Inventory Service
Starting with Puppet 2.6.7, puppet master servers offer API access to the facts reported by every node. You can use this API to get complete info about any node, and to search for nodes whose facts meet certain criteria.
- Puppet Dashboard and Puppet Enterprise’s console use the inventory service to provide a search function and display each node’s complete facts on the node’s page. (PE does this by default. See here for instructions on activating Dashboard’s inventory support.)
- Your own custom applications can access any node’s facts via the inventory service.
What It Is
The inventory is a collection of node facts. The inventory service is a retrieval, storage, and search API exposed to the network by the puppet master. The inventory service backend (AKA the facts_terminus
) is what the puppet master uses to store the inventory and do some of the heavy lifting of the inventory service.
The puppet master updates the inventory when agent nodes report their facts, which happens every time puppet agent requests a catalog. Optionally, additional puppet masters can use the HTTP API to send facts from their agents to the central inventory.
Other tools, including Puppet Dashboard, can query the inventory via the puppet master’s HTTP API. An API call can return:
- Complete facts for a single node
or
- A list of nodes whose facts meet some search condition
Information in the inventory is never automatically expired, but it is timestamped.
Using the Inventory Service
The inventory service is plain vanilla HTTP: Submit HTTP requests, get back structured fact or host data.
To read from the inventory, submit secured HTTP requests to the puppet master’s facts
and facts_search
HTTP endpoints in the appropriate environment. Your API client will have to have an SSL certificate signed by the puppet master’s CA.
Full documentation of these endpoints can be found here, but a summary follows:
- To retrieve the facts for testnode.example.com, send a GET request to https://puppet:8140/production/facts/testnode.example.com.
- To retrieve a list of all Ubuntu nodes with two or more processors, send a GET request to https://puppet:8140/production/facts_search/search?facts.processorcount.ge=2&facts.operatingsystem=Ubuntu.
In both cases, be sure to specify an Accept: pson
or Accept: yaml
header.
Directly Accessing the Inventory Service Backend
If you are using the PuppetDB inventory backend, you also have the option of using its public API instead of the inventory service API. See the following PuppetDB pages for more info:
Setting Up the Inventory Service
Configuring the Inventory Backend
There are two inventory service backends available: PuppetDB and inventory_active_record
.
- If you are using Puppet 2.7.12 or later, use PuppetDB. It is faster, easier to configure and maintain, and also provides resource stashing to enable exported resources. Follow the installation and configuration instructions in the PuppetDB manual, and connect every puppet master to your PuppetDB server:
- If you are using an older version of Puppet, you can use the
inventory_active_record
backend and connect your other puppet masters to the designated inventory master. See the appendix below to enable this backend.- You can upgrade to PuppetDB at a later date after upgrading Puppet; since a node’s facts are replaced every time it checks in, PuppetDB should have the same data as your old inventory in a matter of hours.
Configuring Access
By default, the inventory service is not accessible! This is a reasonable default. Because the inventory service exposes sensitive information about your infrastructure over the network, you’ll need to carefully control access with the rest_authconfig
(a.k.a. auth.conf
) file.
For prototyping your inventory application on a scratch puppet master, you can just permit all access to the facts
endpoint:
path /facts
auth any
method find, search
allow *
(Note that this will allow access to both facts
and facts_search
, since the path is read as a prefix.)
For production deployment, you’ll need to allow find and search access for each application that uses the inventory and deny access to all other machines. (Since agent nodes submit their facts as part of their request to the catalog
resource, they don’t require access to the facts
or facts_search
resources.) One such possible ACL set would be:
path /facts
auth yes
method find, search
allow dashboard.example.com, custominventoryapp.example.com
Configuring Certificates
To connect your application securely, you’ll need a certificate signed by your site’s puppet CA. There are two main ways to get this:
- On the puppet master:
- Run
puppet cert --generate {certname for application}
. - Then, retrieve the private key (
{ssldir}/certs/{certname}.pem
) and the signed certificate ({ssldir}/private_keys/{certname}.pem
) and move them to your application server.
- Run
- Manually:
- Generate an RSA private key:
openssl genrsa -out {certname}.key 1024
. - Generate a certificate signing request (CSR):
openssl req -new -key {certname}.key -subj "/CN={certname}" -out request.csr
. - Submit the CSR to the puppet master for signing:
curl -k -X PUT -H "Content-Type: text/plain" --data-binary @request.csr https://puppet:8140/production/certificate_request/new
. - Sign the certificate on the puppet master:
puppet cert --sign {certname}
. - Retrieve the certificate:
curl -k -H "Accept: s" -o {certname}.pem https://puppet:8140/production/certificate/{certname}
- Generate an RSA private key:
For one-off applications, generating it on the master is obviously easier, but if you’re building a tool for distribution elsewhere, your users will appreciate it if you script the manual method and emulate the way puppet agent gets a cert.
Protect your application’s private key appropriately, since it’s the gateway to your inventory data.
In the event of a security breach, the application’s certificate is revokable the same way any puppet agent certificate would be.
Testing the Inventory Service
On a machine that you’ve authorized to access the facts and facts_search resources, you can test the API using curl
, as described in the HTTP API docs. To retrieve facts for a node:
curl -k -H "Accept: yaml" https://puppet:8140/production/facts/{node certname}
To insert facts for a fictional node into the inventory:
curl -k -X PUT -H 'Content-Type: text/yaml' --data-binary @/var/lib/puppet/yaml/facts/hostname.yaml https://puppet:8140/production/facts/{node certname}
To find out which nodes at your site are Intel Macs:
curl -k -H "Accept: pson" https://puppet:8140/production/facts_search/search?facts.hardwaremodel=i386&facts.kernel=Darwin
Appendix: Enabling the inventory_active_record
Backend
The inventory_active_record
backend works on older puppet masters, all the way back to Puppet 2.6.7. It has reasonable speed, but is generally inferior to PuppetDB, on account of being slightly slower and more difficult to configure.
Unlike PuppetDB, this backend splits your puppet masters into two groups, which must be configured differently:
- The designated inventory puppet master must be configured to access a database. (If you site only has one puppet master, this is it.)
- Every other puppet master must be configured to access the designated inventory puppet master.
Configuring the Inventory Puppet Master
Step 1: Create a Database and User
The inventory puppet master will need access to both a database and a user account with all privileges on that database; setting that up is outside the scope of this document. The database server can be remote or on the local host.
Since database access is mediated by the common ActiveRecord library, you can, in theory, use any local or remote database supported by Rails. In practice, MySQL on the same server as the puppet master is the best-documented approach. See the documentation for the legacy ActiveRecord storeconfigs backend for more details about setting up and configuring a database with Puppet.
Do not use sqlite except as a proof of concept. It is slow and unreliable.
Step 2: Install the Appropriate Ruby Database Adapter
The copy of Ruby in use by puppet master will need to be able to communicate with your chosen type of database server. This will always entail ensuring that Rails is installed, and will likely require installing a specific Ruby library to interface with the database (e.g. the libmysql-ruby
package on Debian and Ubuntu or the mysql
gem on other operating systems). As above, see the old ActiveRecord storeconfigs docs for more help.
Step 3: Edit puppet.conf
Set the following settings in your inventory master’s puppet.conf:
[master]
facts_terminus = inventory_active_record
dblocation = {sqlite file path (sqlite only)}
dbadapter = {sqlite3|mysql|postgresql|oracle_enhanced}
dbname = {database name (all but sqlite)}
dbuser = {database user (all but sqlite)}
dbpassword = {database password (all but sqlite)}
dbserver = {database server (MySQL and PostgreSQL only)}
dbsocket = {database socket file (MySQL only; optional)}
Note that some of these are only necessary for certain databases. As above, see the old ActiveRecord storeconfigs docs for more help.
Step 4: Edit auth.conf (multiple masters only)
Since your other puppet masters will be sending node facts to the designated inventory master, you will need to give each of them save
access to the facts
HTTP endpoint.
path /facts
auth yes
method save
allow puppetmaster1.example.com, puppetmaster2.example.com, puppetmaster3.example.com
Configuring Other Puppet Masters
Edit puppet.conf on every other puppet master to contain the following:
[master]
facts_terminus = inventory_service
inventory_server = {designated inventory master; defaults to "puppet"}
inventory_port = 8140


