The Learning Process
#031: The Learning Process
- Audience Level
- Novice
- Category
- 25 minute talk
- Description
- I get a lot of questions from people looking to get into software development about how to get started in software, what programming language to learn. This talk would try and answer most of the questions as well as clear some common misconceptions related to the learning process in software development
- Abstract
-
Software development is currently the most sought after skill/profession in the world at the moment. It is also the easiest to transition to because unlike traditional professions because all that is required is just a PC and Internet connection. But make no mistake, just because it is easy to transition to, do not necessary mean it is easy to master. Before deciding to transition into software development/programming, It is important to know what to expect and how to go about it. Some of the things you need to know are stated below
- You are not going to be a professional immediately. This doesn’t mean you can’t produce useful software.
- Most of the errors you would run into why learning are syntax errors.
- You need to ask for help when stuck. This cannot be overemphasized.
- If you are learning by watching a video tutorial or reading a blog post, do not expect to get the implementation on first try. You would have to go through the same material at least twice.
- Avoid picking programming books when transitioning into programming for the first time. Make use of coding sites and watch videos. There is a reason why it is said that a picture is worth more than a thousand words. Books only make sense after understanding some initial concepts.
- If you can find others that are willing to learn with you please do. Learning programming is difficult and it is always good to have friends around you when learning.
- You don’t need to learn all the constructs to build a useful application. What is important is having a simple project to base your learning on. Handle the simple case first. Don’t think about the big picture i.e how the whole application would work. Just focus on the current problem in front of you for that point in time.
- If you are finding it difficult to come up with the right questions, Google away what you are thinking. If it is long, try making it shorter. If you run into exceptions/errors, copy and paste it directly in google
- Most if not all the problems you would run into while learning are readily available online.
- Focus on one thing when getting started. This might mean one programming language, one framework etc. Don’t try and juggle multiple languages when getting started.
- Notes
-
Slides for the talk can be found at https://speakerdeck.com/gbozee/the-learning-process
- Documents
- No supporting documents attached to this proposal.
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Ahead Of The AMAs Shania Twain Says ‘I’m A Big Supporter’ Of Taylor Swift
by Corey AtadShania Twain is on Taylor Swift’s side over her dispute with Big Machine Label Group.
Ahead of her upcoming performance at the 2019 American Music Awards, the Canadian singer spoke with USA Today and was asked about Swift’s claim last week that she was being barred from performing her older hits at the awards show.
“I don’t know a lot about the details of that, but I’m a big supporter of Taylor,” Twin said. “I think she’s just incredibly brilliant. I haven’t had a chance to talk to her about it and I don’t really know where everybody is coming from, but I’ll always support the artist and their access to their own music. It seems a bit ridiculous to not have that.”
Swift has since been granted permission to perform all of her songs for her AMAs medley.
Twain also revealed her one wish for the AMAs: “I hope I get a hug in with Post Malone – that would be really great. Just a quick hug and ‘congratulations.’ I love him, and I’ve been such a big fan for a long time.”


apalis.Handler¶
-
class
apalis.
Handler
(obj, affinity=None, param=None)¶ Saves the object into a forked process and provides an interface to interact with it.
- Parameters
- obj :
Object to be saved in a forked process.
- affinityint, optional
cpu affinity of the process. This is usefull when the processed functions take between 1ms and 10ms to complete, by default None
Examples
>>> class A: >>> def f(self, x): >>> return x >>> >>> h = apalis.Handler(A()) >>> token = h.f(5) >>> token() 5
Methods
multiple_run
(tasks)Executes several tasks.
run
(tasks)Runs the tasks and directly returns the ouputs.
single_run
(task)Executes a single task.
-
single_run
(task)¶ Executes a single task.
- Parameters
- taskdict
Dictionary contatining the required fields to execute a function, get an attribute, etc.
- Returns
- Token
A token is returned. If it is called the main process will wait until the child process has returned the outputs of the function.
Examples
>>> h.single_run([{'name': '<function_name>', 'args': args, 'kwargs': kwargs, 'mode': 'run'}])
-
multiple_run
(tasks)¶ Executes several tasks.
- Parameters
- taskdict
Dictionary contatining the required fields to execute a function, get an attribute, etc.
- Returns
- Token
A token is returned. If it is called the main process will wait until the child process has returned the outputs of the function.
Examples
>>> tasks = [] >>> tasks.append({'name': '<function_name>', 'args': args, 'kwargs': kwargs, 'mode': 'run'}) >>> tasks.append({'name': '<attribute_name>', 'mode': 'get_attr'}) >>> h.multiple_run(tasks)
-
run
(tasks)¶ Runs the tasks and directly returns the ouputs. It is faster than multiple_run since it does not need to deal with Tokens.
- Parameters
- taskslist(dict)
List of tasks.
- Returns
- list
List with the output of the tasks.
Examples
>>> tasks = [{name': '<function_name>', 'args': args, 'kwargs': kwargs, 'mode': 'run'}, >>> taks += {name': '<attr_name>', 'value': 5, 'mode': 'setattr'}] >>> h.run(tasks) [result, attr]


Flow Field Following is a method that requires implementation of an object which divides space into tiles.
Every tile has its own vector. When the vehicle is currently positioned on a certain tile,
this tile returns its vector, which becomes a new target for Seek.
The orange vector sticking out of the center of Vehicle is a visualization of the desired velocity and the violet one shows the current velocity vector. The green vector is a flow (a vector) of a tile that the vehicle is occupying right now.
In order to change a vector of a tile use the left mouse button in the tile's space. This action will move the vector to the direction of your cursor.
The orange vector sticking out of the center of Vehicle is a visualization of the desired velocity and the violet one shows the current velocity vector. The green vector is a flow (a vector) of a tile that the vehicle is occupying right now.
In order to change a vector of a tile use the left mouse button in the tile's space. This action will move the vector to the direction of your cursor.
Vehicle position x:
y:
velocity:


Every cup of our quality artisan coffee starts with locally sourced, hand picked ingredients. Once you try it, our coffee will be a blissful addition to your everyday morning routine - we guarantee it!
When you walk into our shop to start your day, we are dedicated to.
When you walk into our shop to start your day, we are dedicated to.
When you walk into our shop to start your day, we are dedicated to.
When you walk into our shop to start your day, we are dedicated to.


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This shōdan represents the dramatic highlight of the first act, recounting heroic battles and introducing the play’s first dance-to-text section. The poetic text, which includes the shite's three ageha lines, is set in hiranori. It is sung by the jiutai, who speaks for the boy, and accompanied by the percussion instruments in strict rhythmic setting, and the nohkan with three non-congruent patterns. These bring the Intermedia Index almost to its highest possible value. The points of intermedia interest are in the music articulating the narrative’s meaning and structure, and the use of mimetic movement illustrating the narrative while providing a gradual formal build-up.
The text tells the story of first century Yamato-dake, the son of legendary Emperor Keikō, and the exploits of his sword Kusanagi no tsurugi. Based on the narrative, the kuse can be divided into three parts. The shite remains kneeling in center stage during the first two. He performs mimetic kata during the third part, providing the text, that recounts the exploits of the Kusanagi no tsurugi, with a visual representation.
The first two parts develop its narrative through stages, deliniated by the shite's salient three ageha and two entrances of the nohkan. In the static first part, small and rare movements take on particular significance.
The first lines of text introduce Yamato-dake. It refers to his journey that took him East, where . The nohkan’s first entrance highlights this exploit with the naka no takane pattern. After learning that Yamato-dake yearned to come home, the music slows down as the shite recalls being kept away by battles in his .
The tempo picks up as the jiutai invokes images of with gory details, heightened by the nohkan’s second entrance and the takane mi kusari pattern. As the description concludes, the boy makes his very first move as he to face Munechika, thus reminding the audience that this story is told for his sake. A slow-down and transitional uchikiri prepare for an introduction to the next battle.
We learn at the beginning of the second part, that at the time, the autumnal colors were fading away. The shite, who now stands for Yamato-dake, slides to the right, grabs his forelock, rises to his knee, and at the fading fall colors. But the circumstances will soon become dire. Right after the second ageha, in which Yamato-dake recounts being , we learn that the enemies set the grass around him on fire. His shows him assessing the danger. The intensity of the moment is underscored by the nohkan’s hishigi takane mi kusari pattern, its last entrance. A moment later, as the jiutai refers to the “enemies’ beating war drums”, his , suggests that the warrior is considering his options. Finally, as the jiutai refers to “…the towering flames as the attack blazed forward," Yamato-dake, about to draw his sword, assesses the situation for the last time as he and faces front, then right.
The kuse’s third part showcases Yamato-dake’s sword prowess. Helped by the kotsuzumi's patterns, which incorporate up-beat strokes, the overall tempo is faster than the preceding section, and the mimetic dance reaches its peak.
As the shite sings “the ”, he uses his fan as a sword, pointing it straight in front of him. He then stands up and takes four steps towards the strong Square 4, where he performs two standard kata, often used at the beginning of a dance: Forward Pointing and Open-retreat. Then, he provides the line “As it mowed down the grasses in all directions”, with a visual representation, by lashing his fan , still using it as a sword.
As he performs a sequence of Four Stamps, the shite Opens the Fan, marking the start of the dance’s high point. He takes a series of steps that bring him from front to back-stage (Square 8), temporarily . From this point on, the shite rebuilds momentum by performing a Scooping Point that carries him back to the strong Square 4 where he performs a Right Circlet to mimic the words “”.
Upon hearing the words “”, he moves to the very strong Square 3, glances Left-Right-Left-Right-Down at the enemy, and uses his fan to aim the flames towards the barbarians, Pointing to the Right as he is Taking the Corner. Then, he starts a Large Circle to the Left, but stops on Square 5 to perform a Scooping Point, followed by a Single Right Stamp that marks , announcing the dance’s imminent conclusion. As the jiutai sings “So in later years”, the speed drops, suggesting that the narration of Yamato-dake’s exploits is over. On his way back to Square 8, the shite Closes his Fan, confirming that the dance is coming to an end, and upon reaching his destination, he concludes the dance commemorating Yamato-dake’s exploits with the standard sequence of Scooping Point, and an Open-retreat.
The words “” confirm the narration of Yamato-dake’s story is over. The return to the present also implies that the jiutai is now speaking for the young boy. As it intones “And now, this auspiciously omened blade that you must forge,”, the tempo gradually increases to underline the urgency of the task ahead.
Finally, the boy turns towards Munechika and but, feeling he must stress his point more strongly, he turns back and goes to Square 1. After pivoting to face Munechika, he performs a Scooping Point and, while insistently pointing his fan, he Takes Six Steps towards the swordsmith just as the jiutai sings “Munechika, heir to a house of proud tradition, set your heart at rest and go home now.” By this point, the tempo has gradually slowed to its lowest point, and the boy, standing center-stage, concludes the kuse with an Open-retreat and Kneels. He finishes the shōdan at the same place and in the same position as he started.


