Unai Emery makes Nicolas Pepe admission after forward's slow start to life at Arsenal
A number of unconvincing performances have left the club's fans feeling short changed, with Pepe unable to hold down a regular starting place
by Matt Maltby, https://www.mirror.co.uk/authors/matt-maltby/Unai Emery has conceded that Nicolas Pepe's progress at Arsenal has been slower than anticipated - and urged the club-record signing to fight for a first-team spot.
The forward joined the Gunners in a club record £70million deal from Lille in the summer.
But a number of unconvincing performances have left the club's fans feeling short changed, with Pepe unable to hold down a regular starting place under Emery.
He has shown glimpses of his undoubted talent on a number of occasions this season, including his stunning brace in the comeback Europa League win over Vitoria.
However Emery knows his star man must do more, although he revealed that he expected the forward to take his time settling in.
"He just needs time, and to be demanding of himself," Emery told Sky Sports.
"We are with him, working individually with him and showing him videos of where he can improve.
"The intensity of the game and the opponents in England demands a lot of you. He has to adapt to that, and we have to help him so that he can keep taking steps forward.
"His process of adapting to the team is positive, but a little slower than what we wanted. We have to be patient as well as demanding and what’s more, we have to ensure that he takes responsibility himself.
"He is not quite at the level yet to be able to play in every game. I believe, and I have always believed, that he needs to play in order to reach that level, but obviously there are also games when other players deserve to play. I use the players who I think are going to help us most in each moment.
"But we bought Pepe knowing that the first year would be difficult, that it would be a year of adaptation.
"We want that to happen as quickly as possible, but we are seeing that we have to have patience."


Library imports for ClusterResolvers.
This library contains all implementations of ClusterResolvers.
ClusterResolvers are a way of specifying cluster information for distributed
execution. Built on top of existing ClusterSpec
framework, ClusterResolvers
are a way for TensorFlow to communicate with various cluster management
systems (e.g. GCE, AWS, etc...).
class ClusterResolver
: Abstract class for all implementations of ClusterResolvers.
class GCEClusterResolver
: ClusterResolver for Google Compute Engine.
class KubernetesClusterResolver
: ClusterResolver for Kubernetes.
class SimpleClusterResolver
: Simple implementation of ClusterResolver that accepts a ClusterSpec.
class SlurmClusterResolver
: ClusterResolver for system with Slurm workload manager.
class TFConfigClusterResolver
: Implementation of a ClusterResolver which reads the TF_CONFIG EnvVar.
class TPUClusterResolver
: Cluster Resolver for Google Cloud TPUs.
class UnionResolver
: Performs a union on underlying ClusterResolvers.


GameBox Complete Desktop 15.6 inch AOC monitor PC CPU Computer (CORE 2 DUO 2.66Ghz & above/ 4 GB / 500GB HDD/LG DVD writer/DOS) ,Mouse/Keyboard with WIFI
₹35,456.00 In stockFeatures
Intel Pentium J3710 Quad Core Processor
4GB DDR3 RAM
500GB SATA HDD with 7200RPM
Microsoft Windows 10 Home (64-bit) OS with Integrated Intel HD Graphics
49.53 cm (19.5 in) diagonal widescreen Full HD VA WLED-backlit display


SLIDER VALUE: 5.4 volts. As letter V [[grows]], letter I [[grows]]. Current now [[10.8]] millamps.
SLIDER VALUE: 3.6 volts. As letter V [[shrinks]], letter I [[shrinks]]. Current now [[7.2]] millamps.
SLIDER VALUE: 600 ohms. As letter R, [[grows]], letter I, [[shrinks]]. Current now [[7.5]] millamps.
SLIDER VALUE: 400 ohms. As letter R, [[shrinks]], letter I, [[grows]]. Current now [[11.3]] millamps.
Scene Summary
In this sim, there are sliders in the Play Area to simultaneously explore two representations of Ohm's Law:
- the equation, V equals I times R, and
- a circuit, two wires connecting a series of batteries to a resistor.
Buttons in the Control Panel mute sound and reset sim.
Right now,
- voltage is [[4.5]] volts
- resistanceis [[500]] ohms
- current is [[9.0]] milliamps
Thus,
- Letter V is [[much larger]] than letter I and [[comparable to]] letter R.
Look sliders to play.
If needed, check out keyboard shortcuts for this sim.
Play Area


4 Lessons After 11 Years in Silicon Valley
Source: Ashley Mayer
Silicon Valley is a magical place with some strange norms—perhaps because companies, careers, and fortunes rise and fall with such astounding speed. Here are a few of the quirky, brutal, and hopefully useful lessons I learned during my 11 years living and working in the technology industry’s epicenter.
1. There’s opportunity in what others undervalue
There’s a rigid hierarchy of functions in Silicon Valley. At the top of the pyramid sit the entrepreneurs, the engineers, the venture capitalists. The closer you are to building or funding, the more respect you get—which probably makes sense. But when I began my career in tech, I wasn’t prepared for how little respect is left over for other functions: recruiting, HR, marketing, communications, etc. There’s an assumption that truly great products market themselves or that truly great companies are magnets for top talent. To work in these superfluous fields is either a sign that your company must compensate for its lack of greatness or that you’re but an intermediary for the inevitable.
Of course, not everyone thinks like this. And that’s where the upside to this warped view comes in. At the company level, it’s quite clear you’ll need to out-innovate your competitors by building a better product. But what about the less obvious vectors for competition? With the benefit of hindsight, it’s easy to see where investments in culture have paid off exceptionally well (and where the lack thereof has halted otherwise unstoppable companies). In nascent industries—especially highly regulated ones—educating customers and stakeholders about your product and market may be as core to survival as the product itself.
This opportunity for differentiation also exists at the individual level. It used to bother me that people made certain assumptions about me based on my profession. I craved validation from my peers and resented the stereotypes that came along with PR. But the longer I’ve been in this field, the more respect I have for how nuanced, impactful, and essential our work is, and consequently, I’m bothered less by other people’s projections. An unfortunate consequence of the hierarchy of functions is that it’s harder to attract top talent to the layers we undervalue, which hurts the industry as a whole. But, as an individual, it means that it’s probably more feasible to distinguish yourself as one of the top recruiters or marketers than it is to become a top engineer in a world where that is the ultimate prize.
2. There’s nothing more dangerous early in your career than success
One of our industry’s oft-repeated (and oft-abused) sayings is, “If you’re offered a seat on a rocket ship, you don’t ask what seat. You just get on.” It’s what Google’s then-CEO Eric Schmidt told Sheryl Sandberg to convince her to come onboard in 2001, and I’ve always appreciated the humility embedded in this perspective (for the seat-taker, that is). But we often fail to dwell on the inevitable follow-up question: If you took a seat on that rocket ship, and it was indeed a rocket ship, how do you know if you made any meaningful impact on its speed or trajectory?
Success is one of the most dangerous things that can happen to you early in your career.  If you’re given responsibilities that exceed your experience, you’re probably plagued by self-doubt. Then, at some point, if you’re lucky, the company you’ve helped build is declared a success. And those many bumps along the way are ironed out into a perfect narrative. Perhaps you’re even tempted to believe it.
Some reputations are built on much less than you would assume.
In Silicon Valley, myths about people and companies tend to beat out carefully considered case studies. Maybe it’s because so much creation happens when companies are still private and therefore less observable. Maybe it’s because there are so many new and invisible forces at work (emerging technology trends, cultural and behavioral shifts) in a company’s meteoric rise that mythology is the only way we can make sense of it. Maybe it’s because we love a good story—and a good creation story most of all.
It’s a balancing act to allow yourself pride in what you’ve helped accomplish without getting caught up in your own personal mythology. Be grateful for the tough times: They will keep you anchored during headier cycles. If you battle insecurity and anxiety on the regular (raises hand), find solace in the fact that you’re probably working harder than you would if you were capable of believing that it was your seat that made all the difference.
3. Some reputations are built on surprisingly little
This is going to seem random, but bear with me: In the 1999 rom-com Never Been Kissed, Drew Barrymore’s character, Josie, is a reporter who goes undercover as a high school student to write about the “cool” high school crowd. But there’s just one problem: She’s super uncool, so she can’t get anywhere near them. Then her naturally cool younger brother decides to relive his high school glory days and salvages her assignment by convincing the cool kids that Josie is, in fact, quite cool. “All you need is for one person to think you’re cool,” he tells her. “And you’re in.”
Silicon Valley can feel a little like high school—in many ways, but especially when it comes to people’s reputations. I’m regularly shocked by how much just one person declaring someone a “rockstar” can open doors and even change the trajectory of a career. And if the person doing the declaring is particularly influential, other people will repeat their pronouncement as a given. The speed and opacity of startup trajectories make it impossible to really know how impactful someone was (how to separate the seat from the rocket ship), so personal endorsements carry a tremendous amount of weight. Which means that some reputations are built on much less than you would assume.
This is troubling, especially because influential people tend to skew white and male, as do their networks, which only reinforces existing power structures. But it’s also an incredible opportunity to elevate deserving but underappreciated and underrepresented people—especially if you yourself are influential. I doubt many people know how much weight their words carry.
Of course, if you’re not accustomed to wielding this power—or asking for it to be wielded on your behalf—it can feel pretty uncomfortable. Women in particular have a harder time transitioning from the personal and emotional to the transactional in their relationships. My female friends and I have discussed this extensively and have even experimented with a “favor swap” event where the whole point is to get transactional. Maybe this is what Lean In Circles should have been all along—lead with the favors, not the feelings.
4. Your former co-workers are your rocks, so keep them close
This one is simple, but important. We all know how critical it is to build strong relationships inside a company, but it wasn’t until I moved on from my first startup job that I realized how incredibly valuable co-worker relationships become after you leave.  And once you’re no longer co-workers, all those pesky work-related complications and politics disappear.
Your non-co-worker friends will of course cheer you on, but if you’re in a rut professionally or trying to figure out if you’re the one being difficult in a dysfunctional work relationship, no one can help you troubleshoot like your former colleagues. Same if you need a substantive ego boost. And because your relationship started in a work context, it’s also much easier to be transactional, whether that means asking for intros, references, funding, or feedback.


