Education and Health Care
Week 14
Levi Sands
Insurance in the US
- Having insurance has a large positive effect on people's health
- Many in the US do not have insurance
- Those who do not have insurance have more financial issues
- You all have limited health insurance through the UI it is included in your
fees
- Go to https://studenthealth.uiowa.edu/services/ for more info
- Oregon Study Medicaid Lottery 2008
Source: New England Journal of Medicine, 2016
Source: New England Journal of Medicine, 2016
Source: New England Journal of Medicine, 2016
Insurance Rates in the US
- Under-insured is defined as insured all year but experienced one of the
following
- Out-of-pocket expenses equaled 10% or more of income
- Out-of-pocket expenses 5% or more of income if low income (<200% type
of poverty)
- Deductibles equaled 5% or more of income
What about since Obamacare or ACA?
Source: Rand Corp.
Source: Gallup
Comparative Health Systems
Bismarck Model
- In Germany, Japan, Belgium, Switzerland
- Private health insurance companies
- Financed through payroll tax and unemployed through federal insurance
employers and employees pay through payroll deduction
- Everybody is covered (this is the big difference between us and Bismarck
Model)
- Also insurers are not for profit (the other big difference between us and
Bismarck Model)
Comparative Health Systems
The Beveridge Model
- Great Britain, Italy, Spain, Most of Scandinavia
- Medical treatment is a public service - financed by the government through
taxes = single payer
- No medical bills
- Public and private doctors (mostly public)
Comparative Health Systems
National Health Insurance
- Canada, Australia, Taiwan, South Korea
- Health care providers are private, but he government-run health insurer is
the payer = single payer
- Every citizen pays into this insurance program
Comparative Health Systems
Out-of-Pocket Model
- Medical care paid for by the patient, out of pocket, with no insurance or
government plan to help
- Utah State University build on land that someone paid to a doctor for a
surgery he had
Comparative Health Systems
The US system
- Working people under 65 (but not all) = Bismarck, but for profit
- Native Americans, military, veterans = Beveridge Model
- Over 65 = National Health Insurance
- Uninsured = Out-of-pocket
- Health Cooperative not for profit insurance and provider
- This is a massive oversimplification
Discussion
- Which system do you think would work best in the US?
- Could any of these systems be implemented in the US?
- What role do drug companies play in all of this?
- Are doctors paid to much?
- Health Cooperatives - is a cooperative entity that has the goal of
providing health insurance and is also owned by the people that the organization insures
- WebMD and other similar sites, how do they affect health care?
- My research into Internet and development of expertise
Group Activity
Choose 4 of the following items, individually write down your answers after
discussing them as a group
- Self-fulfilling prophecy - Coined by Robert K. Merton - a positive or negative prophecy,
strongly held belief, or delusion declared as truth when it is actually false may
sufficiently influence people so that their reactions ultimately fulfill the once-false
prophecy - write down a specific example
- Teacher expectancy effect - teacher expectations effects a student's actual performance -
give a specific example
- Achievement test - test of developed skill or knowledge - write a few sentences about your
opinion regarding standardized testing and if they're needed or not
- Explain the difference between Medicaid and Medicare
- Is obesity stigma good or bad for the US as a whole? write a few sentences about your
opinion


Test if dependency that is a plugin ID with a relative resource name resolves correctly: 101
Check console for messages


