological seasons. This is reflected by the dataset structure. The captured scenes were tiled into patches of 256 X 256 pixels in size and implemented a stride of 128 pixels, resulting in an overlap between adjacent patches of 50% assuming 50% overlap is the ideal trade-off between patch independence and maximization of the number of samples. All patches are provided in the form of 16-bit GeoTiffs containing the following specific information:
| Overlap (%) | Patches |
|---|---|
| 0-15 | 1313 |
| 15-25 | 60195 |
| 25-50 | 82618 |
| 50-95 | 529 |
| >75 | 215 |
Functional Map of the World (fMoW) is a large-scale publicly available remote sensing dataset consisting of approximately 363,571 training images and 53,041 test images across 62 highly granular class categories. It provides images (temporal views) from the same location over time as well as geo-location metadata (lat; lon) for each image. Most of the areas have multiple temporal views from 1 to 21, and on average there is about 2.5-3 years of difference between the images from an area. FMoW is a global dataset consisting of images from seven continents which can be ideal for learning global remote sensing representations. There are two versions of the dataset: fMoW-full and fMoW-rgb. fMoW-full is in TIFF format, contains 4-band and 8-band multispectral imagery, and is quite large at ~3.5TB in size. fMoW-rgb is in JPEG format, all multispectral imagery has been converted to RGB, and it is significantly smaller in size at ~200GB.
RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset was extracted, by the experts in the field of remote sensing image interpretation, from Google Earth (Google Inc.) that maps the Earth by the superimposition of images obtained from satellite imagery, aerial photography and geographic information system (GIS) onto a 3D globe. This dataset contains 31,500 256x256 RGB images, covering 45 scene classes with 700 images in each class and cover more than 100 countries and regions all over the world, including developing, transition, and highly developed economies. It is one of the most comprehensive datasets regarding land use. RESISC45 has a more diverse assortment of classes as well as more samples per class. The authors developed it while keeping in mind the desire to have both urban and rural classes as well as scenes classifiable by large features and small features alike. The dataset includes scenes including large features such as dense_residential, chaparral, mobile_home_park, river; smaller features such as storage-tank and basketball court; and even classes such as sea-ice and cloud.
In this section, we briefly review Contrastive Learning Framework for unsupervised learning and detail our proposed approach to improve Moco-v2, a recent contrastive learning framework, on satellite imagery from multiple sensors data.
| Pre-train / Accuracy (%) | S1 | S2 | S1+S2 |
|---|---|---|---|
| Sup. Learning | 40.0 | 61.0 | 58.6 |
| Sensor-based Augmentation | 62.8 | ||
| Full Fusion | 50.5 | 62.5 | 59.6 |
| Partial Fusion | 47.3 | 58.1 | 60.7 |
| Optional Fusion | 48.2 | 56.1 | 58.8 |
We perform transfer learning experiments with our two proposed methods on land cover scene classification across ten label classes using the SEN12MS dataset to understand the quality of the learned representations. Given that the image samples are randomly sampled to positive and negative pairs in MoCo v2 to compute the loss during training processes, we have finetuned with different hyper-parameters and selected the best results on each of the evaluation datasets in our reporting. Using the pre-trained with our sensor-based geo-aligned pairs approach, our finetune on the 1,024 samples for the multi-class classification shows that the performance on s1 and s2 data together has better performance than the supervised model with the same size of samples. Due to our learned representation being constructed by encoders from two sensor images data with multiple bands information, we argue that the downstream results would perform better on the dataset, including both s1 and s2 data, as it appears in the results. Whereas by looking at the evaluation results with our geo-alignment data fusion approach on 1,024 samples, as shown in the table, we can see that
| Pre-train / Accuracy (%) | S1 | S2 | S1+S2 |
|---|---|---|---|
| Sup. Learning | 40.1 | 54.9 | 54.2 |
| Sensor-based Augmentation | 54.6 | ||
| Full Fusion | 45.6 | 55.3 | 54.6 |
| Partial Fusion | 42.8 | 53.5 | 53.5 |
| Optional Fusion | 44.8 | 51.2 | 52.9 |
We demonstrate similar results finding to sen12ms.
In terms of our future work, further study can be done as:
With many reviews and studies through our processes, we leverage large-scale training data that include versatile remote sensing information from different sensors with global scene distributions to build the sensor-based location invariance momentum contrast for representation learning. With the advantage of the MoCo framework, we proposed two methods to experiment with the learned representations. In our transfer learning tasks, we are excited to see the results are pretty much aligned with our expectations, and that helps us understand the qualities of our learned representations so far. Overall, we demonstrate that natural augmentation and data fusion helps bridge the gaps between self-supervised learning and sup. learning models.
We are humble to explore and pleased to accomplish the solutions so far in remote sensing applications.
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>taeil.goh@berkeley.edu


