Toyota November 2019 Discounts – Innova Crysta, Fortuner, Corolla, Glanza, Etios
by Team GaadiWaadiLike several other car manufacturers, even Toyota Kirloskar Motor (TKM) has been offering some great discounts on models like the Innova Crysta, Fortuner, Glanza and Etios
With the Diwali season over, most car manufacturers have lowered the discounts that they were offering on their product lineup. That said, the existing discounts still make it a pretty good option for the new car buyers to grab their favorite car right now and benefit from lowered prices.
Like Hyundai Motor India Ltd and even Maruti Suzuki India Ltd, even Toyota Kirloskar Motor India has been offering some fantastic discounts across its lineup of models for India. Here is a look at the Toyota Cars discounts in November 2019.
1. Toyota Corolla Altis
Toyota Corolla Altis is a well-rounded D1-segment sedan that has been losing out to fresher rivals like the Hyundai Elantra facelift. However, the good part here is that it’s available with some fantastic discounts amounting to over Rs 1.5 lakh. Right now, this capable sedan is being sold with a cash discount of Rs 1,00,000 and a corporate offer of Rs 50,000. However, currently, there’s no exchange bonus available to the buyers of this model
2. Toyota Innova Crysta
The Innova Crysta, which is basically the second generation of the popular MPV, is being sold with benefits up to Rs 55,000. There’s a corporate discount of Rs 30,000, along with an exchange bonus of Rs 30,000. While not much, this discount is still pretty substantial if you take into account the fact that this highly popular MPV is seldom on sale with any sort of discounts.
3. Toyota Fortuner
The Toyota Fortuner has been undoubtedly the highest-selling SUV in its segment. Still, due to the lackluster sales, the top-selling product has been available with some good discounts. Right now, this full-size SUV is on sale with a corporate cash discount of Rs 40,000 and an exchange bonus of Rs 40,000. This takes the total discount to up to Rs 80,000, which is not bad at all for someone who is willing to invest his money into buying this premium SUV.
| Model | Cash Discount + Corporate Benefit | Exchange Bonus |
| Toyota Corolla Altis | Rs. 1,00,000 + Rs. 50,000 | NIL |
| Toyota Innova Crysta | Rs. 25,000 (Corporate Only) | Rs. 30,000 |
| Toyota Fortuner | Rs. 40,000 (Corporate Only) | Rs. 40,000 |
| Toyota Yaris (Facelift) | Rs. 10,000 + Rs. 15,000 + Rs. 5,000 (Reference Toyota Edge Eligibility Grid) | Rs. 15,000 |
| Toyota Yaris (Pre Faclift) | Upto Rs. 1,00,000 + Rs. 30,000 | Rs. 15,000 |
| Toyota Etios | Rs. 8,000 (Corporate Only) | NIL |
| Toyota Liva | Rs. 8,000 (Corporate Only) | NiL |
| Toyota Etios Cross | Rs. 15,000 + Rs. 8,000 | NIL |
| Toyota Glanza | Rs. 15,000 + Rs. 5,000 + Rs. 5,000 (Reference Toyota Edge Eligibility Grid) | Rs. 15,000 |
4. Toyota Yaris
The Toyota Yaris is a low-selling C2-segment sedan that is easily among the most safe options in its segment, The mid-sizer, which is available with a standard fitment of six airbags across the trims, is on sale with a cash discount of Rs 10,000 and a cash discount of Rs 15,000.
Plus, you can get Ra 5,000 of additional discount through Toyota Edge Eligibility Grid. Also, on buying the pre-facelift version, you can get a cash discount of Rs 1 lakh and a corporate bonus of Rs 30,000. Plus, there’s an exchange bonus of Rs 40,000 on both facelift and pre-facelift models.
5.Toyota Etios
The Etios has been a highly practical C1-segment sedan that takes on sub-4-metre models like the Maruti Swift and Honda Amaze. Right now, this spacious car is on sale with a corporate discount of Rs 8,000. While not much, this offer is definitely somewhat beneficial for buyers of this well-rounded offering.
6.Toyota Etios Liva
Like its sedan sibling, the Toyota Etios Liva is a well-rounded offering that has been a favorite of the fleet operators due to its highly practical cabin. Like the sedan version, the Liva is on sale with a corporate discount of Rs 8,000 at the moment.
7.Toyota Etios Cross
The Toyota Etios Cross is for those who need all the practical bits of the Etios Liva but in a smarter package that has a high street presence due to several SUV-inspired bits. The Etios Cross Is currently on sale with a cash discount of Rs 15,000 and an corporate offer of Rs 8,000.
8.Toyota Glanza
The Glanza is nothing but a rebadged Maruti Baleno that has become the first product of the alliance between the two Japanese automakers. This B2-segment hatchback is currently being sold with a cash discount of Rs 15,000 along with a corporate offer of Rs 5,000. You can also get a benefit of another Rs 5,000 through Toyota Edge Rewards points and an exchange bonus of Rs 15,000.


Compact shRNA Design Tool
Sullivan Lab. Department of Molecular Biosciences. The University of Texas at Austin.
Enter Target Sequence
Output Design
Options
Promoter Architecture
BLV-like
Cutoffs
Max Hits:
Max GC %:
Min GC %:
Exclude Internal Terminator-like
Exclude Repetitive Sequences
Maintain A Box 2
Require 5' U
Require 3' Pairing
Add 5' Flanking Sequence
Generate shRNA


Watch: Shocking video of car crashing through Spar in Knocklyon
by Nick FitzgeraldThe incident occurred at approximately 2:25am this morning
A shocking video is circulating on social media, in which a car is seen crashing through the front door of a Spar in Orlagh Grove, Knocklyon.
The incident occurred at approximately 2:25am this morning, Tuesday, November 19.
The Gardai are treating the incident as a burglary.
A spokesperson for the Gardai confirmed:
“Gardaí responded to a burglary of a premises in Orlagh Grove, Knocklyon on November 19 at approximately 2.25am.
"Significant damage was caused to the premises due to a car colliding into the front of the premises. Investigation are ongoing.”


% include ./header %>
Hacking Pirates developed an application.
This application is using SSO and automatic deployment process by using various technology.
User can see Information of different employeee with the help of this application.


Source code for pyowm.utils.timeutils
#!/usr/bin/env python
"""
Module containing utility functions for time values generation/management
"""
from datetime import datetime, date, timedelta
[docs]def tomorrow(hour=datetime.now().hour, minute=datetime.now().minute):
"""
Gives the ``datetime.datetime`` object corresponding to tomorrow. The default
value for optional parameters is the current value of hour and minute. I.e:
when called without specifying values for parameters, the resulting object
will refer to the time = now + 24 hours; when called with only hour specified,
the resulting object will refer to the tomorrow at the specified hour and at
the current minute.
:param hour: the hour for tomorrow, in the format *0-23* (defaults to
``None``)
:type hour: int
:param minute: the minute for tomorrow, in the format *0-59* (defaults to
``None``)
:type minute: int
:returns: a ``datetime.datetime`` object
:raises: *ValueError* when hour or minute have bad values
"""
tomorrow_date = date.today() + timedelta(days=1)
return datetime(tomorrow_date.year, tomorrow_date.month, tomorrow_date.day,
hour, minute, 0)


Announcing a free plan for small teams
We've always believed that by providing a space where.
Space gives you everything you need to manage business, build great stuff, and reach your goals.
Experience a level of our quality in both design & customization works.
Now that we've aligned the details, it's time to get things mapped out and organized. This part is really crucial in keeping the project in line to completion.
"I love Space! I love the ease of use, I love the fact that I have total creative freedom. The template is really nice and offers quite a large set of options. It's beautiful and the coding is done quickly and seamlessly."
Working together on the daily requires each individual to let the greater good of the team's work surface above their own ego.
"We are incredibly impressed with Space and how well it supports its customers with amazing products and services.
As creatives, it's important that we strive to do work outside of obligation. This lets us stay ahead of the curve for our clients and internal projects.
"Space brings so many benefits to any team that does anything following a process. It is the easiest way for teams to build cool things work—and get results."
Achieve virtually any look and layout from within the one template.
We strive to figure out ways to help your audience grow through all platforms.
Find what you need in one template and combine features at will.
Space gives you everything you need to manage business, build great stuff, and reach your goals.
Our duty towards you is to share our experience we're reaching in our work path with you.
We've always believed that by providing a space where.
At Wake, our mission has always been focused on..
Portfolio offers a user a new way of experiencing design.
Despite the quality of this year's initial crowd offerings.
Follow us on my Instagram account to get the latest updates and deals.


We're on to Seattle
January 25, 2015Unfortunately the Pats/Packers Super Bowl Prediction I made in Week 13 came up just short. To get my mind off of the Patriots’ football inflation tendencies and back on to the upcoming Super Bowl next week, I decided to update my previous football post where I looked at play-by-play data to visualize on-the-field pass distribution between the Pats and the Packers. I do the same here, but I also include rushing plays because the Seahawks are more of a run-heavy offense.
Starting with the aerial attack, I looked at where pass targets typically occurred on the field between Brady and Wilson. Brady has a strong preference for short throws to the left side of the field, which typically come in the form of an out route to Edelman, LaFell, or Vereen. Wilson, on the other hand, has a slight preference for the right side of the field. In total, the Pats obviously have a larger number of pass attempts this season.
Next, I examined how number of targets varied by both location and player (with at least 5 targets). Since only the description of each play was included in the data set, I used regular expressions to identify the targeted player. The first figure shows the data for New England. Again, we see a huge preference for short passes to the left by Brady. However, targets in this location seem to be fairly uniform across the most highly targeted players (Edelman, Gronk, Vereen, and LaFell). Gronk is favored up the middle, while Edelman is slightly favored to the right.
Like Brady, Wilson also does not take many shots down the field, where he typically looks to Kearse down the right. While Baldwin receives the vast majority of the targets, the Seahawks have many others who are involved in the offense. For example, Lynch will be targeted on the left side of the field for short passes, while any one of four Seahawks (Kearse, Willson, Lynch, and Richardson; Harvin is no longer with the team) can be seen getting involved with short passes to the right. Like previously mentioned, Wilson favors short passes to the right side of the field.
I then took a look at both team’s run offenses to finish up the post. I first plotted the number of rushes by the direction of the rush. The Patriots seem to start every week with a different running back. Ever since Ridley was lost for the season, the team has mainly been going with Blount as the power rusher and Vereen as the change-of-pace back, with a little bit of Gray sprinkled here and there. Either that, or they just ignore the run game all together. The Seahawks, on the other hand, favor the man known as “beast mode.” The Seahawks also have quite a few QB scrambles, with almost 50 on the year (these are denoted by the tick mark without a label all the way to the right of the plot). Overall, both teams follow similar patterns in terms of location; the Seahawks just have a lot more rushes. Additionally, the Seahawks put a little less focus on running to the guards (who are probably just pulling to become an extra blocker on these plays).
Finally, I faceted the plot above by player (minimum 10 touches). You can immediately identify the lack of a number one back for the Pats just by glancing at the number of touches given to each player. This is also a product of losing Ridley in the middle of the season, given the number of touches he had in the first few weeks. Blount has been picking up the slack, though, since he was also acquired right after Ridley got injured (and a week after Gray’s monster performance versus the Colts in the regular season). As a fan, seeing Vereen get so many touches up the middle can be annoying, especially because he is a third down back. I know many other Pats fans have not been happy with the play calling this season with that respect. Hopefully this trend does not continue for the Super Bowl.
While the Pats had quite a few players contributing in the rushing game, it’s not too hard to see what the Seahawks like to do.  This is the key to stopping the Seahawks offensive game plan. Notice, however, the number of designed runs and scrambles by Wilson as well. It will be tough to stop both Lynch and contain Wilson, and I expect the pass rush to suffer as a result. I expect the Pats defense will dare Wilson to throw, and rely on their upgraded secondary with Revis, Browner, McCourty, and Chung to win the game.
As always, the code and data can be found on GitHub. GO PATS!