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Search
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SystemThese functions are declared in the main Allegro header file:
al_install_system
Initialize the Allegro system. No other Allegro functions can be called before this (with one or two exceptions). The version field should always be set to ALLEGRO_VERSION_INT. If atexit_ptr is non-NULL, and if hasn't been done already, al_uninstall_system will be registered as an atexit function. Returns true if Allegro was successfully initialized by this function call (or already was initialized previously), false if Allegro cannot be used. See also: al_init al_init
Like al_install_system, but automatically passes in the version and uses the atexit function visible in the current compilation unit. See also: al_install_system al_uninstall_system
Closes down the Allegro system. Note: al_uninstall_system() can be called without a corresponding al_install_system call, e.g. from atexit(). al_get_allegro_version
Returns the (compiled) version of the Allegro library, packed into a single integer as groups of 8 bits in the form You can use code like this to extract them:
The al_get_standard_path
Gets a system path, depending on the
Returns NULL on failure. The returned path should be freed with al_destroy_path. See also: al_set_appname, al_set_orgname, al_destroy_path al_set_appname
Sets the global application name. The application name is used by al_get_standard_path to build the full path to an application's files. This function may be called before al_init or al_install_system. See also: al_get_appname, al_set_orgname al_set_orgname
Sets the global organization name. The organization name is used by al_get_standard_path to build the full path to an application's files. This function may be called before al_init or al_install_system. See also: al_get_orgname, al_set_appname al_get_appname
Returns the global application name string. See also: al_set_appname al_get_orgname
Returns the global organization name string. See also: al_set_orgname al_get_system_driver
Returns the currently active system driver, or NULL. al_get_system_config
Returns the configuration for the installed system, if any, or NULL otherwise. This is mainly used for configuring Allegro and its addons. |
Last updated: 2010-04-01 08:45:00 UTC


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Court slams N1m bail on estate agent over alleged fraudulent property sale
by BankoleA 59-year-old man, Agbomabini Kolawole, who conspired with others and obtained N8 million under false pretences from a woman to sell a building to her was on Monday granted N1 million bail by an Ikeja Magistrates’ Court.
The Chief Magistrate, Mrs Y.O. Aje-Afunwa granted the bail application and ordered that the defendant should produce two sureties as a condition for the bail.
Also read: Cover your breasts before signing endorsement deal, Kemi Olunloyo tells Tacha
Aje-Afunwa said that the sureties must reside within the court’s jurisdiction and should be gainfully employed.
The News Agency of Nigeria (NAN) reports that Kolawole, who resides at Agege area of Lagos, is facing a three-count charge of conspiracy, obtaining money under false pretences and stealing.
The defendant, however, pleaded not guilty to the charges.
The Prosecutor, ASP Peter Nwaongwu, told the court that the defendant and others still at large, committed the offences on July 8, at Fidelity Bank, Agege Branch.
Nwaongwu alleged that the defendant and his accomplices conspired among themselves to obtain N8 million under false pretences from Mrs Chibuike Obi to sell a building to her.
He said that the building which is situated at No 8, Christ Foundation St., Ilupeju, Oke Aro, belonged to the defendant.
According to him, the offences contravened Sections 287, 314 (3) and 411 of the Criminal Law of Lagos, 2015.
Aje-Afunwa adjourned the case till Jan. 9, 2020 for hearing.


{$top_company.department_phone}
{$top_company.service_phone}
{$top_company.address}
{$m.department_name}
电话：{$m.department_phone}
地址：{$m.address}
{$top_company.email}
{$top_company.hr_phone}


Mafia is an online multiplayer roleplaying game that models the conflict between two parties, the mafia, and the town. At the start of the game, each player is secretly assigned a role that belongs to one of the two parties. All the players assigned to the mafia know who their teammates are, while the players assigned to the town are clueless as for who belongs to their team.
The game consists of three alternating phases: day, voting, and night. In each of the phases only certain actions can be performed.
In the night phase, the mafia privately chooses a town citizen to murder. In the day phase, living players debate the identities of the mafia. Finally, in the voting phase, players cast votes to kill the player they suspect to belong to the mafia. You may want to refer to the phases section for more information.
A team wins when they are the only ones that are alive at any point in the game. Anytime a player is killed his role is revealed to the rest of the players. To make the game balanced, players are not distributed evenly among the two parties. The mafia always has fewer players than the town.
Additionally, each role has a special ability that can be performed at night that may help steer the conversation to who belongs to the mafia. Each role is thoroughly described in the roles section.
In order for a game to start there should be 7 players connected through either the Web App or Facebook Messenger. After the game starts, each player is assigned a temporal alias that is the name everyone else sees when one speaks.