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SP4 Questions
First, an update on my friend – she is doing as well as we could hope, healing and recovering in the ICU at Stanford. Many ups and downs but she’s made it clear she doesn’t want to go so all we can do is send her our love and strength.
My knitting progress on Sherbet was pretty good until last night, but then I decided I didn’t like the increases on the sleeves (they were holey) and as a result the side didn’t look right when I stitched it up, so… rrrrriiiippp!!! My husband can’t understand how I can like a hobby where you undo your work so frequently. But I learned good stuff and didn’t waste any material. How cool is that? Tonight I’ll make a *good* sleeve while watching TV, and I’ll stitch that baby on and it will be good. Have to be somewhat expeditious about this since Kaily’s bday is on Saturday.
Here’s my SP4 list for my SP4 pal (and anyone else who might be curious):
1.  Do you avoid Red Heart and Lion Brand? Or is it all the same to you?
I like natural fibers, so I’m kind of a yarn snob… although I have enjoyed working with novelty yarns (for this hat and this scarf)
- Do you spin? Crochet?
While I have admired the wheely things at the yarn shop I have so far resisted their call. And the crochet needles in my possession are only for fixing dropped stitches. So far.
- Do you have any allergies? (smoke, pets, fibers, perfume, etc.)
Smoke.
- How long have you been knitting?
Since Thanksgiving weekend. It’s all Michelle’s fault.
- Do you have an Amazon or other online wish list?
I do, but it’s not very up-to-date.
- What’s your favorite scent? (for candles, bath products etc.)
My favorite smell *ever* is the Coconut Lime Verbeena stuff from bath and body works. I generally like vanilla and tropical scents, I’m not so much for the more ‘grown-up’ smells (like lavender).
- Do you have a sweet tooth?
Yes.
- What other crafts or Do-It-Yourself things do you like to do?
I used to be super-non-crafty but recently I’ve started doing more general stuff. I’m going to spend some time gardening this year, I think. I like sewing, although I’m not very good yet.
- What kind of music do you like? Can your computer/stereo play MP3s? (if your buddy wants to make you a CD)
My PowerBook can play MP3s… my ipod can play them too
- What’s your favorite color?  Do you have any pets?
Husband, 1. Children, 2 (Boy, 5; Girl, 7). Cats, 2. Dogs, 2. Fish, lots.
- What are your life dreams? (really stretching it here, I know)
To achieve zen mastery. Be a good mom. Have fun.
- What is/are your favorite yarn/s to knit with?
Haven’t been knitting long enough to really find a preference. My current project is with cotton tape and I think I might use that for a summer tank – I really like the way it looks. I’d like to make something with Noro…
- What fibers do you absolutely *not* like?
Acrylic. Ew.
- What is/are your current knitting obsession/s?
Socks! I have to make some for Sockapalooza!
- What is/are your favorite item/s to knit?
I’ve enjoyed all of the projects I’ve made, but I am really enjoying the sweater I’m working on now… putting the pieces together into a whole “thing” is really satisfying.
- What are you knitting right now?
Sherbet, due at a birthday party on Saturday.
- What do you think about ponchos?
Haven’t decided yet. I have some baby alpaca which may become a scarf someday.
- Do you prefer straight or circular needles?
Depends on what I’m working on…
- Bamboo, aluminum, plastic?
I like wooden needles (harder wood) and the Inox metal needles, and my Denise needles are great too. I’m not fond of the acrylic needles, even though they are fun to look at.
- Are you a sock knitter?
Not right at this moment. But the deadline for Sockapalooza is March 1 so I’d better get cracking! (right after Sherbet)
- How did you learn to knit?
From Michelle.
- How old is your oldest UFO?
Ha! I don’t keep UFO’s around. I rip them unceremoniously down to their elemental parts. I think I just haven’t been knitting long enough to accumulate UFOs…
- What is your favorite animated character or a favorite animal/bird? ??
Uh, I like cats. I like The Tick. Aladdin is cool. Oh! I liked Elastigirl a lot from The Incredibles! Can you tell from my blog name?
- What is your favorite holiday?
Thanksgiving
- Is there anything that you collect?
Books… cookbooks, knitting books, science fiction books, romance books, parenting books, fitness books… well, pretty much all kinds of books.
KNITTING


Hover over gray box. When you move mouse quickly then it's not activated. When you move slower box get activated.


{% extends "index.html" %} {% block body %}
CALLIG, Computer-Assisted Language Learning using Improvisational Games, is an online platform (in development) for playing verbal improvisational games, which are generally played face-to-face and in collaboration.
In an improvisation performance, suggestions are elicited from the audience. In CALLIG, suggestions are randomly generated by the system. Spontaneity in improvisation is implemented with the use of a timer.
At this stage, only single-player games have been implemented. All games require the cognitive skills of remote association and/or divergent thinking. Both are hallmarks of creativity. These games provide contexts for creative language use.
Our games come from: (i) existing improvisational games; (ii) modifying existing games and (iii) new games designed by the team.
{% endblock %}


Guess The Number
We have selected a random number between 1 - 10.
See if you can guess it.
Enter a guess number:


When it comes to CSS Positing understanding the different positioning types and their uses can be really aggravating. This week I spent over 2 hours messing with some Divs trying to get them all in a row. So here's an attempt to break this down for others, and for my own future reference.
In my opinion Fixed Positioning is the easiest to understand so I'll start with it first. Here's an example:
#myDIV {
position:fixed;
width:100px;
height:100px;
left:10px;
top:100px;
}
So what does that do? It makes the 100x100 sized div always stay 10 px in from the left and 100 px down from the top of the BROWSER WINDOW. This means that if you resize the browser or scroll up or down the div will still sit in that exact spot. Sometimes you see this used for chat popups that stay where they are on the page as you scroll down a page.
This one is a little different, think of relative positioning as a good way to move just one element around without changing any other elements. When you use relative positioning you specify how much to move it with TOP, BOTTOM, LEFT, or RIGHT. Here's what that looks like:
#div-1 {
position:relative;
top:20px;
left:-40px;
}
The amount that the element will move, in this case 20px from the top and 40px further to the left is going to be "relative" to where it would have normally been. So when you move something relatively you move it by just that much, without worrying about what the other elements are doing. Only this element will move, everything else will stay where you put it.
This one can be more confusing because it affects the other elements on the page. Here's an example using Absolute positioning:
#div-2 {
position:absolute;
top:0;
right:0;
}
When you position something absolutely, you're positioning it in that specific location. So if you use Top 0, it will be at the very top of the page. If you move something to the top of the page, it no longer takes up the space it was in, so the other elements will adjust themselves based on the new location of the element that is specified as absolute.
Hopefuly that helps clarify the Fixed, Relative and Absolute positioning!


1: #include <petscsys.h> 2: /*@C
3: PetscGetVersion - Gets the PETSc version information in a string.
5: Input Parameter:
6: . len - length of the string
8: Output Parameter:
9: . version - version string
11: Level: developer
13: Fortran Note:
14: This routine is not supported in Fortran.
16: For doing runtime checking off supported versions we recommend using PetscGetVersionNumber() instead of this routine.
18: Developer Note: The version information is also listed in
19: $ src/docs/tex/manual/intro.tex,
20: $ src/docs/tex/manual/manual.tex.
21: $ src/docs/website/index.html.
23: .seealso: PetscGetProgramName(), PetscGetVersionNumber()
25: @*/
27: PetscErrorCodePetscGetVersion(char version[], size_t len) 28: {
32: #if (PETSC_VERSION_RELEASE == 1)
33: PetscSNPrintf(version,len,"Petsc Release Version %d.%d.%d, %s ",PETSC_VERSION_MAJOR,PETSC_VERSION_MINOR, PETSC_VERSION_SUBMINOR,PETSC_VERSION_DATE);
34: #else
35: PetscSNPrintf(version,len,"Petsc Development GIT revision: %s GIT Date: %s",PETSC_VERSION_GIT, PETSC_VERSION_DATE_GIT);
36: #endif
37: return(0);
38: }
40: /*@C
41: PetscGetVersionNumber - Gets the PETSc version information from the library
43: Not collective
45: Output Parameter:
46: + major - the major version (optional, pass NULL if not requested)
47: . minor - the minor version (optional, pass NULL if not requested)
48: . subminor - the subminor version (patch number) (optional, pass NULL if not requested)
49: - release - indicates the library is from a release, not random git repository (optional, pass NULL if not requested)
51: Level: developer
53: Notes: The C macros PETSC_VERSION_MAJOR, PETSC_VERSION_MINOR, PETSC_VERSION_SUBMINOR, PETSC_VERSION_RELEASE provide the information at
54: compile time. This can be used to confirm that the shared library being loaded at runtime has the appropriate version updates.
56: This function can be called before PetscInitialize()
58: .seealso: PetscGetProgramName(), PetscGetVersion(), PetscInitialize()
60: @*/
61: PetscErrorCodePetscGetVersionNumber(PetscInt *major, PetscInt *minor, PetscInt *subminor,PetscInt *release) 62: {
63: if (major) *major = PETSC_VERSION_MAJOR;
64: if (minor) *minor = PETSC_VERSION_MINOR;
65: if (subminor) *subminor = PETSC_VERSION_SUBMINOR;
66: if (release) *release = PETSC_VERSION_RELEASE;
67: return 0;
68: }


Project Description
Last time, we implemented a web scraper to extract game reviews and store them in a MongoDB collection. Although the last project was valuable on its own, it would be nice to extend it so that we can also explore the data we have collected. One solution is to create a website that allows us to make queries to the MongoDB collection and display the results.
There are a number of full-featured web frameworks available in Python (Django, Pyramid, Web2py, etc ), but as our website will be a simple one, a lightweight framework like Flask will suffice. As for the interaction with MongoDB, we are already familiar with pymongo, as we used it before to store the scraped data into the collection, so we will use it again. Finally, in the front-end, we will make use of AJAX to implement some sort of pagination on the results without having to reload the page.
Before we get to work, I will show you a screenshot of the final webpage for you to get an idea of the intended result :
As you can see our website allows to make the following queries:
- Filter reviews by genre, platform, reviewer and posted date.
- Search reviews by game name.
- Sort the displayed results either by score or posted date.
MongoDB queries
There will be two types of queries in our application: 1) search by name queries; and 2) filter queries. The latter ones have two types of conditions: equality conditions (using the $and operator if more than one condition on the same field) and range conditions (using $gt and/or $lt). Also in the latter case, it is possible to sort the results either by posted date or score of the review.
def _process_result(result):
result["genre"] = ", ".join(result["genre"])
result["platforms"] = ", ".join(result["platforms"])
result["release_date"] = str(result["release_date"]).split(' ')[0]
result["score"] = int(result["score"])
return result
def list_reviews(collection, filter_dict, sort_by="release_date", offset=0, page_size=1):
cursor = collection.find(filter_dict).sort(sort_by, pymongo.DESCENDING)
if offset > 0:
cursor = cursor.skip(offset)
cursor = cursor.limit(page_size+1)
results = [_process_result(result) for result in cursor]
has_next = len(results) > page_size
if has_next:
return results[:-1], has_next
return results, has_next
def search_by_name(collection, name, max_number_results=5):
query = {"game_name":{"$regex": name, "$options": 'i'}}
cursor = collection.find(query).limit(max_number_results)
return [_process_result(result) for result in cursor]
def build_query(eq_filters, range_filters):
filter_dict = {}
and_cond = []
for field, eq_conds in eq_filters.items():
if len(eq_conds) == 1:
filter_dict[field] = eq_conds[0]
else:
and_cond += [{field:value} for value in eq_conds]
if and_cond:
filter_dict["$and"] = and_cond
for range_field, (inf_limit, sup_limit) in range_filters.items():
_range_filter = {range_field: {}}
if inf_limit:
_range_filter[range_field]["$gt"] = inf_limit
if sup_limit:
_range_filter[range_field]["$lt"] = sup_limit
if _range_filter[range_field]:
filter_dict.update(_range_filter)
return filter_dict
Note that we request page_size+1 elements in list_reviews()
so that we can check if there are more than PAGE_SIZE matches. This will be useful later on, when we implement the pagination of results.
Flask Routing and Jinja Templates
There will be only two URLs (or routes) in our application: 1) “/index” (or just “/”) used by the end user to access the webpage in the browser; and 2) “/list” used by the JavaScript code to request results by means of AJAX.
The view function associated with the first route is really simple as it just renders a Jinja Template. This template is what actually does all the heavy lifting here, so we will focus on it.
{% macro display_options(opts_name, opts_type, items, type) -%}