It is of course possible to do the exact same thing with {{ names.name( 'scala' ) }}.
{{ code( 'page/typed-resources/activity.scala', 'scala' ) }}But thanks to the {{ names.name( 'tr' ) }} you can get rid off the cast and therefore simplify the code and improve type safety at the same time.
{{ code( 'page/typed-resources/activity2.scala', 'scala' ) }}The {{ command( 'TR' ) }} mappings are generated on every compile, when the {{ names.name( 'sbt-plugin' ) }} detects changes in the {{ command( 'R' ) }} class. Your {{ names.abbr( 'ide' ) }} usually helps you to keep the {{ command( 'R' ) }} up to date behind the scenes, but it won't do that for the {{ command( 'TR' ) }} class. You can still achieve a similar effect if you open your project {{ names.abbr( 'sbt' ) }} shell and run {{ command( '~compile' ) }}. The tilde before the command tells {{ names.abbr( 'sbt' ) }} to repeat this task whenever it detects a file change.
{{ command( 'TR' ) }} mappings only work for {{ names.abbr( 'xml' ) }} widgets that use the {{ command( 'android:id="@+id/xxx"', 'xml' ) }} property. If you chose not to use the {{ command( 'TR' ) }} class you may want to disable it for performance reasons in your project's {{ names.abbr( 'sbt' ) }} configuration via {{ command( 'typedResources := false', 'scala' ) }}.


The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. This method is similar to that of edge orientation histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid of uniformly spaced cells and uses overlapping local contrast normalization for improved accuracy.
-
Education
University of California, Berkeley
Degree: Master of Engineering
Major: Computer Science
Advisor: Alexandre Bayen
Courses: Machine Learning, Artificial Intelligence, Advanced Robotics, Computer Vision, Deep Reinforcement Learning, Deep Unsupervised Learning, Deep Neural Network, Geometry and Learning for 3D Vision
Nanjing University
Degree: Bachelor of Engineering
Major: Electrical Engineering
Advisor: Zhan Ma
Courses: Data Structure and Algorithms, Digital Image Processing, Database Principle, Semiconductor Devices, etc.
-
Skills
Programming Languages: Python, C++, C, Matlab
Techniques&Tools: ROS, Linux, CUDA, PyTorch, Tensorflow, OpenCV, SUMO, Solidworks, Altium Designer
-
Work Experience
Berkeley Artificial Intelligence Research
Responsibility: Research on Pixel Learning with Deep-RL for Mixed Autonomy Traffic, and Janux, An Operating System for Autonomous City
Advisor: Alexandre BayenSenseTime
Department: Autonomous Driving
Responsibility: Research on data augmentation with image translation
Advisor: Mingyang Huang
-
Research Experience
Arbitrary Image-to-Image Translation via Feature Adaptive Denormalization
- Worked in the VLE (Virtual Learning Evaluation) team to develop image translation models for the data augmentation of autonomous driving
- Designed and implemented a novel model based on SPADE using Python and PyTorch
- The model can transfer the content image to arbitrary styles which are controllable by the style images and achieve better FID performance than the previously published models
- 2nd Author Patent, Multi-style Image Generation with Adaptive Encoder, In application
Instance-level Image Synthesis from Single Label Map
- Developed a model which can generate more realistic images from semantic segmentation generated by the simulator
- Designed sub-modules to generate important instances (e.g. Vehicles and pedestrians in the autonomous driving scenario),then fill and blend the well-generated instances into the background
- The mAP (mean Average Precision) of object detection result on the virtual data our model generated is 3% higher than the previous models
3D Object Detection in Autonomous Driving
- Research on performing reliable and accurate 3D perception with one or multiple RGB images
- Trying to deploy the 3D object detection system to our real autonomous driving car
Pixel learning with deep-RL for mixed autonomy traffic
- Conducted research on FLOW framework (https://flow-project.github.io)
- Developed a real-world single circular lane system including the control and communication system of model cars
- Transferred the deep-RL algorithms (PPO, SAC) from the virtual simulation environment to the real environment
Video Frame Prediction
- Worked in a streaming media team to develop a model to predict future video frames and then reduce the bitstream
- Designed a neural network to utilize the deviation of the sequence of optical flows for a better prediction
- Achieved 31.2dB for average PSNR and 0.96 for average SSIM on the test of UCF-101 dataset
- 1st Author Publication, Looking-ahead: Neural Future Video Frame Prediction, IEEE ICIP2019
Gigapixel Panorama Image Stitching
- Worked on a project for the application of the future Panoramic TV and VR panoramic live broadcast
- Resolved the problems related to C++ multithreading optimization and CUDA parallel programming
- Successfully increased the speed by 15 times from 30s to 1.9s for stitching six images with the resolution of 3000×4000
-
Project Experience
Janux: An Operating System for Autonomous City
- Constructed an automatic system to transform real-world map (Google Map) to SUMO models
- Research on multi-agent policy by abstracting the road system into concrete modules
Baxter Fruit Picking
- Our project uses a Baxter machine to identify and selectively retrieve fruit from a table for storage in a packing bin. We find our project interesting because it achieves successful grasping of desired objects in a scene without having to input information about the shape or pose of the object. Although we used fruits as a simplifying scenario, we believe our system to be adaptable to a more general set of objects given more hardware and minor modification of the grasp and detection subsystems. We believe our work to be useful in situations where particular items need to be selectively retrieved among a greater collection of objects. One such example may be in a production line, where various items of different type need to selected for processing/packaging.
RoboMaster Robotics Competition
- Led a team to design a self-planning, autonomously controlled robot in a projectile shooting competition
- Designed the mechanical structure and the circuits of the robot, implemented the robot control and vision system with ROS
Drones Automatic Tracking
- Led a team to design the control system of the drone to track a model car
- Implemented the visual perception system to detect the AR-tag to locate the model car
Anti-scattered Superconducting Single-photon Detector
- Aimed to control the reception of different frequency signals by using the gate-driven source meter to remove the atmospheric scattering noise
- Responsible for programming the driver of the gate-driven source meter
- Achieved the national first prize in the Challenge Cup
Automatic Control of Experimental Animal Room
- Utilized the Internet of Things to design an automatic system to control the environment of the animal room to meet the national regulations
- Responsible for processing the signals from various sensors and designing the automated control scheme
NAO Robot Golf
- Aimed to proceed golf game among NAO robots
- Responsible for establishing the overall program framework including the robot’s decision, motion, angle measurement and recognition
-
Student Club Experience
President, Robotics Lab
- Recruited undergraduates and graduates who are interested in robotics
- Responsible for lab administration and management
- Led lab members to take an active part in the robotics research and competition
Director, Electronic Technology Innovation Club
- Responsible for some administrative affairs related to technology lectures and experiments
Director, Microsoft Student Club
- Coordinated with other universities in Nanjing and held the Hackathon activities
- Organized technology workshops
- Received the honor of outstanding officer granted by MSRA
-
Honors
- National First Prize of the Challenge Cup
- Third Prize of the RoboMaster Robotics Competition
- Outstanding Capacity Prize of the People’s Scholarship in China
- Third Prize of the People’s Scholarship in China
- First Prize of the Nanjing University Entrepreneurship Competition
-
Publications
- Looking-ahead: Neural Future Video Frame Prediction Changxu Zhang, Tong Chen, Haojie Liu, Qiu Shen, and Zhan Ma, IEEE ICIP2019
-
posts
-
Object Detection with HoG
-
How to use tqdm
Several methods to use tqdm in python.
-
Run Jupyter Notebook on AWS
We can project the 8888 port of the server to other 8xxx port on the local computer.
-
Mean shift Clustering
Mean shift is a non-parametric feature-space analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm.[1] Application domains include cluster analysis in computer vision and image processing.


{% extends 'base.html' %} {% load static %} {% block content %} {% load plotly_dash %}
You can view the analysis by scrolling down.


Page Not Found
The page you’re looking for does not exist. It may have been moved.
If you arrived on this page by clicking on a link, please notify the owner of the site that the link is broken. If you typed the URL of this page manually, please double check that you entered the address correctly.


Meetup.com
Upload pictures and presentations!
Free Developer Subscription
esri.github.com
Today
6:00 - 6:30 Keynote
- Allan Laframboise "the raspberry"
- The wonderful world of GitHub and Open Source projects
6:30 - 8:00 Lightning Talks
- Meghan Burns, "Shopping 'til You Don't Drop"
- Jason Danielson, "Attribute Assistant" add-in
- Allen Armstrong, "Irrigation Ditch Mapping"
- Evan Roberts, "Generic Topological Sort"
- Leslie Zolman, "Some like it 'HOT'"
- Dave Ratz, "Microsoft Spatial Fields and ArcGIS"
- Eric Spangenberg, "Mapping a Vacation Using AGO"
- Josh Gage, "Visualizing spatial/temporal data"
- Gerry Daumiller, "Running with GPS"
- Kyle Balke, "ArcGIS server JS API"
- Erin Fashoway, "Labels for ArcGIS Online…Real Dreams Do Come True"


Alt + Click on a node in the tree to remove it and it's children.
Mouseover on a node and press + or - to adjust its size
Shift + , or Shift + . to move to the next/previous order saved in input file.
Mouseover on a node and press o to find an order for that node and its children using the bitonic algorithm as proposed by us. Non optimal for every graph except 1-level quasi-threshold
Press Shift + T to find a linear ordering using the using the Threshold Algorithm for threshold graphs as proposed by Cutwidth of split graphs and threshold graphs in O(n) time. Optimal only for threshold graph
~ to open up debug information. Then Mouseover a node to see it's specified debug info.


Assignment:
| Repository:
link
Git History
Date
Author
Message
# Changes
Submission History
Submission Date
Status
Labels
Indicators
Actions
Overdue
Final
14/14


To install the features contained here, press Help > Install New Software… in your Eclipse IDE and enter the URL of this site.