Let's revisit the idea of gradient descent, this time with greater focus on its mathematical roots.
Say that we have some multivariable function F(x) that's defined and differentiable in the neighborhood of some point a . The gradient of F, ∇ F , is a function that encodes all partial derivatives of F . It tells us how much F is changing. The direction of steepest descent from the point a is in the direction of
-∇ F(a) . Manipulating our familiar friend y= mx+b , we may derive an expression for the gradient descent equation: xn+1 = xn - η∇ F(a) , where η is our step size, called the learning rate . Starting at a given point, we can eventually get to another special point on F where the gradient is 0: the global minimum of F . When ∇ F is 0, the gradient descent algorithm stops updating.
But wait! What if our cost function has local minima in addition to a global minimum? What if we have saddle points? Excellent questions, dear reader. These are both important problems that face ML engineers. Learning how to fix these problems is beyond the scope of this webpage, but I have attached some wonderful and relevant memes.
This is exactly why gradient descent is useful! Often, but not always, we want to minimize our cost function. The cost function is the average of the loss function evaluated across all training samples (one epoch). Recall that the loss function is the error of the machine learning model for one training sample. By minimizing our cost function, we minimize the total error of the ML model and thus make it as accurate as possible in its predictions.
Here's a visual representation of gradient descent. The black line traces the gradient descent path down to the minimum of the function.
The learning rate, η, as you learned above, is the step size in our gradient descent equation. As the name suggests, it can be adjusted to change how quickly gradient descent reaches the minimum of the cost function, and thus how quickly the machine "learns." Adjusting the learning rate is a delicate process. Too small, and the model may take an unnecessarily long time to learn. Too big, and the model may overshoot the minimum and thus be less accurate than we would like.
One of the most important applications of gradient descent is in neural networks (NN). In NN, the cost function is used to update the weights of the network. We can view a neural network as one big, complex function that relies on weights, biases, and activation functions. It produces a model output. We're trying to minimize the difference between this model output and the target output, which is the actual output. We evaluate the error based on the differences between these two values, calculate the loss or the cost, apply gradient descent to one of those two functions, and adjust our parameters (weights and biases) accordingly.
There are three main types of gradient descent used in NNs.
Batch gradient descent is the most common type, and it is exactly the process we described above, except we apply gradient descent on the cost function instead of the loss function, so we update the weights after training the model on all of the samples once.
Mini-batch gradient descent uses the same technique as batch gradient descent, but the model updates the parameters after evaluating select batches of the training set instead of the entire epoch.
Stochastic gradient descent (SGD) updates the parameters after an individual sample from the training set has been run through the model. It is especially useful when dealing with large training data sets, because going through an epoch and then applying gradient descent to update the parameters can be time-costly. Updating weights after running only a single sample through the model is much quicker. In SGD, the order of the samples in the training data set must be randomized. This process creates a wacky-looking cost function, but it also helps prevent the gradient descent algorithm from getting stuck in a certain location.
The statistics enthusiasts out there will be excited to learn that gradient descent also finds application in linear regression. See this link for more info about applications of gradient descent to linear regression.
Now onto some memes!
Enjoy some high quality ML memes :)
I found this exercise to be extremely helpful in refreshing and deepening my own knowledge of gradient descent. Before making this page I knew the general sketch of gradient descent and how it was helpful in linear regression and neural networks, but I didn't know some of the specifics, like the benefits of SGD vs. batch gradient descent, for example.
What have you directly observed?
Though Shiffman's videos provided a visualization of gradient descent in its application to gradient descent, I didn't get a lot out of seeing it. Looking for other representations of gradient descent, like the picture I attached on this webpage, proved to be more helpful for my understanding.
What do you think?
I certainly think that gradient descent is a powerful tool for operating NNs. It's beautiful to me that we can use a continuous function to control a discrete model.
What do you understand?
I can quite confidently say that I understand everything I wrote down on this page. Knowing multivariable calculus helped significantly with my understanding of the mathematical material; otherwise, there would be been a steeper learning curve for me. I tried to clarify some of the math for my peers, and I hope I helped!
What are you trying to learn more about, or what would you like to understand better?
I would like to learn more about how we handle running into local minima and saddle points in gradient descent algorithms. I'd also like to code some gradient descent applications to NN myself.


Lecture notes for the phyolgenetics workshop. These are taken in real time, so may contain errors. I’ll see if I keep this up throughout the course. See course website.
Huelsenbeck
9a-11a
- Parameters θ
- Observation X
- p(X | θ) is known as the likelihood. Adjust parameter to maximize likelihood.
- Coin toss example
θ = prob of heads on a single toss of a coin, X is # of heads observed on n tosses. Choose a model: binomial:
- Maximize likelihood, find
- Bayesian
likelihood times prior over marginal equals the posterior. The prior is the source of controversy, though mathematically required if you want a posterior probability.
The marginal can be hard to solve.
So Bayesians solved trivial problems or just estimated moments (mean, etc), until along came MCMC and they could do these integrals…
Phylogenetic Model
- Parameters: Tree. (comments on notation). Life is more simple than you think: all phylogenetic models are continuous time Markov models
- “Observations:” could be:
- Alignment (note that it’s not actually observed, it’s an inference from the chronogram from the sequencer! we’ll ignore that.)
- Character matrix
Calculate the probability of alignment given a tree?
- (Lecture moves to slides, will probably ve available on Bodega Wiki?)
Summing over all possible combinations literally is crazy. Felsenstein Pruning algorithm (sum-product algorithm from graphical models) makes this much faster (many of those trees are related).
“This is the exponential distribution, it is the only distribution you’ll see/need in this course”
moments later…
- “This is the gamma distribution, it is the only distribution you’ll need”
- “Oh, and this is the Poisson distribution.”
Dice activity: Exact simulation of finite-state continuous time Markov process
- Interesting hands-on activity to demonstrate Gillespie algorithm simulation.
- (Note, this wiki entry should be expanded).
- Sequence model, sparse matrices with arbitrary correlations. Approximate pruning via MCMC.
Rate variation across sites.
- Approximate the gamma by likelihood catagories, using the mean of a catagory.
| li = | ∑ | p(xi | x) / k |
| k |
- Heterotachy – different models on different parts of the tree.
- Nielsen & Yang 1998
### Jeremy Brown, Mr. Bayes
Using Mr. Bayes
- Can’t calculate the marginal probability. Luckily, in ratio that normalizing constant cancels out: So we can find the ratio of posterior probabilities. This is where MCMC comes in.
Rannala, Divergence Times
- Molecular clock and fossil dating
- branch lengths as estimates
- Bayesian approaches to contain this uncertainty, given sequences times and fossil calibrations:
substitution rate (clock) u, branch length v, age t.
Prior for the divergence times. Priors from fossils. Dangers of hard bounds (will confidently predict the wrong answer).
When do you have enough DNA sequence that your divergence estimate becomes limited only by fossil age uncertainty? Regress width of posterior distribution for divergence times against the mean. Once that regression is sufficiently tight, more DNA data won’t help.
- Irregular Molecular clocks
### Tom Near, Rocks for Gel Jocks
(a.k.a) BEAST tutorial
Ideas/Topics ————
- Reaching the developers
- Help lists.
- uservoice.
- stackoverflow for phylogenetics?
- Managing the literature: Mendeley
- Poll faculty for useful software,tools or tricks. Attempt a talk: 5 minutes, 5 tools to change your life.
- Mendeley: an itunes for your pdfs, pandora for the literature.
### Thoughts/things to investigate or share
- When, if ever, can you actually fit a model with trending rate changes over time. i.e. can we really detect accelerating BM in character evolution?
- Common intuitive mechanisms that generate the familiar distributions. Exponential, Poisson, Gamma, Normal, Log Normal, Binomial, Uniform, Dirchlet.


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Watch Fastest Tractor In The World Hit 135 MPH
Bet you know who's behind the wheel.
by Anthony KarrIt seems that the record for the fastest tractor in the world is quite an honorable award these days. Back in 2018, Top Gear first set a world record in that discipline by doing 87.27 miles per hour (140.44 kilometers per hour) at the Bruntingthorpe Proving Ground in the United Kingdom. This summer, motorcycle racer and TV presenter Guy Martin took down that record with a JBL Fastrac 8000 tractor, modified to hit 103.6 mph (166.72 kph) on Yorkshire's Elvington Airfield. That wasn’t enough for the British engineering company and Martin and the team decided to try and reach even higher speeds.
Gallery: The world's fastest tractor
On October 23, they returned to Elvington and Martin recorded an average speed of 135.191 mph (217.570 kph) and further extended the record with a top speed of 153.7 mph (247.35 kph). The average speed is now registered as a Guinness World Record for the fastest tractor on the planet. It’s important to note that, for a new world speed record, Guinness takes the average speed of two consecutive runs in opposite directions to account for variable conditions such as wind or an incline.
Guy Martin is no stranger to weird speed records as he currently holds records for the fastest speed on a gravity-powered snow sled (83.49 mph/134.368 kph), the fastest soapbox (85.61 mph/137.78 kph), and highest speed on a Wall of Death (78.15 mph/25.77 kph). He admits that he just likes “going fast” and that “if it’s something a bit risky I’m all for it.”
See The Previous Records:
The JCB tractor used for the new speed record is powered by a 7.2-liter diesel engine, modified to deliver about 1,000 horsepower (746 kilowatts) and 1,843 pound-feet (2,500 Newton-meters) of torque. The original CVT gearbox has been replaced by a six-speed transmission and a smaller, lighter, and more aerodynamic cabin has been also installed.
“It doesn’t feel like a tractor [to drive].
Source: Guinness World Records


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This website facilitates finding the best locality for an industry setup depending on the ir quality wholly.
It comprises of the database of all the industries in particular locality and parts per million concentration(ppm) of toxic gases being emitted from it, if any.
It is then evaluated and updated according to the threshold standard set by the Ministry of Environment.
If industrial pollution lies within this threshold level, industrialist get the land availabilities in the region or other best places are suggested accordingly, keeping factors and government policies in consideration.


We are a dynamic and innovative firm whose main goal is to provide, develop and promote compliance in your organisation so that you can focus on your core business with piece of mind. Our aim is to make our culture one driven by excellence and innovation.
We adapt fluidly to different client business models and a changing regulatory environment, whilst maintaining a strong core of integrity, excellence and innovation.
We are proud to be a level 1 B-BBEE black female-owned business.
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She is a member of the South African Institute of Stockbrokers, a member of the South African Institute of Financial Markets Stockbrokers as well as an Affiliate Member of the Compliance Institute of South Africa.


| Interface | Description |
|---|---|
| DispatcherFactory |
This interface must be implemented by all factory classes used to
create instances of subclasses of Dispatcher.
|
| Class | Description |
|---|---|
| BlockThread | |
| CatalogBlockDispatcher |
A dispatching class for exertion blocks in the PUSH mode.
|
| CatalogExertDispatcher | |
| CatalogParallelDispatcher | |
| CatalogSequentialDispatcher | |
| ExertDispatcher | |
| ExertionDispatcherFactory |
This class creates instances of appropriate subclasses of Dispatcher.
|
| ExertionSorter |
SORCER class
User: Pawel Rubach
Date: 23.10.13
Sort a list of exertions taking into account the dependencies in pipes and
the tasks and jobs hierarchy.
|
| JobThread | |
| ProviderProvisionManager | |
| ProvisionManager |
The
ProviderProvisionManager handles the dynamic creation of OperationalString s created
from Exertion s. |
| SpaceParallelDispatcher | |
| SpaceSequentialDispatcher | |
| SpaceTaskDispatcher |
| Exception | Description |
|---|---|
| DispatcherException | |
| SortingException |
The SortingException is thrown when the ExertionSorter finds a problem with the
Flow or the dependencies in the analyzed job
|
Copyright © 2013-2014 Sorcersoft.com S.A.. All Rights Reserved.