<button class="btn btn-default dropdown-toggle" type="button" id="dropdownMenu1" data-toggle="dropdown"
aria-haspopup="true" aria-expanded="true" name="{{opts_type}}">
{{opts_name}}

</button>

{% for value in items %}

<input type="{{type}}" autocomplete="off" name="{{value}}"> {{value}}

{% endfor %}

{%- endmacro %}

<title>Game Reviews</title>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" href="{{ url_for('static', filename='css/bootstrap.min.css') }}">
<link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
<link href='https://fonts.googleapis.com/css?family=Oxygen:400,300,700' rel='stylesheet' type='text/css'>
<link href='https://fonts.googleapis.com/css?family=Lora' rel='stylesheet' type='text/css'>

<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar"
aria-expanded="false" aria-controls="navbar">
Toggle navigation

</button>

Game Reviews

<form class="navbar-form navbar-right">

<input id="search-filter-text" type="text" placeholder="Search" class="form-control">

<button id="search-filter-btn" type="submit" class="btn btn-success">Search</button>
</form>

{{display_options("Genres", "genre", genres, type="checkbox")}}
{{display_options("Platforms", "platforms", platforms, type="checkbox")}}
{{display_options("Reviewers", "reviewer", reviewers, type="radio")}}
{{display_options("Posted date", "posted_date", dates_intervals, type="radio")}}
{{display_options("Sort by", "order", {"release_date":"Posted date", "score":"Score"}, type="radio")}}
<button id="filter-btn" class="btn btn-primary" type="button">Filter </button>

In the html head, not only are we importing css and Javascript files (using url_for()
to get its location) as we would do on plain HTML, but we are also defining two Javascript variables that will be available on the global scope: request_params
and request_url
. The latter is just the URL for “/list” that will be used in the AJAX calls. On the flip side, the former is a Javascript object with the request arguments of the AJAX call. Notice that Flask makes the request object available in the template. An example of request_params would be:
{
genres: ["Acción", "RPG"],
platforms: "PS4",
posted_date: "Anytime",
reviewer: "any",
sort_by: "score"
}
In the html body we define the filtering menu and the search bar. Note that we make use of the display_options()
macro to avoid code duplication. Also note the empty “main-content” div. Here is where the HTML obtained via AJAX will be located.
The view function associated with the second route is more complex as it gets the request arguments, transforms them into a MongoDB query, runs that query and finally renders the HTML that displays re retrieved results. The python code which does that is the following:
def extract_eq_filters():
eq_filters = {}
for key, val in request.args.items():
if key in ["reviewer", "platforms", "genre"]:
if key == "reviewer" and val == "any":
continue
else:
continue
eq_filters[key] = val.split(',')
return eq_filters
def extract_range_filters():
range_filters = {}
date_interval = request.args.get("posted_date", "Anytime")
if date_interval in ["Past week", "Past month"]:
today = datetime.date.now()
range_filters["release_date"] = {"Past week": (today - datetime.timedelta(days=7), today),
"Past month": (today - datetime.timedelta(days=30), today)
}[date_interval]
return range_filters
@app.route('/list')
def list_reviews():
if "search" in request.args:
reviews = db.search_by_name(coll, request.args.get("search"))
has_next = False
else:
eq_filters = extract_eq_filters()
range_filters = extract_range_filters()
query = db.build_query(eq_filters, range_filters)
order = request.args.get("order", "release_date")
page_num = int(request.args.get("page_num", 0))
reviews, has_next = db.list_reviews(coll, query, page_size=PAGE_SIZE,
offset=page_num*PAGE_SIZE,
sort_by=order)
return render_template('list.html', reviews=reviews,
has_next=has_next)
First, we determine whether the request is a search by name query or a filter query. If it is the former, we retrieve some reviews by using db.search_by_name()
. If it is the latter, we extract the equality and range filters, the ordering and the pagination offset from the request object. Next we use db.list_reviews()
to retrieve PAGE_SIZE number of reviews. Finally, we render the results in a template.
{% for review in reviews %}

{{review.score}}

{{ reviewers_logos[review.reviewer] | safe}}

Plataformas: {{review.platforms}}

Géneros: {{review.genre}}

Fecha de publicación: {{review.release_date}}

{{review.title}}

{{review.description}}

{% endfor %}
{% if has_next %}

<button id="next_page" class="btn btn-primary btn-lg">Next page</button>

{% endif %}
Note that if there are more than PAGE_SIZE elements matching the query, we will render a “More Results” button.
Javascript
Earlier in this article, I have stated the reason for using AJAX in our web application: paginate the results coming from the MongoDB collection without having to reload the page each time. Right after the page is loaded an AJAX request will be made to retrieve the first PAGE_SIZE reviews. Thanks to the request_params variable that we created server side, the Javascript code knows the filter parameters to query. Another AJAX request will be made if we click on the “More Results” button. Responses of AJAX requests are inserted in the “main-content” div.
const ajax_url = new URL(request_url, document.location);
const ajax_params = new URLSearchParams(request_params);
ajax_url.search = ajax_params;
var page_num = 0;
function sendGetRequest(requestUrl, responseHandler,isJsonResponse) {
var request = new XMLHttpRequest();
request.onreadystatechange =
function() {
handleResponse(request,
responseHandler,
isJsonResponse);
};
request.open("GET", requestUrl, true);
request.send(null); // for POST only
}
function handleResponse(request,
responseHandler,
isJsonResponse) {
if ((request.readyState == 4) && (request.status == 200)) {
responseHandler(request.responseText);
}
}
function insert_new_content(main_div, responseText) {
var new_div = document.createElement('div');
new_div.innerHTML = responseText;
main_div.appendChild(new_div);
var next_page_button = main_div.querySelector("#next_page");
if (next_page_button){
next_page_button.addEventListener("click", next_page, false);
}
}
document.addEventListener("DOMContentLoaded", function (event) {
sendGetRequest(
ajax_url.toString(),
function (responseText) {
var main_div = document.querySelector("#main-content");
insert_new_content(main_div, responseText);
});
});
function next_page() {
page_num += 1;
ajax_params.set("page_num", page_num);
ajax_url.search = ajax_params;
console.log(ajax_url.toString());
$ajaxUtils.sendGetRequest(
ajax_url.toString(),
function (responseText) {
var main_div = document.querySelector("#main-content");
main_div.querySelector("#next_page").remove();
insert_new_content(main_div, responseText);
},
false);
}
Conclusion
In this post we have created a web application to help us explore the data we collected previously from game reviews websites. We have used Flask to this end as it has all the features we needed while giving us full control over the structure of our project. As for the client side, we made use of AJAX to implement the pagination of the results.


Company Name
Campaign Name
Attempt 1
Attempt 2
Attempt 3
+
Subject
Preheader
From
Body
Subject
Preheader
From
Body
Subject
Preheader
From
Body


| Process Name | Actions |
|---|---|
| {{definition.key}} |
| Started At | Process Defitinion | State | Actions |
|---|---|---|---|
| {{instance.startTime | date:'short'}} | {{instance.processDefinitionKey}} |
{{instance.state}}
|


Gogol Library
Documents
Taken Documents
Add Document
Log Out
Dashboard
/
Taken Documents
Documents on user's hands
Name
Surname
Phone number
Titler
Author
Checkout date
Return date
{{#taken}}
{{name}}
{{surname}}
{{phone}}
{{title}}
{{author}}
{{checkoutDate}}
{{returnDate}}
{{/taken}}


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Fast and light weight revenue forecast with dynamic value generation and basic automated error/data checking. Ideal for last minute investor calls or time crunch situations.
Keying in baseline assumptions in the dashboard panel makes for fast and accurate model building. Easy to understand UI and can be used by non-technical personnel.
Full scale DCF of AT&T Inc. Forecasts assumption balance sheets and income statements, projects FCF and computes WACC. Derives terminal value using exit multiple method and perpetuity/Gordon growth
Originally made for class project back in college. Production level DCF models available for sampling by request only.
Porfolio table for select small cap value stocks. Pulls live stock data from Yahoo! Finance API (YQL) using xlwings library and requests module. Written in Python.
Replaces excel VBA with Python Programming language, leading to more robust and efficient code base.
Flipbook is a company screening and lead generation tool for middle market investment banks and private equity firms. The purpose of this tool is to eliminate unnecessary copy & pasting between your workbook and browser, improving efficiency
Written in C#, served via IIS7. Co-partnered project. Built front end only.
I'm John, thanks for visiting my porfolio. I like to keep things simple, clean and organized.
I do a lot of work in excel and I like to program a bit as well on the side. In my free time, I enjoy strumming away on the guitar and being a tourist in my own city.
I can be reached at


Describe how your culture has influenced your values and identity:
I grew up in a diverse cultural group in Pune, India. My Identity is a product of my upbringing and experiences in life. My mom has a strong influence on my life, she is kind, selfless, hardworking, and dedicated. She maintained the perfect balance of work-life and family and it makes me realize how work and family are equally important. Other than that Strong family values, respect, compassion are some of the other values I have maintained from my culture.
Explain a situation where you have had an ethical decision to make. Discuss how you weighed up the values involved in that decision, the decision you made and the consequences of the action you took.
I remember the incident when I have to resign from my first job, I was working one of the best teams and the work culture and environment were good as well but due to lack of staff movement my growth was limited I have to take the tough decision to move elsewhere after spending a couple of years. I was feeling like I am letting the team down and not trusting them enough, but when I announced my decision my team was actually very happy for me and back me up. Moving forward I realize it was one of the best decisions I have taken to shape my career and personal growth.
Strengths:
I am excellent manager of day-to-day tasks and routine maintenance and make sure that those who are close to me are well cared for. I have a strong sense of responsibility and strive to meet my obligations, though this may sometimes be more from a sense of social expectations than intrinsic drive. I am always loyal and trustworthy and true pillars of any groups I belong to – whether it is their family or a community club, people can always be relied upon me. I seek harmony and care deeply about other people’s feelings, being careful not to offend or hurt anybody. I’m strong team player.
Limitations:
Public speaking, Perfectionism, communication
Describe your core personal values.
The core personal values I hold are compassion, kindness, and honesty to myself and others. Also respecting other people, different opinion and point of views.