Select one of the items that no one is editing to start.
A new tab or new page can simulate different users picking different items to edit:


Creates a StreamTransformer.
The returned instance takes responsibility of implementing (bind).
When the user invokes bind
it returns a new "bound" stream. Only when
the user starts listening to the bound stream, the listen
method
invokes the given closure transformer
.
The transformer
closure receives the stream, that was bound, as argument
and returns a StreamSubscription. In almost all cases the closure
listens itself to the stream that is given as argument.
The result of invoking the transformer
closure is a StreamSubscription.
The bound stream-transformer (created by the bind
method above) then sets
the handlers it received as part of the listen
call.
Conceptually this can be summarized as follows:
-
var transformer = new StreamTransformer(transformerClosure);
creates aStreamTransformer
that supports thebind
method. -
var boundStream = stream.transform(transformer);
binds thestream
and returns a bound stream that has a pointer tostream
. -
boundStream.listen(f1, onError: f2, onDone: f3, cancelOnError: b)
starts the listening and transformation. This is accomplished in 2 steps: first theboundStream
invokes thetransformerClosure
with thestream
it captured:transformerClosure(stream, b)
. The resultsubscription
, a StreamSubscription, is then updated to receive its handlers:subscription.onData(f1)
,subscription.onError(f2)
,subscription(f3)
. Finally the subscription is returned as result of thelisten
call.
There are two common ways to create a StreamSubscription:
-
by creating a new class that implements StreamSubscription. Note that the subscription should run callbacks in the Zone the stream was listened to.
-
by allocating a StreamController and to return the result of listening to its stream.
Example use of a duplicating transformer:
stringStream.transform(new StreamTransformer<String, String>(
(Stream<String> input, bool cancelOnError) {
StreamController<String> controller;
StreamSubscription<String> subscription;
controller = new StreamController<String>(
onListen: () {
subscription = input.listen((data) {
// Duplicate the data.
controller.add(data);
controller.add(data);
},
onError: controller.addError,
onDone: controller.close,
cancelOnError: cancelOnError);
},
onPause: () { subscription.pause(); },
onResume: () { subscription.resume(); },
onCancel: () => subscription.cancel(),
sync: true);
return controller.stream.listen(null);
});
Source
const factory StreamTransformer( StreamSubscription<T> transformer(Stream<S> stream, bool cancelOnError)) = _StreamSubscriptionTransformer<S, T>;


Originally written by .
[Scene: Monica and Rachel's apartment. Phoebe enters to see Monica,
Richard, Ross, and Rachel tearfully watching TV.]
PHOEBE: Hey. Watcha guys doin?
RICHARD: Monica's making us watch Old Yeller.
PHOEBE: Why are you guys so upset? It's Old Yeller, it's a happy movie.
RACHEL: What?
ROSS: What're you talkin' about?
PHOEBE: C'mon, happy family gets a dog, frontier fun.
ROSS: Yeah but Phoebs, what about the end?
PHOEBE: What when Yeller saves saves the family from the wolf and everyone's happy?
RACHEL: That's not the end.
PHOEBE: Yu-huh. That's when my mother would shut off the TV and say 'The end'.
MONICA: What about the part where he has rabies?
PHOEBE: He doesn't have rabies, he has babies. That's what my mom said.
RICHARD: Uh, Phoebe, I don't think your mom would want you to see what's about to happen.
PHOEBE: What, what's about to happen? [starts watching] I've never
seen this part before. Hey, Travis, watcha doin' with that gun? Oh no, no no Travis, put
down the gun. No no no no, he he's your buddy, he's your Yeller, no, no no, the end, THE
END. [hear the gunshot from the TV]
[Scene:Monica and Rachel's apartment. Richard is on the balcony smoking and Monica is
on the phone.]
MONICA: Hey, have you guys eaten, because uh, Richard and I just
finished and we've got leftovers... Chicken and potatoes... What am I wearing?...Actually,
nothing but rubber gloves.

MONICA: Alright, I've got a leg, three breasts and a wing.
CHANDLER: Well, how do you find clothes that fit?
JOEY: Oh, hey, Monica, we've got a question.
MONICA: Alright, for the bizillionth time, yes I see other women in shower at the gym, and
no I don't look.
JOEY: No, not that one. We're trying to figure out who to bring to the Knicks game
tonight, we have an extra ticket.
CHANDLER: Yeah, Ross can't go so it's between my friend Eric Prower who has breath issues
and Dan with the poking. [starts poking Monica in the shoulder] 'Did you see that play? Do
you want some more beer? Is that Spike Lee?'
MONICA: Ok. [Richard walks in] Hey, why don't you ask Richard?
JOEY: Ok, uh, hey Richard, if you had an extra ticket to the Knicks game and you had to
choose between a friend who smells and one who bruises you who would you pick?
RICHARD: Wow.
CHANDLER: Ok, that's Eric.
RICHARD: Glad to be of help. Matches. [walks out to the balcony]
MONICA: I meant, why don't you take Richard to the game? What?
JOEY: I don't know.
MONICA: C'mon. Keeps his fingers to himself and he's always minty fresh.
CHANDLER: I don't know, Richard's really nice and everything, uh,
it's just that we don't know him really well, ya know, and plus he's, ya know, old [Monica
gives him a glare] -er than some people, but, uh, younger than some buildings.
MONICA: So what, he's a little older, big deal, I mean he's important to me. Ya know if
you ask him, he might take you on his Jag. [walks off]
JOEY: How do we say yes now and make it seem like we're not doin' it
just to ride in the cool car?
CHANDLER: Ok, this could be tough.
JOEY: [Monica walks back in the kitchen] Ok ok ok ok. Monica, we'll bring him, but only if
he takes the Jaguar.
CHANDLER: Ooh, you almost had it.
[Scene: Ross's apartment. Ross is holding Ben.]
ROSS: [smells Ben's butt] No no, you're fine, you're fine.
CAROL: [enters with Susan] Hello
ROSS: Hi.
SUSAN: Hey.
CAROL: Hi honey.

CAROL: What?
SUSAN: What?
ROSS: Ok, we were sitting over there playing on the floor and he
grabs the table and he pulls himself up. He pulled himself up. Standing man. I'm sorry you
guys missed it but I did tape it so it you guys want to see it.
CAROL: Uhh, we know, he already did it last week.
SUSAN: You can watch our tape if you want.
ROSS: I don't believe this. I miss, I miss the first time of everything. I missed, what,
the first time he rolled over, the first time he crawled. What else did I miss? Has he
spoken yet, is he driving, does he have a favorite liquour?
CAROL: Actually, he is getting closer on the talking thing. He can't quite say mama yet,
but once he said yumen.
  No listen , I mean, I feel like-
CAROL and SUSAN: Great. That would be fine.
ROSS: Really?

ROSS: Oh look, did, did you just see that? Did you see? He just waved, he just waved, he's
never waved before, you've never waved before. Yes he has. Very good.
[Scene: Central Perk. Monica and Rachel are there. Phoebe enters carrying video tapes.]
PHOEBE: Hey.
RACHEL: Hey Phoebs, whatcha got there?
PHOEBE: Ok, Love Story, Brian's Song, and Terms of Endearment.
MONICA: Wow, all you need now is The Killing Fields and some guacamole and you've
got yourself a part-ay.
PHOEBE: Yeah, I talked to my grandma about the Old Yeller
incident, and she told me that my mom used to not show us the ends of sad movies to shield
us from the pain and sadness. You know, before she killed herself.
[Chandler and Joey enter]
CHANDLER: Hey.
JOEY: Hey.
RACHEL: Hey.
MONICA: Hey. Where is he, where's Richard? Did you ditch him?
JOEY: Yeah right after we stole his lunch money and gave him a wedgie. What's the matter
with you, he's parking the car.
MONICA: So'd you guys have fun?
CHANDLER: Your boyfriend is so cool.
MONICA: Really?
CHANDLER: Yeah, he let us drive his Jaguar. Joey for 12 blocks, me
for 15.
RACHEL: Wow, he must like you the best.
JOEY: Oh, what about that thing he did when he tipped the guy who
showed us to our seats. You never even saw the money, it was like this. [With money in his
palm] Hey Chandler, thanks for showing us to our seats [shakes his had and passes the
dollar].
CHANDLER: You're welcome. Hey Joey, thanks for parking the car [passes the dollar back].
JOEY: No problem. Hey Chandler
CHANDLER: I think they get it.
JOEY: Ok.
[Richard enters]
CHANDLER: There's the man.
JOEY: He-he-eyy. [Shakes his had and passes the dollar]
RICHARD: Hey, you're gettin' better. I'm gonna keep this by the way.
JOEY: Ok. He kept my dollar.
[Scene: Monica and Rachel's apartment. Carol and Susan are dropping off Ben.]
MONICA: So your first whole weekend without Ben, what're you guys gonna do?
CAROL: Uh, we're going down to Colonial Williamsburg.
SUSAN: Yeah, a woman I went to college with just became the first female blacksmith down
there.
ROSS: Well, ya know, they're a little behind the times in Colonial Williamsburg.
CAROL: Think I better go before mommy starts weeping.
ROSS: Buy mommy.
CAROL and SUSAN: We love you. Buy.
ROSS: Have a good time. Ok, Ben.
MONICA: Ross.
ROSS: Yeah.
MONICA: Look. [they look at Joey in the kitchen with a cigar in his
mouth, looking at his reflection in a spatula] Joey, do you know we can see you from here?
JOEY: How come Richard looks so much cooler with one of these than
me?
ROSS: Well for starters, you may want to light it and lose the spatula.
MONICA: You know what, I think it's cute, you trying to be more like Richard.
JOEY: Not like him, per-se, just not un-like him.