Tetrisnator
Use Arrow keys to Move.
Use Mouse to aim!
Survive by destroying the block before they reach the top!
This little game was made by bewelge in 48 hours for the Ludum Dare #41 compo
Use Arrow keys to Move.
Use Mouse to aim!
Survive by destroying the block before they reach the top!
Game was made in its entirety by bewelge, originally for the Ludum Dare #41 Compo


My conscious appreciation of design and art started at the age of eight when my Mom gave me the gift of a scrapbook, Its wide empty pages filled my imagination with possibilities and there begun my journey as a designer.
I could say that's how my journey as a designer began. I would cut out comic strips and beautiful ads from my Dad's old magazines and newspapers
This scrapbook is not much different from the scrapbook of my childhood, as I'm still collecting images I love. It's just no longer from my dad's magazines but snippets from large projects I worked on, my photography or simply just experiments from the past 10 years.
I like to listen.


---
layout: default
title: Getting Started
---
Getting Started
We've built a CLI (Command Line Interface) which is supported by OSX, Linux and Windows. To create your own blog;
npm install -g jsonblog-cli
The command line tool uses an ecosystem of modules that we've open sourced to convert your blog to different formats:
blog init
blog build
blog serve
Will be build some free community hosting soon.


|
Earthquakemodels
Here is the official documentation for generating Earthquake Risk Models using Genetic Algorithms.
|
Functions | |
| def | createSytheticModel (year, region, depth, withMag=True, save=False) |
| def | ExecGASynthetic (region, depth, year=1990, times=5) |
| def | ExecEtasGASynthetic (region, depth, year=1990, times=5) |
| def | callGAModelSynthetic (region, depth) |
| def | callEtasGAModelSynthetic (region, depth) |
| def | main () |
| def applySytheticsModels.callEtasGAModelSynthetic | ( | region, | |
| depth | |||
| ) |
It is a wrapper to the function that generates the list model synthetic data It cover the years of 2000 to 2005, and the models are from 2005 to 2010
Definition at line 110 of file applySytheticsModels.py.
| def applySytheticsModels.callGAModelSynthetic | ( | region, | |
| depth | |||
| ) |
It is a wrapper to the function that generates the GAModel with synthetic data It cover the years of 2000 to 2005, and the models are from 2005 to 2010
Definition at line 99 of file applySytheticsModels.py.
| def applySytheticsModels.createSytheticModel | ( | year, | |
| region, | |||
| depth, | |||
withMag = True , |
|||
save = False |
|||
| ) |
Creates a sythetic poisson data model to experiments
Definition at line 10 of file applySytheticsModels.py.
| def applySytheticsModels.ExecEtasGASynthetic | ( | region, | |
| depth, | |||
year = 1990 , |
|||
times = 5 |
|||
| ) |
Creates the list Model with Synthetic catalog
Definition at line 65 of file applySytheticsModels.py.
| def applySytheticsModels.ExecGASynthetic | ( | region, | |
| depth, | |||
year = 1990 , |
|||
times = 5 |
|||
| ) |
Creates the GAModel with Synthetic catalog
Definition at line 36 of file applySytheticsModels.py.
| def applySytheticsModels.main | ( | ) |
This function creates the needed enviroment needed to generate both GAModel and List Model with sythetic catalog for the regions: EastJapan, Kanto, Kansai, Tohoku from 2000 to 2005 to create models from 2005 to 2010
Definition at line 121 of file applySytheticsModels.py.


Press F12 on your keyboard to view the message in the console view.
This example demonstrates how an object is displayed in the console view.


Welcome أهلا وسهلا
The Organization of Arab Students (OAS) is an organization at the Ohio State University that aims to provide an environment where Arab students and non-Arab students can come together and celebrate the Arab diversity through educational and entertaining activities.