Oxford Union president resigns amid row over blind student
The president of the Oxford Union has resigned after a blind student was forcibly removed from a debate event by a security guards.
Postgraduate student Ebenezer Azamati was taken from the venue by a security guard when he tried to return to a seat before the debate last month.
Union president Brendan McGrath wrote in a statement posted to Facebook late on Tuesday that he had resigned because it was his “job to ensure that every Member feels welcome at the Oxford Union”.
McGrath was set for a vote of impeachment, which had required the necessary 150 signatures to take place, but has instead decided to quit.
Sorry, this content isn't available on your device.
“For all of my shortcomings, and all of my mistakes, I apologise profusely and unqualifiedly,” he added.
Since the incident took place the Oxford Union's Secretary, the Chief of Staff, the Director of Press and the Director of Operations, have all left their roles, according to The Cherwell.
Mr Azamati, an international relations scholar from Ghana, said his treatment left him feeling “unwelcome in the Union, Oxford and even the country”.
He successfully appealed on Saturday against what he told the BBC were claims of “false violent disorder” by the union.
The society operates independently of the university and has hosted speakers and debates since 1823.


Hi. Inever played a Bard, nor Blackguard before. I wanted to play this combo though SOU & HOTU. I was thinking Bard 4/ PM 6/ BG 10, pre epic, but then what? Starting Attributes? What feats? Any ideas? Thanks for any help.
Edited By Dra'vex on 01/24/09 00:45
There are 8 builds listed in the search engine that use this combo. Have you looked over any of them yet?
My personal favorite is Immortal Beloved, but you won't find that in the Index. _________________ Got Hommlet? World of Greyhawk Action Server (with 1/2 price ales on Mondays!)
Ariel, Ookla, RIDE!
Bard 26/BG 4/PM 10 (Bard 16/BG 4) preepic sounds good. Pump CHA up to 20 and the rest into STR. Divine Might and Shield, Lasting Inspiration will be key features. One could go for Bard 20/BG 4/PM 16 too and grab epic spells. _________________ They can't stop us Let them try For Heavy Metal We will die
Quote: Posted 01/24/09 01:51 (GMT) -- galelabriel
There are 8 builds listed in the search engine that use this combo. Have you looked over any of them yet?
Forgot. Found them, gave me some ideas, but i'm going a little bit different.
Bard 26/BG 4/PM 10 (Bard 16/BG 4) preepic sounds good. Pump CHA up to 20 and the rest into STR. Divine Might and Shield, Lasting Inspiration will be key features. One could go for Bard 20/BG 4/PM 16 too and grab epic spells.
Thanks. I've decided on Bard 11/ PM 16/ BG 13, with Devastating Crit and Divine Might. Currently, i'm in SOU at Bard 5/ PM 3. Pre-epic, i'll go to Bard 8/ PM 4/ BG 8.


|
libfly
6.2.2
C++20 utility library for Linux, macOS, and Windows
|
#include <huffman_encoder.hpp>
Public Member Functions | |
| HuffmanEncoder (const std::shared_ptr< CoderConfig > &config) noexcept | |
| Public Member Functions inherited from fly::coders::Encoder | |
| virtual | ~Encoder ()=default |
| virtual bool | encode_string (const std::string &decoded, std::string &encoded) |
| virtual bool | encode_file (const std::filesystem::path &decoded, const std::filesystem::path &encoded) |
Protected Member Functions | |
| bool | encode_binary (std::istream &decoded, fly::BitStreamWriter &encoded) override |
| Protected Member Functions inherited from fly::coders::BinaryEncoder | |
| bool | encode_internal (std::istream &decoded, std::ostream &encoded) final |
Implementation of the Encoder interface for Huffman coding. Forms length-limted, canonical Huffman codes to encode symbols.
|
explicitnoexcept |
Constructor.
| config | Reference to coder configuration. |
|
overrideprotectedvirtual |
Huffman encode a stream.
If the input stream is large, in order to limit memory usage, the stream is encoded in chunks. Each chunk is treated as its own input stream, and the encoding sequence is repeated for each chunk.
The first bytes of the output stream are reserved as a header. Currently, the header contains: the incurred BitStream header, the version of the Huffman coder used to encode the stream, the maximum chunk length used to to split large streams (in kilobytes), and the maximum allowed Huffman code length:
| 8 bits | 8 bits | 16 bits | 8 bits | -------------------------------------------------------------------- | BitStream header | Version | Chunk length (KB) | Max code length |
The sequence to encode a stream is:
1. Create a Huffman tree from the input stream. 2. Generate standard Huffman codes from the Huffman tree. 3. Length-limit the standard Huffman codes. 4. Convert the length-limited codes to canonical Huffman codes. 5. Encode the canonical codes. 6. Encode the input stream using the canonical codes.
This sequence involves iterating over the entire input stream twice (to create the Huffman tree and to encode the stream).
The coder does not assume the Huffman codes are retained between calls. Thus, the codes are encoded before the input stream (step 5) so that they may be learned during decoding.
Length-limiting is performed on the generated Huffman codes to improve decoder performance. Worst-case, a Huffman code could have the same length as the maximum number of symbols. Limiting the length of Huffman codes awards a significant decoder performance improvement, while only incurring a small cost in compression ratio.
Canonical form is used for its property of generally being describable in fewer bits than standard form. When in canonical form, the Huffman codes are sorted by code length. With this sorting, the count of the number of symbols for each code length is computed: (N0, N1, N2, ..., Nn), where N<n> is the number of symbols of code length <n>. Call the length of this list NN.
The encoding of canonical Huffman codes then becomes:
NN,N0,N1,N2,...,Nn,S0,S1,S2,...,Sn
Where S<n> is all symbols of code length <n>.
Encoding the input stream (step 6) consists of reading each symbol from the input stream and outputting that symbol's canonical Huffman code.
| decoded | Stream holding the contents to encode. |
| encoded | Stream to store the encoded contents. |
Implements fly::coders::BinaryEncoder.


What is your long term goal and or career pathway?
My long term career pathway is to become an accomplished Web Developer. I don’t want to be limited to just “Front-end”
or one single area. I want to have the chance to explore all sides of the tech industry.  But whatever “tech” is in 20 years I want to be at the forefront of it.
What skills (non-technical) would you like to see developed in yourself while at EDA?
The skills in myself that I would like to see developed while at EDA are my mindset, and my leadership skills. I like that EDA
exposes us to the idea of having a “growth mindset”. This is something I really want to see myself take on and push. The other
skill is leadership. I want to be able to motivate and enable others in my space to move forward.
A description of your own strengths and limitations
One of the strengths I have is my self-awareness. I am usually quite aware of my surroundings and I try to work with how the
energy is around me. I think I can feel when people aren’t happy with something, or something is off. This also helps me think
of alternative solutions to problems by kind of taking a step back and analysing the situation.
Another strength I believe I have is my honesty. This is a hard one because honesty can be painful. But I think being honest
especially when it comes to critiquing someone’s work is vital for them to maximise their potential.
Something I struggle with is self-doubt. Seeing people who are highly skilled at something can make me doubt my own abilities,
I really would love to stop this thinking and become more comfortable with not knowing everything. I think this ties in with the
“growth mindset”.
I don’t consider myself an optimist, I see myself as more of a realist. I analyse situations like I mentioned before and often
forget to be ‘present’ and happy in the moment. I would like to change this and have a more optimistic outlook on life.
Workload management
To manage my workload in this programme I need to be pushed. Not by someone else but I need a sense of urgency in what I do.
The pressure helps to fuel my motivation. To work with this, I will continue writing daily goals and if I achieve them I will
make them bigger, and If I don’t I will try to find out why. The goals help me to stay motivated and productive. I am also very
driven to become a developer, It has been my long term goal for a number of years now.
To seek help I will try to communicate with my cohort as much as possible, I think with a collective mind we can all help each
other.
I expect timely responses from the facilitation team. Also I expect them to ask me questions when I am stuck to help my problem
solving skills.
I currently have no other commitments for the duration of my time at EDA. But I will take breaks If I mentally feel the need to.


Eve Muirhead: GB curling skip pushes through pain for 'last' Olympic place
by Heather DewarGreat Britain curler Eve Muirhead has admitted that continuing hip trouble means the next Winter Olympics in Beijing could be her last.
The 29-year-old Scot, who took bronze at Sochi 2014, says she will push for an Olympic spot in 2022 then "see what happens".
Despite surgery in 2018, she told BBC Scotland it is "difficult to play with this pain".
"You have good days and bad days; I can't really control it," she said.
Muirhead will take part in her 10th European Championships in Helsingborg, Sweden, starting this weekend.
She has gathered eight medals for Scotland from her nine previous visits, including gold in 2017 and 2011.
"Every day I do rehab and try to keep on top of it," she said of her condition. "But I would rather be tired and pain free than be all jolly. There's a lot of pain. It's a lot of time and effort. Being a full-time athlete, its always going to be hard.
"I feel like I'm getting old. I'm 30 in a few months and I feel like I'm getting to the mid to end of my career, so I really want to push for that last Olympic spot and from then on, who knows what I will do."
Muirhead was just 19 when she made her Olympic debut in Vancouver in 2010 and went into the 2014 Games in Russia as world champion.
After narrowly missing out on another bronze medal at Pyeongchang 2018, the skip had keyhole surgery, similar to that undertaken by tennis star Andy Murray 12 months before he had a form of hip replacement.
"The next few years I know I'm in a good spot and can still play well, so I will buckle down, keep working hard with the physios, the coaches, and see what happens," she said of her quest for a fourth Olympic appearance.
"When I went through my op and off the back of coming fourth at the Olympics, it wasn't a great year to be honest. It was a bit of a struggle. I dealt with it quite well and I asked for help when I needed to.
"Every athlete goes through good times and bad times, that's a fact. It can be very tough at times, but then at others, its great. I try not to let anything get me down."