CHANDLER: Just tryin' somethin' here, ya know.
MONICA: So Joey, why didn't you grow a moustache?
JOEY: Oh we flipped for it. I got the cigar, he got the moustache. Figured if we both grew
it, we'd look like dorks.
ROSS: Yeah, you really sidestepped that land mine.
CHANDLER: Hey listen, we've gotta go, I promised Richard we'd meet him downstairs.
MONICA: You're meeting Richard?
JOEY: Yeah, we're goin' to a Ranger game.
CHANDLER: Yeah, didn't he tell ya?
MONICA: Well, he told me he was going out with the guys, I just didn't know that you were
the guys.
CHANDLER: You hear that? We're the guys.
JOEY: We're the guys.
MONICA: With that moustache doesn't Chandler remind you of Aunt Sylvia?
ROSS: Thank you.
[Scene: Central Perk. Rachel is working. Ross enters with Ben.]
ROSS: Hi, we're visitiing. It's Ben and his da-da. Da-da. Can you
say da-da? Look, I'm gonna tell your momies you said it anyway so you might as well try.
RACHEL: No luck huh?
ROSS: Naa. A while ago I got a sah out of him, which I thought, ya know, might turn into
sah-condary caregiver but... Hey, would you uh, would you hold him for a sec, 'cause I, I
gotta take this off.
RACHEL: Oh, yeah sure, Ok. [she takes Ben and holds him at arms length]
ROSS: What're you doing?
RACHEL: Uh, I'm holding Ben.
ROSS: Yeah, well, he's a baby not a bomb.
RACHEL: Ok.
ROSS: Well just hold him like you'd hold a football.
RACHEL: This is how I would hold a football.
ROSS: Ok, here, here. There we go.
RACHEL: Ok, I'm sorry, I'm just not very good with babies.
ROSS: It's alright, it's no big deal.
RACHEL: Really?
ROSS: Yeah, definitely, I'm sure you'll feel totally different when it's our baby.
RACHEL: What?
ROSS: What?
RACHEL: You think about stuff like that?
ROSS: Uhh, yeah. I mean, actually I kinda think that we'll have, we'll have two babies.
RACHEL: Two, two babies?
ROSS: Yeah. Ya know, a boy and a girl. Hopefully the girl will come first so Ben here
won't feel too competitive.
RACHEL: Then what's gonna happen?
ROSS: Well, we won't wanna raise kids in the city so we'll probably move to uh, Scarsdale.
RACHEL: Uh-huh.
ROSS: Yeah, that way I figure, ya know, we'll be far enough away from our parents that we
don't have to see them all the time but close enough that they can come over and babysit
whenever we want. And yes, I know, the taxes are a little higher than, let's say, Nassau
county but the school system's supposedly great.
RACHEL: Wow. Wow, that's great. Great. Ok, wow, you know what.
ROSS: Huh?
 Ok. Oh look at that, I don't have a pot.
I don't have a pot. Well, hey, maybe I've got one at home, or in Scarsdale. Hey is that a
door? [leaves]
[Scene: Monica and Rachel's apartment. Monica and Richard are there. Phoebe enters.]
PHOEBE: Hey.
RICHARD: Hey Phoebs, what's happening?
PHOEBE: Oh, ok, murder, cancer, soccer teams eating each other in the Andes.
MONICA: So you watched the movies huh?
PHOEBE: Uh huh, what is happening to the world?
RICHARD: Charlotte who?
PHOEBE: With the web, the spider she dies, she does. She has babies and dies. It's like ya
know, hey welcome home from the hospital, thud.
MONICA: Alright, you wanna feel better?
PHOEBE: Yeah.
MONICA: Ok, here, watch this.
PHOEBE: It's a Wonderful Life. Yes I've heard of this.
MONICA: So you can't lose, it's there in the title. Wonderfullness is baked right in.
PHOEBE: Please, I almost fell for that with, uh, Pride of the Yankees, I thought I
was gonna see a film about Yankee pride and then, boom, the guy gets Lou Gehrig's disease.
RICHARD: Uh, the guy was Lou Gehrig. Didn't you kinda see it coming?
PHOEBE: Phoebe, just watch that, I promise it will resotre all your faith in humanity.
CHANDLER: [runs in] Hey, big guy, game time.
RICHARD: Hey, be right there.
MONICA: There's a game?
CHANDLER: Uh, yeah, I just got my pick-up sticks back from the shop. Bring your nerves of
steel.
RICHARD: It's the basketball playoffs.
MONICA: Listen, um honey, I appreciate this but you don't have to keep hanging out with
them for me, I mean, they have each other.
RICHARD: Oh, no, honey, I mean, don't worry, I like hanging out with
those guys. It's fun for me. They're different than my other friends, they don't start
sentences with, 'You know who just died shoveling snow?'
MONICA: Alright that's great, then just go. Go Knicks.
RICHARD: Uh, it's the college playoffs.
MONICA: Oh, then go Vassar.
RICHARD: Uh, they're not in it.
MONICA: Ok, then just go.
RICHARD: Ok. [leaves]
MONICA: Oh, why does this bother me so much? I mean I don't wanna be one of those people
who tells their boyfriend they wanna spend 24 hours a day with them.
PHOEBE: Sure.

PHOEBE: Does it matter?
RACHEL: [enters] Aghh.
PHOEBE: Me too. [leaves]
MONICA: I've got a question. Richard made plans again with the guys.
RACHEL: Yeah well, Ross just made plans for the whole century.
MONICA: Ya know what, I think I'm gonna go to my room and read
Cosmo, maybe there's something helpful in there. Know what, at least maybe I can learn how
to do an at home bikini wax with leftover Cristmas candles.
ROSS: [enters] Ok, what the hell happened back there?
RACHEL: I don't know, you tell me. One minute I'm holding Ben like a football, the next
thing I know, I've got two kids, I'm living in Scarsdale complaining about the taxes.
ROSS: Well I'm sorry, I think about stuff.
RACHEL: Ross, you have planned out the next 20 years of our lives, we've been dating for
six weeks.
ROSS: C'mon, what, you never think about our future?
RACHEL: Yes, but I, I think about who's apartment we're gonna sleep
at tomorrow night and, and where we're gonna have dinner next Saturday night. I do not
think about what our childrens' names are gonna be.
ROSS: No, no, I mean, ya know, I, I read a book and there was a girl named Emily and I
thought, I thought that might be good.
RACHEL: What was the book?
ROSS: The big book of childrens' names.
RACHEL: Ok, Ross, Ross, ok listen, what we have is amazing.
ROSS: Yeah.
RACHEL: But I do not want to have everything decided for me. I spent my whole life like
that. It's what I had with Barry, that was one of the reasons I left. I, I like not
knowing right now and I'm sorry if that scares you but if you want to be with me you are
gonna have to deal with that.
ROSS: Ok fine.
RACHEL: Thank you.
ROSS: We're not done.
RACHEL: I didn't know that.

RACHEL: Fine, I will.
ROSS: Good, 'cause I love you.
RACHEL: Oh yeah.
ROSS: Yeah.
RACHEL: Well I love you too.
ROSS: Well that's the first time we've said that.
RACHEL: Yes it is.

RACHEL: Well you better.
[Scene: Monica and Rachel's apartment. Monica is in the kitchen, Phoebe enters.]
PHOEBE: Hey. Oh thanks for the great movie tip.
MONICA: Did you like it?
PHOEBE: Oh yeah. You know, I don't know if I was happier when um George Bailey destroyed
the family business or um, Donna Reid cried, or when the mean pharmacist made his ear
bleed.
MONICA: Alright, I'll give you the ear thing but don't you think the ending was pretty
wonderful?
PHOEBE: I didn't watch the ending, I was too depressed.
[Scene: Chandler and Joey's apartment. Chandler and Joey are playing Richard at
foosball.]
CHANDLER: Kick save and... denied.
RICHARD: But... he gets it back, pass to the middle, lines it up
and... BAM! Yes! Could that shot BE any prettier?
JOEY: Man you are incredible.
RICHARD: Well, we had a table in college.
CHANDLER: Oh really, I didn't know they had foosball in the 1800's.
RICHARD: Nice moustache by the way. When puberty hits that thing's
really gonna kick in.
MONICA: [enters] Honey. Uh, not to sound too Florence Henderson but, dinner's on the
table.
RICHARD: Ok, just one more point.
MONICA: [grabs the other two bars on Richard's side and scores] Score! Now can we go?
CHANDLER: See, that's why we don't let her play.
RICHARD: Is everything all right?
MONICA: Um-hmm.
RICHARD: Uh-oh.
 I'm sorry we've been hoggin
so much of his time.
JOEY: Yeah, he's just really great to hang around with.
RICHARD: Well.
JOEY: No no, seriously, Chandler and I were just talkin about this.
He is so much cooler than our dads.  What are you kickin me
for? Huh? I'm tryin to talk here.
RICHARD: Uh, you guys see me as a dad?
JOEY: Oh yeah.
CHANDLER: No.
JOEY: Nooooo.
CHANDLER: Your just, your just clearly not familiar with our young
persons vernacular. See, when we say dad, we mean buddy. We mean pal.
RICHARD: Uh-huh, yeah.
CHANDLER: No no, seriously, Joey's my dad, Monica's my dad. I've even got some dads down
at work.
RICHARD: That's fine. Well, your other dad and I are gonna go have a romantic evening and
I guess I'll just see you kids around.
MONICA: Nighty-nite.
CHANDLER: You're not a dad. You're not a dad.
JOEY: Not a dad.
CHANDLER: I can't believe you got us into trouble. [slaps Joey on the arm. Joey takes
exception and slaps him back]
MONICA: [her and Richard return to her place] So are you ok?
RICHARD: Yeah, just, I feel like I'm about a hundred. I thought I was just one of the
guys.
MONICA: Come here. I'll make you feel like one of the guys.
RICHARD: What're you talkin' about, I was killin' 'em.

[Scene: Ross's apartment. Rachel is changing Ben's diaper under Ross's supervision.]
ROSS: Ok, and then you take the poopie diaper and you put it in the
poopie diaper pail.
RACHEL: Ok Ross, just so you know, calling it a poopie diaper doesn't make this process
any cuter.
ROSS: [doorbell buzzes] Hello.
CAROL: It's us.
ROSS: Come on up. I'm gonna get the rest of his stuff together. [walks in his room]
RACHEL: Ok, we can do this now, can't we Ben? Yes we can, yes we can. [finishes the
diaper] There. I did it. I did it. Look at that, oh, stays on and everything. Hi.
BEN: Hi.
RACHEL: I'm sorry, what did you just say? Did you just say hi? Oh my God, Ross, Ross, Ben
just said 'Hi'.
ROSS: Wha, what?
RACHEL: Ben just said hi.
ROSS: What, the word hi?
RACHEL: Ye-, no, my Uncle Hi.
ROSS: Great, great, and I miss that too, I miss everything.
RACHEL: Oh, I'm sorry, I guess I just bring it out in him.
CAROL and SUSAN: Hello.
RACHEL: Guess what. Ben just said his first word.
CAROL: What did he say?
ROSS: Something about hi.
SUSAN: That's so exciting.

CAROL: Hi.
RACHEL: Hi.
CAROL: Hi.
RACHEL: Hi.
CAROL: Hi.
RACHEL: Hi.
CAROL: Hi.
SUSAN: Ok, this could go on for a while.
CAROL: We've got a cab waiting downstairs.
ROSS: Well, this was fun. Uh, we should really do it again sometime, wha'dya say? Ok.
Alright so I've got him.
CAROL: Tuesday.
ROSS: Tuesday right. Ok, bye you guys.
RACHEL: Take care.
ROSS: Bye Ben.
BEN: Bye.
RACHEL: Did, did he just, did he, did he just say, he said bye. He said bye. You said, you
said bye to me. You said bye to me.
SUSAN: Suddenly I'm seeing him go off to college.
CAROL: We've gotta go, we've got that cab waiting.
ROSS: Alright, alright, ok. Bye.
BEN: Bye.
ROSS: Bye.
BEN: Bye.
ROSS: Bye.
BEN: Bye.
ROSS: Bye.
CLOSING CREDITS
[Scene: Monica and Rachel's apartment. Phoebe is watching Bert and Ernie with Ben.]
ERNIE: Oh wow, look at this nice deep hole I've been digging. Hey Bert, isn't this a
nice hole here. Hey.
PHOEBE: [pauses the tape] Ok, Ben, this is the part where Ernie
buries Bert in the sand and can't find him. Now, I've looked ahead on the tape and he does
find him again. But, ok, before that happens, there's some pretty rough goin' for a while
but I think we can handle it. And, there's just the alphabet but we know that ends well
so. Ok, here we go. [starts the tape again]
ERNIE: Bert, Bert. Bert. Hey, what happened to my friend Bert? He was here just a
moment ago. Oh no, my old friend Bert is lost.
PHOEBE: Oh, I'm so glad you're here.
END


| Peter's Page | About | Services | Contact |
|
STUDENT & DESIGNER
|
|
At Peter's Design we're an award-winning professional web design company that focuses on lead generation and eCommerce website design services to grow your business online. For over 16 years our expert team of in-house project managers, web designers, website developers, and search marketing strategists have used the latest website design technology to create actionable results. We're skilled in development across all major platforms including Wordpress, Shopify, Magento and many others. No matter the type of website project you need, the Peter's Design team will take your online image to the next level. |
Graphics Design
Get your unique and creative designs at AFFORDABLE PRICE!
Web Design and Development
Bring you business online; we bring your brand to the World Wide Web
SEO Expert
We rank your site higher in Search Engines
Project Management
Leave your project Management to Experts, we manage your project for you.
Branding Development
We help you Development your brand.