Amanita muscaria var. formosa & Cannabis
| DOSE: T+ 0:00 |
3 caps |
oral |
Amanitas |
(dried) |
| T+ 0:30 |
1 cig. |
smoked |
Cannabis |
(plant material) |
Me and a friend where bored…I had found a couple yellow orange fly agarics the day before and dried them at 250 degrees. I took one and my brother took one with no effects.
I suggested to my bored friend that we look for more and up the dose a little. He agreed and we went for a short hike. It didn't take us long as we found a ring of yellow fly's around a pine tree…we picked six of them and dried them out the same way as the first.
We ate 3 each at about 10 PM or so at a friends house…we got some pot and about a half hour later my stomach was a little upset. We went outside and sparked a blunt, that helped. We then went to a friends house to sleep over. On the bike ride there I noticed a little energy burst.  My friend didn't have the same experience just yet.
Later…about 12 or so, I started to sweat, it lasted for about a half hour and got my shirt pretty wet. My friends mouth then started to salivate. My friend didn't sweat more than usual the whole time…that goes to show the different effects on different people.
I turned on some music and I drifted to a relaxed state. I then started to get floaters in my vision…I asked my friend if he had the same and he said he did. The floaters were like blurry spots, with the lights on or off. If we looked at something straight on we couldn't see what we were looking at. A little later the spot grew a little bigger and colors started growing in the middle…first it was blue…then blue and yellow…then blue and red in different patterns…at one point the pattern resembled a cartoon face of an old man…I asked my friend what he was seeing and he explained the same sort of thing. The visuals didn't last too long…it wasn't difficult to speak to each other…we could think clearly…no confusion…no loss of equilibrium…no memory loss.
After the trip we both enjoyed a peaceful sleep.
I enjoyed my experience alot and the only bad effect was the sweating, which my friend didn't even get. I actually liked the increased salivation. I want to do more next time, and hopefully extend the visual stage by doing so.
Peace!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Pros of Javascript:
After reading a few articles on the web, most sources point to the big benefits of Javascript being the fact that it's the language of broswers (does it get any better than this), does have server side support (not only for the front-end developers), its lightening fast speed, and wealth of libraries. To be honest, the last two bullet points don't mean much to me at this stage. I can understand how speed and robust libraries is of value as they aren't constraints in the work flow. However, I've not experience the benefit firsthand so it's more of a benefit to be seen. Quora had quite a few threads on Javascript and here is the link to the one I found most helpful: Quora on Popularity of Javascript.
In summary the pros of Javascript are:
- Language of the browsers
- Does have server side side support
- Speed
- Libraries
Cons of Javascript:
There are a few things that immediately pop into my mind when I think of cons of Javascript. These cons are the syntax of the language seems a bit weak. It's almost as if rules are not really rules, but more suggestions in the language which can be confusing for a n00b. From research, it seems that different browsers will render Javascript differently, which creates headaches as there isn't one consistent standard. Another commonly cited con is a security issues although I haven't really bumped into this issue, but I do realize how this could be a big problem. Here is a link to a Quora article that helped further my understanding: Quora on limitations of Javascript.
In summary the cons of Javascript are:
- Poorly structured syntax - Can I even claim that with my coding knowledge?
- Inconsistent rendering across browsers
- Weak security


The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested. The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested.
Lorem ipsum
Dolor signutos
The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested. The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested.
Lorem ipsum
Dolor signutos
The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested. The standard chunk of Lorem Ipsum used since the 1500s is reproduced below for those interested.


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Bachelor of Science in Environmental Economics and Policy, GPA: 3.428
Associate of Arts in Natural Sciences, Cumulative GPA: 3.87 Honors: Dean’s List (all semesters), Administrative Honors
UCPath is a project launched by the University of California to modernize its current payroll system, which is nearly 40 years old. UCPath introduces new technology that will unify and standardize the payroll, benefits, and human resources for all UC employees.


This is component zero.
A good link to component 1
A good link to component 2
A bad link to component 3 (which does not exist)
A bad link to component 1 from the wrong relative root, which matches the component id
A good link to component 1 from the absolute root
A good link to an external address


File downloads are currently available in the vertx rest channel and servlet rest.
First, producer
1. Download normal files
return new File(......);
2. Download temporary files
In this scenario, you need to create temporary files based on the request parameters dynamically. After the download is complete, you need to delete the temporary files.
return new FilePart(file).setDeleteAfterFinished(true);
3. Download org.springframework.core.io.Resource
Because the resource does not necessarily mean file download, you need to identify this file download scenario by swagger annotation (@ApiResponse).
Take ByteArrayResource as an example:
@GetMapping(path = "/resource")
@ApiResponses({
@ApiResponse(code = 200, response = File.class, message = "")
})
public Resource resource() {
......
return new ByteArrayResource(bytes) {
@Override
public String getFilename() {
return "resource.txt";
}
};
}
In the above example, because ByteArrayResource does not have the concept of a file name, you need to implement the resource's getFilename method, or you can wrap it with ResponseEntity:
@GetMapping(path = "/resource")
@ApiResponses({
@ApiResponse(code = 200, response = File.class, message = "")
})
public ResponseEntity<Resource> resource() {
......
return ResponseEntity
.ok()
.header(HttpHeaders.CONTENT_TYPE, MediaType.TEXT_PLAIN_VALUE)
.header(HttpHeaders.CONTENT_DISPOSITION, "attachment;filename=resource.txt")
.body(resource);
}
4.Download InputStream
Because InputStream does not mean file downloading for sure, it needs to be annotated by 'swagger annotation' (@ApiResponse). This is a file download scenario.
In some scenarios, resources are not stored locally.
return ResponseEntity
.ok()
.header(HttpHeaders.CONTENT_TYPE, MediaType.TEXT_PLAIN_VALUE)
.header(HttpHeaders.CONTENT_DISPOSITION, "attachment;filename=resource.txt")
.body(stream);
After the download is complete, ServiceComb will automatically close the stream, and developers don't have to pay attention
5. File type determination
As long as the HttpHeaders.CONTENT_TYPE is not set directly via ResponseEntity, ServiceComb will try to automatically determine the file name suffix in File, Part, and Resource.
ServiceComb uses java's mime type mechanism for file type determination. If the file suffix in the business scenario cannot be identified, ServiceComb will default to application/octet-stream.
If this does not meet the requirements, assuming the file suffix is, and the expected file type is application/file-xyz, any of the following methods can be resolved:
1) Extend via Java's mime type mechanism
In the META-INF directory, create a mime. Types file with the contents:
application/file-xyz xyz
2) Specify by Part in the business code
return new FilePart(null, file).contentType("application/file-xyz");
3) specified in the business code by ResponseEntity
return ResponseEntity
.ok()
.header(HttpHeaders.CONTENT_TYPE, "application/file-xyz")
.body(……);
.body(...);
6.File name
As long as HttpHeaders.CONTENT_DISPOSITION is not set directly via ResponseEntity, ServiceComb will try to generate HttpHeaders.CONTENT_DISPOSITION through the file names in File, Part, and Resource. Assuming the file name is file.txt, the generated data is as follows:
Content-Disposition: attachment;filename=file.txt;filename*=utf-8’’file.txt
Not only the filename is generated, but also filename* is generated. This is because if there is Chinese, space, and filename correctly in the file name, i.e., chrome is fine, but firefox directly treats the string after the encoding as a text. The name of the item is used directly. Firefox only decodes filename* according to [https://tools.ietf.org/html/rtf6266] (https://tools.ietf.org/html/rtf6266).
If Content-Disposition is set directly in the business code, you need to handle the problems supported by multiple browsers.
Second, Consumer
The consumer side uses org.apache.servicecomb.foundation.vertx.http.ReadStreamPart to process file downloads.
1. Transparent RPC
public interface ......{
ReadStreamPart download1(...);
ReadStreamPart download2(...);
}
2.RestTemplate
Take get as an example:
ReadStreamPart part = restTemplate.getForObject(url, ReadStreamPart.class);
3. Read data from ReadStreamPart
ReadStreamPart provides a set of methods to save the data stream as local data:
org.apache.servicecomb.foundation.vertx.http.ReadStreamPart.saveAsBytes()
org.apache.servicecomb.foundation.vertx.http.ReadStreamPart.saveAsString()
org.apache.servicecomb.foundation.vertx.http.ReadStreamPart.saveToFile(String)
org.apache.servicecomb.foundation.vertx.http.ReadStreamPart.saveToFile(File, OpenOptions)
note:
-
When the ReadStreamPart instance is obtained, the file content is not downloaded. The save or other methods is called to start reading the file data from the network.
-
If you use saveAsBytes, saveAsString, the data is directly stored in the memory; if the downloaded file is large, there will be a risk of memory explosion.
-
The save series method returns all CompletableFuture objects:
* If you want to block waiting for the download to complete, you can use future.get() * If asynchronous callback processing is performed through future.whenComplete, be aware that callbacks occur in network threads, and you must follow the reactive thread rules.