Prediction Game
In this game, you are a chipmunk digging around underground in search of food.
You need to be able to get to the right place at the right time in order to eat.
Before we begin, please read the instructions on how to play the game.
- 1. IMPORTANT: Make sure you are using Google Chrome on fullscreen in INCOGNITO mode.
- 2. IMPORTANT: Enable Popups! Click (in the top right corner of your browser) then click "Always allow pop-ups from https://jwzhang.com".
If you don't see that icon, click here.
- 3. Turn on your audio!
- 4. Close all other open applications. The game may lag if you don't do this.
- 5. Make sure you get to the end screen that will say "You are finished!" on it.
- 6. Use the arrow keys to select where you think the food will go. Make sure you click PRIOR to the food moving.
- 7. After you click start, the game will start. Pay close attention!
- 8. Try your best and have fun!
Begin Practice Mode


@extends("shared/layout.html") @require(sub, report, reported_user, related_reports, related_reports_json, banuserform, is_sub_banned, post, comment, subInfo, subMods, logs, createreportenote) @def title(): Mod |\ @end @def sidebar(): @include('shared/sidebar/mod.html') @end @def main():
@{_('Reason')}: @{report['reason']!!e}
@{_('Reporter')}: @{report['reporter'] }
@{_('Status')}: @if report['open'] == True: @{_('Open')} @else: @{_('Closed')} @end
@{_('[post deleted by mod or admin]')}
@elif post['deleted'] == 1:@{_('[post deleted by user]')}
@end@{_('[post deleted by user]')}
@end@{_('No logs have been made yet on this report')}
@else:
@{_('[comment deleted by mod or admin]')}
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@end


Real Madrid team news: Injury, suspension list vs. Paris Saint-Germain
Real Madrid will be looking to keep their hopes of finishing top of Group A alive when they welcome Paris Saint-Germain to the Bernabeu in the Champions League on Tuesday night.
by Matt LawLos Blancos are currently second in the section on seven points, five behind leaders PSG, who recorded a 3-0 win in the reverse clash in Paris earlier this season.
Madrid would book their spot in the knockout round with a victory, although they could lose and still advance providing that third-placed Club Brugge do not overcome Galatasaray in the group's other match.
Here, Sports Mole rounds up Madrid's latest injury and suspension news ahead of Tuesday's European tie in the Spanish capital.
Status: Out
Type of injury: Knee
Possible return date: Unknown
Marco Asensio faces a battle to play again this season after suffering a serious knee injury during the team's preparations for the 2019-20 campaign. A timeframe on his return has not yet been set by the club.
Status: Out
Type of injury: Knee
Possible return date: Unknown
Nacho trained with Madrid on Sunday as the defender steps up his recovery from a knee injury, although the Spaniard will not be considered for Tuesday's European affair at the Bernabeu.
Status: Out
Type of injury: Knee
Possible return date: Unknown
James Rodriguez could miss the remainder of 2019 after suffering a knee injury during a training session with Colombia. The attacker was allowed to join up with his country despite his injury problems in recent weeks but is now facing an extended period on the sidelines.
Status: Out
Type of injury: Toe
Possible return date: Unknown
Lucas Vazquez was ruled out of Saturday's clash with Real Sociedad after suffering a fractured toe in his left foot during a session in the gym. As a result, the Spaniard is unlikely to play again until 2020.
REAL MADRID'S SUSPENSION LIST
Madrid have no players suspended for the European fixture.
body check tags ::


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/zmodp.html ---
Would it make sense for zmodp
to take a primes
, rather than a pair?
it's probably fine? there will be a lot of coercions
it seems potentially a lot more convenient in a situation where some prime is fixed for a long time, since the prime will probably be inferrable from everything while a separate hp : prime p
will not
prime
is a typeclass, so you can have variables (p : nat) [prime p]
and it will be inferred
See also #1648
Oh, I just noticed that you changed the definition of prime
in that PR. I thought it was just @[class] def prime := ...
BTW, does it make sense to have nat.prime
defined differently from prime
in algebra/associated
?
I think that a better approach would be to reuse the general definition, and prove equivalence to the current version of nat.prime
.
I have an innate dislike of this idea but maybe it's not well grounded
Of which idea?
I think it should be okay but we need to be careful to keep the dependencies down
the idea to merge the two definitions
I don't think _root_.prime
was defined in an area that was being conscientious about dependencies
OK, algebra/associated
depends on multiset
, and nat.prime
uses list.perm
relation instead of equality in multiset
.
Moving definition of _root_.prime
with some properties to a file not import
ing multiset
should be easy.
Why does nat.prime
need list.perm
?
For factors_unique
I think we could split the file into two parts, with the second part dealing with complete factorizations and the unique factorization theorem
Why nat/prime
should care about dependencies?
Sometimes you just want to be able to say 5
is prime and maybe evaluate some primes
also it's needed by norm_num
I think that we can move multisets out of algebra/associated
.
About merging definitions: for a newcomer, having two definitions for the same notion is really confusing.
Having a fancy definition for a simple concept is also confusing
E.g., it took me a lot of time to understand the situation with cau_seq
and cauchy
.
see filters
There should be a theorem that relates cau_seq
and cauchy
. That's how I prefer to resolve these situations
In the first place there should be a clear documentation (and for cau_seq
vs cauchy
, I'd use cauchy
everywhere).
I agree you should use cauchy
preferentially if you are doing anything using or depending on topology
cau_seq
is mostly an implementation detail for the construction of the reals
which escaped a bit in order to help with building the p-adic numbers
If I have h : prime p
in the context and I want to apply a theorem taking prime p
, then I expect it to work.
open nat
causes a lot of confusion
Is _root_.prime
really that complicated?
/-- prime element of a semiring -/ def prime [comm_semiring α] (p : α) : Prop := p ≠ 0 ∧ ¬ is_unit p ∧ (∀a b, p ∣ a * b → p ∣ a ∨ p ∣ b)
What are the dependencies of that definition that you would like to avoid?
The definition itself is not complicated, but it's embedded in a file with many dependencies last I checked
So... merge the definitions and maybe split the file to avoid deps
import algebra.group algebra.group.is_unit data.multiset
If is_unit
is too complicated because it depends on units
then we could even spell it out.
actually my memory is faulty or else I missed the refactor because it's in the algebra directory now and the imports are not as bad as I thought
Actually data.multiset
depends on lots of things, including group_power
.
yeah, that's definitely the biggest dependency in the list
group_power is hard to avoid when talking about prime numbers
but actually there are a lot of imports in data.multiset
that have creeped in about algebra that are surprising
that's kind of the whole reason algebra.big_operators
exists, to act as the join of data.finset
and algebra.*
See https://leanprover.zulipchat.com/#narrow/stream/113489-new-members/topic/Contribution.20to.20mathlib.20with.20the.20Ostrowski.20theorem/near/187592253 for the last time someone tried to convince Mario to drop _root_.prime
. Maybe we can ask once per month and I'll get tired at some point :wink:
Now I'm trying to convince Mario to drop nat.prime
, and use _root_.prime
as the only definition ;)
Any thoughts on my suggestion to have two files, one about prime
(and applied to nat
) and one about factorization and unique factorization
Or should there still be a separate file for @prime nat
?
What information about nat.prime
is important for norm_num
?
The computation of min_fac
cau_seq
is mostly an implementation detail for the construction of the reals
Yury, if you really have too much time and energy, you can try to revert the change that introduced cau_seq
. This was motivated mostly by mysterious performance issues. We used to have a construction of reals (by Johannes Hölzl) which was half-way between the Cauchy sequence construction and Bourbaki construction (using completion of general topological rings). But it caused many time-outs. This was probably because some definition should have been marked as irreducible but wasn't. But nobody could figure out a solution before Mario's desire to stick to low-tech maths kicked in. I'm very grateful to see real numbers that work, but I wouldn't mind getting rid of cau_seq
.
Now I'm trying to convince Mario to drop nat.prime, and use _root_.prime as the only definition ;)
Sorry, I meant dropping nat.prime
.
Please don't remove cau_seq
. For some reason Kevin loves to use data.real.basic
in examples, and I have the luxury of a compiled mathlib at most 60% of the time
If you can make the definition of cauchy
and relevant properties similarly speedy I would be okay with it but we have done nothing to improve this situation since I wrote cau_seq
AFAIK
I want the real numbers to be something that can be considered part of the core library
Filters and uniform spaces can be considered part of the core library, right?
Like I said, if that library got slimmer and faster it might be possible to do away with cau_seq
. But it needs a big cleanup / refactor for that
One way to solve the problem is to use dedekind cuts for the construction instead :D
Nooo!
Also cau_seq
is "computable". I was discouraged at the time from using explicit moduli but without it the actual computable content is pretty useless, but with it we could conceivably have an exact real arithmetic library built on real
Which sounds like a useful thing to have.
At some point we want to know that e > 2.71
for some reason or other. So we'll need tactics and a library that can "compute" with real numbers.
We already did this kind of thing for pi day
For the proof producing version we will need a norm_num
style prover, but a computable function would be useful for computations with real numbers in #eval
which, by the way, would probably be used in the prover itself
Do I remember correctly that there are no computable well-defined non-constant functions from real
to nat
?
Yes, with the current implementation
Which implementation has computable functions and is a field
?
You can, however, inhabit trunc (psum (r < 1) (0 < r))
It will not be a computable field
, but it will have an inv
function that requires a proof that the argument is nonzero
You guys are really not fun. I was talking about Bourbaki reals and you managed to drift to computable reals... I'm going back to normal maths in my geometry class.
I think it would be nice to have both
Once you go in mathematician mode, all the distinctions fade away and you've clearly constructed the actual real numbers
it can be a field
and everything else
@Patrick Massot Did Boubaki ever need an approximation of a transcendental real number?
I've never read any of their treatment of analyisis
I'm not sure that we can make sufficiently flexible computable (in the sense of eval
) real numbers.
More or less, a computable real number is a (trunc
of) a function λ n, {r : rat | abs (x - r) ≤ 1/n
(or (1/2)^n
).
If you want to define, e.g., a + b
, then you need to decide how to distribute the error.
Splitting in halves is not a way to go because in a + b + c + d + e + f
you'll need to know a
with a very high accuracy.
So we either need a definition that automatically distributes error margins, or a definition that asks the user how to distribute them.
(1) You can always have more fancy functions that allow you to distribute the error more precisely (2) Dividing the error in half isn't so bad. Usually you use 1/2^n
for the modulus, and most of the basic operations generate bits in linear time, so asking for one more bit is not unreasonable
I think that a possible way to go is to have classical noncomputable real
s and something like approximates (x : real) (f : nat → rat) : Prop := ∀ n, abs (x - f n) < (1 / 2)^n
. Then we can have theorems like approximates x f → approximates y g → approximates (x + y) (λ n, f n + g n)
.
but isn't that just a cau_seq
and its representative real
?
No, I prescribe the rate of convergence.
I'm talking here about a hypothetical change to the definition of cau_seq
to add a modulus so that you can use real
for computation
Ah, so the new cau_seq
will converge at a prescribed rate (e.g., (1/2)^n
)?
right
Then inv x
will require psum (x < 0) (0 < x)
, not just x ≠ 0
.
actually it will need more than that, it will need |x| > 1/n
Yes.
i.e. x is apart from 0
My point being, it will have some funny intuitionistic constraints on it and the mathematicians won't need to touch it but it's there if you want to actually compute
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Top Grades-As above and make a 2 x 3 invisible table border = 0 with 3 x images and 3 x links,
and an unordered list of food types with ordered sub lists of at least 5 foods foods in each type.
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The Router Expanded:
Let's Get Modular
Created by > Ndimphiwe Mayekiso & Siphathisiwe Hlakaza
Contents
What is the Router?
What does this give us?
What are the side effects?