rotten > Library > Biographies > Artists > Mike Diana
Mike Diana
Genius Florida cartoonist Mike Diana was arrested and jailed in 1994 for distributing his disturbing comic "Boiled Angel".    He has since moved to greener pastures where he continues violating his Florida state court ruling and risking imprisonment due to his flagrant regard for the 1st Amendment. This is an important artist who deserves your support. He also has a nice bod.
Pornopolis |
Rotten |
Faces of Death |
Famous Nudes
|


HireSpark
Home
About Us
Our Products
Customer Registration
Contact Us
HireSpark
HireSpark helps you build your dream, your future when you hire our WORLD CLASS HIGH QUALITY machinery.
Get Started
Hire Our Equipment
Customer Name
Make
Komatsu
Caterpilar
JCB
Mitsubishi
Type
Grader
Tractor
Bulldozer
Crane
Daily Rate
Number of Days
Collection Point
HireSpark
Customer
Print Proforma
Close


|
|
| Calibrator (const std::string &name) |
| |
|
virtual void | display (std::ostream &os) const |
| |
|
virtual const std::string & | getClassName (void) const |
| |
|
|
SignalPtr< dg::Vector, int > | positionSIN |
| |
|
SignalPtr< dg::Vector, int > | velocitySIN |
| |
|
SignalPtr< dg::Vector, int > | calibration_torqueSIN |
| |
|
SignalPtr< dg::Vector, int > | hardstop2zeroSIN |
| |
|
SignalTimeDependent< dg::Vector, int > | positionSOUT |
| |
|
SignalTimeDependent< dg::Vector, int > | controlSOUT |
| |
|
SignalTimeDependent< int, int > | calibrated_flagSOUT |
| |
|
|
static const double | TIME_STEP_DEFAULT = .001 |
| |
|
static const std::string | CLASS_NAME |
| |
|
|
double & | setsize (int dimension) |
| |
|
dg::Vector & | calibrate (dg::Vector &tau, int t) |
| |
|
dg::Vector & | compute_position (dg::Vector &pos, int t) |
| |
|
int & | is_calibrated (int &calibrated_flag, int t) |
| |
|
|
double | TimeStep |
| |
|
double | _dimension |
| |
|
SignalTimeDependent< int, int > | internal_signal_refresher_ |
| |
|
int | threshold_time |
| |
|
double | threshold_velocity |
| |
|
int | t_start |
| |
|
int | init_flag |
| |
|
int | num_joints |
| |
|
int | calibrated_flag_ |
| |
|
dg::Vector | calibrated |
| |
|
dg::Vector | des_vel |
| |
|
dg::Vector | error |
| |
|
dg::Vector | start2hardstop |
| |
The documentation for this class was generated from the following files:


Regridding operations
Introduction
HARP makes a distinction between ‘regridding’ and ‘rebinning’. With regridding you use interpolation to find values at new grid points by using a weighted average based on distance between the values at neighbouring grid points. With rebinning you consider intervals and assign each interval a value based on the (weighted) average of all values that intersect with that interval.
Note that regridding and rebinning is closely linked to the HARP convention regarding axis variables.
There are also special operations called ‘binning’ and ‘spatial binning’. With ‘binning’ all values in the time dimension are averaged together based on a time-dependent reference variable that defines the bin values. With ‘spatial binning’ a product that does not depend on the ‘latitude’ and ‘longitude’ dimensions is gridded to a specified lat/lon grid.
Regridding
For regridding we define the source grid as \(x_{s}(i)\) with \(i=1..N\) and the target grid as \(x_{t}(j)\) with \(j=1..M\). For each target grid point \(x_{t}(j)\) the nearest points below and above that point in the source grid are located (\(x_{s}(i)\) and \(x_{s}(i+1)\)) and then an interpolation is performed to convert the values.
If a target grid point lies outside the source grid then the behaviour depends on the regrid_out_of_bounds option that can be set using the set() operation.
Both the source and target grid variables need to be strict monotic (either ascending or descending) for the regridding to work.
Variables \(y_{s}(i)\) that depend on the grid dimension are regridded to a target version \(y_{t}(j)\) as follows:
\begin{eqnarray} y_{t}(j) & = & \begin{cases} y_{s}(0) + \frac{x_{t}(j) - x_{s}(0)}{x_{s}(0) - x_{s}(1)}\left(y_{s}(0) - y_{s}(1)\right), & x_{t}(j) < x_{s}(0) \wedge \textrm{regrid_out_of_bounds=extrapolate} \\ y_{s}(0), & x_{t}(j) < x_{s}(0) \wedge \textrm{regrid_out_of_bounds=edge} \\ \mathit{nan}, & x_{t}(j) < x_{s}(0) \wedge \textrm{regrid_out_of_bounds=nan} \\ \left(1 - \frac{x_{t}(j) - x_{s}(i)}{x_{s}(i+1) - x_{s}(i)}\right)y_{s}(i) + \frac{x_{t}(j) - x_{s}(i)}{x_{s}(i+1) - x_{s}(i)}y_{s}(i+1) , & x_{s}(i) < x_{t}(j) < x_{s}(i+1) \\ y_{s}(i), & x_{t}(j) = x_{s}(i) \\ y_{s}(N) + \frac{x_{t}(j) - x_{s}(N)}{x_{s}(N) - x_{s}(N-1)}\left(y_{s}(N) - y_{s}(N-1)\right), & x_{t}(j) > x_{s}(N) \wedge \textrm{regrid_out_of_bounds=extrapolate} \\ y_{s}(N), & x_{t}(j) > x_{s}(N) \wedge \textrm{regrid_out_of_bounds=edge} \\ \mathit{nan}, & x_{t}(j) > x_{s}(N) \wedge \textrm{regrid_out_of_bounds=nan} \end{cases} \\ \end{eqnarray}
Variables will be left unmodified if they do not depend on the to be regridded dimension.
Variables will be removed if they:
depend more than once on the given dimension (e.g. 2D averaging kernels when using the vertical dimension).
do not have a unit attribute (note that variables with an empty unit or unit=1 are considered to have a unit attribute).
have a ‘string’ data type.
are an uncertainty variable.
are a bounds axis variable for the given dimension
In most cases the axis variable is directly mapped to \(x\) and the variables that will be regridded to \(y\). The special cases are:
when regridding in the vertical dimension and the axis variable is pressure then \(x\) is set to the logarithm of the pressure.
when regridding in the spectral dimension and the variable to be regridded is an aerosol_optical_depth or aerosol_extinction_coefficient variable then a log/log interpolation is performed. This means that the logarithm of the axis variable and the logarithm of the to be regridded variable is used for the interpolation.
A special version of interpolation, called interval interpolation is used for variables that provide an integrated quantity in the given dimension. The algorithm for this is the same algorithm as for rebinning of integrated quantities as described in the next section.
Rebinning
Rebinning uses a weighted average of the overlapping intervals of the source grid with the interval of the target grid. Each interval is defined by its upper and lower boundary. We define the source interval grid as \(x^{B}_{s}(i,l)\) with \(i=1..N\) and \(l=1..2\), and the target interval grid as \(x^{B}_{t}(j,l)\) with \(j=1..M\) and \(l=1..2\).
The rebinning is then performed as follows:
\begin{eqnarray} x^{min}_{s}(i) & = & \min_{l}{x^{B}_{s}(i,l)} \\ x^{max}_{s}(i) & = & \max_{l}{x^{B}_{s}(i,l)} \\ x^{min}_{t}(j) & = & \min_{l}{x^{B}_{t}(j,l)} \\ x^{max}_{t}(j) & = & \max_{l}{x^{B}_{t}(j,l)} \\ w(i,j) & = & \frac{\max(\min(x^{max}_{s}(i), x^{max}_{t}(j)) - \max(x^{min}_{s}(i), x^{min}_{t}(j)), 0)} {x^{max}_{s}(i) - x^{min}_{s}(i)} \\ y_{t}(j) & = & \begin{cases} \frac{\sum_{i}{w(i,j)y_{s}(i)}}{\sum_{i}{w(i,j)}}, & \sum_{i}{w(i,j)} > 0 \\ \mathit{nan}, & \sum_{i}{w(i,j)} = 0 \end{cases} \\ \end{eqnarray}
For variables that provide an integrated quantity in the given dimension, the end result is the sum of the weighted contributions instead of the average. Such variables are, for example, partial column density profiles for the vertical dimension. Other such vertical variables are column averaging kernels and degree-of-freedom profiles.
The rebinning operation for integrated variables uses the following revised calculation of \(y_{t}(j)\):
\begin{eqnarray} y_{t}(j) & = & \begin{cases} \sum_{i}{w(i,j)y_{s}(i)}, & \sum_{i}{w(i,j)} > 0 \\ \mathit{nan}, & \sum_{i}{w(i,j)} = 0 \end{cases} \\ \end{eqnarray}
Binning
Binning uses a variable that depends on the time dimension to define the bins. For each variable, all elements that have the same value for the associated element in the binning variable are then averaged into a final value for the bin.
Even though the bins will be represented by the time dimension, this resulting time dimension may not end up in any chronological order. This all depends on the variable that is used for the bins.
The algorithm for binning is somewhat similar to that of rebinning, except that each interval is represented by a single value. The binning variable is defined as \(x_{s}(i)\) with \(i=1..N\) and the target bins as \(x_{t}(j)\) with \(j=1..M\). The value \(M\) represents the number of unique values in \(x_{s}(i)\).
The value \(y_{t}(j)\) for each bin \(j\) is then determined using:
\begin{eqnarray} x_{t}(j) & = & x_{s}(\arg \min_{i}{x_{s}(i) \ne x_{t}(k) \forall k < j}) \\ N_{j} & = & \sum_{i}{\begin{cases} 1, & x_{s}(i) = x_{s}(j) \\ 0, & x_{s}(i) \ne x_{s}(j) \\ \end{cases}} \\ y_{t}(j) & = & \frac{ \sum_{i}{\begin{cases} y_{s}(i), & x_{s}(i) = x_{s}(j) \\ 0, & x_{s}(i) \ne x_{s}(j) \\ \end{cases}} }{N_{j}} \end{eqnarray}
In most cases, each variable is directly mapped to \(y\). The special cases are:
random uncertainty variables are averaged using the square of each value. The final value is given by: \(y_{t}(j) = \frac{\sqrt{\sum_{i,x_{s}(i) = x_{s}(j)}{y_{s}(i)^{2}}}}{N_{j}}\).
total uncertainty variables are averaged based on the propagate_uncertainty option that can be set using the set() operation. If it is set to uncorrelated then the variable is averaged as a random uncertainty variable (using its square), otherwise a regular average is taken.
variables that define an angle (such as latitude, longitude, angle and direction) are averaged using their unit vector representation (\(\textbf{y}_{s} = (\textrm{cos}(y_{s}) , \textrm{sin}(y_{s}))\). The final average is converted back into an angle using \(\textrm{atan2}(\textbf{y}_{t})\). The norm \(\|\textbf{y}_{t}\|\) is stored as a weight variable.
Spatial binning
Spatial binning grids the data to a rectilinear lat/lon grid. Depending on what latitude/longitude variables are available the gridding either uses a point average or an area weighted average.
If the product contains latitude_bounds and longitude_bounds variables (that only depend on the time dimension) then an area weighted average is performed. Otherwise, if the product contains latitude and longitude variables (that only depend on the time dimension) then a point average is performed.
Spatial binning can only be performed on a product that does not already depend on the latitude and longitude dimensions. Regridding an existing lat/lon grid can be done by individually rebinning the existing latitude and longitude dimensions.
The target grid is defined by the lat/lon positions of the cell edge corners. This edge grid is represented as \(\phi^{E}_{t}(j)\) with \(j=1..(M_{\phi}+1)\) for latitude and \(\lambda^{E}_{t}(k)\) with \(j=k..(M_{\lambda}+1)\) for longitude.
In the resulting HARP product the edge grid is stored as latitude_bounds and longitude_bounds variables \(\phi^{B}_{t}(j,l)\) and \(\lambda^{B}_{t}(k,l)\) with \(j=1..M_{\phi}\), \(k=1..M_{\lambda}\), and \(l=1..2\) using the relation:
\begin{eqnarray} \phi^{B}_{t}(j,1) & = & \phi^{E}_{t}(j) \\ \phi^{B}_{t}(j,2) & = & \phi^{E}_{t}(j + 1) \\ \lambda^{B}_{t}(k,1) & = & \lambda^{E}_{t}(k) \\ \lambda^{B}_{t}(k,2) & = & \lambda^{E}_{t}(k + 1) \\ \end{eqnarray}
The spatial binning maps each source variable \(y_{s}(i)\) with \(i=1..N_{t}\) to a gridded target variable \(y_{t}(j,k)\). Each target grid cell is represented by \((j,k)\) with \(j=1..M_{\phi}\) and \(k=1..M_{\lambda}\) providing the latitude and longitude indices within the spatial grid.
The source coordinates can be \(\phi_{s}(i)\) and \(\lambda_{s}(i)\) for latitude and longitude in case of points, and \(\phi^{B}_{s}(i,l)\) and \(\lambda^{B}_{s}(i,l)\) for the latitude and longitude boundaries in case of areas (with \(l=1..N_{V}\) being the number of vertices for the area polygon).
The weight \(w(i,j,k)\) determines the contribution of the point/polygon \(i\) to the target grid cell \((j,k)\).
In case of point averages each weight is determined by:
\begin{eqnarray} w(i,j,k) & = & \sum_{i}{\begin{cases} 1, & \left( \phi^{E}_{t}(j) \le \phi_{s}(i) < \phi^{E}_{t}(j+1) \vee \phi_{s}(i) = \phi^{E}_{t}(M_{\phi}+1) \right) \wedge \left( \lambda^{E}_{t}(k) \le \lambda_{s}(i) < \lambda^{E}_{t}(k+1) \vee \lambda_{s}(i) = \lambda^{E}_{t}(M_{\lambda}+1) \right) \wedge x_{s}(i) \ne \mathit{nan} \\ 0, \textrm{otherwise} \\ \end{cases}} \end{eqnarray}
In case of area weighted averages we consider \(\textbf{P}_{t}(j,k)\) as the polygon that represents the target grid cell at position \((j,k)\), and \(\textbf{P}_{s}(i)\) as the polygon that is defined by the boundary coordinates \(\phi^{B}_{s}(i,l)\) and \(\lambda^{B}_{s}(i,l)\). The weights are then determined using:
\begin{eqnarray} w(i,j,k) & = & \sum_{i}{\begin{cases} \frac{\textrm{area}(\textbf{P}_{t}(j,k) \wedge \textbf{P}_{s}(i))}{\textrm{area}(\textbf{P}_{t}(j,k))}, & x_{s}(i) \ne \mathit{nan} \\ 0, & x_{s}(i) = \mathit{nan} \\ \end{cases}} \end{eqnarray}
The algorithms for the polygon area calculation \(\textrm{area}(\textbf{P})\) and polygon intersection \(\textbf{P}_{a} \wedge \textbf{P}_{b}\) are those for polygons in a 2D Cartesian plane (i.e. these calculations are not performed using polygons on a sphere).
With the calculated weights each variable is then regridded using:
\begin{eqnarray} y_{t}(j,k) & = & \frac{\sum_{i}{w(i,j,k)y_{s}(i)}}{\sum_{i}{w(i,j,k)}} \end{eqnarray}
In most cases, each variable is directly mapped to \(y\). The special cases are:
random uncertainty variables are averaged using the square of each value. The final value is given by: \(y_{t}(j,k) = \frac{\sqrt{\sum_{i}{\left(w(i,j,k)y_{s}(i)\right)^{2}}}}{\sum_{i}{w(i,j,k)}}\).
total uncertainty variables are always averaged as correlated (i.e. using a regular average).
variables that define an angle (such as latitude, longitude, angle and direction) are averaged using their unit vector representation (\(\textbf{y}_{s} = (\textrm{cos}(y_{s}) , \textrm{sin}(y_{s}))\). The final average is converted back into an angle using \(\textrm{atan2}(\textbf{y}_{t})\). The norm \(\|\textbf{y}_{t}\|\) is stored as the weight for this variable.