http://www.speakers-podium.com/susangranger.
Susan Granger's review of "DETROIT ROCK CITY" (New Line Cinema)
It's 1978, and four Cleveland teens are trying to crash a KISS concert in Detroit to see their idols after their tickets are burned by a religious fanatic parent who swears that the group's name is an acronym for "Knights in Satan's Service" and is a member of M.A.T.M.O.K. (Mothers Against the Music of KISS). Edward Furlong, Giuseppe Andrews, Sam Huntington, and newcomer James De Bello are die-hard KISS fanatics and they're determined to attend the concert - whether it involves winning front-row center seats in a radio contest, spiking a teacher's pizza with hallucinogenic mushrooms, stealing a car, selling their bodies, robbing a convenience store, or sneaking by security.  While the KISS members are prominently listed as part of the cast and Gene Simmons is a producer, they only appear for a couple of minutes on-stage amid pyrotechnics. But Mrs. Gene Simmons - better known as Playboy bunny Shannon Tweed - does drop in as a romantic interest, along with Natasha Lyonne and Melanie Lynskey. (The latter two are named 'Christine' and 'Beth,' which are names of KISS songs.) Carl V. Dupre's screenplay seems to be lifted piecemeal from a myriad of coming-of-age movies and has so many hateful caricatures of Catholicism as to be quite offensive. Vulgar mockery of any religion is not funny.  And do you really want to watch Edward Furlong vomiting? On the Granger Movie Gauge of 1 to 10, "Detroit Rock City" is a lame, loud 1 - one of the most feeble pictures of the year so far.
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27 unique kitchen gifts under $50 that any home cook or baker will love, Business Insider - Business Insider Singapore
by Remi Rosmarin, Business Insider US- Whether it’s for Mom, Dad, a friend, or a coworker, small kitchen accessories make nice gifts for just about everyone on your list.
- From practical additions like a serving board to quirky gadgets like a s’mores maker, there are plenty of great kitchen gifts out there, and they won’t cost you much.
- We rounded up 27 fun and practical kitchen accessories that make great gifts, and they’re all $50 or less.
- Check out all of our 2019 holiday gift guides for more inspiration.
Whether they have a cookware collection that rivals Martha Stewart’s or their most prized kitchen accessory is their drawer of takeout menus, any friend or family member on your gift list will appreciate a nice new kitchen tool to add to their space.
For a solid gift you can bank on them loving, check out our list of kitchen accessories that’ll please kitchen pros and newbies alike. It runs the gamut from practical to quirky and everything in between, plus you can find all of it for $50 or less.
Most of these items are available with two-day shipping if you have Amazon Prime, so don’t stress too hard about if shopping on a time crunch – just remember that the sooner you order, the better your chances of a timely arrival.
The top 5 best affordable kitchen gifts:
- A gadget that cooks eggs with little time and effort
- A ravioli mold for easy homemade pasta
- A sleek cheese board for serving and snacking
- A s’mores maker to satisfy their sweet tooth
- An old fashioned mortar and pestle
Keep reading for 27 great kitchen gifts under $50:
A stovetop espresso maker
Bialetti Moka Express Stovetop Coffee Maker (1-cup), $19.49, available at Amazon
For the coffee-loving types who have already tried just about every way to get their morning brew, give them this Moka pot. Bialetti actually invented the first stovetop coffee maker in the early 20th century and, even after all these years, the original model is our favorite option out there.
A cheese melt pan
Cuisinart BBQ Cheese Melt Pan, $9.99, available at Amazon
If they’re not already melting cheese on all of their favorite foods… they should probably start. Help them out with this pan that makes the ooey, gooey, cheesy topping of their dreams a reality.
A smoothie set
Chef’n Smoothie Set, $34.95, available at Williams Sonoma
This set has all the little prep tools they never thought they needed. A strawberry huller, citrus juicer, and kale and green stripper will help them quickly turn their favorite fresh produce into a delicious smoothie.
A rapid egg cooker
Dash Rapid Egg Cooker, $14.99, available at Amazon
Whether they like them hard boiled, poached, or scrambled, this rapid egg cooker lives up to its name – it makes cooking eggs way faster and easier than ever before.
A foldable cutting board
Joseph Joseph Foldable Plastic Cutting Board, 9.83, available at Amazon
They probably already have a cutting board, but this one is a seriously smart upgrade. With a sturdy handle and sides that fold up when squeezed, this board will help them seamlessly chop and then pour into a pot or pan, without dropping any of the good stuff along the way.
Mickey Mouse ramekins
Le Creuset Disney Mickey Mouse Ramekins (set of 2), $50, available at Williams Sonoma
Good ramekins are useful for baking and serving just about any dish – and these Mickey Mouse ones are some of the cutest out there. They’re adorable, but they’re also seriously durable stoneware that’ll last them a lifetime.
A 3-in-1 avocado slicer
OXO 3-in-1 Avocado Slicer, $9.95, available at Williams Sonoma
The only tool avocado lovers need – it can halve, pit, and slice an avocado all in one go. Plus, it’s all plastic so they don’t have to worry about any avocado-related injuries.
 Any frozen pizza will be given an upgrade when cooked on this pizza stone.
A popcorn maker
Whirley Stainless-Steel Induction Popcorn Maker, $49.95, available at Williams Sonoma
A vintage-style stovetop popcorn maker that you crank by hand – this thing is the real deal. It’s a little more effort, but they’ll never want to return to the microwave stuff after this.
A ravioli mold
Ravioli Mold with Roller, $26.95, available at Williams Sonoma
They could just pop some frozen ravioli into a pot of boiling water and call it a day, but the homemade version is undeniably more fun (and delicious) to make. This mold and roller set is a simple way to get them started.
A mini rice cooker
Dash Mini Rice Cooker, $19.99, available at Amazon
This appliance is small, but mighty – it can make rice, stews, soups, pasta, and more. The two-cup capacity is good for making small meals and won’t take up too much of their precious counter space.
A s’mores maker
S’mores Maker, $49.95, available at Williams Sonoma
When it’s too cold to head out to the fire pit, they’ll appreciate this thoughtful set with all of the tools they need to get outdoor quality s’mores – without ever having to leave the kitchen.
A spiralizer
Paderno 4-Blade Spiralizer, $39.96, available at Williams Sonoma
Zucchinis, sweet potatoes, carrots, beets – anything can be a noodle if you really believe, or if you use a spiralizer. Plus, these spindly shapes make eating veggies a lot more fun.
A splatter screen
Silicone Splatter Screen, $24.95, available at Williams Sonoma
Save them from one of the perils of frying foods at home: the dreaded hot oil splatter. This screen has holes to release steam, so everything stays crunchy, but keeps all of the grease in the pan as it’s intended to be.
A state pride dish towel
State Dish Towel, $20, available at Uncommon Goods
Add some sentimentality to their kitchen with a dish towel of their home state. The bright towels include iconic names, landmarks, and images of what makes their favorite state so special.
A sushi kit
DIY Sushi Kit, $29.95, available at Williams Sonoma
If they love sushi, and a good challenge, they’ll love trying to make their own rolls with this set. It comes with tools and ingredients like rice, nori, and wasabi powder so they can recreate recipes their favorite sushi spot from the comfort of their own kitchen.
A molcajete
Molcajete, $39.96, available at Williams Sonoma
An old-fashioned mortar and pestle set is the key to crushing and grinding everything from fresh herbs and spices to making perfectly smooth guacamole.
An Oreo cup
Dunky Cup, $19.95, available at Amazon
A thoughtful gift that shows you understand their Oreo obsession, this cup helps them get the perfect cookie dunk every time. It also comes with a pack of Oreos for good measure.
A bento box
Modern Large Bento Box, $35, available at Food52
This bento box is made up of multiple stackable components. It’s dishwasher- and microwave-safe, and surely an upgrade to the brown paper bag.
A microwave grill
Microwave Grill, $40, available at Uncommon Goods
If a small kitchen or lack of an outdoor space is stopping them from grilling their favorite foods, they’ll love this. They can put their favorite veggies in between the two plates and just pop in the microwave for perfect grill marks every time.
A serving board
Anolon Marble/Teak Serving Board, $39.99, available at Amazon
This serving board makes a beautiful backdrop for charcuterie boards, appetizers, and more. The piece even made it onto Jennifer Lawrence’s Amazon wedding registry.
A martini shaker and glasses
Libbey Capone Entertaining Set, Shaker and 4 glasses, $36.47, available at Amazon
Amateur and experienced mixologists alike will get a kick out of shaking up their own cocktails, and presenting them in these classy martini glasses.
Fancy olive oil
Alive Extra Virgin Heirloom Olive Oil, $37, available at Brightland
A drizzle of good olive oil adds a flavorful punch to any dish.
A cookie spoon pan
Cookie Spoon Pan, $18, available at Uncommon Goods
Indulge their sweet tooth with this innovative pan that lets them turn their favorite cookie doughs into spoon-shaped cookies. They can use them to scoop ice cream, stir their coffee, or dip into the Nutella jar – every use is equally delicious.
An automatic pan stirrer
Automatic Pan Stirrer with Timer, $25, available at Uncommon Goods
Help them save time and elbow grease with this little gadget. All they have to do is place it in the pan, set the timer, and let it whiz around so they can do other kitchen tasks while their sauce is stirred for them.
A pancake pan
Nordic Ware Silver Dollar Pancake Pan, $34.95, available at Crate & Barrel
This pan lets them cook seven silver dollar pancakes at once and ensures that each one is a perfectly round circle.
A food scale
Bamboo Food Scale, $29.95, available at Crate & Barrel
If they’re serious about baking, they’ll appreciate this food scale. It’ll help them make sure they’re using the exact measurements every time. Plus, the bamboo and sleek LED display make it look nice in their space.


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1. Download a REST pdf from Pure Support, choose the file i.e. REST_API_1.13.pdf, then wait a bit for it to convert.
2. To use "Try it Now" Input your array IP or Hostname i.e. <IP_ADDRESS> etc.


Pay as you go by minutes!
Just update plan and pay correspondingly
SLA is guarded by blockchain. No hassal for dispute!
Start with whatever make sense to you.


Ocean [aj57], ocean, in The Inner Sea
Last updated Turn #96
Ocean [aj57], ocean, in The Inner Sea
------------------------------------------------------------------------
11: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
11: 22% damaged, arrived from Ocean [aj58], owner:
11: Phantasm [n4b], Imperial Empire, duke, with eight sailors,
11: one riding horse
12: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
12: 22% damaged, departed for Ocean [aj56].
Routes leaving Ocean:
North, to Ocean [ai57], 6 days
East, to Ocean [aj58], 6 days
South, to Ocean [ak57], 6 days
West, to Ocean [aj56], 6 days