WITNESS LIST Government Reform Committee July 7, 2003-9:00A HB 53 On: Shorter, III, Coby (Office of the Governor) HB 54 Against: Mitchell, Kathy (Consumers Union) Whalen, Ken (TX Daily Newspaper Ass. & TX Press Ass.) On: Roberts, Wayne (Office of the Governor) HB 57 On: Gilman, Tony (Ed Perez, Executive Director, Texas Office of State-Federal Relations) Roberts, Wayne (Office of the Governor) HB 58 On: Adams, Greg (State Auditor's Office) Ray, Jim (Texas Association of Regional Councils) HB 64 For: Thompson, Jay (Prudential Ins. Co.) On: Biles, Susan (Texas Comptroller of Public Accounts)


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Imagine you’re a machine learning practitioner and you want to solve some classification problem, like classifying groups of colored squares as being either 1s or 0s. Here’s what you would typically do: collect a large dataset of examples, label the data, and train a classifier:
But humans don’t learn like this. We have a very powerful and intuitive mechanism for communicating information about the world - language!
With just the phrase at least 2 red squares, we’ve summarized the entire dataset presented above in a much more efficient manner.
Language is a crucial medium for human learning: we use it to convey beliefs about the world, teach others, and describe things that are hard to experience directly. Thus, language ought to be a simple and effective way to supervise machine learning models. Yet past approaches to learning from language have struggled to scale up to the general tasks targeted by modern deep learning systems and the freeform language explanations used in these domains. In two short papers presented at ACL 2020 this year, we use deep neural models to learn from language explanations to help tackle a variety of challenging tasks in natural language processing (NLP) and computer vision.
- ExpBERT: Representation Engineering with Natural Language Explanations
- Shaping Visual Representations with Language for Few-shot Classification
What’s the challenge?
Given that language is such an intuitive interface for humans to teach others, why is it so hard to use language for machine learning?
The principal challenge is the grounding problem: understanding language explanations in the context of other inputs. Building models that can understand rich and ambiguous language is tricky enough, but building models that can relate language to the surrounding world is even more challenging. For instance, given the explanation at least two red squares, a model must not only understand the terms red and square, but also how they refer to particular parts of (often complex) inputs.
Past work (1,
2,
3) has relied on semantic
parsers which
convert natural language statements (e.g. at least two red squares) to formal
logical representations (e.g. Count(Square AND Red) > 2
). If we can easily
check whether explanations apply to our inputs by executing these logical
formulas, we can use our explanations as features to train our model.
However, semantic parsers only work on simple domains
where we can hand-engineer a logical grammar of explanations we might expect to
see. They struggle to handle richer and vaguer language or scale up to more
complex inputs, such as images.
Fortunately, modern deep neural language models such as BERT are beginning to show promise at solving many language understanding tasks. Our papers propose to alleviate the grounding problem by using neural language models that are either trained to ground language explanations in the domain of interest, or come pre-trained with general-purpose “knowledge” that can be used to interpret explanations. We will show that these neural models allow us to learn from richer and more diverse language for more challenging settings.
Representation Engineering with Natural Language Explanations
In our first paper, we examine how to build text classifiers with language explanations. Consider the task of relation extraction, where we are given a short paragraph and must identify whether two people mentioned in the paragraph are married. While state-of-the-art NLP models can likely solve this task from data alone, humans might use language to describe ways to tell whether two people are married—for example, people who go on honeymoons are typically married. Can such language explanations be used to train better classifiers?
In the same way that we might take an input , and extract features (e.g. the presence of certain words) to train a model, we can use explanations to provide additional features. For example, knowing that honeymoons are relevant for this task, if we can create a honeymoon feature that reliably activates whenever the two people in a paragraph are described as going on a honeymoon, this should be useful signal for training a better model.
But creating such features requires some sort of explanation interpretation mechanism that tells us whether an explanation is true for an input. Semantic parsers are one such tool: given and went on honeymoon, we could parse this explanation into a logical form which, when run on an input, produces 1 if the word honeymoon appears between and . But what about a vaguer explanation like and are in love? How can we parse this?
While semantic parsing is efficient and accurate in small domains, it can be
overly brittle, as it can only interpret explanations which adhere to a fixed
set of grammatical rules and functions that we must specify in advance (e.g.
contains
and extract_text
).
Instead, we turn to the soft reasoning
capabilities of BERT, a neural language model. BERT is particularly effective
at the task of textual entailment: determining whether a sentence implies or
contradicts another sentence (e.g. does She ate pizza imply that She ate
food? Yes!). In our proposed ExpBERT model, we take a BERT model
trained for textual entailment, and instead ask it to identify whether a
paragraph in our task entails an explanation. The features produced by BERT
during this process replace the indicator features produced by the semantic
parser above.
Does the soft reasoning power of BERT improve over semantic parsing? On the marriage identification task, we find that ExpBERT leads to substantial improvements over a classifier that is trained on the input features only (No Explanations). Importantly, using a semantic parser to try to parse explanations doesn’t help much, since there are general explanations (in love) that are difficult to convert to logical forms.
In the full paper, we compare to more baselines, explore larger relation extraction tasks (e.g. TACRED), conduct ablation studies to understand what kinds of explanations are important, and examine how much more efficient explanations are compared to additional data.
Shaping Visual Representations with Language
The work we’ve just described uses natural language explanations for a single task like marriage identification. However, work in cognitive science suggests that language also equips us with the right features and abstractions that help us solve future tasks. For example, explanations that indicate whether person is married to also highlight other concepts that are crucial to human relationships: children, daughters, honeymoons, and more. Knowing these additional concepts are not just useful for identifying married people; they are also important if we would later like to identify other relationships (e.g. siblings, mother, father).
In machine learning, we might ask: how can language point out the right features for challenging and underspecified domains, if we ultimately wish to solve new tasks where no language is available? In our second paper, we explore this setting, additionally increasing the challenge by seeing whether language can improve the learning of representations across modalities—here, vision.
We’re specifically interested in few-shot visual reasoning tasks like the following (here, from the ShapeWorld dataset):
Given a small training set of examples of a visual concept, the task is to determine whether a held-out test image expresses the same concept. Now, what if we assume access to language explanations of the relevant visual concepts at training time? Can we use these to learn a better model, even if no language is available at test time?
We frame this as a meta-learning task: instead of training and testing a model on a single task, we train a model on a set of tasks, each with a small training set and an accompanying language description (the meta-train set). We then test generalization to a meta-test set of unseen tasks, for which no language is available:
First, let’s look at how we might solve this task without language. One typical approach is Prototype Networks, where we learn some model (here, a deep convolutional neural network) that embeds the training images, averages them, and compares to an embedding of the test image:
To use language, we propose a simple approach called Language Shaped Learning (LSL): if we have access to explanations at training time, we encourage the model to learn representations that are not only helpful for classification, but are predictive of the language explanations. We do this by introducing an auxiliary training objective (i.e. it is not related to the ultimate task of interest), where we simultaneously train a recurrent neural network (RNN) decoder to predict the explanation(s) from the representation of the input images. Crucially, training this decoder depends on the parameters of our image model , so this process should encourage to better encode the features and abstractions exposed in language.
In effect, we are training the model to “think out loud” when representing concepts at training time. At test time, we simply discard the RNN decoder, and do classification as normal with the “language-shaped” image embeddings.
We apply this model to both the ShapeWorld dataset described above, and a more realistic Birds dataset, with real images and human language:
In both cases, this auxiliary training objective improves performance over a no-explanation baseline (Meta), and Learning with Latent Language (L3), a similar model proposed for this setting that uses language as a discrete bottleneck (see the paper for details):
In the full paper, we also explore which parts of language are most important (spoiler: a little bit of everything), and how much language is needed for LSL to improve over models that don’t use language (spoiler: surprisingly little!)
Moving Forward
As NLP systems grow in their ability to understand and produce language, so too grows the potential for machine learning systems to learn from language to solve other challenging tasks. In the papers above, we’ve shown that deep neural language models can be used to successfully learn from language explanations to improve generalization across a variety of tasks in vision and NLP.
We think this is an exciting new avenue for training machine learning models, and similar ideas are already being explored in areas such as reinforcement learning (4, 5). We envision a future where in order to solve a machine learning task, we no longer have to collect a large labeled dataset, but instead interact naturally and expressively with a model in the same way that humans have interacted with each other for millennia—through language.
Acknowledgments
Thanks to our coauthors (Pang Wei Koh, Percy Liang, and Noah Goodman), and to Nelson Liu, Pang Wei Koh, and the rest of the SAIL blog team for reviewing and publishing this blog post. This research was supported in part by the Facebook Fellowship (to Pang Wei Koh), the NSF Graduate Research Fellowship (to Jesse Mu), Toyota Research Institute, and the Office of Naval Research.


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Anyframe Common Eclipse Plug-inNoticeNOTICE file corresponding to the section 4 d of the Apache License, Version 2.0, in this case for the Anyframe Common Eclipse Plug-In distribution. Anyframe Common Eclipse Plug-In Copyright 2007-2012 Samsung SDS Co., Ltd. This product includes software developed by Samsung SDS Co., Ltd. (http://www.sds.samsung.com/). The latest distribution of this product and related information is available at http://www.anyframejava.org/. Some other features may be built by linking with various open source software. The original software and related information is available at each open source software information page. |
|
|


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Both the Environment
and
Database
classes provide methods that are useful
for manipulating databases. These methods are:
Database.getDatabaseName()
Returns the database's name.
String dbName = myDatabase.getDatabaseName();
Database.getEnvironment()
Returns the Environment
that contains this database.
Environment theEnv = myDatabase.getEnvironment();
Database.preload()
Preloads the database into the in-memory cache. Optionally takes
a long
that identifies the maximum number of bytes to load into the
cache. If this parameter is not supplied, the maximum memory usage
allowed by the evictor thread is used.
myDatabase.preload(1048576l); // 1024*1024
Environment.getDatabaseNames()
Returns a list of Strings of all the databases contained by the environment.
import java.util.List; ... List myDbNames = myDbEnv.getDatabaseNames(); for(int i=0; i < myDbNames.size(); i++) { System.out.println("Database Name: " + (String)myDbNames.get(i)); }
Environment.removeDatabase()
Deletes the database. The database must be closed when you perform this action on it.
String dbName = myDatabase.getDatabaseName(); myDatabase.close(); myDbEnv.removeDatabase(null, dbName);
Environment.renameDatabase()
Renames the database. The database must be closed when you perform this action on it.
String oldName = myDatabase.getDatabaseName(); String newName = new String(oldName + ".new", "UTF-8"); myDatabase.close(); myDbEnv.renameDatabase(null, oldName, newName);
Environment.truncateDatabase()
Deletes every record in the database and optionally returns the number of records that were deleted. Note that it is much less expensive to truncate a database without counting the number of records deleted than it is to truncate and count.
int numDiscarded = myEnv.truncate(null, // txn handle myDatabase.getDatabaseName(), // database name true); // If true, then the // number of records // deleted are counted. System.out.println("Discarded " + numDiscarded + " records from database " + myDatabase.getDatabaseName());


Navigation:
Question 1 (Bar Chart)
Question 2 (Bar Chart)
Question 3 (Line Graph)
Question 4 (Network Graph)
Question 5 (Bar Chart)
Construct a citation web for the base paper with title “ Low-density parity check codes over GF(q)” .