This site covers a simple analysis of weather around the globe. Our question in particular: how does latitude effect different components of the weather?
To create a random collection of 500 cities, we randomly generated coordinates and used an app called Citipy to determine the nearest cities to those coordinates.
We then ran our collection of cities through OWM (Open WeatherMap), an API that returns an assortment of weather metrics. The collected data is visible under the 'Data' tab.
After, we plotted latitude (N/S orientation) versus a few other variables: cloudiness, humidity, maximum temperature, and wind speed.


My name is Joseph Ortiz and I was born in Frankfurt, Germany. I then moved to the Philippines momentarily before I settled in New Jersey where I spent most of my life. My family than relocated to Georgia, and have have been here for abour 4 years. I am a senior Information Technology student here at Georgia Gwinnett college and have 6 more classes left before graduation! My concentration is Software Development and plan on specializing in web development. I currently work as a programmer at Careerbuilder.com and enjoy it there. In my freetime, I enjoy playing video games and over the past few months, started rock climbing.


Mike Tyson Explains Chilling Reason He Had To Sell His Pet Tiger
by Julia BanimMike Tyson has opened up about the reason why he had to sell his beloved pet tiger, Kenya.
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The former professional boxer paid a reported £55,000 to buy Kenya, who was by his side for 16 years; even staying in Tyson’s bedroom.
However, Tyson was forced to give her up after she ripped somebody’s arm off. Further evidence, which really shouldn’t be needed, that tigers are far better off in the wild than they are in somebody’s back garden.
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You can watch the interview for yourself below:
Speaking with GQ Sports, Tyson, 53, said:
I did have a pet tiger, I had a great affection for her. I kept her, I slept with her, I kept her in my room. She stayed with me – I had her about 16 years.
But she got too old, and I had to get rid of her when her eyes and her head got bad. Oh, and she ripped somebody’s arm off.
Earlier in the year, Tyson spoke out about his reasoning behind purchasing such a dangerous creature during an appearance on The Joe Rogan Experience podcast.
Tyson has apparently owned no fewer than three tigers during his life before being forced to give them up. He apparently decided to buy them on a whim after discovering he could trade his cars for them.
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Speaking on the podcast, Tyson said:
This is really interesting. I’m in prison at the time. So I’m in prison, I’m talking to my car dealer at the time… and he’s discussing ‘if he doesn’t pay for these cars, I’m going to sell these cars to somebody and get some horses.’
I said ‘what, you can get horses? And trade horses in for cars?’ Because I had a lot of cars, I’d probably get some horses too.”
And he said ‘yeah man, you can get cougars, lions, tigers…’ I said ‘you do?! Can you get me some tigers?’
The guy said ‘imagine how cool that would be?’ Because I had a bunch of fancy cars, ‘imagine that man, you’d be in an Aston Martin or a Ferrari. You’d have a tiger right next to you, man. That’s be so awesome.’
And I’m a young guy. I’m saying to myself ‘wow, that would be cool. Get me some cubs, man.’
For anyone interested in getting a pet, it’s probably best in the long run to just get a nice, small-toothed hamster or something…
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Jules studied English Literature with Creative Writing at Lancaster University before earning her masters in International Relations at Leiden University in The Netherlands (Hoi!). She then trained as a journalist through News Associates in Manchester. Jules has previously worked as a mental health blogger, copywriter and freelancer for various publications.