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002
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Effect Of Area Level Deprivation On Body Mass Index: Analysis Of NZ Health Surveys
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003
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Online Learning For Bayesian Nonparametrics: Weakly Conjugate Approximation
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004
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The Joint Models For Nonlinear Longitudinal And Time-To-Event Data Using Penalized Splines: A Bayesian Approach
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005
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Transfer Regression And Predictive Distributions
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007
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Specification Of GARCH Model Under Asymmetric Error Innovations
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008
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Lattice Polytope Samplers
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009
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Promoting Your R Package
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010
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Incorporating Genetic Networks Into Case-Control Association Studies With High-Dimensional DNA Methylation Data
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011
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Presenting Flexi, A Statistical Program For Fitting Variance Models
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012
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Calendar-Based Graphics For Visualising People’s Daily Schedules
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014
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Bayesian Optimum Warranty Length Under Type-II Unified Hybrid Censoring Scheme
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015
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Adaptive Model Checking For Functional Single-Index Models
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016
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An EWMA Chart For Monitoring Covariance Matrix Based On Dissimilarity Index
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017
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Analysis Of Multivariate Binary Longitudinal Data: Metabolic Syndrome During Menopausal Transition
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018
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Test For Genomic Imprinting Effects On The X Chromosome
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019
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A Unified Regularized Group PLS Algorithm Scalable To Big Data
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020
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Bayesian Analyses Of Non-Homogeneous Gaussian Hidden Markov Models
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021
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Clustering Using Nonparametric Mixtures And Mode Identification
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022
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Robust Principal Expectile Component Analysis
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023
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Estimation Of A High-Dimensional Covariance Matrix
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024
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Identifying Clusters Of Patients With Diabetes Using A Markov Birth-Death Process
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025
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R&D Policy Regimes In France: New Evidence From A Spatio-Temporal Analysis
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026
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Adaptive Model Averaging In High-Dimensional Linear Regression
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027
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Fluctuation Reduction Of Value-At-Risk Estimation And Its Applications
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028
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Spatial Surveillance With Scan Statistics By Controlling The False Discovery Rate
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029
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Genetic Map Estimation Using Hidden Markov Models In The Presence Of Partially Observed Information
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030
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Estimation Of A Semiparametric Spatiotemporal Models With Mixed Frequency
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031
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Vector Generalized Linear Time Series Models
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032
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gridSVG: Then And Now
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033
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Evaluation Of Spatial Cluster Detection Method Based On All Geographical Linkage Patterns
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034
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On Optimal Group Testing Designs: Prevalence Estimation, Cost Considerations, And Dilution Effects
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035
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Evidence Of Climate Change From Nonparametric Change-Point Analysis
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036
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Nonparametric Test For Volatility In Clustered Multiple Time Series
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037
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Bayesian Curve Fitting For Discontinuous Function Using Overcomplete Representation With Multiple Kernels
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038
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Variable Selection Algorithms
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039
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Towards An Informal Test For Goodness-Of-Fit
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040
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Genetic Approach And Statistical Approach For Association Study On DNA Data
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041
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The Use Of Bayesian Networks In Grape Yield Prediction
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042
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Comparison Of Tests Of Mean Difference In Longitudinal Data Based On Block Resampling Methods
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043
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Dissimilarities Between Groups Of Data
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044
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The Uncomfortable Entrepreneurs: Bad Working Conditions And Entrepreneurial Commitment
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045
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Mixed Models For Complex Survey Data
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046
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Intensity Estimation Of Spatial Point Processes Based On Area-Aggregated Data
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047
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How Does The Textile Set Describe Geometric Structures Of Data?
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048
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Selecting Generalised Linear Models Under Inequality Constraints
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049
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Bayesian Structure Selection For Vector Autoregression Model
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050
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Improving The Production Cycle At Stats NZ With RStudio
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051
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An Incomplete-Data Fisher Scoring With An Acceleration Method
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052
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Smooth Nonparametric Regression Under Shape Restrictions
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053
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R – A Powerful Analysis Tool To Improve Official Statistics In Romania
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Statistical Disclosure Control With R: Traditional Methods And Synthetic Data
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055
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Innovative Bayesian Estimation In The von Mises Distribution
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Cluster-Wise Regression Models Combined By A Quasi-Linear Function
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Improvement Of Computation For Nonlinear Multivariate Methods
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Clusterwise Low-Rank Correlation Analysis Based On Majorization
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059
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Empirical Comparison Of Some Algorithms For Automatic Univariate ARMA Modeling Using RcmdrPlugin.SPSS
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Robustness Of Temperature Reconstruction For Past 500 Years
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061
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Estimation Of Animal Density From Acoustic Detections
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Random Search Global Optimization Using Random Forests
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A Max-Type Multivariate Two-Sample Baumgartner Statistic
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Performance Of Bayesian Credible Interval For Binomial Proportion Using Logit Transformation
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Semiparametric Mixed Analysis Of Covariance Model
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Bringing Multimix From Fortran To R
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Geographically Weighted Principal Component Analysis For Spatio-Temporal Statistical Dataset
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Dimension Reduction Strategies For Modeling Bi-Clustered High Dimensional Data
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A Package For Multiple Precision Floating-Point Computation On R
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071
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Simultaneous Test For Mean Vectors And Covariance Matrices In High-Dimensional Settings
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072
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Detecting Change-Points In The Stress-Strength Reliability P(X<Y)
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073
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Two Stage Approach To Data-Driven Subgroup Identification In Clinical Trials
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074
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Adjusted Adaptive Index Model For Binary Response
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Dimension Reduction For Classification Of High-Dimensional Data By Stepwise SVM
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076
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A Simple Method For Grouping Patients Based On Historical Doses
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077
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Real-Time Transit Network Modelling For Improved Arrival Time Predictions
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078
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Ranking Potential Shoplifters In Real Time
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079
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Canonical Covariance Analysis For Mixed Numerical And Categorical Three-Way Three-Mode Data
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080
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Bayesian Survival Analysis Of Batsmen In Test Cricket
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081
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Flight To Relative Safety: Learning From A No-Arbitrage Network Of Yield Curves Model Of The Euro Area
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083
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Rolling Survival Extrapolation Algorithm For Estimating Life Years Lost Among Subjects Exposed To Long-Term Air Pollution
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084
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Analysing Scientific Collaborations Of New Zealand Institutions Using Scopus Bibliometric Data
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085
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Genetic Predictors Underlying Long-Term Cognitive Recovery Following Mild Traumatic Brain Injury
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087
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Symbolic Data Analytical Approach To Unauthorized-Access Logs
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Bayesian Static Parameter Inference For Partially Observed Stochastic Systems
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089
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Interactive Visualization Of Aggregated Symbolic Data
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090
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Measure Of Departure From Marginal Average Point-Symmetry For Two-Way Contingency Tables With Ordered Categories
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091
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Optimizing Junior Rugby Weight Limits
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092
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BIG-SIR A Sliced Inverse Regression Approach For Massive Data
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093
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Local Canonical Correlation Analysis For Multimodal Labeled Data
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094
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Estimating Causal Structures For Continuous And Discrete Variables
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Meta-Analysis With Symbolic Data Analysis And Its Application For Clinical Data
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Deep Learning High-Dimensional Covariance Matrices
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097
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Analysis Of Spatial Data With A Gaussian Mixture Markov Random Field Model
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098
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Imputation Of The 2016 Economic Census For Business Activity In Japan
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099
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SSREM: A Summary-Statistics-Based Random Effect Model To Estimating Heritability, Co-Heritability And Effect Sizes In GWAS Data Analysis
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100
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A Practitioners Guide To Deep Learning For Predictive Analytics On Structured Data
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101
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Pattern Prediction For Time Series Data With Change Points
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102
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Regression With Random Effects For Analysing Correlated Survival Data: Application To Disease Recurrences
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103
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Comparison Of Exact And Approximate Testing Procedures In Clinical Trials With Multiple Binary Endpoints
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104
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Modified Gene Shaving Algorithm - A Dimension REduction And Clustering Method
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105
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Applying Active Learning Procedure To Drug Consumption Data
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106
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Consistency Of Linear Mixed-Effects Model Selection With Inconsistent Covariance Parameter Estimators
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107
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Testing For Presence Of Clustering Effect In Multilevel Model With High-Dimensional Predictors
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108
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High Dimensional Asymptotics For The Naive Canonical Correlation Coefficient
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109
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Factors Influencing On Growth Of Garments Industry In Bangladesh
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111
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New Zealand Crime And Victims Survey: Filling The Knowledge Gap
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112
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Analysis Of Official Microdata Using Secure Statistical Computation System
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Statistical Models For The Source Attribution Of Zoonotic Diseases: A Study Of Campylobacteriosis
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A New Approach To Distribution Free Tests In Contingency Tables
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Bayesian Inference For Population Attributable Measures
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Dimensionality Reduction Of Multivariate Data For Bayesian Analysis
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Computation Of Influence Functions For Robust Statistics
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118
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Analysis Of A Brief Telephone Intervention For Problem Gambling And Examining The Impact On Co-Existing Depression?
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120
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Forward Selection In Regression Models Based On Robust Estimation
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Feature Selection In High-Dimensional Models With Complex Block Structures
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IGESS: A Statistical Approach To Integrating Individual Level Genotype Data And Summary Statistics In Genome Wide Association Studies
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Sparse Estimates From Dense Precision Matrix Posteriors
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Whitebait In All Its Varieties: One Fish, Two Fish, Three, Four, Five Fish.
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Adaptive False Discovery Rate Regression With Application In Integrative Analysis Of Large-Scale Genomic Data
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Computing Entropies With Nested Sampling
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Missing Data In Randomised Control Trials: Stepped Multiple Imputation
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Author Name Identification For Evaluating Research Performance Of Institutes
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Separation Of Symmetry For Square Contingency Tables With Ordinal Categories
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Elastic-Band Transform: A New Approach To Multiscale Visualization
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Bayesian Continuous Space-Time Model Of Burglaries
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Penalized Vector Generalized Additive Models
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Scoring Rules For Prediction And Classification Challenges
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Breeding Value Estimation In Partially-Genotyped Populations
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An Overview Of The Correspondence Analysis Family
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137
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Relationships Between Linguistic Characteristics And The Use Of Māori Loanwords In New Zealand English.
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Including Covariate Estimation Error When Predicting Species Distributions: A Simulation Exercise Using Template Model Builder
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Nonparametric Causal Inference By The Kernel Method
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Sparse Group-Subgroup Partial Least Squares With Application To Genomic Data
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R In Industry – Application On Pipe Renewal Planning
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Covariate Discretisation On Big Data
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R Package For New Two-Stage Methods In Forecasting Time Series With Multiple Seasonality
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Spatial Scan Statistics For Matched Case-Control Data
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Model-Based Clustering For Multivariate Categorical Data With Dimension Reduction
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Talk Data To Me
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An Information Criterion For Prediction With Auxiliary Variables Under Covariate Shift
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LSMM: A Statistical Approach To Integrating Functional Annotations With Genome-Wide Association Studies
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149
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Visualization And Statistical Modeling Of Financial Big Data
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150
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Modeling Of Document Abstraction Using Association Rule Based Characterization
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151
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BIVAS: A Scalable Bayesian Method For Bi-Level Variable Selection
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153
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Enhancing The Flexibility Of Regression Modeling By Liquid Association
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154
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High Mortality Predictions With Lines Or Curves Fitted To Over-Dispersed Exposure-Mortality Data
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A Computational Tool For Detecting Copy Number Variations From Whole Genome And Targeted Exome Sequencing
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Modelling The Distribution Of Lifetime Using Compound Time-Homogenous Poisson Process
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157
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An Advanced Approach For Time Series Forecasting Using Deep Learning
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A Smoothing Filter Modelling Approach For Time Series
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Structure Of Members In The Organization To Induce Innovation: Quantitatively Analyze The Capability Of The Organization
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Statistical Modelling And Analysis Of Cosmic Microwave Background Data
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Joint Analysis Of Individual Level Genotype Data And Summary Statistics By Leveraging Pleiotropy
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162
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Adjusting For Linkage Bias In The Analysis Of Record-Linked Data
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163
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Spline-Based Drift Models For High Temperature Operating Life Tests
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164
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Tolerance Limits For The Reliability Of Semiconductor Devices Using Longitudinal Data
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Statistical Generalized Derivative Applied To The Profile Likelihood Estimation In A Mixture Of Semiparametric Models
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166
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Cross Covariance Estimation For Integration Of Multi-Omics Data
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167
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Regularized Noise-Reduction Methodology For High-Dimensional Data
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Selecting The Number Of Principal Components
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169
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Clustering Of Curves On A Spatial Domain Using A Bayesian Partitioning Model
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Family Structure And Academic Achievements Of High School Students In Tonga
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Hierarchical Structural Component Analysis Of Gene-Environment Interactions
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Towards A Sparse, Scalable, And Stably Positive Definite (Inverse) Covariance Estimator
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Testing For Genetic Associations In Arbitrarily Structured Populations
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Bayesian Temporal Density Estimation Using Autoregressive Species Sampling Models
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175
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Prior-Based Bayesian Information Criterion
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Bayesian Semiparametric Hierarchical Models For Longitudinal Data Analysis With Application To Dose-Response Studies
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Mobile Learning In Teaching Bioinformatics For Medical Doctors
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Wavelet-Based Power Transformation Of Non-Gaussian Long Memory Time Series
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Threshold Determination For The Meteorological Data Quality Control In Korea
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Meta-Analytic Principal Component Analysis In Integrative Omics Application
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Fitting Additive Hazards Model Using Calibrated Weights For Case-Cohort Data
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Three-Dimensional Data Visualization Education With Virtual Reality
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Bayesian Analysis For Fitting Zero-Inflated Count Data With Data Augmentation
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Inverse Regression For Multivariate Functional Data
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Multiple Function-On-Function Linear Regression With Application To Weather Forecast Calibration
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Phylogenetic Tree-Based Microbiome Association Test
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A Study Of The Influence Of Articles In The Large-Scale Citation Network
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Probabilistic Outlier Detection And Visualization Of Smart Meter Data
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Model-Checking For Regressions: A Local Smoothing-Based Global Smoothing Test
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Clustering Of Research Subject Based On Stochastic Block Model
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Latent Variable Models And Multivariate Binomial Data
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My Knee Still Hurts; The Statistical Pathway To The Development Of A Clinical Decision Aid
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Asking About Sex In General Health Surveys: Comparing The Methods And Findings Of The 2010 Health Survey For England With Those Of The Third National Survey Of Sexual Attitudes And Lifestyles
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R For Everything
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Tick-By-Tick Effect On The Inference Of Ultra-High Frequency Data
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A Simple Method To Construct Confidence Bands In Functional Linear Regression
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Space And Circular Time Log Gaussian Cox Processes With Application To Crime Event Data
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Could Do Better … A Report Card For Statistical Computing
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ALTREP: Alternate Representations Of Basic R Objects
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Zen And The aRt Of Workflow Maintenance
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R In Times Of Growing User Base And Data Sizes
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A Bayesian Inference For Time Series Via Copula-Based Markov Chain Models
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203
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Estimating Links Of A Network From Time To Event Data
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204
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E-Learning Courses On Introductory Statistics Using Interactive Educational Tools
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Towards A Big Data CPI For New Zealand
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Investigating Methods To Produce Price Indexes From Big Data
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The Potential Of Web Scraping
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Early Childhood Dental Decay
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Co-expression network of the differentially expressed lncRNAs and mRNAs in ovary tissues in H1 vs H3 ( A) H3 vs H8 ( B). Triangle and circle nodes represent lncRNA and mRNAs, respectively. The nodes with red colour represent up-regulation, whereas those with green represents down-regulation. The dotted line represents the target relationship between two nodes.