#include <ApplicationSamplerImp.hpp>
|
| | ApplicationSamplerImp () |
| |
| | ApplicationSamplerImp (std::shared_ptr< ApplicationStatus > status, const PlatformTopo &platform_topo, const std::map< int, m_process_s > &process_map, bool is_filtered, const std::string &filter_name, const std::vector< bool > &is_cpu_active, bool do_profile, const std::string &profile_name, const std::map< int, std::set< int > > &client_cpu_map, std::shared_ptr< Scheduler > scheduler) |
| |
| virtual | ~ApplicationSamplerImp ()=default |
| |
| void | update (const geopm_time_s &curr_time) override |
| | Update the record buffer by clearing out old records and providing a new cache for subsequent calls to the get_records() method. Also update cache of application status for use by hint and progress APIs. More...
|
| |
| std::vector< record_s > | get_records (void) const override |
| | Get all of the application events that have been recorded since the last call to update_records(). More...
|
| |
| short_region_s | get_short_region (uint64_t event_signal) const override |
| |
| uint64_t | cpu_region_hash (int cpu_idx) const override |
| | Get the region hash associated with a CPU. More...
|
| |
| uint64_t | cpu_hint (int cpu_idx) const override |
| | Get the hint associated with a CPU. More...
|
| |
| double | cpu_hint_time (int cpu_idx, uint64_t hint) const override |
| | Get the amount of time a CPU has been measured running with a hint. More...
|
| |
| double | cpu_progress (int cpu_idx) const override |
| | Get the progress reported on a CPU. More...
|
| |
| void | connect (const std::vector< int > &client_pids) override |
| | Connect with an application using a key. More...
|
| |
| std::vector< int > | client_pids (void) const override |
| |
| std::set< int > | client_cpu_set (int client_pid) const override |
| |
| bool | do_shutdown (void) const override |
| |
| double | total_time (void) const override |
| |
| double | overhead_time (void) const override |
| |
| int | sampler_cpu (void) |
| |
| virtual | ~ApplicationSampler ()=default |
| |
◆ ApplicationSamplerImp() [1/2]
| geopm::ApplicationSamplerImp::ApplicationSamplerImp |
( |
| ) |
|
◆ ApplicationSamplerImp() [2/2]
| geopm::ApplicationSamplerImp::ApplicationSamplerImp |
( |
std::shared_ptr< ApplicationStatus > |
status, |
|
|
const PlatformTopo & |
platform_topo, |
|
|
const std::map< int, m_process_s > & |
process_map, |
|
|
bool |
is_filtered, |
|
|
const std::string & |
filter_name, |
|
|
const std::vector< bool > & |
is_cpu_active, |
|
|
bool |
do_profile, |
|
|
const std::string & |
profile_name, |
|
|
const std::map< int, std::set< int > > & |
client_cpu_map, |
|
|
std::shared_ptr< Scheduler > |
scheduler |
|
) |
| |
◆ ~ApplicationSamplerImp()
| virtual geopm::ApplicationSamplerImp::~ApplicationSamplerImp |
( |
| ) |
|
|
virtualdefault |
◆ client_cpu_set()
| std::set< int > geopm::ApplicationSamplerImp::client_cpu_set |
( |
int |
client_pid | ) |
const |
|
overridevirtual |
◆ client_pids()
| std::vector< int > geopm::ApplicationSamplerImp::client_pids |
( |
void |
| ) |
const |
|
overridevirtual |
◆ connect()
| void geopm::ApplicationSamplerImp::connect |
( |
const std::vector< int > & |
client_pids | ) |
|
|
overridevirtual |
Connect with an application using a key.
Called by the Controller to set up all channels of communication with the application using the provided string as a key.
Implements geopm::ApplicationSampler.
◆ cpu_hint()
| uint64_t geopm::ApplicationSamplerImp::cpu_hint |
( |
int |
cpu_idx | ) |
const |
|
overridevirtual |
Get the hint associated with a CPU.
Returns the most recently sampled value for the hint associated with the Linux logical CPU specified by the user. An exception is raised if the value of cpu_idx is negative or greater or equal to platform_topo().num_domain(GEOPM_DOMAIN_CPU).
- Parameters
-
| [in] | cpu_idx | The index of the linux logical CPU to query. |
- Returns
- The hint associated with the CPU.
Implements geopm::ApplicationSampler.
◆ cpu_hint_time()
| double geopm::ApplicationSamplerImp::cpu_hint_time |
( |
int |
cpu_idx, |
|
|
uint64_t |
hint |
|
) |
| const |
|
overridevirtual |
Get the amount of time a CPU has been measured running with a hint.
Returns a total amount of time in seconds that a CPU was measured to be running with the hint value on the Linux logical CPU specified by the user. An exception is raised if the value of cpu_idx is negative or greater or equal to platform_topo().num_domain(GEOPM_DOMAIN_CPU) or if the specified hint is invalid.
- Parameters
-
| [in] | cpu_idx | The index of the linux logical CPU to query. |
- Returns
- The total time in seconds since the applications started.
Implements geopm::ApplicationSampler.
◆ cpu_progress()
| double geopm::ApplicationSamplerImp::cpu_progress |
( |
int |
cpu_idx | ) |
const |
|
overridevirtual |
Get the progress reported on a CPU.
Returns the most recently sampled value for the fraction of the work units completed by the thread running on the specified CPU. If the CPU queried is not currently executing a thread that is reporting progress, then the value NAN is returned. An exception is raised if the value of cpu_idx is negative or greater or equal to platform_topo().num_domain(GEOPM_DOMAIN_CPU).
- Parameters
-
| [in] | cpu_idx | The index of the linux logical CPU to query. |
- Returns
- Value between 0.0 and 1.0 representing the fraction of work completed, or NAN.
Implements geopm::ApplicationSampler.
◆ cpu_region_hash()
| uint64_t geopm::ApplicationSamplerImp::cpu_region_hash |
( |
int |
cpu_idx | ) |
const |
|
overridevirtual |
Get the region hash associated with a CPU.
Returns the most recently sampled value for the region hash associated with the Linux logical CPU specified by the user. An exception is raised if the value of cpu_idx is negative or greater or equal to platform_topo().num_domain(GEOPM_DOMAIN_CPU).
- Parameters
-
| [in] | cpu_idx | The index of the linux logical CPU to query. |
- Returns
- The region hash associated with the CPU.
Implements geopm::ApplicationSampler.
◆ do_shutdown()
| bool geopm::ApplicationSamplerImp::do_shutdown |
( |
void |
| ) |
const |
|
overridevirtual |
◆ get_records()
| std::vector< record_s > geopm::ApplicationSamplerImp::get_records |
( |
void |
| ) |
const |
|
overridevirtual |
Get all of the application events that have been recorded since the last call to update_records().
- Returns
- Vector of application event records.
Implements geopm::ApplicationSampler.
◆ get_short_region()
| short_region_s geopm::ApplicationSamplerImp::get_short_region |
( |
uint64_t |
event_signal | ) |
const |
|
overridevirtual |
◆ overhead_time()
| double geopm::ApplicationSamplerImp::overhead_time |
( |
void |
| ) |
const |
|
overridevirtual |
◆ sampler_cpu()
| int geopm::ApplicationSamplerImp::sampler_cpu |
( |
void |
| ) |
|
◆ total_time()
| double geopm::ApplicationSamplerImp::total_time |
( |
void |
| ) |
const |
|
overridevirtual |
◆ update()
| void geopm::ApplicationSamplerImp::update |
( |
const geopm_time_s & |
curr_time | ) |
|
|
overridevirtual |
Update the record buffer by clearing out old records and providing a new cache for subsequent calls to the get_records() method. Also update cache of application status for use by hint and progress APIs.
Implements geopm::ApplicationSampler.
The documentation for this class was generated from the following files:


|
Fusion3D
Version 6.04
|
Does coordinate transformations SGI GL style. More...
#include <transform.h>
Public Member Functions | |
| jfd_transform () | |
| Constructor. More... | |
| ~jfd_transform () | |
| Destructor. More... | |
| int | reset () |
| Reset tranformation to unity transform. More... | |
| int | rotate_x (float ang) |
| Rotate ang degrees around the x-axis. More... | |
| int | rotate_y (float ang) |
| Rotate ang degrees around the y-axis. More... | |
| int | rotate_z (float ang) |
| Rotate ang degrees around the z-axis. More... | |
| int | transl (float xt, float yt, float zt) |
| Translate. More... | |
| int | scale (float xs, float ys, float zs) |
| Scale. More... | |
| int | get (Matrix &out_matrix) |
| Get the resulting composite matrix. More... | |
| int | sar_rotation (float azim, float elev, float sqint) |
| Set matrix for a SAR rotation – shortcut. More... | |
Does coordinate transformations SGI GL style.
Coordinate transformations
Author: J.F. Dammann
Revision 2.0
Date: 94/03/11
Transformations are SGI GL style. Homogeneous coordinates are used and a single 4x4 transformation matrix is maintained. Compositing matrices is done by pre-multiplying the new matrix onto the existing matrix:
[ new ] [ pre-existing] = [ result ]
This matrix is used to generate a new point (x',y',z') by
[x,y,z,w] = [ 4x4 matrix[row][col] ] = [x',y',z',w']
xt = matrix[0][0]*x[i] + matrix[1][0]*y[i] + matrix[2][0]*z[i] + matrix[3][0];
yt = matrix[0][1]*x[i] + matrix[1][1]*y[i] + matrix[2][1]*z[i] + matrix[3][1];
zt = matrix[0][2]*x[i] + matrix[1][2]*y[i] + matrix[2][2]*z[i] + matrix[3][2];
See Appendix C of Graphics Library Programming Guide for trans. matrices
Notes
scale, translate and rotate should all composit properly
The SGI matrix stack has not yet been implemented.
| jfd_transform::jfd_transform | ( | ) |
Constructor.
| jfd_transform::~jfd_transform | ( | ) |
Destructor.
| int jfd_transform::get | ( | Matrix & | out_matrix | ) |
Get the resulting composite matrix.
| int jfd_transform::reset | ( | ) |
Reset tranformation to unity transform.
| int jfd_transform::rotate_x | ( | float | ang | ) |
Rotate ang degrees around the x-axis.
| int jfd_transform::rotate_y | ( | float | ang | ) |
Rotate ang degrees around the y-axis.
| int jfd_transform::rotate_z | ( | float | ang | ) |
Rotate ang degrees around the z-axis.
| int jfd_transform::sar_rotation | ( | float | azim, |
| float | elev, | ||
| float | sqint | ||
| ) |
Set matrix for a SAR rotation – shortcut.
| int jfd_transform::scale | ( | float | xs, |
| float | ys, | ||
| float | zs | ||
| ) |
Scale.
| int jfd_transform::transl | ( | float | xt, |
| float | yt, | ||
| float | zt | ||
| ) |
Translate.


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|
OR-Tools
7.1
Google Optimization software suite
|
#include <ebert_graph.h>
Public Types | |
| typedef NodeIndexType | NodeIndex |
| typedef ArcIndexType | ArcIndex |
Public Member Functions | |
| ForwardEbertGraph () | |
| ForwardEbertGraph (NodeIndexType max_num_nodes, ArcIndexType max_num_arcs) | |
| ~ForwardEbertGraph () | |
| bool | CheckArcBounds (const ArcIndexType arc) const |
| bool | CheckArcValidity (const ArcIndexType arc) const |
| bool | CheckTailIndexValidity (const ArcIndexType arc) const |
| NodeIndexType | Tail (const ArcIndexType arc) const |
| bool | IsIncoming (ArcIndexType arc, NodeIndexType node) const |
| void | BuildRepresentation () |
| bool | BuildTailArray () |
| void | ReleaseTailArray () |
| bool | TailArrayComplete () const |
| std::string | DebugString () const |
| NodeIndexType | Head (const ArcIndexType arc) const |
| bool | IsNodeValid (NodeIndexType node) const |
| Public Member Functions inherited from operations_research::EbertGraphBase< NodeIndexType, ArcIndexType, DerivedGraph > | |
| bool | Reserve (NodeIndexType new_max_num_nodes, ArcIndexType new_max_num_arcs) |
| ArcIndexType | AddArc (NodeIndexType tail, NodeIndexType head) |
| template<typename ArcIndexTypeStrictWeakOrderingFunctor > | |
| void | GroupForwardArcsByFunctor (const ArcIndexTypeStrictWeakOrderingFunctor &compare, PermutationCycleHandler< ArcIndexType > *annotation_handler) |
| ArcIndexType | end_arc_index () const |
| bool | IsNodeValid (NodeIndexType node) const |
| Public Member Functions inherited from operations_research::StarGraphBase< NodeIndexType, ArcIndexType, DerivedGraph > | |
| NodeIndexType | num_nodes () const |
| ArcIndexType | num_arcs () const |
| NodeIndexType | end_node_index () const |
| ArcIndexType | end_arc_index () const |
| NodeIndexType | max_num_nodes () const |
| ArcIndexType | max_num_arcs () const |
| NodeIndexType | max_end_node_index () const |
| ArcIndexType | max_end_arc_index () const |
| bool | IsNodeValid (NodeIndexType node) const |
| ArcIndexType | LookUpArc (const NodeIndexType tail, const NodeIndexType head) const |
| NodeIndexType | Head (const ArcIndexType arc) const |
| std::string | NodeDebugString (const NodeIndexType node) const |
| std::string | ArcDebugString (const ArcIndexType arc) const |
Static Public Attributes | |
| static const ArcIndexType | kFirstArc |
| static const NodeIndexType | kFirstNode |
| static const ArcIndexType | kNilArc |
| static const NodeIndexType | kNilNode |
| Static Public Attributes inherited from operations_research::EbertGraphBase< NodeIndexType, ArcIndexType, DerivedGraph > | |
| static const ArcIndexType | kFirstArc |
| static const NodeIndexType | kFirstNode |
| static const ArcIndexType | kMaxNumArcs |
| static const NodeIndexType | kMaxNumNodes |
| static const ArcIndexType | kNilArc |
| static const NodeIndexType | kNilNode |
| Static Public Attributes inherited from operations_research::StarGraphBase< NodeIndexType, ArcIndexType, DerivedGraph > | |
| static const NodeIndexType | kNilNode = -1 |
| static const ArcIndexType | kNilArc |
| static const NodeIndexType | kFirstNode = 0 |
| static const ArcIndexType | kFirstArc = 0 |
| static const NodeIndexType | kMaxNumNodes |
| static const ArcIndexType | kMaxNumArcs |
Additional Inherited Members | |
| Protected Member Functions inherited from operations_research::EbertGraphBase< NodeIndexType, ArcIndexType, DerivedGraph > | |
| EbertGraphBase () | |
| ~EbertGraphBase () | |
| void | Initialize (NodeIndexType max_num_nodes, ArcIndexType max_num_arcs) |
| ArcIndexType | FirstOutgoingOrOppositeIncomingArc (const NodeIndexType node) const |
| ArcIndexType | NextAdjacentArc (const ArcIndexType arc) const |
| ArcIndexType | NextOutgoingArc (const NodeIndexType unused_node, const ArcIndexType arc) const |
| Protected Member Functions inherited from operations_research::StarGraphBase< NodeIndexType, ArcIndexType, DerivedGraph > | |
| StarGraphBase () | |
| ~StarGraphBase () | |
| NodeIndexType | StartNode (NodeIndexType node) const |
| ArcIndexType | StartArc (ArcIndexType arc) const |
| NodeIndexType | NextNode (const NodeIndexType node) const |
| ArcIndexType | NextArc (const ArcIndexType arc) const |
| ArcIndexType | FirstOutgoingArc (const NodeIndexType node) const |
| Protected Attributes inherited from operations_research::EbertGraphBase< NodeIndexType, ArcIndexType, DerivedGraph > | |
| ZVector< ArcIndexType > | next_adjacent_arc_ |
| bool | representation_clean_ |
| ZVector< ArcIndexType > | first_incident_arc_ |
| ZVector< NodeIndexType > | head_ |
| ArcIndexType | max_num_arcs_ |
| NodeIndexType | max_num_nodes_ |
| ArcIndexType | num_arcs_ |
| NodeIndexType | num_nodes_ |
| Protected Attributes inherited from operations_research::StarGraphBase< NodeIndexType, ArcIndexType, DerivedGraph > | |
| NodeIndexType | max_num_nodes_ |
| ArcIndexType | max_num_arcs_ |
| NodeIndexType | num_nodes_ |
| ArcIndexType | num_arcs_ |
| ZVector< NodeIndexType > | head_ |
| ZVector< ArcIndexType > | first_incident_arc_ |
Definition at line 190 of file ebert_graph.h.
| typedef NodeIndexType operations_research::ForwardEbertGraph< NodeIndexType, ArcIndexType >::NodeIndex |
Definition at line 1598 of file ebert_graph.h.
| typedef ArcIndexType operations_research::ForwardEbertGraph< NodeIndexType, ArcIndexType >::ArcIndex |
Definition at line 1599 of file ebert_graph.h.
|
inline |
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|
friend |
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|
friend |
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|
static |
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|
static |
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|
static |
Definition at line 219 of file ebert_graph.h.
|
static |
Definition at line 216 of file ebert_graph.h.


ngx-extended-pdf-viewer ships with two versions of pdf.js:
- The
assets
folder contains the latest release of pdf.js.
- The
bleeding-edge
folder contains a developer build of pdf.js. That code is newer
than the latest release, but it's not an officially supported version, so it's primarily there for testing purposes.
You can switch to the newer either by modifying the
angular.json
or
by setting
pdfDefaultOptions.assetsFolder
.
This option also allows to use use a non-standard project directory layout.
Note that it's not possible to change the pdf.js engine at runtime. You need to set it
before <ngx-extended-pdf-viewer/>
is loaded. This demo uses the
"bleeding edge" version of pdf.js after a full-page reload (i.e. after hitting the F5 key).