--- layout: home category: home ---
Raising adults since 2000
In 2009, Pat and Sarah Schloss moved to Webster Township, Michigan nestled between Dexter and Ann Arbor with four kids and no experience raising livestock. The number of children and our love for the ever growing number of sheep, cows, pigs, chickens, and bees continues to grow. We have become quite proud of our ability to provide our family and people in our community with an all-natural source of meat from animals raised the way you would expect them to be raised. The responsibility and comroderie of raising animals has also proven to be an excellent approach to nurturing our children to become responsible and compassionate adults. Be sure to check out our Farm Diary to learn the latest news at Eight Kids Farm.
Our farm specializes in producing meat, eggs, honey, and wool from a diverse collection of animals that are raised without the use of anitibiotics or artificial hormones. Be sure to check out our animals and husbandry practices to learn more about the breeds of animals and how we raise them. Although we do sell some meat by the cut, we commonly sell whole, half, and quarter animals. Many are overwhelmed by receiving cuts of meat that they've never cooked. Be sure to check out our collection of family favorite recipes. The kids also love making wool products that are always for sale and can be made to order. More than anything, we hope to offer members of our community an experience that improves their well-being.


JERRY MAGUIRE A film review by Stairwell Studios Copyright 1997 Stairwell Studios
GO LONG, TOM: CROWE AND CRUISE COMBINE FOR PERFECT PLAY
Ok, here's the deal. To me, Tom Cruise has always been like a big chunk of apple pie after a steak dinner. Sometimes, it's just what you want, sometimes it's just too much to swallow, and sometimes it's perfect when it's covered in cheese.
He's just what we wanted in Risky Business and The Firm.  And, yes, he showed us a glimpse or two of star quality in Mission Impossible, confusing the bejesus out of us, but making such a show of catching sweat droplets, who really cared? Indeed, it was smothered with the finest of big-screen cheeses, but Tom's a star just the same.
But, on the eighth day, the Hollywood gods created the biggest miracle ever. They gave us Tom Cruise, the actor. Not the smilin' bartender from Cocktail, not the clenching overachiever from A Few Good Men, not the brazen award-ham from Rain Man, and, thank heaven, not the curly-permed bloodsucker from Interview with the Vampire. Tom Cruise, the actor, who amazes in Jerry Maguire.
And, good giggly-wiggly, who knew this guy could act like THIS?!? Simply put, Tom is brilliant as the only sports agent with a troubled conscience, tired of allowing greed to rule his life. His role is a meaty, conflicted, multidimensional one, a character study of a guy at the top, his fall to the bottom, and his rise to fulfillment.
A lot of the credit for this performance has to go to Cameron Crowe, the writer/director of Jerry Maguire. Crowe's ability to write with words that speak to an audience rather than at an audience has always been his strong suit. He directs the same way. He crafts the moments, but does it so quietly that you don't notice it until much later. Remember that haunting image of John Cusack holding a boombox in the rain from Say Anything? Simple, not cinematic or glamorous, but silently powerful. The best leaders are those that command with a whisper. Crowe whispers, and miracles occur.
Crowe and Cruise make an excellent team, yes, but they are not what make Jerry Maguire such a fantastic movie. The greatness comes from the fact that Crowe's script did not need a superstar to carry it. It needed an actor who was confident in his talent. It needed someone who would show himself in the best and worst of his character's being. And, most importantly, it needed an actor who could relax and let his fellow actors guide his performance as he guides theirs.
It is this last step that has always restricted The Cruiser. He is the "star" and everyone else in the movie revolves around him. I don't begrudge him this. I say it, though, so I can say this: by allowing himself to leave his orbit in the "solar system" approach, he has become the actor I'd hope he has always wanted to be. I offer him this name to think about for future reference - Tom Hanks, who went from "star" to "actor" in just a few years. If Tom Cruise can keep the other Tom in mind, his career could last forever. Go back to the "solar system," and, well, does the name "Burt Reynolds" come to mind?
Crowe also gave Cruise two valuable gifts to make this movie with, namely Cuba Gooding, Jr. and Renee Zellweger. If there is any mental strength in the Academy this year, Cuba Gooding will be nominated for and win the Best Supporting Actor award next march. His performance as the fame-hungry philosopher and wide receiver Rod Tidwell is the role Cuba has needed to make that last step to the level of "star." He is not on the screen every second, but his presence is.
Miss Zellweger is another story entirely. She manages her own impossible mission and steals many scenes from Cruise, just by the power of her silence. With this one role, she has touched on the magic of people like Barbara Stanwyck and Jean Arthur. She can say much by saying nothing. Her eyes welling with joy is a thing of beauty. Her lower lip quivering, trying to keep from crying, is a gesture so touching, it brought tears to this crotchety man's ducts. She is beautiful, yet common. She is reserved, yet lively. She is sexy, yet proper. She is a find for the ages.
Friends and patients, Jerry Maguire may well be the best film of the year. No funnelclouds, no golf clubs, no boogermen from space. Just acting at the top of its form, and a script that should be bronzed and given to new screenwriters as a playbook for perfection.
Crowe passes to Cruise. Touchdown.
NOTE: This review and more available at "Dr. Daniel's MOVIE EMERGENCY" http://www.stairwell.com/doc/
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