Durban beach stabbing mystery: Victims' identities released
KwaZulu-Natal police have released the identities of the woman and man who were found stabbed on the Durban beachfront on Monday.
Fathima Muhammad, a 50-year-old Chatsworth mother of three, sustained multiple stab wounds to her body, while tiler Kresen Chandiah, 24, was found with a kitchen knife sticking out of his chest, Times Live reported.
A plastic and cardboard seller was taking a walk on a Durban sand dune when found the dead couple of Indian origin whose bodies were riddled with stab wounds, News24 earlier reported.
"He found the bodies of an Indian male and female on the sand dunes with stab wounds. The male still had the knife embedded in his chest," police spokesperson Colonel Thembeka Mbele told News24 on Monday.
According to The Mercury, Muhammad's bag and ID were found near the scene. Muhammad's family reportedly said they did not know Chandiah.
READ | Dead couple found on Durban sand dune, man still had knife in his chest
Muhammad's family told Times Live she had left home shortly before 12:00 on Monday, telling them she was going to Suncoast Casino for lunch.
She reportedly last spoke to one of her daughters shortly after 13:00. When her daughter attempted to contact her an hour later, her phone was switched off.
"The police told us they discovered her body around 16:00. We were informed at 18:30. We don't understand what could have happened," a family member, who asked not to be named, told Times Live.
Provincial police spokesperson, Brigadier Jay Naicker, said two cases of murder were opened at the Durban Central police station.
- Compiled by Riaan Grobler


Exploring Instacart Data with PCA
Recently, Instacart released a dataset of ~3 million orders made by ~200,000 users at different days of week and times of day. There is also an ongoing Kaggle competition to predict which products a user will buy again. My goal here is more modest where I just wanted to explore the dataset to find patterns of purchasing behaviour by hour of day, day of week and number of days since prior order.


Car class
Car type
Daily
Weekly
Monthly
{{c.categoryName }}
{{c.dailyRates | currency:'INR'}}
{{c.weeklyRates | currency:'INR'}}
{{c.monthlyRates | currency:'INR'}}
Select
Cancel


GetValuelistOfValues - a list of carriage return-delimited valuesvalueNumber - the value to return from the listWhen the listOfValues parameter is a literal string, you must insert a literal carriage return character (¶) between each item in the string. To insert a literal carriage return character, click the ¶ button in the Specify Calculation dialog box.


{{ define "content" }}
Fetching {{ .Url }}
You can close this window and your download will continue. Check the Status page to see all downloads in progress.


Requesting data
Fetch is a popular new Browser API to request data and files. However, older browsers can't deal with fetch()
. How do you use it while maintaining compatibility with older browsers?
Progressive enhancement
Instead of writing a polyfill for everything fetch()
entails, including Promises, I'd like to build on what's already there in the browser while providing a unified API you can use and expect the same results from. That means XMLHttpRequest()
and XDomainRequest()
. Implementing then()
and catch()
is probably beyond the scope of the article, but we'll see where we end up. I'm aiming for IE8+ support here.
The first thing we need to know is what we would expect a request to return as a response. Right? I would expect either a response with the thing I requested, or an error telling me what went wrong.
I think request might be a good name for the API we'll be building, so let's go ahead and create some boilerplate. We need this functionality in the global scope, so I'm putting this in a script
tag right in the HTML.
function request() {
};
That is very barebones. What else could we need? Some parameters maybe? Both fetch()
and XMLHttpRequest()
need a URL to be able to do anything, so that seems like a good first candidate. Additionally, both can have options. Now, as fetch()
and XMLHttpRequest()
handle returning their response in a different way, we need to be able to get those results to where we need them. A single callback is an easy way to do so. Let's add that to the parameters as well.
In addition to defining the parameters, we need to define what they are able to contain. The URL obviously has to be a valid URL. Luckily, both methods will warn you when that's not the case, so we can forget about that. We do need to set some defaults for the options object and make sure they are actually used.
Now, because XMLHttpRequest()
can't directly request images (but can insert source URLs which might be the better way to go), we will assume that we are going to either request text or JSON. Of course your text could be a piece of HTML which you would insert with whatever method you'd like.
function request(url, options, callback) {
var type = options && options.type ? options.type : 'text';
// XMLHttpRequest only needs the method, but let's deal with that later.
var opts = Object({
method: 'GET',
mode: 'cors',
});
for (var key in options) {
// Don't put the type key in the options for requests
if (key === 'type') {
continue;
}
opts[key] = options[key];
}
};
For me, writing this with fetch()
first is the easy way to go. We should first see if we even have fetch available using feature detection. If fetch is available, we can safely use it to do our request and call the callback when it finishes. Do note the return which stops function execution right there.
function request(url, options, callback) {
var type = options && options.type ? options.type : 'text';
var opts = Object({
method: 'GET',
mode: 'cors',
});
for (var key in options) {
if (key === 'type') {
continue;
}
opts[key] = options[key];
}
// self refers to the Window or Worker object
if (fetch in self) {
// now fetch! 🎉
return fetch(url, options)
.then(function(response) {
return response[type]();
})
.then(function(content) {
if (content.cod !== 200) {
throw Error(content.message);
}
return callback(null, content);
})
.catch(function(err) {
console.error(err);
callback(err);
})
}
};
Now only if we do not have fetch()
available, we ever get below the if-statement. We do a XMLHttpRequest()
there, so everyone can enjoy their new content. In the next code snippet, I omitted some parts for brevity.
In IE8 and IE9, you use XDomainRequest()
for CORS requests instead of XMLHttpRequest()
, so we need to check if it exists and use it if it does.
function request(url, options, callback) {
… code omitted for brevity …
// self refers to the Window or Worker object
if (self && fetch in self) {
… … code omitted for brevity …
}
// XDomainRequest is a IE8 and IE9 specific version of XMLHttpRequest,
// providing CORS for those browsers.
var req = self.XDomainRequest ? new self.XDomainRequest() : new XMLHttpRequest();
req.onerror = function(event) {
callback(req);
}
req.onload = function() {
if (req.status === 200) {
var response = type === 'json' ? JSON.parse(req.responseText) : req.responseText;
return callback(null, response);
}
callback(JSON.parse(req.responseText));
}
req.open(opts.method, url);
setTimeout(function() {
req.send();
}, 0);
};
That setTimeout()
is weird, but needed in some scenario's where browsers require req.open()
to be executed before req.send()
can be. This is it. The whole thing. There you are. Now click the button and see it work.
Unfortunately, the button is broken on HTTPS domains due to fetch()
's security features and would require a proxy server to work on a HTTPS domain. It should work fine on a regular HTTP domain.


EXPERTISE
- iOS DEVELOPMENT
- TEST DRIVEN DEVELOPMENT
- AGILE/SCRUM METHODOLOGIES
- PROJECT MANAGEMENT
Occidental College
Mathematics 2007 - 2011
I'm an engineer with an eye for design and a passion for creating captivating user experiences. I love sports, travel, technology, and craft beer. My experience includes a wide variety of iOS applications for both iPhone and iPad using almost every framework of the SDK. I've also worked with Android, OSX, and Ruby applications in personal and professional projects.
The Brewery | San Francisco, CA | Aug 2015 - Present
Work with clients to turn ideas into product requirements that can be estimated and implemented. Create application infrastructure for deployment of beta and production builds. Manage projects development of multiple ongoing projects.
TaskRabbit | San Francisco, CA | Feb 2015 - Aug 2015
Made architectural decisions about application design. Created and managed continuous integration servers with automated deployment systems. Integrated Swift into multiple applications while maintaining testing structure. Created coding style guides and enforced style and quality through code reviews. Created screening process for candidates and conducted interviews.
TaskRabbit | San Francisco, CA | Aug 2012 - Feb 2015
Built iOS applications with the latest Apple technologies in a test-driven and agile environment. Maintained multiple applications for consumers and internal release. Developed and maintained a shared library of custom UI components and domain objects for use in multiple applications. Researched new libraries and technologies for use in our products.
Freelance | May 2011 - Oct 2012
Miso Media | Venice, CA | Oct 2011 - July 2012
Greatly improved stability and feature set of Miso Music : Plectrum for iPad through major refactors. Adapted the user interface and functionality for iPhone/iPod. Responsible for building majority of the user interface. Handled functional testing and test build releases.