Jet with flaming engine returns safely to LA airport
by ROBERT JABLONLOS ANGELES (AP) - A Philippine Airlines jet with flames spurting from one engine returned safely to Los Angeles International Airport just after takeoff Thursday.
Flight 113, a Boeing 777 bound for Manila, reported a problem with the right engine after takeoff. It turned around and landed at about noon, said Ian Gregor of the Federal Aviation Administration.
“All 342 passengers and 18 crew members are safe and were able to disembark from the airplane using regular airstairs,” Philippine Airlines said in a statement.
However, it wasn’t immediately clear when they might board other flights.
Passengers and people on the ground videotaped repeated blasts of flame coming from the right engine of the Boeing 777 minutes after takeoff.
“I could see, like, flashes of light. I thought it was … just from the sunlight and then I just started hearing, like ‘boom, boom, boom,’” passenger Walter Baumann told KABC-TV.
Andrew Ames was a passenger in a car speeding down a freeway near the airport when he got a video of the plane.
“The back was spewing fire bolts,” he said. “It looked like when you see a backfire from a motorcycle. Then I thought, ‘I don’t think a plane is supposed to do that.’”
“It went on for about 20 seconds of repeated fire and then it just stopped and I saw the plane start to veer hard to the left more than normal,” Ames said. “It looked like it was turning around.”
The tower reported that the plane was “at emergency with an engine out.”
An air traffic controller observed a “compressor stall” on the plane and told a Skywest aircraft on the ground to turn.
“I’m sure you saw, but the engine on that traffic ahead and to your left had some flames coming out of it, so we are getting you out of the way of it,” the controller said, according to LiveATC.net, an independent website that records and plays communications between pilots and towers at many airports.
There were no flames showing from the plane when it landed but firefighters were on hand as a precaution, airport spokesman Heath Montgomery said.
The fire team did hose down some of the heated plane wheels as a precaution.
Brian Skoloff in Phoenix, Arizona and David Koenig in Dallas contributed to this story.
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Maker of the first Philippine flag. Born in Taal, Batangas, on June 24, 1859.
Married to Felipe Agoncillo. She labored to make the Filipino flag in Hong Kong with the help of Delfina Herbosa de Natividad, a niece of Rizal.
Died on May 30, 1946 in Taal, Batangas.


Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks <http://www.jmlr.org/papers/volume6/cowell05a/>.
| Version: | 1.4.0 |
| Depends: | R (≥ 3.3.0), stats, graphics, utils |
| Imports: | Rgraphviz, igraph, RColorBrewer, fields, doBy, qtlnet, methods, qtl, graph, bnlearn |
| Suggests: | shiny, googleVis |
| Published: | 2018-08-22 |
| Author: | Han Yu |
| Maintainer: | Han Yu <hyu9 at buffalo.edu> |
| BugReports: | https://github.com/hyu-ub/BayesNetBP/issues |
| License: | GPL-2 |
| URL: | https://github.com/hyu-ub/BayesNetBP |
| NeedsCompilation: | no |
| CRAN checks: | BayesNetBP results |
| Reference manual: | BayesNetBP.pdf |
| Package source: | BayesNetBP_1.4.0.tar.gz |
| Windows binaries: | r-devel: BayesNetBP_1.4.0.zip, r-release: BayesNetBP_1.4.0.zip, r-oldrel: BayesNetBP_1.4.0.zip |
| OS X binaries: | r-release: BayesNetBP_1.4.0.tgz, r-oldrel: BayesNetBP_1.4.0.tgz |
| Old sources: | BayesNetBP archive |
Please use the canonical form https://CRAN.R-project.org/package=BayesNetBP to link to this page.


ALARMS
{% for item in list_of_alarms %}
{{ item }}
{% endfor %}
Alarm Clock
Alarm name:
Include news briefing?
Include weather briefing
Submit
Covid19 Updates
{% for item in covid_list %}
{{item}}
{% endfor %}


Preparation time
30 mins to 1 hour
Cooking time
1 to 2 hours
Serves
Serves 4
Test your chef skills with this complex dessert - served with a final flourish at the table.
For the wild strawberry and yuzu sorbet, place in the strawberries in a blender and blend until smooth. Pass through a sieve.
Place the sugar and 150ml/5½fl oz water in a sauce pan and boil until the sugar is dissolved and you have a syrup.
Add the strawberry purée and yuzu juice to the syrup. Place in an ice cream maker and churn following the manufacturer’s instructions until frozen.
For the meringue, preheat the oven to 80C/150F/Gas ¼, or the lowest possible setting.
Combine the ingredients in a large freestanding mixer and whisk until stiff peaks form when the whisk is removed. Spoon into a piping bag and pipe small dots onto a silicone mat or a baking tray lined with baking parchment.
Bake for 2-3 hours, or until firm. Pound the basil leaves in a pestle and mortar. When you remove the meringues from the over, stir them through the crushed basil and set aside.
For the strawberry jam, combine all ingredients in a thermo mix or blender and pulse until fully incorporated. Strain through a sieve and pour into sterilized jars.
For the basil soup, place the sugar and 350ml/12fl oz water in a large saucepan and bring to the boil. Squeeze the gelatine to remove any excess water, then add to the pan. Set aside to cool.
Place the basil leaves and sugar in a blender and pulse to form a smooth paste. Add the lemon juice and the gelatine mixture, blend again then pass through a fine sieve.
For the gariguettes and fraise des bois, dress the gariguettes with a drizzle of vanilla syrup and mix with the fraise des bois.
To serve, arrange the dressed strawberries in small bowls, place five mini meringues around the strawberries, a few dabs of strawberry jam and sprinkle a few micro basil leaves over the top. Place a scoop of sorbet on top of the strawberries and grate some fresh yuzu or lime zest over the top.
Drizzle with some vanilla syrup and scatter some marshmallows over and serve. Finally, at the table, poor some chilled basil soup into the bowls.


BLE2MQTT
Management
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The MD4SG initiative presented a tutorial focused on the MD4SG, with applications in healthcare, online labor markets, and matching platforms at the 13th Conference on Web and Internet Economics (WINE), held at the Indian Institute of Science, Bangalore, India, in December, 2017.
Slides can be found here.
Tutorial: Youtube
link
Organizers
Kira Goldner, Columbia University
Kira Goldner is a postdoctoral researcher at Columbia University hosted by Tim Roughgarden in the Computer Science department. Specifically, she is an NSF Mathematical Sciences Postdoctoral Research Fellow and a Data Science Institute Postdoctoral Fellow. She received her PhD in computer science and engineering from the University of Washington under the advisement of Anna Karlin, during which she was been supported by a 2017-19 Microsoft Research PhD Fellowship and a 2016-17 Google Anita Borg Scholarship. Her research is in algorithmic mechanism design, with work ranging from relaxing traditional behavioral and informational assumptions, maximizing revenue in settings motivated by practice, and applying mechanism design to social good. She co-founded and co-organized MD4SG from Fall 2016 to 2018.


|
If you're like me. You might be an internet veteran. You could have hundreds or thousands of funny pictures that
you have saved over time on your computer. They are nice to go back to, but they're basically collecting dust...
What I propose is that users who come to the site, upload their collection of memes, image macros, nsfw images, or whatever to help a meme-deprived person in need. In this example, its a hobo. You start by making an account, and then you can upload an archive of files.  Here you can give the character items and food to keep them alive.
It will detract your funds to buy a new hat and pants... You can also buy new people to support.
Eventually you will have a huge collection of people you're supporting with your memes. |


Hi, I’m Bhanu Pratap, a young creative developer from India.
I have multiple skills in development, design and project management.
My areas of interest include creating beautiful,interactive, responsive and user friendly websites.
Thanks for your visit on my portfolio, feel free to share and draw more on
Dribbble.


Winners in key categories at the 2019 Latin Grammy Awards
by The Associated PressA list of winners in key categories at the 2019 Latin Grammy Awards, held Thursday at the MGM Grand Garden Arena in Las Vegas.
- Album of the year: “El Mal Querer,” Rosalía
- Record of the year: “Mi Persona Favorita,” Alejandro Sanz and Camila Cabello
TOP STORIES
Franklin Graham calls on nation to pray for Trump as impeachment effort gains speed
Skeptics chuckle as climate activist Greta Thunberg sets sail for Europe on 'plastic yacht'
Bill Clinton offers Trump advice on impeachment
- Song of the year: “Calma,” Pedro Capó, Gabriel Edgar González Pérez and George Noriega
- Best new artist: Nella
- Best contemporary pop vocal album: “El Mal Querer,” Rosalía
- Best traditional pop vocal album: “Agustín,” Fonseca
- Best pop song: “Mi Persona Favorita,” Alejandro Sanz and Camila Cabello
- Best urban song: “Con Altura,” Rosalía, J Balvin, Mariachi Budda, Frank Dukes, El Guincho, Teo Halm and Alejandro Ramirez
- Best urban music album: “X 100Pre,” Bad Bunny
- Best urban fusion/performance: “Calma (Remix),” Pedro Capó and Farruko
- Best rock album: “Monte Sagrado,” Draco Rosa
- Best pop/rock album: “Cargar La Suerte,” Andrés Calamaro
- Best alternative music album: “Norma,” Mon Laferte
- Best salsa album: “Mas De Mi,” Tony Succar
- Best traditional tropical album: “Andrés Cepeda Big Band (En Vivo),” Andrés Cepeda
- Best contemporary/tropical fusion album: “Literal,” Juan Luis Guerra 4.40
- Best singer-songwriter album: “Contra El Viento,” Kany García
- Best ranchero/mariachi album: “Ahora,” Christian Nodal
- Best folk album: “Tiempo Al Tiempo,” Luis Enrique + C4 Trio
- Best tango album: “Revolucionario,” Quinteto Astor Piazzolla
- Best Latin jazz/jazz album: “Jazz Batá 2,” Chucho Valdés
- Producer of the year: Tony Succar
- Best short form music video: “Banana Papaya,” Kany García and Residente
- Best long form music video: “Lo Que Fui Es Lo Que Soy,” Alejandro Sanz
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Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