One of those things beginners in C find difficult is the concept of pointers. The purpose of this tutorial is to provide an introduction to pointers and their use to these beginners.
I have found that often the main reason beginners have a problem with pointers is that they have a weak or minimal feeling for variables, (as they are used in C). Thus we start with a discussion of C variables in general.
A variable in a program is something with a name, the value of which can vary. The way the compiler and linker handles this is that it assigns a specific block of memory within the computer to hold the value of that variable. The size of that block depends on the range over which the variable is allowed to vary. For example, on PC's the size of an integer variable is 2 bytes, and that of a long integer is 4 bytes. In C the size of a variable type such as an integer need not be the same on all types of machines.
When we declare a variable we inform the compiler of two things, the name of the variable and the type of the variable. For example, we declare a variable of type integer with the name k by writing:
int k;
On seeing the "int" part of this statement the compiler sets aside 2 bytes of memory (on a PC) to hold the value of the integer. It also sets up a symbol table. In that table it adds the symbol k and the relative address in memory where those 2 bytes were set aside.
Thus, later if we write:
k = 2;
we expect that, at run time when this statement is executed, the value 2 will be placed in that memory location reserved for the storage of the value of k. In C we refer to a variable such as the integer k as an "object".
In a sense there are two "values" associated with the object k. One is the value of the integer stored there (2 in the above example) and the other the "value" of the memory location, i.e., the address of k. Some texts refer to these two values with the nomenclature rvalue (right value, pronounced "are value") and lvalue (left value, pronounced "el value") respectively.
In some languages, the lvalue is the value permitted on the left side of the assignment operator '=' (i.e. the address where the result of evaluation of the right side ends up). The rvalue is that which is on the right side of the assignment statement, the 2 above. Rvalues cannot be used on the left side of the assignment statement. Thus: 2 = k; is illegal.
Actually, the above definition of "lvalue" is somewhat modified for C. According to K&R II (page 197): [1]
"An object is a named region of storage; an lvalue is an expression referring to an object."
However, at this point, the definition originally cited above is sufficient. As we become more familiar with pointers we will go into more detail on this.
Okay, now consider:
int j, k; k = 2; j = 7; <-- line 1 k = j; <-- line 2
In the above, the compiler interprets the j in line 1 as the address of the variable j (its lvalue) and creates code to copy the value 7 to that address. In line 2, however, the j is interpreted as its rvalue (since it is on the right hand side of the assignment operator '='). That is, here the j refers to the value stored at the memory location set aside for j, in this case 7. So, the 7 is copied to the address designated by the lvalue of k.
In all of these examples, we are using 2 byte integers so all copying of rvalues from one storage location to the other is done by copying 2 bytes. Had we been using long integers, we would be copying 4 bytes.
Now, let's say that we have a reason for wanting a variable designed to hold an lvalue (an address). The size required to hold such a value depends on the system. On older desk top computers with 64K of memory total, the address of any point in memory can be contained in 2 bytes. Computers with more memory would require more bytes to hold an address. Some computers, such as the IBM PC might require special handling to hold a segment and offset under certain circumstances. The actual size required is not too important so long as we have a way of informing the compiler that what we want to store is an address.
Such a variable is called a pointer variable (for reasons which hopefully will become clearer a little later). In C when we define a pointer variable we do so by preceding its name with an asterisk. In C we also give our pointer a type which, in this case, refers to the type of data stored at the address we will be storing in our pointer. For example, consider the variable declaration:
int *ptr;
ptr is the name of our variable (just as k was the name of our integer variable). The '*' informs the compiler that we want a pointer variable, i.e. to set aside however many bytes is required to store an address in memory. The int says that we intend to use our pointer variable to store the address of an integer. Such a pointer is said to "point to" an integer. However, note that when we wrote int k; we did not give k a value. If this definition is made outside of any function ANSI compliant compilers will initialize it to zero. Similarly, ptr has no value, that is we haven't stored an address in it in the above declaration. In this case, again if the declaration is outside of any function, it is initialized to a value guaranteed in such a way that it is guaranteed to not point to any C object or function. A pointer initialized in this manner is called a "null" pointer.
The actual bit pattern used for a null pointer may or may not evaluate to zero since it depends on the specific system on which the code is developed. To make the source code compatible between various compilers on various systems, a macro is used to represent a null pointer. That macro goes under the name NULL. Thus, setting the value of a pointer using the NULL macro, as with an assignment statement such as ptr = NULL, guarantees that the pointer has become a null pointer. Similarly, just as one can test for an integer value of zero, as in if(k == 0), we can test for a null pointer using if (ptr == NULL).
But, back to using our new variable ptr. Suppose now that we want to store in ptr the address of our integer variable k. To do this we use the unary & operator and write:
ptr = &k;
What the & operator does is retrieve the lvalue (address) of k, even though k is on the right hand side of the assignment operator '=', and copies that to the contents of our pointer ptr. Now, ptr is said to "point to" k. Bear with us now, there is only one more operator we need to discuss.
The "dereferencing operator" is the asterisk and it is used as follows:
*ptr = 7;
will copy 7 to the address pointed to by ptr. Thus if ptr "points to" (contains the address of) k, the above statement will set the value of k to 7. That is, when we use the '*' this way we are referring to the value of that which ptr is pointing to, not the value of the pointer itself.
Similarly, we could write:
printf("%d\n",*ptr);
to print to the screen the integer value stored at the address pointed to by ptr;.
One way to see how all this stuff fits together would be to run the following program and then review the code and the output carefully.
------------ Program 1.1 --------------------------------- /* Program 1.1 from PTRTUT10.TXT 6/10/97 */ #include <stdio.h> int j, k; int *ptr; int main(void) { j = 1; k = 2; ptr = &k; printf("\n"); printf("j has the value %d and is stored at %p\n", j, (void *)&j); printf("k has the value %d and is stored at %p\n", k, (void *)&k); printf("ptr has the value %p and is stored at %p\n", ptr, (void *)&ptr); printf("The value of the integer pointed to by ptr is %d\n", *ptr); return 0; }
Note: We have yet to discuss those aspects of C which require the use of the (void *) expression used here. For now, include it in your test code. We'll explain the reason behind this expression later.
To review:


Enums
The following enums are available globally.
-
Undocumented
See more
-
Undocumented
See more
-
See more
See also
::delete-from-cursor
.Declaration
Swift
public enum DeleteType: RawRepresentable
-
Used to control what selections users are allowed to make.
See moreDeclaration
Swift
public enum SelectionMode: RawRepresentable
-
Describes the image data representation used by a
See moreImage
. If you want to get the image from the widget, you can only get the currently-stored representation. e.g. if theImage.storageType
isImage.Type.Pixbuf
, then you can callImage.getPixbuf()
but notImage.getStock()
. For empty images, you can request any storage type (call any of theget
functions), but they will all returnnil
values.Declaration
Swift
public enum ImageType: RawRepresentable
-
Whenever a container has some form of natural row it may align children in that row along a common typographical baseline. If the amount of verical space in the row is taller than the total requested height of the baseline-aligned children then it can use a BaselinePosition to select where to put the baseline inside the extra availible space.
See moreDeclaration
Swift
public enum BaselinePosition: RawRepresentable
-
Represents the orientation of widgets and other objects which can be switched between horizontal and vertical orientation on the fly, like
See moreToolbar
orGesturePan
.Declaration
Swift
public enum Orientation: RawRepresentable
-
Undocumented
See more
-
Undocumented
See more
-
Built-in stock icon sizes.
See moreDeclaration
Swift
public enum IconSize: RawRepresentable
-
Describes which edge of a widget a certain feature is positioned at, e.g. the tabs of a Notebook, the handle of a HandleBox or the label of a Scale.
See moreDeclaration
Swift
public enum PositionType: RawRepresentable


Hypertext Markup Language (HTML) is the most common language used to create documents on the World Wide Web. HTML uses hundreds of different tags to define a layout for web pages. Most tags require an opening <tag> and a closing </tag>.
Example: On a webpage, this sentence would be in bold print.
Below is an example of a very simple page:
This is the code used to make the page:

<TITLE>Your Title Here</TITLE>

Link Name
is a link to another nifty site

This is a Header

This is a Medium Header

Send me mail at <a<EMAIL_ADDRESS><EMAIL_ADDRESS>
 This is a new paragraph!

 This is a new paragraph!

 This is a new sentence without a paragraph break, in bold italics.