{% load i18n %}
{{ case.citizen.fbr_number }}
{{ case.citizen.mother_name }}
{% trans "Ask the citizen" %}:
{% if case.field_staff %} {% trans "Provide either the citizen's Family Book Record Number or the citizen's Mother's Name." %} {% else %} {% trans "Provide either your Family Book Record Number or your Mother's Name." %} {% endif %}
{% trans "Did they provide either correctly?" %}


This div contains 3 addresses :
A first address with normal background
A second address that should have a green background
A third address that should have a green background


Upcoming Concerts
June
15
Laura Marling
Bowery Ballroom
June 15
June
15
Wilco
Bowery Ballroom
June 15
June
15
The National
Bowery Ballroom
June 15
June
15
Arcade Fire
Bowery Ballroom
June 15
June
15
Hot Chip
Bowery Ballroom
June 15
June
15
Beirut
Bowery Ballroom
June 15
June
15
Belle & Sebastian
Bowery Ballroom
June 15
June
15
Fanfarlo
Bowery Ballroom
June 15
June
15
The Head & The Heart
Bowery Ballroom
June 15
June
15
The Black Keys
Bowery Ballroom
June 15


GAuth
Certificate Authentication for your Google Forms
{% block content %}{% endblock %}
Created by
Ben Weissmann
· Source on
GitHub


Why not pip
, wheels, virtualenvs?
site-packages
?apt
, brew
, yum
) and the included compiler (e.g. clang
, gcc
).Scientific Python community identified that there was a gap not being addressed by the core Python infrastructure, limiting their ability to get packages into the hands of users
Industry standard built by people who care about this space – Continuum Analytics
It solves a hard problem:
Built for Python first, but it really solves a much broader infrastructural issue.
meta.yaml
, and a build recipe.What can it install? Not just scientific packages. It can help with:
See conda-recipes for a comprehensive set of build recipes. Everything from applications to compilers to Python modules, hundreds of maintained recipes across many problem domains.
Conda packages can come from a variety of locations:
file://
)Command line interface
Will show what we’re working on to make this easier, especially for non-developers
To start:
conda --help
Activating environments, a couple ways:
cd /d C:\ArcGIS\bin\Python\Scripts
activate arcgispro-py3
Once you’re in an environment get details with info
:
conda info
Conda info is the starting point – it tells you the state of the environment.
conda info
Current conda install:
platform : win-64
conda version : 4.0.4
conda-build version : not installed
python version : 3.5.1.final.0
requests version : 2.9.1
root environment : C:\ArcGIS\bin\Python (writable)
default environment : C:\ArcGIS\bin\Python\envs\arcgispro-py3
envs directories : C:\ArcGIS\bin\Python\envs
package cache : C:\ArcGIS\bin\Python\pkgs
channel URLs : https://conda.anaconda.org/esri/win-64/
https://conda.anaconda.org/esri/noarch/
https://repo.continuum.io/pkgs/free/win-64/
https://repo.continuum.io/pkgs/free/noarch/
config file : C:\ArcGIS\bin\Python\.condarc
conda list
# packages in environment at C:\ArcGIS\bin\Python\envs\arcgispro-py3:
#
arcgispro 1.0 0 esri
matplotlib 1.4.3 np19py34_0 defaults
nose 1.3.7 py34_0 defaults
numpy 1.9.3 py34_0e [arcgispro] esri
pandas 0.17.1 np19py34_0 esri
pip 8.0.3 py34_0 defaults
pyparsing 2.0.3 py34_0 defaults
pypdf2 1.25.1 py_0 esri
python 3.4.4 2 defaults
python-dateutil 2.4.2 py34_0 defaults
pytz 2015.7 py34_0 defaults
scipy 0.16.1 np19py34_0e [arcgispro] esri
setuptools 20.1.1 py34_0 defaults
six 1.10.0 py34_0 defaults
sympy <IP_ADDRESS> py34_0 defaults
wheel 0.29.0 py34_0 defaults
xlrd 0.9.4 py34_0 defaults
xlwt 1.0.0 py34_0 defaults
Creating new environments:
conda create --name my_env python=3.4 numpy flask dask
Can also use a file which includes all the dependencies:
conda create --name my_env --file my_sweet_depends.txt
These can contain explcit information about channels, to ensure that the new environment precisely matches the requirements.
| Name | Means | Will Ship? |
|---|---|---|
| Conda | The command itself | ✓ |
| Miniconda | A minimum set of Python packages to build and run Conda. | ✓ |
| Anaconda | A distribution 200+ packages built with Conda | |
| Anaconda Server | Host the full infrastructure internally |
How’s it work?
Lock it down: Don’t use network
Can vet the installation
Will work out of the box with default packages without any network connectivity
.condarc
conda config
, just like using git config
to update the default configurationStraightforward:
meta.yaml
) specifying the contents and dependenciesbld.bat
, build.sh
) specifying how to buildmeta.yaml
:
package:
name: pypdf2
version: "1.25.1"
source:
fn: PyPDF2-1.25.1.tar.gz
url: https://pypi.python.org/packages/source/P/PyPDF2/PyPDF2-1.25.1.tar.gz
md5: ee5e5b01d00b120805e5049e56c6fd7c
requirements:
run:
- python
bld.bat
:
"%PYTHON%" setup.py install
Currently:
| Platform | Python version |
|---|---|
| Desktop | Python 2.7.x (2.7.10) |
| Pro | Python 3.4.x (3.4.3) |
Upgrade code? Python migration for ArcGIS Pro
But… this can be costly. For many organizations, a significant burden, even if the language changes are relatively small.
With Conda, we can support multiple platforms:
Create a new environment, target a different Python, users can now use that with the Py2 code
Still need to change arcpy.mapping
to arcpy.mp
when moving from Desktop to Pro, but no Python language level changes needed.
Have to make sure you’re running the right Python (what happens when you type python
at the command line?)
Requires some user education over the “only one Python on the box” model
requests
)conda
today. Miniconda is fully stand-alone, won’t affect your global Python (unless you tell it to)Effectively manage complex software dependencies with Conda. Thousands of packages exist today, can integrate it into your organization’s needs.
Esri Conda Team:
Continuum Analytics for creating and open sourcing Conda
iOS, Android: Feedback from within the app
Windows Phone, don’t use a smartphone?: Cuniform tablets accepted (sorry! limitation).
Windows Phone, or no smartphone? Cuneiform tablets accepted.


(Tested on 13.10 & 14.04 64 bit. Also works on equavelant mint distro 64 bit)
In your terminal:
apt-get update
#apt-get upgrade
apt-get install mercurial ssh mc curl python-pip -y
curl http://install.jumpscale.org:85/cmds/jsbox_stable.sh > /tmp/init.sh;sh /tmp/init.sh
source /opt/jsbox/activate
This installs JumpScale and puts it in sandboxed mode.
echo "export JSBASE=/opt/jsbox
export PATH=/opt/jsbox/bin:/opt/jsbox/tools:\$PATH" >> ~/.bashrc
jpackage mdupdate
Now you'll have the automation framework JumpScale installed and sandboxed and jpackages ready to use!
To know more about JumpScale, see JumpScale


{% extends "header.html" %} {% block body %}
Math is a pretty popular topic, so we should probably learn how to do it in Python 3. Luckily for us, math is so very popular that it works extremely simply.
1+3
Here, we have some simple addition, the returned number is 4.
4*4
Next up, multiplication, fancy stuff! The return is 16.
5-2
Subtraction, even fancier. Return will be 3
5/2
Division, now this is actually fancy, based on Python's history.
In Python 2, the division does not necessarily work in the way expected. If you divide two whole numbers, you will be returned a whole number. You must divide one of the numbers as a float in order to get a proper return. This was changed in Python 3+, and now you always get expected division, yay!
#exponents 4**4
There is another way to do exponents, but this works just fine, and is a familiar syntax for it.
That's it for basic math. It really is quite simple. Math is quite integral to a lot of programs, so it is very nice that it is kept simple enough.


Class Relationship
- java.lang.Object
-
- edu.cnm.deepdive.tunefull.model.entity.Relationship
-
@Entity public class Relationship extends java.lang.Object
The
Relationship
entity holds the data for each user-to-user relationship in the TuneFull server and provides a method to facilitate getting the other user in the relationship.- Since:
- 1.0
- Version:
- 1.0
- Author:
- Robert Dominguez, Roderick Frechette, Laura Steiner
-
-
Constructor Summary
Constructors Constructor Description Relationship()
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description java.lang.Boolean
getFriendAccepted()
Returns the Boolean indicating whether a friend relationship has been accepted, denied, or not yet responded to.java.lang.Long
getId()
Returns the id for the relationship.User
getRequested()
Returns theUser
entity associated with the user who received the relationship request.User
getRequester()
Returns theUser
entity associated with the user who requested the relationship.boolean
isFriendRelationship()
Returns a boolean indicating whether the relationship is a friendship or simply a follow relationship.User
other(User self)
Returns the user on the other side of the relationship from the current user: the requested if the current user is the requester, and the requester if the current user is the requested.void
setFriendAccepted(java.lang.Boolean friendAccepted)
Sets the Boolean indicating whether a friend relationship has been accepted, denied, or not yet responded to.void
setFriendRelationship(boolean friendRelationship)
Sets the boolean indicating whether the relationship is a friendship or simply a follow relationship.void
setRequested(User requested)
Sets theUser
entity associated with the user who received the relationship request.void
setRequester(User requester)
Sets theUser
entity associated with the user who requested the relationship.
-
-
-
Method Detail
-
getId
public java.lang.Long getId()
Returns the id for the relationship.- Returns:
-
getRequester
@NonNull public User getRequester()
Returns theUser
entity associated with the user who requested the relationship.- Returns:
-
setRequester
public void setRequester(@NonNull User requester)
Sets theUser
entity associated with the user who requested the relationship.- Parameters:
requester
-User
-
getRequested
@NonNull public User getRequested()
Returns theUser
entity associated with the user who received the relationship request.- Returns:
-
setRequested
public void setRequested(@NonNull User requested)
Sets theUser
entity associated with the user who received the relationship request.- Parameters:
requested
-User
-
isFriendRelationship
public boolean isFriendRelationship()
Returns a boolean indicating whether the relationship is a friendship or simply a follow relationship.- Returns:
-
setFriendRelationship
public void setFriendRelationship(boolean friendRelationship)
Sets the boolean indicating whether the relationship is a friendship or simply a follow relationship.- Parameters:
friendRelationship
- boolean
-
getFriendAccepted
public java.lang.Boolean getFriendAccepted()
Returns the Boolean indicating whether a friend relationship has been accepted, denied, or not yet responded to.- Returns:
-
setFriendAccepted
public void setFriendAccepted(java.lang.Boolean friendAccepted)
Sets the Boolean indicating whether a friend relationship has been accepted, denied, or not yet responded to.- Parameters:
friendAccepted
- Boolean
-
other
public User other(User self)
Returns the user on the other side of the relationship from the current user: the requested if the current user is the requester, and the requester if the current user is the requested.- Parameters:
self
-User
- Returns:
- the user in the relationship that is not the current user
-
-


Pair held by Taliban finally free
Australian academic Tim Weeks has been released by the Taliban, along with his American colleague Kevin King, three years after they were abducted in Afghanistan.
Mr Weeks (50) and Mr King (63), who were grabbed outside the American University in Kabul in 2016, were freed in exchange for three members of the Taliban's Haqqani Network.
The two men were handed over to US forces in Afghanistan's southern Zabul province and flown out in a US helicopter, The Associated Press reports.
White House press secretary Stephanie Grisham indicated Mr Weeks, from Wagga Wagga in NSW, was in the care of US officials and receiving medical care.
Mr Weeks' father Mervyn was notified of his release in a phone call from the Department of Foreign Affairs and Trade.
Mervyn Weeks told the ABC he was very relieved but that he had yet to speak to his son and was unsure of his whereabouts and plans.
A Weeks' family statement on Tuesday night asking for privacy.
"We are grateful for the efforts of the Australian government in securing Tim's release," it read.
"We would like to thank the United States government for the significant role it played in securing Tim's freedom and acknowledge the important contribution of the government of Afghanistan.
"We thank our friends and extended family for their love and support over the past three years during this very difficult time.
"While we understand the intense public interest in Tim's release, we do not want to comment further."
Australian Prime Minister Scott Morrison late on Tuesday night tweeted his relief at Mr Weeks' release and said he was "profoundly pleased".
Mr Morrison issued a joint statement with Foreign Affairs Minister Marise Payne which thanked US President Donald Trump and Afghan President Ashraf Ghani.
"We regard this release as one of a series of confidence-building measures that are taking place in Afghanistan. We hope that such measures will set the stage for a ceasefire and intra-Afghan dialogue," the statement read.
Mr King's family released a statement saying he was also safe with US officials and getting the medical care he needs before his return home to be reunited with his family.