|
Compute Library
18.08
|
#include "arm_compute/core/NEON/INEKernel.h"
#include "arm_compute/core/NEON/INESimpleKernel.h"
#include <array>
#include <cstdint>
#include <vector>
Go to the source code of this file.
Data Structures | |
| class | NEConvolutionKernel< matrix_size > |
| Interface for the kernel to run an arbitrary size convolution on a tensor. More... | |
| class | NESeparableConvolutionHorKernel< matrix_size > |
| Kernel for the Horizontal pass of a Separable Convolution. More... | |
| class | NESeparableConvolutionVertKernel< matrix_size > |
| Kernel for the Vertical pass of a Separable Convolution. More... | |
| class | NEConvolutionRectangleKernel |
| Kernel for the running convolution on a rectangle matrix. More... | |
Namespaces | |
| arm_compute | |
| This file contains all available output stages for GEMMLowp on OpenCL. | |
Typedefs | |
| using | NEConvolution3x3Kernel = NEConvolutionKernel< 3 > |
| Interface for the kernel which applied a 3x3 convolution to a tensor. More... | |
| using | NEConvolution5x5Kernel = NEConvolutionKernel< 5 > |
| Interface for the kernel which applied a 5x5 convolution to a tensor. More... | |
| using | NEConvolution7x7Kernel = NEConvolutionKernel< 7 > |
| Interface for the kernel which applied a 7x7 convolution to a tensor. More... | |
| using | NEConvolution9x9Kernel = NEConvolutionKernel< 9 > |
| Interface for the kernel which applied a 9x9 convolution to a tensor. More... | |
| using | NESeparableConvolution5x5HorKernel = NESeparableConvolutionHorKernel< 5 > |
| Interface for the kernel which applied a 5x1 horizontal convolution to a tensor. More... | |
| using | NESeparableConvolution7x7HorKernel = NESeparableConvolutionHorKernel< 7 > |
| Interface for the kernel which applied a 7x1 horizontal convolution to a tensor. More... | |
| using | NESeparableConvolution9x9HorKernel = NESeparableConvolutionHorKernel< 9 > |
| Interface for the kernel which applied a 9x1 horizontal convolution to a tensor. More... | |
| using | NESeparableConvolution5x5VertKernel = NESeparableConvolutionVertKernel< 5 > |
| Interface for the kernel which applied a 1x5 vertical convolution to a tensor. More... | |
| using | NESeparableConvolution7x7VertKernel = NESeparableConvolutionVertKernel< 7 > |
| Interface for the kernel which applied a 1x7 vertical convolution to a tensor. More... | |
| using | NESeparableConvolution9x9VertKernel = NESeparableConvolutionVertKernel< 9 > |
| Interface for the kernel which applied a 1x9 vertical convolution to a tensor. More... | |