The Bluffer is mugged in BA and how to report a crime in Irish
by Robert McMillenXin chào to everyone as the Bluffer is back from another unforgettable holiday in Vietnam - more about that soon!
Some people are neirbhíseach - nervous when they go away on holidays, fearing they may be mugged or robbed or take sick thousands of miles away from home - but not the Bluffer.
Robáladh uair amháin é - he was robbed once, in Buenos Aires, but d’éirigh leis a chártaí a fháil ar ais - he managed to get his cards back – that’s another story for another day – so he ended up losing a few hundred peso ie not very much.
It’s time now to move to a parallel universe where no-one in Ireland understands English and everyone speaks Irish.
Say you were robbed and had to report an coir - the crime, to your local Robo-Garda.
Robáil is a robbery so you would say to Garda or PC Plod robáladh mé - I’ve been robbed.
The officer would then lick the end of his pencil and start to take down your particulars.
He might ask you cad é a goideadh? - What was stolen? and you can explain it was your sparán - your purse, or your guthán póca - mobile phone or airgead - money.
An bhfaca tú cé a rinne é? - did you see who did it? is usually a superfluous question as pickpockets move with the poise and alacrity of ballet dancers as they separate you from your belongings.
You might also be asked an raibh finnéithe ar bith ann? Were there any witnesses?
This, of course can all be a lot to take in as you will no doubt be distraught by the horrific event, even when no violence was used but generally, na péas - the police are very kind and understanding as the Bluffer explained.
“The lady who helped me out in BA – there was a special office to help stricken tourists at the St Telmo market we were visiting at the time – was very kind but the chances of me getting my wallet back was always going to be less than zero.
“Then, go tobann - suddenly, fuair mé ríomphost - I got an e-mail from a girl who said she had found my wallet, without the money but with all the cards, and if I wanted to collect it, I could call to her work, a hospital clinic in Rosario, a 45-minute taxi drive away. It was nearly meán oíche – midnight.
“The Clash played in my head. Should I stay or should I go?
I went. And met a lovely nurse who had been walking with her boyfriend when she saw my wallet on the ground. It contained my sonraí teagmhála - contact details which she used to e-mail me and so I was re-united with my cártaí creidmheasa - credit cards.
“There are kind people all over the world!”
It might be misplaced but the Bluffer feels sábháilte - safe wherever he goes.
Having said that, he would never take a risk with is own or others’ safety.
Chuaigh sé sa seans - means he took a risk but safety is always the main priority.
He’s just back from walking the streets of Hanoi, Hoi An, Nha Trang and Saigon and never once felt threatened. So here’s to more adventures!
CÚPLA FOCAL
neirbhíseach (nerveeshakh) - nervous
Róbáladh uair amháin é (rawlaloo oor awine ay) - he was robbed once
d’éirigh leis a chártaí a fháil ar ais (jeeree lesh a khartee a iyl er aash ) - he managed to get his cards back
an coir (un kir) - the crime
róbáil (rawbaal) - a robbery
robáladh mé (rawbaaloo may) - I’ve been robbed
cad é a goideadh? (cadge ay a gudge-oo) - What was stolen?
sparán (sparaan) - a purse
guthán póca (goohaan pawka) - mobile phone
airgead (aragid) - money
An bhfaca tú cé a rinne é? (an waca too cay a rin ay) - did you see who did it?
an raibh finnéithe ar bith ann? (an roe finyaha ar bee un) Were there any witnessess?
na péas (ne payss) - the police
go tobann (gaw tuban) - suddenly
fuair mé ríomphost (foor may reeoofust) - I got an e-mail
meán oíche (maan eeha) - midnight
sonraí teagmhála (sonree chagwaala) - contact details
cártaí creidmheasa (cartee credge-vasa) - credit cards
Chuaigh sé sa seans (khooee shay sa shanss) - he took a risk


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Olson Camera Stemming from my frustration with current equipment-heavy professional video solutions and control-light consumer video solutions, I created a concept for a control-centric prosumer point-and-shoot video camera. 2014 Non-commercial Modeled in Solidworks, rendered in KeyShot. -
Fujifilm X100 Lens Shade 3D printed lens shade for the Fujifilm X100. 2014 Non-commercial ABS plastic. -
Andover Community Skatepark Expansion Drawings Drawings of the concrete addition for the Andover Community Skatepark. These drawings were used for fundraising and promotion. This skatepark addition was constructed summer 2010. 2012 Andover Youth Services Modeled in Sketchup. -
Candor Headphones Studio headphones concept. 2012 Non-commercial. Modeled in Solidworks, rendered in Keyshot.


This is the course website for IS445(AOG) in Fall 2020
Time to start talking about interactivity in more detail!
During class today, we will explore creating interactive visualizations using bqplot. For next week, we will discuss the taxonomy of interactive visualizations and how we can use that understanding to better guide our construction.


Brook Miller is a data analyst in Orlando, Fl. He is currently completing a data analytics program through the University of Central Florida.
Brook's primary interest is in natural language processing


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@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import controllers.register.individual.routes._ @this( main_template: MainTemplate ) @(draftId: String, isTaxable: Boolean)(implicit request: Request[_], messages: Messages) @main_template( title = messages("otherIndividualsInfo.5mld.title") ) { @components.back_link() @components.heading_with_caption("otherIndividualsInfo.5mld")
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