Hi, FIRSTNAME.
I am glad that you have decided to take the time to write me. This shows that you're dedicated and determined to do the best possible job, while simultaneously getting to know your colleagues. Keep that attitude, and you will have a great career ahead of you!
However, I'm afraid I'm way too busy to answer mails all the time. Furthermore, I'm no longer your direct superior. If you have something you want to discuss, you should notify Miss Sháo or Mr. Manduca. If they feel it's important, they will let me know!
Keep up the good work!
Sandra Silvern
Managing Consultant / Haka Manager


class: center, middle, inverse, title-slide # Spatial Beta-Convergence Forecasting Models: ## Evidence from Municipal Homicide Rates in Colombia ### Felipe Santos-Marquez
Master’s student (M2)
Graduate School of International Development
Nagoya University, JAPAN ### Prepared for the 57th Annual Conference of JSLA
[ slides available at:
https://felipe-santos.rbind.io/
&
https://jsla-2020.netlify.app/
] --- ## Motivation: - Policy development and management should consider not only current challenges but also **the potential challenges in the near future**. - Persistent income differences, differences in health indicators and in "general" regional inequality in Colombia. - Scarce academic literature on the **forecasting of crime** at the municipal level in Colombia. ## Research Objective: - **To build a forecasting model that can predict homicide rates 4 years into the future** - Analyze the spatial distribution of the forecasted homicide rates in 2022 ## Methods: - Exponential smoothing - ARIMA models - STAR models - **Classical convergence framework (Barro and Sala-i-Martin 1992)** --- class: middle # Main Results: 1. **Beta and Spatial-beta forecasting models** are more accurate than other conventional forecasting models. 2. **768 out of 1120** Colombian municipalities are expected to reach the 2022 national SDG target. (**23 extra municipalities when compared to 2018**) 3. **Increasing spatial polarization of the data**. higher crime areas are more likely to be surrounded by high crime areas and conversely for low-low cluster formation. 4. **Crime spillovers are forecasted** for municipalities in the pacific region and positive spillovers are forecasted in the north and north-eastern parts of the country. --- class: middle # Outline of this presentation 1. **Data description** administrative levels in Colombia and homicide rates. 3. **Forecasting models** - Exponential smoothing, ARIMA and STAR models 2. **Beta convergence models** - Beta convergence and forecasting 4. **Main results and prediction:** - Best models and forecasted data 5. **Policy discussion** - The Colombian National Development Plan 2018-22 5. **Concluding Remarks** --- # Colombian administrative levels and the spatial distribution of crime .pull-left[ ## States and Municipalities ![](figs/col_mun.png) ] .pull-right[ ## Crime ![](figs/01_eb_mr_2018.png) **(In Japan in 2018 about 0.02 per 10.000 people)**] --- # Data: - Total number of **homicides** and in Colombia per year from 2003 until 2018 (**data taken from Municipal data-set Universidad de los Andes, Bogota, Colombia**). - Data is aggregated at the municipal and departmental levels. - Population census and estimates for states and municipalities (data from the same dataset). - Raw rates computed `$$raw\space rates = crimes / population$$` - Non crime rates computed `$$NCR= 10000- raw\ rate * 10000$$` - **Survival rates** are chosen because positively defined variables are a **standard** in the convergence literature. - The variables are transformed from raw rates to Empirical Bayes rates in order to control for variance instability. --- class: center # Global trend of crime  --- class: middle # Exponential smoothing methods In general terms, these methods use a **weighted average of past observations** in order to draw a forecast of future observations `$$\hat{y}_{T+1 \mid T}=\alpha y_{T}+\alpha(1-\alpha) y_{T-1}+\alpha(1-\alpha)^{2} y_{T-2}+\cdots$$` # ARIMA models An autoregressive model of order p `$$y_{t}=c+\phi_{1} y_{t-1}+\phi_{2} y_{t-2}+\cdots+\phi_{p} y_{t-p}+\varepsilon_{t}$$` A moving average model considers past forecast errors in a linear model: `$$y_{t}=c+\varepsilon_{t}+\theta_{1} \varepsilon_{t-1}+\theta_{2} \varepsilon_{t-2}+\cdots+\theta_{q} \varepsilon_{t-q}$$` An autoregressive model, a moving average model and the differentiating of the data can be combined into a non-seasonal ARIMA model: `$$y_{t}^{\prime}=c+\phi_{1} y_{t-1}^{\prime}+\cdots+\phi_{p} y_{t-p}^{\prime}+\theta_{1} \varepsilon_{t-1}+\cdots \theta_{q} \varepsilon_{t-q}+\varepsilon_{t}$$` --- class: middle # Space time Autoregressive models (STAR) `$$y_{i t}=\sum_{k=1}^{p} \sum_{l=0}^{\lambda_{k}} \phi_{k l} \sum_{j=1}^{N} w_{i j}^{(l)} y_{j t-k}+a_{i t}$$` - `\(y_it\)` is the variable of interest at location `\(i\)` during time `\(t\)` - `\(a_{it}\)` is the random normal error associated with that location and time. - `\(y_{jt-k}\)` are the time lagged variables at location `\(j\)` - `\(w_{i j}^{(l)}\)` is the distance weight matrix of order (l). These models have been **successfully used to forecast crime data in the United States**. See Shoesmith GL (2013) Space-time autoregressive models and forecasting national, regional and state crime rates. --- class: center # **Beta convergence and forecasting** (catch-up process)  When studying regional data sets, Barro and Sala-i Martin (1995) proposes the following equation: `$$\log \frac{y_{i T}}{y_{i 0}}=\alpha-\left[1-e^{-\beta T}\right] \cdot \log \left(y_{i 0}\right)+w_{i, 0 T}$$` Solving the equation for `\(y_{i T}\)`: `$$\log \left(y_{i T}\right)=\alpha+e^{-\beta T} \cdot \log \left(y_{i 0}\right)+w_{i, 0 T}$$` Therefore, a plausible 4-year ahead forecast is: `$$\log \left(\hat{y}_{i(t+4) \mid t}\right)=\hat{\alpha}_{t+4 \mid t}+\hat{\beta}_{t+4 \mid t} \cdot \log \left(y_{i t}\right)+w_{i, t}$$` --- class: middle, justify #**Spatial Beta convergence model** - Spatial beta convergence models have been used to model the dynamics of crime **(Santos-Marquez and Mendez, 2020)** - In order to simplify the computations instead of a SAR (spatial autoregressive model) or a SEM (spatial error model), **an SLX model (Spatially Lagged X) is proposed to forecast homicide**. - the spatial equivalent of the equation in the previous slide is: `$$\log \frac{y_{i T}}{y_{i 0}}=\alpha-\left[1-e^{-\beta T}\right] \cdot \log \left(y_{i 0}\right)+\theta W \cdot \log \left(y_{i 0}\right)+\epsilon_{i, 0 T}$$` Therefore, a plausible 4-year ahead forecast is: `$$\log \left(\hat{y}_{i(t+4) \mid t}\right)=\hat{\alpha}_{t+4 \mid t}+\hat{\beta}_{t+4 \mid t} \cdot \log \left(y_{i t}\right)+\hat{\theta}_{t+4 \mid t} W \cdot \log \left(y_{i t}\right)+\epsilon_{i, t}$$` Where three coefficients need to be computed: `\(\alpha\)`, `\(\beta\)` and `\(\theta\)`. --- class: middle, center # Time series forecast accuracy and cross-validation #How can the best forecasting model be chosen? `$$\begin{aligned} &\text { Mean absolute error: } \operatorname{MAE}=\operatorname{mean}\left(\left|e_{t}\right|\right)\\ &\text { Root mean squared error: RMSE }=\sqrt{\operatorname{mean}\left(e_{t}^{2}\right)} \end{aligned}$$` # Cross-validation of forecasts   --- class: center, middle # The prediction of the coefficients of the beta models  Linear regressions are used to compute the coefficients in: `$$\log \left(\hat{y}_{i(t+4) \mid t}\right)=\hat{\alpha}_{t+4 \mid t}+\hat{\beta}_{t+4 \mid t} \cdot \log \left(y_{i t}\right)+\hat{\theta}_{t+4 \mid t} W \cdot \log \left(y_{i t}\right)+\epsilon_{i, t}$$` `$$\log \left(\hat{y}_{i(t+4) \mid t}\right)=\hat{\alpha}_{t+4 \mid t}+\hat{\beta}_{t+4 \mid t} \cdot \log \left(y_{i t}\right)+w_{i, t}$$` --- class: center, middle # Cross-validation results (Beta and spatial Beta)  --- class: justify # Forecasting homicide rates for 2022 - In the Colombian National Development Plan 2018-2022 DNP (2018) there are national targets for a variety of SDG related variables for the year 2022. - In terms of homicides the baseline for 2018 is 2.58 (per 10.000 people) and the **2022 target is 2.32 per 10.000 people (nmr= 9997.68)**  --- class: center # Forecasting homicide rates for 2022 # (2022 Target = 9997.68)  **A noticeable error in this forecasting model is the fact that a total of 68 municipalities have a predicted non crime rate slightly above 10000 (WHICH IS PRACTICALLY IMPOSSIBLE)** --- class: center ## The spatial distribution of the forecasted crime **municipalities in dark green are lagging behind**  --- class: center ## The spatial distribution of the forecasted crime **municipalities in dark green are lagging behind**  --- class: center # Measuring the polarization of regional crime ## Local join count statistic (Anselin (1995), Anselin and Li (2019))  --- # Policy discussion .pull-left[ - Vertical and horizontal policy coordination, spillovers and borders. - Spatial spillovers from neighbors can have **both positive and negative effects**. - It could be more appropriate for the formulation of development plans to have targets at the **state level or regional level**. - For example, there is a clear negative spill-over in the municipalities in **the pacific region (in purple)**. ] .pull-right[ ## Colombian natural regions ![](figs/col_regions.png) ] --- # Concluding Remarks ## Uplifting results "on average" : - The dispersion of non-crime (crime) rates at the has decreased level **has decreased**. - It was found that the forecasted mean in 2022 is still below the 2022 target. - Nevertheless, major improvements are foreseen as **23 extra municipalities are expected to reach the SDG homicide target by 2022**. This will bring the total of municipalities that have accomplished the target to 768 out of 1120 in 2022. ## The Role of Space - There are **positive and negative spillovers**. - **Policy should be localized in order to reverse the creation of crime clusters**. ## Further Research - **Other determinants** may help to improve the forecasting accuracy of the beta models. - A **damped trend method** may help to correct for municipalities that are forecasted to have 0.0 crime rates (over 10.000 non crime rates) --- class: center, middle # Thank you very much for your attention You can find more about my research outputs on my website https://felipe-santos.rbind.io If you are interested in our research please check the QuaRCS lab website
 https://quarcs-lab.org/  **Quantitative Regional and Computational Science Lab**