Async::Reactor
An asynchronous, cooperatively scheduled event reactor.
Definitions
def self.run(*arguments, **options, &block)
The preferred method to invoke asynchronous behavior at the top level.
- When invoked within an existing reactor task, it will run the given block asynchronously. Will return the task once it has been scheduled.
- When invoked at the top level, will create and run a reactor, and invoke the block as an asynchronous task. Will block until the reactor finishes running.
Implementation
def self.run(*arguments, **options, &block)
if current = Task.current?
reactor = current.reactor
return reactor.async(*arguments, **options, &block)
else
reactor = self.new
begin
return reactor.run(*arguments, **options, &block)
ensure
reactor.close
end
end
end
def run(*arguments, **options, &block)
Run the reactor until all tasks are finished. Proxies arguments to #async
immediately before entering the loop, if a block is provided.
Implementation
def run(*arguments, **options, &block)
raise RuntimeError, 'Reactor has been closed' if @selector.nil?
initial_task = self.async(*arguments, **options, &block) if block_given?
while self.run_once
# Round and round we go!
end
return initial_task
ensure
logger.debug(self) {"Exiting run-loop because #{$! ? $! : 'finished'}."}
end
def async(*arguments, **options, &block)
Start an asynchronous task within the specified reactor. The task will be executed until the first blocking call, at which point it will yield and and this method will return.
This is the main entry point for scheduling asynchronus tasks.
Implementation
def async(*arguments, **options, &block)
task = Task.new(self, **options, &block)
# I want to take a moment to explain the logic of this.
# When calling an async block, we deterministically execute it until the
# first blocking operation. We don't *have* to do this - we could schedule
# it for later execution, but it's useful to:
# - Fail at the point of the method call where possible.
# - Execute determinstically where possible.
# - Avoid scheduler overhead if no blocking operation is performed.
task.run(*arguments)
# logger.debug "Initial execution of task #{fiber} complete (#{result} -> #{fiber.alive?})..."
return task
end
def interrupt
Interrupt the reactor at the earliest convenience. Can be called from a different thread safely.
Implementation
def interrupt
@guard.synchronize do
unless @interrupted
@interrupted = true
@selector.wakeup
end
end
end
def << fiber
Schedule a fiber (or equivalent object) to be resumed on the next loop through the reactor.
Implementation
def << fiber
@ready << fiber
end
def yield(fiber = Fiber.current)
Yield the current fiber and resume it on the next iteration of the event loop.
Implementation
def yield(fiber = Fiber.current)
@ready << fiber
Fiber.yield
end
def run_once(timeout = nil)
Run one iteration of the event loop.
Implementation
def run_once(timeout = nil)
# logger.debug(self) {"@ready = #{@ready} @running = #{@running}"}
if @ready.any?
# running used to correctly answer on `finished?`, and to reuse Array object.
@running, @ready = @ready, @running
@running.each do |fiber|
fiber.resume if fiber.alive?
end
@running.clear
end
if @ready.empty?
interval = @timers.wait_interval
else
# if there are tasks ready to execute, don't sleep:
interval = 0
end
# If we are finished, we stop the task tree and exit:
if self.finished?
return false
end
# If there is no interval to wait (thus no timers), and no tasks, we could be done:
if interval.nil?
# Allow the user to specify a maximum interval if we would otherwise be sleeping indefinitely:
interval = timeout
elsif interval < 0
# We have timers ready to fire, don't sleep in the selctor:
interval = 0
elsif timeout and interval > timeout
interval = timeout
end
# logger.debug(self) {"Selecting with<EMAIL_ADDRESS>children with interval = #{interval ? interval.round(2) : 'infinite'}..."}
if monitors = @selector.select(interval)
monitors.each do |monitor|
monitor.value.resume
end
end
@timers.fire
# We check and clear the interrupted flag here:
if @interrupted
@guard.synchronize do
@interrupted = false
end
return false
end
# The reactor still has work to do:
return true
end
def close
Stop each of the children tasks and close the selector.
Implementation
def close
# This is a critical step. Because tasks could be stored as instance variables, and since the reactor is (probably) going out of scope, we need to ensure they are stopped. Otherwise, the tasks will belong to a reactor that will never run again and are not stopped.
self.stop(false)
@selector.close
@selector = nil
end
def closed?
Check if the selector has been closed.
Implementation
def closed?
@selector.nil?
end
def sleep(duration)
Put the calling fiber to sleep for a given ammount of time.
Implementation
def sleep(duration)
fiber = Fiber.current
timer = @timers.after(duration) do
if fiber.alive?
fiber.resume
end
end
Task.yield
ensure
timer.cancel if timer
end
def with_timeout(timeout, exception = TimeoutError)
Invoke the block, but after the specified timeout, raise class Async::TimeoutError
in any currenly blocking operation. If the block runs to completion before the timeout occurs or there are no non-blocking operations after the timeout expires, the code will complete without any exception.
complete.
Implementation
def with_timeout(timeout, exception = TimeoutError)
fiber = Fiber.current
timer = @timers.after(timeout) do
if fiber.alive?
error = exception.new("execution expired")
fiber.resume error
end
end
yield timer
ensure
timer.cancel if timer
end


An inline frame is marked up as follows:
<!DOCTYPE html>   <title>HTML Iframes</title>
Document content goes here...
  Sorry your browser does not support inline frames.
Document content also go here...

| Attribute | Value | Description |
|---|---|---|
| align | left right top middle bottom |
Not supported in HTML5. Specifies the alignment of an  according to surrounding elements |
| frameborder | 1 0 |
Not supported in HTML5. Specifies whether or not to display a border around an  |
| height | pixels | Specifies the height of an  |
| longdesc | URL | Not supported in HTML5. Specifies a page that contains a long description of the content of an  |
| marginheight | pixels | Not supported in HTML5. Specifies the top and bottom margins of the content of an  |
| marginwidth | pixels | Not supported in HTML5. Specifies the left and right margins of the content of an  |
| name | text | Specifies the name of an  |
| sandbox | allow-forms allow-pointer-lock allow-popups allow-same-origin allow-scripts allow-top-navigation |
Enables an extra set of restrictions for the content in an  |
| scrolling | yes no auto |
Not supported in HTML5. Specifies whether or not to display scrollbars in an  |
| src | URL | Specifies the address of the document to embed in the  |
| srcdoc | HTML_code | Specifies the HTML content of the page to show in the  |
| width | pixels | Specifies the width of an  |


--- title: What is the bias in the estimation of an effect given an omitted interaction term? author: Jeff Walker date: '2019-07-31' slug: what-is-bias-in-the-estimation-of-an-effect-giving-an-omitted-interaction-term categories: - stats 101 tags: - bias - causal graph - covariance - categorical X - effect size - fake data - Wright style path analysis keywords: - tech ---
Given a generating model:
\[\begin{equation} y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 x_3 \end{equation}\]where \(x_3 = x_1 x_2\); that is, it is an interaction variable.
The total effect of \(x_1\) on \(y\) is \(\beta_1 + \frac{\mathrm{COV}(x_1, x_2)}{\mathrm{VAR}(x_1)} \beta_2 + \frac{\mathrm{COV}(x_1, x_3)}{\mathrm{VAR}(x_1)} \beta_3\).
If \(x_3\) (the interaction) is missing, its component on the total efffect is added to the coefficient of \(x_1\). This added component is the bias due to the omitted interaction.
This computation assumes an interaction effect is defined as the “leftover” residual after adding up the intercept plus additive effects – that is, the coefficient estimated using dummy coding (which is R’s default coding). See Is the power to test an interaction effect less than that for a main effect? for other definitions/parameterizations of an interaction.
I use data.table
library(data.table)
Let’s simulate this with a \(2 \times 2\) factorial design. In a balanced fatorial design, \(\frac{\mathrm{COV}(x_1, x_3)}{\mathrm{VAR}(x_1)} = 0.5\), so the bias is \(\frac{\beta_3}{2}\)
n <- 10^6 # a high n is used so that the estimate is very close to the true expected value
beta_0 <- 0
beta_1 <- 1
beta_2 <- 0.5
beta_3 <- 0.5
beta <- c(beta_0, beta_1, beta_2, beta_3)
sigma <- 1
fd <- data.table(
x1=factor(rep(c("CnA", "TrA"), each=2*n)),
x2=factor(rep(rep(c("CnB", "TrB"), each=n), 2))
)
X <- model.matrix(~x1*x2, data=fd)
cov_X <- cov(X)
fd[, y:=(X%*%beta)[,1] + rnorm(n*4)]
The bias is
b <- cov_X[4,2]/cov_X[2,2] # this will be 0.5 in balanced design
b*beta_3
## [1] 0.25
The expected coefficient given the known true effect plus the bias is
beta_1 + b*beta_3
## [1] 1.25
The coefficient of \(x_1\) in the linear model with the missing interaction estimates this biased effect.
coef(lm(y ~ x1 + x2, data=fd))[2]
## x1TrA
## 1.248929
n <- 10^6
# generate two correlated x
rho <- 0.6 # true correlation between the two x variables
z <- rnorm(n)
sigma_x <- sqrt(1-rho)
x1 <- sqrt(rho)*z + rnorm(n, sd=sigma_x) # expected variance is 1
x2 <- sqrt(rho)*z + rnorm(n, sd=sigma_x) # expected variance is 1
x3 <- x1*x2
beta_0 <- 0
beta_1 <- 1
beta_2 <- 0.5
beta_3 <- 0.5
beta <- c(beta_0, beta_1, beta_2, beta_3)
sigma <- 1
fd <- data.table(
x1=x1,
x2=x2
)
X <- model.matrix(~x1*x2, data=fd)
cov_X <- cov(X)
fd[, y:=(X%*%beta)[,1] + rnorm(n)]
The bias is
b <- cov_X[4,2]/cov_X[2,2] # this will be 0.5 in balanced design
b*beta_3
## [1] -0.001261114
huh. Is this generally the case that the regression of the interaction on the main variable is near zero? If it is, then an omitted interaction would have little bias, as here.
The expected coefficient given the known true effect plus the bias is
beta_1 + b*beta_3
## [1] 0.9987389
The coefficient of \(x_1\) in the linear model with the missing interaction estimates this biased effect.
coef(lm(y ~ x1 + x2, data=fd))[2]
## x1
## 0.9966902


Your account name is currently set to {{account.name}}
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If you need to block a user screen while loading some data or doing heavy computations, you can use this snippet to show modal dialog with progress bar.
Fork it on github
waitingDialog.show();
waitingDialog.show('Custom message');
waitingDialog.show('Custom message', {dialogSize: 'sm', progressType: 'warning'});
waitingDialog.show('Dialog with callback on hidden',{onHide: function () {alert('Callback!');}});


Tic Tac Toe
Tenis Game
Simon Game
Tic Tac Toe
Select your choice:
X
O
Tenis Game
Simon
1
Count
Start
Strict