1 reStructuredText Demonstration¶
1.1 Examples of Syntax Constructs¶
| Author: | David Goodger |
|---|---|
| Address: | 123 Example Street Example, EX Canada A1B 2C3 |
| Contact: | |
| Authors: | Me; Myself; I |
| organization: | humankind |
| date: | $Date: 2012-01-03 19:23:53 +0000 (Tue, 03 Jan 2012) $ |
| status: | This is a “work in progress” |
| revision: | $Revision: 7302 $ |
| version: | 1 |
| copyright: | This document has been placed in the public domain. You may do with it as you wish. You may copy, modify, redistribute, reattribute, sell, buy, rent, lease, destroy, or improve it, quote it at length, excerpt, incorporate, collate, fold, staple, or mutilate it, or do anything else to it that your or anyone else’s heart desires. |
| field name: | This is a generic bibliographic field. |
| field name 2: | Generic bibliographic fields may contain multiple body elements. Like this. |
| Dedication: | For Docutils users & co-developers. |
| abstract: | This document is a demonstration of the reStructuredText markup language, containing examples of all basic reStructuredText constructs and many advanced constructs. |
Table of Contents
- 1 reStructuredText Demonstration
- 1.1 Examples of Syntax Constructs
- 1.1.1 Structural Elements
- 1.1.2 Body Elements
- <IP_ADDRESS> Paragraphs
- <IP_ADDRESS> Bullet Lists
- <IP_ADDRESS> Enumerated Lists
- <IP_ADDRESS> Definition Lists
- <IP_ADDRESS> Field Lists
- <IP_ADDRESS> Option Lists
- <IP_ADDRESS> Literal Blocks
- <IP_ADDRESS> Line Blocks
- <IP_ADDRESS> Block Quotes
- <IP_ADDRESS> Doctest Blocks
- <IP_ADDRESS> Tables
- <IP_ADDRESS> Footnotes
- <IP_ADDRESS> Citations
- <IP_ADDRESS> Targets
- <IP_ADDRESS> Directives
- <IP_ADDRESS> Substitution Definitions
- <IP_ADDRESS> Comments
- 1.1.3 Field Lists
- 1.1.4 Error Handling
- 1.1 Examples of Syntax Constructs
1.1.1 Structural Elements¶
<IP_ADDRESS> Section Title¶
That’s it, the text just above this line.
1.1.2 Body Elements¶
<IP_ADDRESS> Paragraphs¶
A paragraph.
<IP_ADDRESS>.1 Inline Markup¶
Paragraphs contain text and may contain inline markup: emphasis, strong emphasis, inline literals, standalone hyperlinks (http://www.python.org), external hyperlinks (Python [5]), internal cross-references (example), external hyperlinks with embedded URIs (Python web site), footnote references (manually numbered [1], anonymous auto-numbered [3], labeled auto-numbered [2], or symbolic [*]), citation references ([CIT2002]), substitution references (), and inline hyperlink targets (see Targets below for a reference back to here). Character-level inline markup is also possible (although exceedingly ugly!) in reStructuredText. Problems are indicated by |problematic| text (generated by processing errors; this one is intentional).
The default role for interpreted text is Title Reference. Here are some explicit interpreted text roles: a PEP reference (PEP 287); an RFC reference (RFC 2822); a subscript; a superscript; and explicit roles for standard inline markup.
Let’s test wrapping and whitespace significance in inline literals: This is an example of --inline-literal --text, --including some-- strangely--hyphenated-words. Adjust-the-width-of-your-browser-window to see how the text is wrapped. -- ---- -------- Now note the spacing between the words of this sentence (words should be grouped in pairs).
If the --pep-references option was supplied, there should be a live link to PEP 258 here.
<IP_ADDRESS> Bullet Lists¶
A bullet list
- Nested bullet list.
- Nested item 2.
Item 2.
Paragraph 2 of item 2.
- Nested bullet list.
- Nested item 2.
- Third level.
- Item 2.
- Nested item 3.
<IP_ADDRESS> Enumerated Lists¶
Arabic numerals.
- lower alpha)
- (lower roman)
- upper alpha.
- upper roman)
- upper alpha.
- (lower roman)
- lower alpha)
Lists that don’t start at 1:
- Three
- Four
- C
- D
- iii
- iv
List items may also be auto-enumerated.
<IP_ADDRESS> Definition Lists¶
- Term
- Definition
- Term : classifier
Definition paragraph 1.
Definition paragraph 2.
- Term
- Definition
<IP_ADDRESS> Field Lists¶
| what: | Field lists map field names to field bodies, like database records. They are often part of an extension syntax. They are an unambiguous variant of RFC 2822 fields. |
|---|---|
| how arg1 arg2: | The field marker is a colon, the field name, and a colon. The field body may contain one or more body elements, indented relative to the field marker. |
<IP_ADDRESS> Option Lists¶
For listing command-line options:
| -a | command-line option “a” |
| -b file | options can have arguments and long descriptions |
| --long | options can be long also |
| --input=file | long options can also have arguments |
| --very-long-option | |
The description can also start on the next line. The description may contain multiple body elements, regardless of where it starts. | |
| -x, -y, -z | Multiple options are an “option group”. |
| -v, --verbose | Commonly-seen: short & long options. |
| -1 file, --one=file, --two file | |
| Multiple options with arguments. | |
| /V | DOS/VMS-style options too |
There must be at least two spaces between the option and the description.
<IP_ADDRESS> Literal Blocks¶
Literal blocks are indicated with a double-colon (”::”) at the end of the preceding paragraph (over there -->). They can be indented:
if literal_block:
text = 'is left as-is'
spaces_and_linebreaks = 'are preserved'
markup_processing = None
Or they can be quoted without indentation:
>> Great idea!
>
> Why didn't I think of that?
<IP_ADDRESS> Line Blocks¶
Take it away, Eric the Orchestra Leader!
A one, two, a one two three fourHalf a bee, philosophically,must, ipso facto, half not be.But half the bee has got to be,vis a vis its entity. D’you see?But can a bee be said to beor not to be an entire bee,when half the bee is not a bee,due to some ancient injury?Singing...
<IP_ADDRESS> Block Quotes¶
Block quotes consist of indented body elements:
My theory by A. Elk. Brackets Miss, brackets. This theory goes as follows and begins now. All brontosauruses are thin at one end, much much thicker in the middle and then thin again at the far end. That is my theory, it is mine, and belongs to me and I own it, and what it is too.
—Anne Elk (Miss)
<IP_ADDRESS> Doctest Blocks¶
>>> print 'Python-specific usage examples; begun with ">>>"'
Python-specific usage examples; begun with ">>>"
>>> print '(cut and pasted from interactive Python sessions)'
(cut and pasted from interactive Python sessions)
<IP_ADDRESS> Tables¶
Here’s a grid table followed by a simple table:
| Header row, column 1 (header rows optional) | Header 2 | Header 3 | Header 4 |
|---|---|---|---|
| body row 1, column 1 | column 2 | column 3 | column 4 |
| body row 2 | Cells may span columns. | ||
| body row 3 | Cells may span rows. |
|
|
| body row 4 | |||
| body row 5 | Cells may also be empty: --> |
| Inputs | Output | |
|---|---|---|
| A | B | A or B |
| False | False | False |
| True | False | True |
| False | True | True |
| True | True | True |
<IP_ADDRESS> Footnotes¶
| [1] | (1, 2) A footnote contains body elements, consistently indented by at least 3 spaces. This is the footnote’s second paragraph. |
| [2] | (1, 2) Footnotes may be numbered, either manually (as in [1]) or automatically using a “#”-prefixed label. This footnote has a label so it can be referred to from multiple places, both as a footnote reference ([2]) and as a hyperlink reference (label). |
| [3] | This footnote is numbered automatically and anonymously using a label of “#” only. |
| [*] | Footnotes may also use symbols, specified with a “*” label. Here’s a reference to the next footnote: [†]. |
| [†] | This footnote shows the next symbol in the sequence. |
| [4] | Here’s an unreferenced footnote, with a reference to a nonexistent footnote: [5]_. |
<IP_ADDRESS> Citations¶
| [CIT2002] | (1, 2) Citations are text-labeled footnotes. They may be rendered separately and differently from footnotes. |
Here’s a reference to the above, [CIT2002], and a [nonexistent] citation.
<IP_ADDRESS> Targets¶
This paragraph is pointed to by the explicit “example” target. A reference can be found under Inline Markup, above. Inline hyperlink targets are also possible.
Section headers are implicit targets, referred to by name. See Targets, which is a subsection of Body Elements.
Explicit external targets are interpolated into references such as “Python [5]”.
Targets may be indirect and anonymous. Thus this phrase may also refer to the Targets section.
Here’s a `hyperlink reference without a target`_, which generates an error.
<IP_ADDRESS>.1 Duplicate Target Names¶
Duplicate names in section headers or other implicit targets will generate “info” (level-1) system messages. Duplicate names in explicit targets will generate “warning” (level-2) system messages.
<IP_ADDRESS>.2 Duplicate Target Names¶
Since there are two “Duplicate Target Names” section headers, we cannot uniquely refer to either of them by name. If we try to (like this: `Duplicate Target Names`_), an error is generated.
<IP_ADDRESS> Directives¶
These are just a sample of the many reStructuredText Directives. For others, please see http://docutils.sourceforge.net/docs/ref/rst/directives.html.
<IP_ADDRESS>.1 Document Parts¶
An example of the “contents” directive can be seen above this section (a local, untitled table of contents) and at the beginning of the document (a document-wide table of contents).
<IP_ADDRESS>.2 Images¶
An image directive (also clickable – a hyperlink reference):
A figure directive:
| re | Revised, revisited, based on ‘re’ module. |
| Structured | Structure-enhanced text, structuredtext. |
| Text | Well it is, isn’t it? |
This paragraph is also part of the legend.
A figure directive with center alignment
<IP_ADDRESS>.3 Admonitions¶
Attention
Directives at large.
Caution
Don’t take any wooden nickels.
Danger
Mad scientist at work!
Error
Does not compute.
Hint
It’s bigger than a bread box.
Important
- Wash behind your ears.
- Clean up your room.
- Call your mother.
- Back up your data.
Note
This is a note.
Tip
15% if the service is good.
Warning
Strong prose may provoke extreme mental exertion. Reader discretion is strongly advised.
And, by the way...
You can make up your own admonition too.
<IP_ADDRESS>.6 Replacement Text¶
I recommend you try Python, the best language around [5].
<IP_ADDRESS>.7 Compound Paragraph¶
This paragraph contains a literal block:
Connecting... OK
Transmitting data... OK
Disconnecting... OK
and thus consists of a simple paragraph, a literal block, and another simple paragraph. Nonetheless it is semantically one paragraph.
This construct is called a compound paragraph and can be produced with the “compound” directive.
<IP_ADDRESS> Substitution Definitions¶
An inline image () example:
(Substitution definitions are not visible in the HTML source.)
1.1.3 Field Lists¶
| Field List: | Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. |
|---|
some text
| Field List 2: | Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor |
|---|
1.1.4 Error Handling¶
Any errors caught during processing will generate system messages.
|*** Expect 6 errors (including this one). ***|
There should be six messages in the following, auto-generated section, “Docutils System Messages”:
demo.rst from: http://docutils.sourceforge.net/docs/user/rst/demo.txt