Recipe 11.14. Using a wxPython Notebook with PanelsCredit: Mark Nenadov ProblemYou want to design a wxPython GUI comprised of multiple panelseach driven by a separate Python script running in the backgroundthat let the user switch back and forth (i.e., a wxPython Notebook). SolutionNotebooks are an effective GUI approach, as they let the user select the desired view from several options at any time with an instinctive button click. wxPython supports this feature by supplying a wxNotebook widget. Here is a "frame" class that holds a notebook and adds to it three panes, each driven by a different Python module (not shown) through a function in each module named runPanel: from wxPython.wx import * class MainFrame(wxFrame): # # snipped: mainframe class attributes # def _ _init_ _(self, parent, id, title): # # snipped: frame-specific initialization # # Create the notebook object self.nb = wxNotebook(self, -1, wxPoint(0,0), wxSize(0,0), wxNB_FIXEDWIDTH) # Populate the notebook with pages (panels), each driven by a # separate Python module which gets imported for the purpose: panel_names = "First Panel", "Second Panel", "The Third One" panel_scripts = "panel1", "panel2", "panel3" for name, script in zip(panel_names, panel_scripts): # Make panel named 'name' (driven by script 'script'.py) self.module = _ _import_ _(script, globals( )) self.window = self.module.runPanel(self, self.nb) if self.window: self.nb.AddPage(self.window, name) # # snipped: rest of frame initialization # DiscussionwxPython provides a powerful notebook user-interface object, with multiple panels, each of which can be built and driven by a separate Python script (actually a module, not a "main script"). Each panel's script runs in the background, even when the panel is not selected, and maintains state as the user switches back and forth. This recipe isn't a fully functional wxPython application, but it adequately demonstrates how to use notebooks and panels (which it loads by importing files). This recipe assumes that you have files named panel1.py, panel2.py, and panel3.py, each of which contains a runPanel function that takes two arguments (a wxFrame and a wxNotebook in the frame) and returns a wxPanel object. The notebook-specific functionality is easy: the notebook object is created by the wxNotebook function, and an instance of this recipe's MainFrame class saves its notebook object as the self.nb instance attribute. Then, each page (a wxPanel object), obtained by calling the separate script's runPanel functions, is added to the notebook by calling the notebook's AddPage method, with the page object as the first argument and a name string as the second. Your code only needs to make the notebook and its panels usable; the wxWidgets framework, as wrapped by the wxPython package, handles all the rest on your behalf. See AlsowxPython, and the wxWidgets toolkit it depends on, are described in detail at http://www.wxPython.org and http://www.wxWidgets.org. |


document.trigger
document.trigger(evtName, options)
evtNameA string containing a JavaScript event type, such as click or submit.
optionsAdditional parameters to pass along to the event handler { once: true, passive: true, capture: true } once => true, the event can only be called once, polyfill for IE
Execute all handlers and behaviors attached to the matched elements for the given event type.


A visitor count application for your GitHub README.md
Audit duration since this repository goes live of course. :)
| Repository | Total visitors | Rank |
| {{repo_total.repo_id}} | {{repo_total.count}} | {{loop.index}} |
| Repository | Visitors within today({{ visit_date }}) | Rank |
| {{repo_today.repo_id}} | {{repo_today.count}} | {{loop.index}} |


Trying since 2014.
Software developer started trying since 2014. Good team player and a self-learner, worked in different companies on variety of projects including web, mobile and desktop apps development. concerned a lot about the code and the quality and eager to learn more everyday to extend my capabilities and my knowledge.


This page is made to test the accessibility markup that style-elements will use.
The second headingWe want to use aria attributes, but we have to see if they have the correct effect or not.
This page is made to test the accessibility markup that style-elements will use.
We want to use aria attributes, but we have to see if they have the correct effect or not.


Press F or upload a file to enter file mode.
Press D or click on the canvas to enter draw mode.
Press C to cycle through drawing colors, or use the color picker to the bottom-right of the canvas to select any custom color.
Click on the canvas to start drawing a polyline. Subsequent clicks will be added to the same line. If B is held, the current polyline will be completed and a new one will be started.


contextual sense ‘to drive away, expel with a curse’ has been suggested with reference to OS forhuātan ‘to curse’ (OS Gen 77); other suggestions include ‘to repudiate’, ‘to excite’, with reference to ahwettanq.v.
GenB 405: þonne weorð he him wrað on mode, ahwet hie from his hyldo.


Options
Sign In
View PortFolio
Skype
Message Someone
About
Services
Contact
Sign In Here
Site by
Rave Media
Show Options


Hand Painted
Prototype
Line Icons
2.5D
Synthesis
Banner
Plant.
Macha Cake.
Logo.
Line icons.
Hotel.
Character.
Porsche-GT3.
Camera.
Basketball.
Angela's birthday.


Hi, I'm
Web Designer And Developer
from India. I have recently completed a master's in Computer Science. I like Cycling and playing Table Tennis.