This is a paragraph in header
Started in 2009, blabla bla.
The make blable ble
You better cut the pizza in five pieces because I'm not hungry enough to eat six
A unique combination of VE
texto aqui de cesar
texto aqui de fulano
texto aqui de geranio
| 2013 | 2014 | 2015 | |
|---|---|---|---|
| Employees | 15 | 30 | 40 |
| Guests Served | 15000 | 45000 | 100000 |
| Special Events | 3 | 20 | 45 |


NamChain - Open Initiative Research Lab
Webinar Certificate Issuer
Issue Certificate
Name :
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IPFS Hash :
Transaction ID:
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:


Enumerable#map
Enumerable is an extremely useful Ruby module, or library of methods, that can be used across multiple classes - such as the Array class or Hash class. The Enumerable module enables programmers to cycle through various elements in collection objects like arrays and hashes, and perform various actions to each object within the collection. These methods can produce much simpler versions of other looping options, and can also perform more specific repeated tasks in an elegant way.
The Enumerable method I would like to focus on in this post is Enumerable#map. To preface my explanation of this method, here is a simple example of how it works:
arr = [1,3,5]

arr.map { |n| n * n }

returns the new array [1, 9, 25]
| The enumerable map method, as seen above, enables the creation of a new array which has been modified in a way specified by the programmer. It can either be thought of as performing a certain action and collecting the results, as in the similar #collect method, or as “re-mapping” the original object ( | n | in our example), and producing a new array of changed versions of the original object. The new array is filled with whatever is returned by the block each time it runs - for each object in the collection object. |
This method and the concept of enumerators can be intimidating at first, but the methods in the Enumerable module are fairly intuitive and extremely useful once you realize that they are simply a form of going through each element in a collection object and performing a certain task to each object.


Day 01 : Arrive- Delhi On arrival at Delhi airport meet with our representative and transfer to hotel for overnight stay.
Day 02 :Delhi - Haridwar - Uttarkashi (200 Kms/6 hrs drive Approx) In the morning board Shatabdi Express for Haridwar at 0600 hrs. Arrive Haridwar and meet our tour Guide. One of the seven holy cities of Hinduism. The train journey takes about 5 1/2 hours. Legend has it that Vishnu's footprint was found on the bank of the holy river Ganges, giving it it's sanctity.
Day 03 : Uttarkashi - Sangamchatti - Manjhi (16 kms/6-7 hrs) Set out for Sangam Chatti after breakfast (14 kilometers) on a half an hour drive. This is the starting point of the trek. From Sangam Chatti the trail is well marked and climbs to the true right of the valley and continues to the village of Agoda. From this point it is a 2 kilometer trek to the settlement at Bhebra - an idellic village set near a gushing mountain stream, and full of green fields growing potatoes, beans and the famous Rajma.
Day 04 : Manjhi - Dodital (3307 10847 ft) (6 Kms/ 3 - 4 hrs) Ascending a zig zaging path and gaining altitude slowly, the gradual climb to Dodital is a shade milder than the previous day's backbreaking climb.The view become more and more attractive and scenic with every step taken towards the famed lake. The trail becomes very spectacular, as the mountain transforms into a vertical cliff face, and the Asi Ganga looks like a thin thread snaking through its course in the valley below.
Day 05 : Dodital (3307 mts/10847 ft) Day free to enjoy the beauty and tranquility of the Lake and surrounding dense forest. The scenic beauty of Dodital can compel any nature lover to camp here for days together, the lake, the surrounding pine forest, the towering Darwa Bugyal on one side and the valley on the other is serene and enticing. The circumference around the lake is roughly 1.5 kms. A few streams coming from Darwa Bugyal feed the lake situated at a height of 3,024 metres (9,921 ft ).
Day 06 : Dodital - Darwa Top (5 kms/ 3 hrs) Early in the morning trek up to Darwa Top with packed breakfasts. The walk to Darwa top is 5 kms (2-3 hrs). It is a pass at a height of 4150 mts. The entire walk is an uphill climb. It starts with the climb along the feeder to Dodital breaking into a thick birch forest opening into the high meadows.
Day 07 : Dodital - Agoda (2250 mts/7380 ft) (17 kms/4-5 hrs) After breakfast trek down till Agoda- a swift descent of 17 kms, retracing the same path back, but this time without the sweat and pain!. Overnight in camp at Agoda.
Day 08 : Agoda - Rishikesh (4 kms trek + 140 kms Drive) After breakfast trek back to Sangamchatti 04 Kms, and thereafter, drive down to Rishikesh 140 Km away. On arrival check in at the hotel. Evening free at leisure. Visit the Ganga river bank for the spectacular and moving Aarti- spectacle of sights, sounds and chanting in the evening..
Day 09: Rishikesh - Haridwar - Delhi Early in the morning transfer to Haridwar railway station to catch a train for Delhi. Arrival at Delhi by afternoon.
Tour & Service End.


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Vim recognizes three file formats (Unix, DOS, and Mac) that determine line ending characters.
Visit Vim's Download Page and select the correct download link for your operating system. Vim comes pre-installed on Mac OSX as well as most Linux Distributions, so if you're using Mac or Linux, simply open a Terminal, type in "vim", hit enter and it should open for you! If it doesn't you can follow the steps on the Vim Download page to install it. Below is how to install Vim in Windows.
Vim surprisingly doesn't come pre-installed in Ubuntu Linux anymore. The installation in Ubuntu is simple, simply type in the following command in a terminal.
If you stick with the default installation options on Windows, three Vim icons will appear on your computer desktop: gVim 7.4, gVim Easy 7.4, and gVim Read only 7.4. The only difference between standard and easy is that gVim Easy begins and locks in insert mode, whereas standard starts in normal mode and you can choose to toggle between modes. Learn more about modes.
In Mac, Linux and Unix based operating systems, open a terminal and type "vim" followed by a space and name of the file you want to edit or create. It's that easy! An example can be seen below.


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4.1 Overview
This chapter describes a number of modules that can be used to convert
between Python objects and other data representations. These modules are often
used to read and write foreign file formats and to store or transfer
Python variables.
4.1.1 Binary Data
Python provides several support modules that help you decode and
encode binary data formats. The struct module can convert
between binary data structures (like C structs) and Python tuples.
The array
module wraps binary arrays of data (C arrays) into a Python sequence
object.
4.1.2 Self-Describing Formats
To pass data between different Python programs, you can
marshal
or pickle
your data.
The marshal module uses a simple self-describing
format that supports most built-in datatypes, including code
objects. Python uses this format itself to store compiled code on
disk (in PYC files).
The pickle module provides a more sophisticated
format, which supports user-defined classes, self-referencing data
structures, and more. This module is available in two versions; the
basic pickle module is written in Python and is
relatively slow, while cPickle is written in C
and is usually as fast as marshal.
4.1.3 Output Formatting
The modules in this group supplement built-in formatting functions like
repr and the % string
formatting operator.
The pprint
module can print almost any Python data structure in a nice, readable
way (as readable as it can make things, that is).
The repr module
provides a replacement for the built-in function with the same name.
The version in this module applies tight limits on most things: it
doesn't print more than 30 characters from each string, it doesn't
print more than a few levels of deeply nested data structures, etc.
4.1.4 Encoded Binary Data
Python supports most common binary encodings, such as base64, binhex (a Macintosh format),
quoted
printable, and uu encoding.
|


text
subtext
Percent of Vote for Marine Le Pen in 2011
Lower to Higher Percentages
Ethnic Enclaves Outlined in Dark Red


EBD - Easton Bible DIctionary
Kine
KINE
"KINE "(Hebrews sing. parah, i.e., “fruitful”), mentioned in Pharaoh’s dream (Genesis 41: 18). Here the word denotes “buffaloes,” which fed on the reeds and sedge by the river’s brink.
"KINE "(Hebrews sing. parah, i.e., “fruitful”), mentioned in Pharaoh’s dream (Genesis 41: 18). Here the word denotes “buffaloes,” which fed on the reeds and sedge by the river’s brink.


[1]
thank you ritz battery park for undoubtedly the best possible service we received from this hotel.
[2]
thank you ritz battery park for undoubtedly the best possible service we received from this hotel property.


Let's talk work
We're a team of four people doing our bachelors in computer science from Bennett University. We're skilled in working with drones, full stack web development, app development using flutter, desktop app development, ML and AI.
Watch video
Drone Projects
For over an year we have been working with UAVs now. As a semester project we deployed live image recognition on drone to identify wastes on streets. We have made some successful efforts towards automating a quadcopter's flight from one location to another in collaboration with our seniors as a project for AWS in SIH (Smart India Hackathon).
See moreFull Stack web development
A part of our latest efforts in expanding our horizons in the field of computer science. We have completed some online Web Dev courses and are confident to start as a team of developers working to develop websites at minimal costs for people that need them. This very website is the first one that we developed.
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We are fast learners and have experience working with Artificial Neural Networks, penetration testing, and can do app development using flutter.
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Antriksh Malik
Full stack web developer
He loves to explore new technologies and is the jack of all trades. Mostly a full stack web developer having a good knowledge on ethical hacking and drones. Here is his LinkedIn account. You can find and easily contact him here.
Apurva Bhardwaj
Drone Specialist
He is the glue which binds the team together. An expert on drones and an AI enthusiast that can develop Windows applications. Here is his LinkedIn account. You can find and easily contact him here.
Tejas Hirawat
Pen tester and security
The smartest guy in the room. A really fast learner who has a strong grip on topics like penetration testing and ethical Hacking. Here is his LinkedIn account. You can find and easily contact him here.
Aditya Ojha
ML and AI expert
If creativity was a crime, then he would be in jail. The most creative person in the team, handles all the video editing and poster making. Has an in depth knowledge about using python for ML and AI projects. Here is his LinkedIn account. You can find and easily contact him here.


{% extends "layout.html" %} {% block page_title %} What did you do before using this service? {% endblock %} {% block content %} {% include "includes/phase_banner_alpha.html" %} {% include "includes/backlink.html" %} What did you try before using this service? Select all that apply. What did you try before using this service? Telephoned HMRC Wrote to HMRC Completed an online form Read guidance on GOV.UK Spoke to your employer, agent or accountant Spoke to a friend or family member None of these {% endblock %}
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