| why can't we just get a really light container which encloses a vacuum? |
| If it's only the density that matters, why can't we just get a really light container which encloses a vacuum? Sure it'd be expensive, but would it float? |
| @MiiNiPaa: is my scenario mathematically impossible, or just highly impractical? I never mentioned how strong the vacuum is - it doesn't need to be a true vacuum. |
| Buoyancy is a force that arises from differences in density; this difference in density (manifested as pressure) may be caused by any force - gravitational, centripetal, magnetic ... |
| Buoyancy is a force that arises from differences in density; this difference in density (manifested as pressure) may be caused by any force - gravitational, centripetal, magnetic ... Though experiment: rotate, at high speed, a vessel containing a mixture of helium and nitrogen in a zero gravity field. Buoyancy will cause the helium molecules to move towards the centre of the vessel (and the nitrogen molecules to move towards the periphery). |
| All fictitious forces are proportional to the mass of the object upon which they act, which is also true for gravity. This led Albert Einstein to wonder whether gravity was a fictitious force as well. He noted that a freefalling observer in a closed box would not be able to detect the force of gravity; hence, freefalling reference frames are equivalent to an inertial reference frame (the equivalence principle). Following up on this insight, Einstein was able to formulate a theory with gravity as a fictitious force; attributing the apparent acceleration of gravity to the curvature of spacetime. https://en.wikipedia.org/wiki/Fictitious_force#Gravity_as_a_fictitious_force |
| With general relativity, Einstein managed to blur forever the distinction between real and fictitious forces. General relativity is his theory of gravity, and gravity is certainly the paradigmatic example of a "real" force. The cornerstone of Einstein's theory, however, is the proposition that gravity is itself a fictitious force (or, rather, that it is indistinguishable from a fictitious force). Now, some 90 years later, we have innumerable and daily confirmations that his theory appears to be correct. http://www.scientificamerican.com/article/what-is-a-fictitious-force/ |
| Though experiment: rotate, at high speed, a vessel containing a mixture of helium and nitrogen in a zero gravity field. |
| I guess it is... but really I think of it as just a different manifestation of gravity. No gravity = no buoyancy. |


Parking Prototyp that uses the latest Sails.js and Angular to easily create realtime, single page web applications.


You don't need to do this unless we request you to do so.
Initially make sure you have DT added as an upstream remote in your fork. Do this by :
git remote add upstream https://github.com/DefinitelyTyped/DefinitelyTyped.git
Now whenever you want the latest changes from DT added to your local branch just run these two commands :
git fetch upstream
# Fetches any new changes from DefinitelyTyped
git merge upstream/master
# Merges any changes fetched into your working files
If tests fail and you know how to fix them, just add a commit to fix that error in your local branch and push it to your DT fork. If the Pull Request uses the same branch it will be update automatically and the tests will run again.
If you need multiple tries to make it work then please flatten your commits so the history is moderately clean (please also minimise the commit message). You could git push --force
(careful!) the flat commit over your earlier commits in the same branch and the PR will be automatically updated.
It is recommended to split your contributions for different JS packages into their own git branches so you can have multiple pending PR's.
You can enable Travis for your own DT fork if you like: go to Travis and sign-up for free if you haven't yet. Then go to your account and enable your DT fork. This will run Travis same as on DT and allows you to experiment freely. You can review your fork's build status via the menu on the left side of the Travis homepage after you enabled the hook AND pushed some code to the repo.
If you plan to start a new definition make sure to check the issues first and/or leave a message that you are working on it so you're not do duplicating work. Coordination with fellow users is always recommended.
If you think you need to make big changes to a existing definition it could be handy to review the git history and create a new Issue where you @mention previous authors and see if they agree and/or want to get involved again.
DefinitelyTyped is managed by a group of enthusiasts who volunteer their free time to review and merge the contributions. This means it can potentially take a few days or up to a week before someone has the focus+time to look at a PR and merge it. If your contributions stays pending and get buried then feel free to politely ping/bump.


contourlines(3f) - [M_contourplot] calculate contour lines from ungridded data f(x,y) and call user-supplied routine with results
Synopsis
Description
Authors
Comments
Options
Example
SUBROUTINE ContourLines(x,y,z,ismopt,iexp,jexp,clist,epsilon,ierr,cntcrv)
real,intent(in),dimension(:) :: x real,intent(in),dimension(:) :: y real,intent(in),dimension(:) :: z integer,intent(in) :: ismopt integer,intent(in) :: iexp integer,intent(in) :: jexp real,intent(in),dimension(:) :: clist real,intent(out) :: epsilon integer,intent(out) :: ierr external cntcrv
contourlines(3f) is a general algorithm for the construction of contour plots. It computes contour lines of constant z for the function z = f(x,y).
From the original COSMIC description:
The graphical presentation of experimentally or theoretically generated data sets frequently involves the construction of contour plots. A general computer algorithm has been developed for the construction of contour plots. The algorithm provides for efficient and accurate contouring with a modular approach which allows flexibility in modifying the algorithm for special applications. The algorithm accepts as input data values at a set of points irregularly distributed over a plane. The algorithm is based on an interpolation scheme in which the points in the plane are connected by straight line segments to form a set of triangles. In general, the data is smoothed using a least-squares-error fit of the data to a bivariate polynomial. To construct the contours, interpolation along the edges of the triangles is performed, using the bivariable polynomial if data smoothing was performed. Once the contour points have been located, the contour may be drawn.
This is a public domain routine from PDAS (Public Domain Aeronautical Software) converted to modules; which was derived from COSMIC Program Number ARC-11441; which originated at NASA Ames Research Center, circa 1981.
From the original PDAS description:
There are many additional algorithms available for computation of contour lines. There are several in the Transactions for Mathematical Software and elsewhere. This routine was deemed worthy of inclusion in the NASA COSMIC collection, but I cannot swear to its status among contour generators.
x input list of x values y input list of y values z input list of z values ismopt smoothing option flag (0=no/off, 1=yes/on) iexp,jexp i and j exponent value for smoothing If smoothing is used, these values are used to define a polynomial used for a least-squares fit of the data. clist list of constant contour values epsilon error function (normalized value) returned to caller if ismopt is non-zero ierr return error flag
0. for normal return 1. for invalid value for number of values in x,y,z 2. for number of ismopt coefficients greater than maxcof or number of values in x,y,z
TEST1:
! program demo_contourlines and user routine program TestCase use M_ContourPlot, only : contourlines implicit none ! integer,parameter :: NPTS=121 real,parameter,dimension(8):: c = & [0.1, 0.2, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0] real :: eps integer :: errCode integer,parameter :: DBG=2 integer :: ierr !integer :: iexp=0, jexp=0, ism=0 integer :: iexp=2, jexp=3, ism=1 integer :: i,j,k real,dimension(NPTS) :: x,y,z external :: my_CntCrv ! k=0 do j=0,10 do i=0,10 k=k+1 x(k)=0.1*real(i) y(k)=0.1*real(j) end do end do ! z=(x-0.5)**2 + (y-0.5)**2 z(:)=16.0*z(:) ! ! write out the input values for inspection open(unit=dbg, file=test.log, status=replace, & & iostat=errCode, action=write, position=rewind) write(DBG,(I4,3F12.6)) (k,x(k),y(k),z(k),k=1,npts) ! call ContourLines(x,y,z, ism,iexp,jexp, c, eps,ierr,my_CntCrv) END PROGRAM TestCase ! ---------------------------------------------------------------- subroutine my_CntCrv(x,y,n,z) ! User-supplied routine used to plot or process the contour lines implicit none integer,intent(in) :: n real,intent(in),dimension(n):: x,y real,intent(in) :: z ! integer,save :: gnu=0 integer :: k integer :: errCode if(gnu.eq.0)then ! on first call, set up output file gnu=1 open(unit=gnu, file=test1.gnu, status=replace, & & iostat=errCode, action=write, position=rewind) write(*,*) "File test1.gnu added to your directory" endif ! write a contour line out to a file followed by a blank line write(gnu,("# level ",g0)) z write(gnu,(2f12.5)) (x(k),y(k),k=1,n) write(gnu,(a)) " " ! end subroutine my_CntCrv ! end program demo_contourlines and user routine#!/bin/sh # Example execution of gnuplot(1) to plot the output file gnuplot <<\EOF plot test1.gnu with lines pause mouse "click mouse to exit ..." quit
| Nemo Release 3.1 | contourlines (3) | July 22, 2023 |