Hi! I'm a Kindness Bag! I might have been a gift or you might want to give me to someone you care about. My purpose is to share kindness and pass it on to someone else. Fill me with a surprise, an encouraging note, a memory, etc. If you can put a smile on someone's face, then I've done my job.
"Be kind whenever possible. It is always possible." -Dalai Lama
"No act of kindness, no matter how small, is ever wasted."- Aseop
"Kindness is the language which the deaf can hear and the blind can see."- Mark Twain
Find Us
on Social Media


Welcome to the Internet,
Neopolitan!
Its not like I like you or anything...
Github
Startpage
Conflict Generator
Fictional Writing
Space Battles
Sufficient Velocity
Archive Of Our Own
Questionable Questing (18+)
FanFiction.Net
Werewolf
MafiaScum
Mafia451
RNG Roles
Table Converter
Torn
Torn City
TornStats
YATA
Mahjong
Riichii Mahjong
Mahjong Rules
Mahjong Scoring
Anime
AnimeBytes
Anilist
Hyperbeam
Productivity
Google Drive
Dropbox
Cryptpad.fr
Music Metasearch
Social
Misskey
Reddit
4Chan
Learning
Exercism.io
edX
Wolfram Alpha
Python Book
Writing
Hemingway
GPT2
Cliche Finder
Tabletop
Dice Roller
Roll20.net
Char Sheets
Games Pt1
PkmnBattle
Spyfall
Online-Go
Learn Go
Games Pt2
GameFAQs
SINoALICE DB
D2JSP
D.Progress
Misc
YouTube
Odysee
PlaylistGen
Buy Fumos


No page exists here. Are you sure this where you want to be?
Return to joshldavis.com for his posts, projects, and more information about him.
This might be showing up because GitHub Pages might be down. Check out GitHub Pages status to see if that is the cause.
Template borrowed from GitHub's Error Pages.


Hello, and welcome!
I am Peter Kariuki Mwangi, a UI/UX Designer.
As a budding UI/UX Designer, I create positively memorable and intuitive experiences for web, mobile, and desktop software users by applying strategic and innovative approaches. This serves the interests of both providers and users of the technologies.
I have a certificate in UI/UX Design earned through Coursera's practical-based competent course.Through the course and working on personal projects, I have grasped some major aspects of interface design such as: User-centered design, User Empathy, Typography & Iconography, Information Architecture, Design Iteration, Prototyping, and User Research.
My working knowledge is in Figma and Adobe XD tools, which I have used to deliver the projects that are linked below.
I am currently freelancing and working on personal projects as I look for an organization to offer my UI/UX Design services.
"A user interface is like a joke. If you have to explain it, it's not that good." ~ Martin Leblanc


GitHub for teams
GitHub brings teams together to work through problems, move ideas forward, and learn from each other along the way.
Collaboration makes perfect. The conversations and code reviews that happen in Pull Requests help your team share the weight of your work and improve the software you build.
Take a deep breath. On GitHub project management happens in Issues and Projects, right alongside your code. All you have to do is mention a teammate to get them involved.
Security and administration
We worried about your administrative and security needs so you don’t have to. From flexible hosting to authentication options, GitHub can help you meet your team’s requirements.
Prevent problems before they happen. Protected branches, signed commits, and required status checks protect your work and help you maintain a high standard for your code.
Encourage teams to work together while limiting access to those who need it with granular permissions and authentication through SAML/SSO and LDAP.
Securely and reliably host your work on GitHub.com. Or, deploy GitHub Enterprise on your own servers or in a private cloud using Amazon Web Services, Azure or Google Cloud Platform.
Integrations
Customize your process with hundreds of integrations and an intuitive API. Integrate the tools you already use or discover new favorites to create a happier, more efficient way of working.
GitHub for teams
GitHub is home to the world’s largest community of developers and their projects. Whether you’re making your first commit or sending a Rover to Mars, there’s room for you here, too.
Developers use GitHub for personal projects, from experimenting with new programming languages to hosting their life’s work.
See how developers use GitHubGitHub’s users create and maintain influential technologies alongside the world's largest open source community.
Learn about open sourceBusinesses of all sizes use GitHub to support their development process and to securely build software.
See how businesses use GitHubJoin the millions of developers already using GitHub to share their code, work together, and build amazing things. Sign up for GitHub
Sign up for GitHub


Health-AI is a project which uses advanced techniques like AI to detect sudden health issues
The symptoms you are concerned about might be caused by a condition that is reversible.
Detecting a disease before clinical detection can be extremely beneficial for the person as they can visit the doctor with prior knowledge.
Our project aims on multiple diseases and detect them using Artificial Intelligence techniques.
The following diseases require you to enter NUMERIC details
Numeric Data Required
Enter the details in the form that appears
Numeric Data Required
Enter the details in the form that appears
Numeric Data Required
Enter the details in the form that appears
Numeric Data Required
Enter the details in the form that appears
Numeric Data Required
Enter the details in the form that appears
Numeric Data Required
Enter the details in the form that appears
Numeric Data Required
Enter the details in the form that appears
The following diseases require you to enter IMAGES
Image Required
Enter the image in the dropbox that appears
Image Required
Enter the image in the dropbox that appears
Image Required
Enter the image in the dropbox that appears
Image Required
Enter the image in the dropbox that appears
The following section gives details about the Diseases
Corona Virus
Most Recent
The novel coronavirus outbreak that began in December in Wuhan, China, has now spread to more than 25 countries and regions.
Cases rise: There are a total of 28,273 confirmed cases and 565 deaths globally, the vast majority of which are in mainland China. Some countries have seen a surge in cases; Japan now has 45 cases, the highest number for a country outside China.
Youngest patient: Two newborn infants are infected in Wuhan, according to China's state broadcaster. The youngest baby was diagnosed just 30 hours after being born to a mother who had also been infected. Experts warn it's unclear how the baby got infected, as there could be various ways it became exposed to the virus.
Recoveries: There are reports of patients recovering; the first patient in the US was discharged from hospital earlier this week, in Washington state, and the first patient in Taiwan will also be discharged soon.
Quarantined cruises: More than 7,300 people are quarantined on two cruise ships docked in Hong Kong and Japan, after former passengers were confirmed to have the virus. A total of 20 people were found to be infected on the ship in Japan, tests showed today.
Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces.
Insulin is a hormone that regulates blood sugar.
Hyperglycaemia, or raised blood sugar, is a common effect of uncontrolled diabetes and over time leads to serious damage to many of the body's systems, especially the nerves and blood vessels.
According to the World Health Organization (WHO), India had 69.2 million people living with diabetes in 2015.
India actually has the highest number of diabetics of any one country in the entire world.
In 2016, diabetes was the direct cause of 1.6 million deaths and in 2012 high blood glucose was the cause of another 2.2 million deaths worldwide.
Diabetes
Most Common
Liver Disease
Very Common
Liver disease can be inherited (genetic) or caused by a variety of factors that damage the liver, such as viruses and alcohol use. Obesity is also associated with liver damage.
Over time, damage to the liver results in scarring (cirrhosis), which can lead to liver failure, a life-threatening condition.
According to the latest WHO data published in 2017 Liver Disease Deaths in India reached 259,749 or 2.95% of total deaths.
The age adjusted Death Rate is 22.93 per 100,000 of population ranks India #63 in the world.
Chronic kidney disease, also called chronic kidney failure, describes the gradual loss of kidney function.

When chronic kidney disease reaches an advanced stage, dangerous levels of fluid, electrolytes and wastes can build up in your body.
Studies estimate that the number of new patients diagnosed with End Stage Kidney Disease (ESKD) who are started on dialysis or transplantation is over 100,000 per year in India
Kidney Disease
Pernicious
Pneumonia
Pernicious
Pneumonia is an infection of the lungs with a range of possible causes. It can be a serious and life-threatening disease.
It normally starts with a bacterial, viral, or fungal infection.
Pneumonia can occur in young and healthy people, but it is most dangerous for older adults, infants, people with other diseases, and those with impaired immune systems.
Fifty percent of the world’s pneumonia deaths occur in India which means approximately 3.7 lakh children die of pneumonia annually in India.
India’s under-five death toll is higher than the deaths in Nigeria, Congo and Pakistan put together.
A skin lesion is a part of the skin that has an abnormal growth or appearance compared to the skin around it.
Two categories of skin lesions exist: primary and secondary. Primary skin lesions are abnormal skin conditions present at birth or acquired over a person’s lifetime.
Secondary skin lesions are the result of irritated or manipulated primary skin lesions.
The diagnosis and treatment of nonmelanoma skin cancers increased by 77 percent between 1994 and 2014, worldwide.
Skin Lesions
Harmful
Heart Disease
Most Pernicious
Heart disease describes a range of conditions that affect your heart.
Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease;
heart rhythm problems (arrhythmias); and heart defects you're born with (congenital heart defects), among others.
Heart diseases are the number 1 cause of death globally, taking an estimated 17.9 million lives each year.
One third of these deaths occur prematurely in people under 70 years of age.
Breast cancer is cancer that develops from breast tissue. Signs of breast cancer may include a lump in the breast, a change in breast shape,
dimpling of the skin, or a red or scaly patch of skin.
Breast cancer is the most frequent cancer among women, impacting 2.1 million women each year, and also causes the greatest number of cancer-related deaths among women.
In 2018, it is estimated that 627,000 women died from breast cancer – that is approximately 15% of all cancer deaths among women.
While breast cancer rates are higher among women in more developed regions, rates are increasing in nearly every region globally.
Breast Cancer
Very Common
Skin Cancer
Harmful
Skin cancer is an abnormal growth of skin cells. It generally develops in areas that are exposed to the sun, but it can also form in
places that don’t normally get sun exposure.
There are two main types of skin cancer: melanoma and non-melanoma. The most common non-melanoma tumours are basal cell carcinoma and squamous cell carcinoma.
Melanoma of the skin is the 19th most commonly occurring cancer in men and women. There were nearly 300,000 new cases in 2018.
Benign skin cancer is the 5th most commonly occurring cancer in men and women, with over 1 million diagnoses worldwide in 2018, although this is likely to be an underestimate.
Sepsis arises when the body’s response to an infection injures its own tissues and organs, potentially leading to death or significant morbidity.
The global epidemiological burden of sepsis is difficult to ascertain. It is estimated to affect more than 30 million people worldwide every year, potentially
leading to 6 million deaths. The burden of sepsis is most likely highest in low- and middle-income countries.
It is estimated that 3 million newborns and 1.2 million children suffer from sepsis globally every year.
Three out of every ten deaths due to neonatal sepsis are thought to be caused by resistant pathogens.
One in ten deaths associated with pregnancy and childbirth is due to maternal sepsis with over 95% of deaths
due to maternal sepsis occurring in low- and middle-income countries. One million newborn deaths are associated with maternal infection, such as maternal sepsis, each year.
Sepsis
Pernicious
Optic Nerve Disease
Harmful
The optic nerve is a bundle of more than 1 million nerve fibers that carry visual messages.
You have one connecting the back of each eye (your retina) to your brain. Damage to an optic nerve can cause vision loss. The type of vision loss and how severe it is depends on where the damage occurs. It may affect one or both eyes.
There are many different types of optic nerve disorders, including:
Glaucoma is a group of diseases that are the leading cause of blindness in the United States.
Glaucoma usually happens when the fluid pressure inside the eyes slowly rises and damages the optic nerve.
Optic neuritis is an inflammation of the optic nerve. Causes include infections and immune-related illnesses such as multiple sclerosis. Sometimes the cause is unknown.
Optic nerve atrophy is damage to the optic nerve. Causes include poor blood flow to the eye, disease, trauma, or exposure to toxic substances.
Optic nerve head drusen are pockets of protein and calcium salts that build up in the optic nerve over time


--- layout: post title: Metadata for nothing... date: 2004-11-21 00:00:00 category: mobile ---
A comment the mysterious John made on a previous post has got me thinking: what metadata can we get for free in a mobile context, without any intervention from the user? What can I automatically tag a photo (say) with when I take it? So far I've got:
1. Who I am: my phone number and some sort of ID (phone number, IMEI, bluetooth ID, whatever)
2. Where I am: cell ID, country (inferred from the network I'm on, or my phone number perhaps) GPS co-ordinates, postcode (or whatever networks can offer)
3. When I am: what time-zone, what time of day, date (and therefore day/night, season)
4. What format (JPEG, PNG) and size my image is.