Before Change
config = self.config
ops = self.ops
if(config.discriminator == None):
d_real = gan.graph.d_real
d_fake = gan.graph.d_fake
else:
d_real = gan.graph.d_reals[config.discriminator]
d_fake = gan.graph.d_fakes[config.discriminator]
net = tf.concat([d_real, d_fake], 0)
net = config.reduce(net, axis=1)
s = [int(x) for x in net.get_shape()]
net = tf.reshape(net, [s[0], -1])
After Change
d_fake = tf.slice(net, [shape[0]//2,0], [shape[0]//2,-1])
a,b,c = config.labels
square = ops.lookup("square")
d_loss = square(d_real - b) + square(d_fake - a)
g_loss = square(d_fake - c)
if config.gradient_penalty:


http://tetherless-world.github.io/whyis/tutorial/
Note: these slides are meant to be used as part of an interactive tutorial with a trainer. If you use them unsupervised, you're on your own.
To follow this tutorial, please start by installing Whyis onto a virtual or physical machine of your choice.
To peform the following administrative tasks, you need to connect to the VM (if you’re not running directly):
$ vagrant ssh
$ screen -R -DD
C-a c
C-a a
Once you are in the server, you need to change to the whyis user, go to the whyis app directory:
# Create a virtual terminal with C-a c
$ sudo su - whyis
$ cd /apps/whyis
$ git pull
$ git checkout master
$ pip install --upgrade -r requirements/dev.txt
$ python manage.py configure
project_name [My Knowledge Graph]: Dataset Catalog
project_short_description []: A catalog of interesting datasets.
project_slug [dataset_catalog]:
location [/apps/dataset_catalog]:
author []:
email []:
linked_data_prefix [http://localhost]: http://dev.whyis.io
version [0.1]:
packages []:
SECRET_KEY []:
SECURITY_PASSWORD_SALT []:
$
$ cd /apps/dataset_catalog
$ pip install -e .
In your first terminal (cycle through using C-a a
:
$ sudo service apache2 restart
$ sudo service celeryd restart
$ curl -X POST --data-binary @knowledge.properties -H 'Content-Type:text/plain' http://localhost:8080/blazegraph/namespace
$ curl -X POST --data-binary @admin.properties -H 'Content-Type:text/plain' http://localhost:8080/blazegraph/namespace
In your whyis user terminal (cycle through using C-a a
:
$ python manage.py createuser -e [email] -p [password] -f [First Name] \
-l [Last Name] -u [user handle] --roles=admin
$ python manage.py runserver -h <IP_ADDRESS>
Could not configure logger, using defaults.
* Serving Flask app "dataset_catalog" (lazy loading)
* Environment: production
WARNING: Do not use the development server in a production environment.
Use a production WSGI server instead.
* Debug mode: on
* Running on http://<IP_ADDRESS>:5000/ (Press CTRL+C to quit)
* Restarting with stat
Could not configure logger, using defaults.
* Debugger is active!

We can use dbpedia.org/sparql and the following query to resolve keywords to dc:subject values.
select distinct ?s ?o ?sc ?score where {
?s rdfs:label ?o .
?o bif:contains 'Protein'
OPTION (score ?sc) .
FILTER (lang(?o) = 'en')
filter (regex(str(?s), "http://dbpedia.org/resource"))
bind (?sc/ strlen(?o) as ?score)
} order by desc(?score) LIMIT 1
Add the following to the config.py 'namespaces' entry:
importer.FileImporter(
prefix = LOD_PREFIX+'/pubmed/file/',
url = 'https://www.ncbi.nlm.nih.gov/pubmed/%s?report=xml'
),
Add the following as a predicate of the dataset object:
"dc:references" : [
"@for" : "pmid in row['Reference(s) - PubMed ID'].split('||')",
"@do" : {
"@id" : "https://www.ncbi.nlm.nih.gov/pubmed/{{pmid}}",
"@type" : [ "dcterms:BibliographicResource","bibo:AcademicArticle"],
"rdfs:seeAlso" : {
"@id" : "https://www.ncbi.nlm.nih.gov/pubmed/{{pmid}}",
"@type" : "http://dev.whyis.io/PubMedXMLMetadataFile"
}
}
]
Download, customize/expand, and add the pubmed_xml.setl.ttl file.
$ python manage.py load -i ../dataset_catalog/setl/pubmed_xml.setl.ttl -f turtle
Additional metadata for PubMed Articles will now be included. Try adding more metadata from the XML, or adding an article view.


When you lend him your ears, John Kageche has the rare and amazing talent for breaking down speeches. He is able to see what is usually overlooked -- especially in message development he will see the whole program and how your message fits into it; his ability to combine ones personality with one's message is especially refreshing for any speaker to forge emotional bonds with their audiences.  Most probably, yes. Pain is when you are subjected to listening to a drone of a voice devoid of content and direction in the name of a public address. Whether presenting your thesis, defending your proposal, addressing the board, a rally, a congregation, or funeral gathering, the ability to get the audience to comprehend what you are saying while keeping them awake and responsive is pegged to scientifically proven tools and techniques which you can harness. Speak for Yourself! and give much needed relief to audiences accustomed to being on the receiving end of a painful address.
Jagi Gakunju, Group CEO, AAR
One size cannot fit all. The sales skills set for a business to customer (B2C) service industry are different from those of an FMCG or oil industry and both different still from B2B selling. For instance, selling a bank account and selling elevators have widely varied sales cycles and require different skills. It is for this reason that we prefer crafting a program depending on your needs.
He didn’t need to say it but it was quickly obvious to me that Kageche was not sharing theory with my sales team. His practical style of delivery had all the hallmarks of one who had been to the trenches. I found him a great educator, totally engaging, with contagious enthusiasm. We witnessed unprecedented growth after lending him our ears and, more importantly, as senior management, were forced to relook our sales strategy, based on our interactions with him. He’s reliable professional and a real pleasure to work with.
James Waweru, Human Resource & Admin Manager, Kenya Postel Directories Ltd
This is not a program but a service we offer.
John Kageche conceptualized and facilitated a turn-around sales conference for KETEPA in which my heads of department and myself were present; the objectives were well articulated and I consider the conference a success.
Collins Cheruiyot, MD, KETEPA


This yummy South Korean coffee treat that's all over the internet only requires four ingredients to make - all of
which you probably have in the cupboard – you feel like you’ve won the jackpot, right?
Ingredients
Instant Coffee
Sugar
Hot Water
Milk
Dalgona Making Process
Step 1: Combine the instant coffee, sugar, and hot water:
Add 2 tablespoons (tbsp) of instant coffee, 2 tbsp of sugar, and 2 tbsp of hot water to a medium mixing bowl
Step 2: Whisk, whisk, whisk until the coffee is light and fluffy!
Using an electric hand-held mixer, whip the coffee mixture until it is light brown, fluffy, and holds stiff
peaks when the whisk is removed.
Step 3: Serve with Milk:
Use a spoon to garnish a glass of hot or cold milk with the coffee cream. Serve with a straw, and stir,
stir, stir to enjoy!
Did you find this recipe useful?
Yes
No
A recipe contributed & coded by ProGrad Junior, Atan ❤


The Bartender's Guide has already supplanted the great Codex Bartactica as the standard repository of all bar/pub/saloon/watering-hole knowledge and wisdom, for though it has many omissions, it scores over the older, more pedestrian work in two important respects: First, it is cheaper; and secondly it has the words CALM DOWN inscribed in large friendly letters on its cover.
The Bartender's Guide to the Galaxy is an indispensable companion to all those who are keen to make sense of bar life in an infinitely complex and confusing multi-bar-verse. Where it is inaccurate it is at least definitely inaccurate. In cases of major discrepancy it's always reality that's got it wrong.
"The Guide is definitive. Reality is often inaccurate."


public final class QualityModelUtils
extends java.lang.Object
| Modifier and Type | Method and Description |
|---|---|
static java.util.Map<java.lang.String,QualityModel> |
createModelMap(java.util.List<QualityModel> models)
Creates a map relating model names to quality models
|
static AbstractEntity |
findEntity(java.util.Map<java.lang.String,QualityModel> modelMap,
java.lang.String id)
Finds an entity by its identifier within the provided model map, if such
an entity exists.
|
static java.util.List<MeasurementMethod> |
getAllMeasurementMethods(java.util.List<QualityModel> models)
Retrieves all MeasurementMethod entities from a list of known quality
models.
|
static Evaluation |
getEvaluates(INode dest,
java.util.Map<java.lang.String,QualityModel> modelMap)
Retrieves an Evaluation entity from a map of quality models indexed by
name when given a node representing an entity evaluated by the returned
evaluation.
|
static Factor |
getFactor(INode source,
java.util.Map<java.lang.String,QualityModel> modelMap)
Retrieves a factor object from a quality model provided in the model map,
that is represented by the provided node.
|
static Measure |
getMeasure(INode source,
java.util.Map<java.lang.String,QualityModel> modelMap)
Retrieves a measure object from a quality model provided in the model
map, that is represented by the provided node.
|
public static java.util.Map<java.lang.String,QualityModel> createModelMap(java.util.List<QualityModel> models)
models
- a list of existing quality modelspublic static AbstractEntity findEntity(java.util.Map<java.lang.String,QualityModel> modelMap, java.lang.String id)
modelMap
- A map of models indexed by their names.id
- The unique identifying string associated with the entity.public static java.util.List<MeasurementMethod> getAllMeasurementMethods(java.util.List<QualityModel> models)
models
- The list of quality modelspublic static Evaluation getEvaluates(INode dest, java.util.Map<java.lang.String,QualityModel> modelMap)
dest
- Node that whose quality model representation is evaluated by
the desired evaluation.modelMap
- Map of quality models indexed by their names.public static Factor getFactor(INode source, java.util.Map<java.lang.String,QualityModel> modelMap)
source
- Node which represents the factor.modelMap
- Map of quality models indexed by name.public static Measure getMeasure(INode source, java.util.Map<java.lang.String,QualityModel> modelMap)
source
- Node which represents the measure.modelMap
- Map of quality models indexed by name.


UI5con 2017 - Create Your Own UI5 Controls – What’s Coming Up
Example 4: a container control, which internally creates a Button for each Item given into the aggregation