Although Neataptic networks accepts non-normalized values as input, normalizing your input makes your network converge faster. I see a lot of questions where people ask how to normalize their data correctly, so I decided to make a guide.
You have gathered this information, now you want to use it to train/activate a neural network:
{ stock: 933, sold: 352, price: 0.95, category: 'drinks', id: 40 }
{ stock: 154, sold: 103, price: 5.20, category: 'foods', id: 67 }
{ stock: 23, sold: 5, price: 121.30, category: 'electronics', id: 150 }
So some information on the above data:
stock
: the amount of this item in stock
sold
: the amount of this item sold ( in the last month )
price
: the price of this item
category
: the type of product
* id
: the id of the product
So we want to represent each of these inputs as a number between 0
and 1
, however, we can not change the relativity between the values. So we need to treat each different input the same (stock
gets treated the same for every item for example).
We have two types of values in our input data: numerical values and categorical values. These should always be treated differently.
Numerical values are values where the distance between two values matters. For example, price: 0.95
is twice as small as price: 1.90
. But not all integers/decimals are numerical values. Id's are often represented with numbers, but there is no relation between id: 4
and id: 8
. So these should be treated as categorical values.
Normalizing numerical values is quite easy, we just need to determine a maximum value we divide a certain input with. For example, we have the following data:
stock: 933
stock: 154
stock: 23
We need to choose a value which is >= 933
with which we divide all the stock
values. We could choose 933
, but what if we get new data, where the stock
value is higher than 933
? Then we have to renormalize all the data and retrain the network.
So we need to choose a value that is >=933
, but also >= future values
and it shouldn't be a too big number. We could make the assumption that the stock
will never get larger than 2000
, so we choose 2000
as our maximum value. We now normalize our data with this maximum value:
// Normalize the data with a maximum value (=2000)
stock: 933 -> 933/2000 -> 0.4665
stock: 154 -> 154/2000 -> 0.077
stock: 23 -> 23/2000 -> 0.0115
Categorical data shows no relation between different categories. So each category should be treated as a seperate input, this is called one-hot encoding. Basically, you create a seperate input for each category. You set all the inputs to 0
, except for the input which matches the sample category. This is one-hot encoding for our above training data:
| Sample | Drinks | Foods | Electronics |
|---|---|---|---|
| 1 | 1 | 0 | 0 |
| 2 | 0 | 1 | 0 |
| 3 | 0 | 0 | 1 |
But this also allows the addition of new categories over time: you just a need input. It has no effect on the performances of the network on the past training data as when the new category is set to 0
, it has no effect (weight * 0 = 0
).
So applying what I have explained above creates our normalized data, note that the relativity between inputs has not been changed. Also note that some values may be rounded in the table below.
{ stock: 0.4665, sold: 0.352, price: 0.00317, drinks: 1, foods: 0, electronics: 0, id40: 1, id67: 0, id150: 0 }
{ stock: 0.077, sold: 0.103, price: 0.01733, drinks: 0, foods: 1, electronics: 0, id40: 0, id67: 1, id150: 0 }
{ stock: 0.0115, sold: 0.005, price: 0.40433, drinks: 0, foods: 0, electronics: 1, id40: 0, id67: 0, id150: 1 }
Max values:
stock
: 2000sold
: 1000price
: 300Please note, that these inputs should be provided in arrays for neural networks in Neataptic:
[ 0.4665, 0.352, 0.00317, 1, 0, 0, 1, 0, 0 ]
[ 0.77, 0.103, 0.01733, 0, 1, 0, 0, 1, 0 ]
[ 0.0115, 0.005, 0.40433, 0, 0, 1, 0, 0, 1 ]


Data Annotation Platform for Siamese Nets¶
Quesadiya is an open source project that provides a data annotation project management platform for developing Siamese Neural Networks by SiameseLab.
- With Quesadiya, you can:
- manage a data annotation project through Command Line Interface.
- annotate data through Web GUI.
Get started by following this Quick Start.


public class X509Factory
extends java.security.cert.CertificateFactorySpi
CertificateFactorySpi
,
Certificate
,
CertPath
,
CRL
,
X509Certificate
,
X509CRL
,
X509CertImpl
,
X509CRLImpl
| Modifier and Type | Field and Description |
|---|---|
static java.lang.String |
BEGIN_CERT |
static java.lang.String |
END_CERT |
| Constructor and Description |
|---|
X509Factory() |
| Modifier and Type | Method and Description |
|---|---|
java.security.cert.Certificate |
engineGenerateCertificate(java.io.InputStream is)
Generates an X.509 certificate object and initializes it with
the data read from the input stream
is . |
java.util.Collection<? extends java.security.cert.Certificate> |
engineGenerateCertificates(java.io.InputStream is)
Returns a (possibly empty) collection view of X.509 certificates read
from the given input stream
is . |
java.security.cert.CertPath |
engineGenerateCertPath(java.io.InputStream inStream)
Generates a
CertPath object and initializes it with
the data read from the InputStream inStream. |
java.security.cert.CertPath |
engineGenerateCertPath(java.io.InputStream inStream,
java.lang.String encoding)
Generates a
CertPath object and initializes it with
the data read from the InputStream inStream. |
java.security.cert.CertPath |
engineGenerateCertPath(java.util.List<? extends java.security.cert.Certificate> certificates)
Generates a
CertPath object and initializes it with
a List of Certificate s. |
java.security.cert.CRL |
engineGenerateCRL(java.io.InputStream is)
Generates an X.509 certificate revocation list (CRL) object and
initializes it with the data read from the given input stream
is . |
java.util.Collection<? extends java.security.cert.CRL> |
engineGenerateCRLs(java.io.InputStream is)
Returns a (possibly empty) collection view of X.509 CRLs read
from the given input stream
is . |
java.util.Iterator<java.lang.String> |
engineGetCertPathEncodings()
Returns an iteration of the
CertPath encodings supported
by this certificate factory, with the default encoding first. |
static X509CertImpl |
intern(java.security.cert.X509Certificate c)
Return an interned X509CertImpl for the given certificate.
|
static X509CRLImpl |
intern(java.security.cert.X509CRL c)
Return an interned X509CRLImpl for the given certificate.
|
public static final java.lang.String BEGIN_CERT
public static final java.lang.String END_CERT
public java.security.cert.Certificate engineGenerateCertificate(java.io.InputStream is) throws java.security.cert.CertificateException
is
.engineGenerateCertificate
in class java.security.cert.CertificateFactorySpi
is
- an input stream with the certificate data.java.security.cert.CertificateException
- on parsing errors.public static X509CertImpl intern(java.security.cert.X509Certificate c) throws java.security.cert.CertificateException
java.security.cert.CertificateException
public static X509CRLImpl intern(java.security.cert.X509CRL c) throws java.security.cert.CRLException
java.security.cert.CRLException
public java.security.cert.CertPath engineGenerateCertPath(java.io.InputStream inStream) throws java.security.cert.CertificateException
CertPath
object and initializes it with
the data read from the InputStream
inStream. The data
is assumed to be in the default encoding.engineGenerateCertPath
in class java.security.cert.CertificateFactorySpi
inStream
- an InputStream
containing the dataCertPath
initialized with the data from the
InputStream
java.security.cert.CertificateException
- if an exception occurs while decodingpublic java.security.cert.CertPath engineGenerateCertPath(java.io.InputStream inStream, java.lang.String encoding) throws java.security.cert.CertificateException
CertPath
object and initializes it with
the data read from the InputStream
inStream. The data
is assumed to be in the specified encoding.engineGenerateCertPath
in class java.security.cert.CertificateFactorySpi
inStream
- an InputStream
containing the dataencoding
- the encoding used for the dataCertPath
initialized with the data from the
InputStream
java.security.cert.CertificateException
- if an exception occurs while decoding or
the encoding requested is not supportedpublic java.security.cert.CertPath engineGenerateCertPath(java.util.List<? extends java.security.cert.Certificate> certificates) throws java.security.cert.CertificateException
CertPath
object and initializes it with
a List
of Certificate
s.
The certificates supplied must be of a type supported by the
CertificateFactory
. They will be copied out of the supplied
List
object.
engineGenerateCertPath
in class java.security.cert.CertificateFactorySpi
certificates
- a List
of Certificate
sCertPath
initialized with the supplied list of
certificatesjava.security.cert.CertificateException
- if an exception occurspublic java.util.Iterator<java.lang.String> engineGetCertPathEncodings()
CertPath
encodings supported
by this certificate factory, with the default encoding first.
Attempts to modify the returned Iterator
via its
remove
method result in an
UnsupportedOperationException
.
engineGetCertPathEncodings
in class java.security.cert.CertificateFactorySpi
Iterator
over the names of the supported
CertPath
encodings (as String
s)public java.util.Collection<? extends java.security.cert.Certificate> engineGenerateCertificates(java.io.InputStream is) throws java.security.cert.CertificateException
is
.engineGenerateCertificates
in class java.security.cert.CertificateFactorySpi
is
- the input stream with the certificates.java.security.cert.CertificateException
- on parsing errors.public java.security.cert.CRL engineGenerateCRL(java.io.InputStream is) throws java.security.cert.CRLException
is
.engineGenerateCRL
in class java.security.cert.CertificateFactorySpi
is
- an input stream with the CRL data.java.security.cert.CRLException
- on parsing errors.public java.util.Collection<? extends java.security.cert.CRL> engineGenerateCRLs(java.io.InputStream is) throws java.security.cert.CRLException
is
.engineGenerateCRLs
in class java.security.cert.CertificateFactorySpi
is
- the input stream with the CRLs.java.security.cert.CRLException
- on parsing errors.


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A | |
| Ast | The raw Abstract Syntax Tree for grammars. |
C | |
| Common | Some common facilities. |
D | |
| Default [Printers] | The default plain text printer. |
| Default | |
E | |
| ExtendedAst | The simplified Abstract Syntax Tree for grammars. |
G | |
| GenericPrinter | Provide a generic functor to build a specific printer. |
H | |
| Helper | The generic signature for the printer helpers. |
| Html [Printers] | The HTML printer. |
| Html | |
L | |
| LatexBacknaur [Printers] | The LaTeX "backnaur" package based printer. |
| LatexBacknaur | |
| LatexSyntax [Printers] | The LaTeX "syntax" package based printer. |
| LatexSyntax | |
| LatexTabular [Printers] | The LaTeX default table-based printer. |
| LatexTabular | |
M | |
| M [Reduce] | A map with identifiers keys. |
| M [Common.Symbols] | |
| Main | The main driver of the application. |
| Make [GenericPrinter] | The functor to build a printer of signature |
| MiniHelper | |
| MiniLatex | |
N | |
| Normalize | This module provides a way to "flatten" the groups of productions. |
P | |
| Position | A generic module for handling locations of errors. |
| Printer | The generic signature for a printer |
| Printers | This module provides the actual printers, of signature |
R | |
| Reduce | Pattern-recognition and inlining. |
S | |
| Scan | Build the set of symbols appearing in a grammar. |
| Symbols [Common] | The actual implementation for the set of symbols, see |
T | |
| Transform | Transform a normalized |


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