19
1 When King Jehoshaphat returned safely to his palace in Jerusalem,
2 the prophet Jehu son of the prophet Hanani, went out of the city to meet the king, and said to him, "It was not right for you to help a wicked man and to love those who hate Yahweh. Because of what you have done, Yahweh is angry with you.
3 But you have done some good things; you got rid of the poles in this country for worshiping the goddess Asherah, and you have been determined to do what pleases God."
4 Jehoshaphat lived in Jerusalem. But one time, like he had done once previously, he went out among all the people in the country, from the city of Beersheba in the far south to the hill country of the tribe of Ephraim in the far north, and he convinced them to return to worshiping Yahweh, the God whom their ancestors worshiped.
5 He appointed judges throughout Judah, in each of the cities that had walls around them.
6 He told them, "Make your decisions carefully, because you are judging cases not in order to please people, but to please Yahweh. And he will be watching you whenever you make a decision.
7 So now revere Yahweh and judge cases carefully, and do not forget that Yahweh our God never acts unjustly, and that he never does what people want because they offer him money"
8 In Jerusalem, Jehoshaphat also appointed some priests and other descendants of Levi and some leaders of Israelite families to be judges. He told them to do what Yahweh's laws said was right when they settled disputes. Those men lived in Jerusalem.
9 He told them this: "You must always do your work faithfully, honoring Yahweh.
10 In every dispute that your fellow Israelites who live in the cities want you to settle, you must warn them to not sin against Yahweh by telling lies during the trial—regardless of whether the trial is about murder, or about various laws, or about various royal decress. If you do not warn them, God will punish you.  And the descendants of Levi will assist you. Act courageously, and I pray that Yahweh will help those who do their work well."


How many Park & Ride places have been made per year?
Hover over the pieces to see how many the number is.
The example I have used you can find
here
.


The client side of ownCloud
What is coming up soon on the ownCloud desktop sync client.
## Michael Stingl Team Lead Desktop & Mobile, Branding Services GitHub: [@michaelstingl](https://github.com/michaelstingl) Twitter: [@michaelstingl](href="https://twitter.com/michaelstingl) elsewhere: …
## 2.3.0 Features - Allow to not sync the server's external storages by default - Add 'Mail link' button - Caja file manager plugin
## 2.3.0 Performance - Decreased memory usage during sync - Overlay icons: Lower CPU usage
## 2.3.0 Tech - Switch Windows and OS X build to Qt 5.6.2 - New WebDAV endpoint & Chunking - A ton of other bugfixes, Refactoring, Crash fixes
## 2.3.1 & 2.3.2 - Fix more crashes - UI improvement
## 2.3.3 - Switch Linux build also to Qt 5.6.2 (#5470) - Stopped maintaining Qt 4 buildability
## Policy for supported Linux distros > For Linux distributions, we support, if technically feasible, the latest 2 versions per platform and the previous LTS. ([docs](https://doc.owncloud.com/server/10.0/admin_manual/installation/system_requirements.html#desktop))
## 2.4.0 Features I - OAuth2 authentication support - Sharing: Add support for multiple public link shares - Sharing: Add option to copy/email private links - Sharing: Many UI improvements
## 2.4.0 Features II - Sync Issues: More functional error view including filters and conflicts - macOS & Win 7,8: Finder sidebar icon - Logging improvements (with Qt logging categories), new `--logdebug` parameter - GUI: Allow to add multiple sync folder connection of the same folde - Settings Dialog: Display the user server avatar
## 2.4.0 Performance - Sync: Dynamic sizing of chunks in chunked uploads for improved big file upload performance - Reduce memory usage (avoid memory copying)
## 2.4.0 Tech - Exclude list: remove .htaccess form list of excluded files - Maintenance Mode: Detect maintenance mode (Add a 1 to 5 min reconnection delay) - Sync: Introduce overall errors that are not tied to a file - CSync: Switch rest of codebase from C to C++ to enable faster development speed
## 2.4.0 Supported Platforms - Windows 7+ - Mac OS X 10.10+ (64-bit only) - All Linux (64-bit only) - CentOS 6 & 7 - Debian 7.0 & 8.0 & 9.0 - Fedora 24 & 25 & 26 - Ubuntu 16.04 & 16.10 & 17.04 & 17.10
## Work in progress - New sync algorithms - Win 10 Integration - GNOME Integration - Win: MSI Installer ([docs](https://github.com/owncloud/client/blob/msi-doc/doc/installing.rst)) - Automated QA: Next level
## 2.4.0 & 2.5.0 2.4.0 alpha soon
## Upcoming - Qt 5.9.x ==> HTTP2 - Guest App support - Bring more server features to client: Versions, Trashbin, Search (hybrid approach) - Virtual File System - Design/QML
## Thank You! This presentation is available on GitHub: View: https://michaelstingl.github.io/occon17-desktop-client/ Fork: https://github.com/michaelstingl/occon17-desktop-client/ Run local: `php -S localhost:8080`


Machine learning has shown promise in predicting stellar properties (temperature, composition, surface gravity, etc) given an input spectra. In previous attempts, the performance has been underwhelming for low mass and young stars. We aimed to correct that with a new machine learning algorithm that was properly trained in these domains, and had some success.
Abstract: Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and spectral resolutions, but most often for main sequence or evolved stars, where reliable synthetic spectra provide labels and data for training. Spectral models of young stellar objects (YSOs) and low mass main sequence (MS) stars are less well-matched to their empirical counterparts, however, posing barriers to previous approaches to classify spectra of such stars. In this work we generate labels for YSOs and low mass MS stars through their photometry. We then use these labels to train a deep convolutional neural network to predict log g, Tef f and Fe/H for stars with APOGEE spectra in the DR14 dataset. This “APOGEE Net” has produced reliable predictions of log g for YSOs, with uncertainties of within 0.1 dex and a good agreement with the structure indicated by pre-main sequence evolutionary tracks, and correlate well with independently derived stellar radii. These values will be useful for studying pre-main sequence stellar populations to accurately diagnose membership and ages.


EvolveCircuitError
public enum EvolveCircuitError : Error
Errors throwed by GeneticFactory.evolveCircuit(configuration:useCases:gates:)
-
Throwed when
GeneticConfiguration.depth
starts with a negative numberDeclaration
Swift
case configurationDepthHasToBeAPositiveNumber
-
Throwed when
GeneticConfiguration.depth
is an emptyRange
Declaration
Swift
case configurationDepthIsEmpty
-
Throwed when
GeneticConfiguration.populationSize
starts on 0 which is not valid because that would be the size of the initial population and an empty initial population can not produce new circuitsDeclaration
Swift
case configurationPopulationSizeHasToBeBiggerThanZero
-
Throwed when
GeneticConfiguration.populationSize
is an emptyRange
Declaration
Swift
case configurationPopulationSizeIsEmpty
-
Throwed when
GeneticConfiguration.tournamentSize
is 0 which is not valid because a reproduction operation requires at least 1 circuit in the tournamentDeclaration
Swift
case configurationTournamentSizeHasToBeBiggerThanZero
-
Throwed if
gate
requires more qubits thanGeneticUseCase.Circuit.qubitCount
specifiesDeclaration
Swift
case gateInputCountIsBiggerThanUseCaseCircuitQubitCount(gate: ConfigurableGate)
-
Throwed if
useCase.Circuit.output
is not composed exclusively of 0’s and 1’sDeclaration
Swift
case useCaseCircuitOutputHasToBeANonEmptyStringComposedOnlyOfZerosAndOnes(useCase: GeneticUseCase)
-
Throwed if any
GeneticUseCase.Circuit.qubitCount
is 0 or a negative numberDeclaration
Swift
case useCaseCircuitQubitCountHasToBeBiggerThanZero
-
Throwed if
useCases
is an empty listDeclaration
Swift
case useCaseListIsEmpty
-
Throwed when a circuit evolved to solve
useCase
throwederror
while measuring the probabilities of all possible outputsDeclaration
Swift
case useCaseMeasurementThrowedError(useCase: GeneticUseCase, error: ProbabilitiesError)
-
Throwed when
GeneticUseCase.Circuit.qubitCount
is not the same in alluseCases
Declaration
Swift
case useCasesDoNotSpecifySameCircuitQubitCount
-
Throwed if
useCase.TruthTable.qubitCount
is 0Declaration
Swift
case useCaseTruthTableQubitCountHasToBeBiggerThanZeroToMakeOracle(useCase: GeneticUseCase)


How it Works
In the form below, you can input a piece of text in one of four languages and the analyzer will translate it and understand its sentiment score. The score ranges from -1.0 to 1.0 with a negative score indicating a negative sentence and a positive score indicating a positive sentence. Now go on and give it a try!
Supported Languages: English, Spanish, French, and Chinese


mNotes is recommended for daily works which is best for managing different activities and tasks with different levels of priorities. It really helps you to focus on what really matters in your life.
For AndroidTask lists are accessible anywhere, on any device.
Take all the details to add to any task.
Prioritize your tasks according to their importance.
User-friendly task management.
You can attach images and add URLs to any notes.
Organize notes into different images and colors.
A quick search helps to find out a particular task.
Use can use this app offline.
You can create notes and keep track of your ideas & plans. Enter your notes with different colors and designs.
Read MoreIncrease productivity! Make a daily to do list, take notes.
Copyright © 2020 | All Right Revered | Md Rubel Rana


nips nips2005 nips2005-41 nips2005-41-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Sanjoy Dasgupta
Abstract: We characterize the sample complexity of active learning problems in terms of a parameter which takes into account the distribution over the input space, the speciﬁc target hypothesis, and the desired accuracy.
[1] D. Angluin. Queries revisited. ALT, 2001.
[2] M.-F. Balcan and A. Blum. A PAC-style model for learning from labeled and unlabeled data. Eighteenth Annual Conference on Learning Theory, 2005.
[3] D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201–221, 1994.
[4] S. Dasgupta. Analysis of a greedy active learning strategy. NIPS, 2004.
[5] S. Dasgupta. Full version of this paper at www.cs.ucsd.edu/˜dasgupta/papers/sample.ps.
[6] S. Dasgupta, A. Kalai, and C. Monteleoni. Analysis of perceptron-based active learning. Eighteenth Annual Conference on Learning Theory, 2005.
[7] Y. Freund, S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning Journal, 28:133–168, 1997.
[8] S. Goldman and M. Kearns. On the complexity of teaching. Journal of Computer and System Sciences, 50(1):20–31, 1995.
[9] D. Haussler. Decision-theoretic generalizations of the PAC model for neural net and other learning applications. Information and Computation, 100(1):78–150, 1992.
[10] J. Shawe-Taylor, P. Bartlett, R. Williamson, and M. Anthony. Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory, 44(5):1926–1940, 1998.