Public Member Functions | |
| Patient (DBHandler db) | |
| void | addRequest (int req_id, int item, int pid) |
| void | run () |
Public Attributes | |
| DBHandler | dbh |
Static Public Attributes | |
| static final int | WATER = 0 |
| static final int | MEDICINE = 1 |
| static final int | PENDING = 0 |
| static final int | ASSIGNED = 1 |
| static final int | SERVED = 2 |
Definition at line 5 of file Patient.java.
| pss::server::Patient::Patient | ( | DBHandler | db | ) | [inline] |
Definition at line 10 of file Patient.java.
| void pss::server::Patient::addRequest | ( | int | req_id, | |
| int | item, | |||
| int | pid | |||
| ) | [inline] |
Definition at line 16 of file Patient.java.
| void pss::server::Patient::run | ( | ) | [inline] |
Definition at line 20 of file Patient.java.
final int pss::server::Patient::ASSIGNED = 1 [static] |
Definition at line 14 of file Patient.java.
Definition at line 8 of file Patient.java.
final int pss::server::Patient::MEDICINE = 1 [static] |
Definition at line 7 of file Patient.java.
final int pss::server::Patient::PENDING = 0 [static] |
Definition at line 14 of file Patient.java.
final int pss::server::Patient::SERVED = 2 [static] |
Definition at line 14 of file Patient.java.
final int pss::server::Patient::WATER = 0 [static] |
Definition at line 7 of file Patient.java.


Bottom-up segmentation¶
Description¶
Bottom-up change point detection is used to perform fast signal segmentation and is implemented in
ruptures.detection.BottomUp
.
It is a sequential approach.
Contrary to binary segmentation, which is a greedy procedure, bottom-up segmentation is generous:
it starts with many change points and successively deletes the less significant ones.
First, the signal is divided in many sub-signals along a regular grid.
Then contiguous segments are successively merged according to a measure of how similar they are.
See for instance [BUKCHP01] or [BUFry07] for an algorithmic
analysis of ruptures.detection.BottomUp
.
The benefits of bottom-up segmentation includes low complexity (of the order of
\(\mathcal{O}(n\log n)\), where \(n\) is the number of samples), the fact that it can extend
any single change point detection method to detect multiple changes points and that it can work
whether the number of regimes is known beforehand or not.
See also
Usage¶
Start with the usual imports and create a signal.
import numpy as np
import matplotlib.pylab as plt
import ruptures as rpt
# creation of data
n, dim = 500, 3 # number of samples, dimension
n_bkps, sigma = 3, 5 # number of change points, noise standart deviation
signal, bkps = rpt.pw_constant(n, dim, n_bkps, noise_std=sigma)
To perform a bottom-up segmentation of a signal, initialize a ruptures.detection.BottomUp
instance.
# change point detection
model = "l2" # "l1", "rbf", "linear", "normal", "ar"
algo = rpt.BottomUp(model=model).fit(signal)
my_bkps = algo.predict(n_bkps=3)
# show results
rpt.show.display(signal, bkps, my_bkps, figsize=(10, 6))
plt.show()
In the situation in which the number of change points is unknown, one can specify a penalty using
the 'pen'
parameter or a threshold on the residual norm using 'epsilon'
.
my_bkps = algo.predict(pen=np.log(n)*dim*sigma**2)
# or
my_bkps = algo.predict(epsilon=3*n*sigma**2)
See also
Change point detection: a general formulation for more information about stopping rules of sequential algorithms.
For faster predictions, one can modify the 'jump'
parameter during initialization.
The higher it is, the faster the prediction is achieved (at the expense of precision).
algo = rpt.BottomUp(model=model, jump=10).fit(signal)
Code explanation¶
-
class
ruptures.detection.
BottomUp
(model='l2', custom_cost=None, min_size=2, jump=5, params=None)[source]¶ Bottom-up segmentation.
-
__init__
(model='l2', custom_cost=None, min_size=2, jump=5, params=None)[source]¶ Initialize a BottomUp instance.
- Parameters
model (str, optional) – segment model, [“l1”, “l2”, “rbf”]. Not used if
'custom_cost'
is not None.custom_cost (BaseCost, optional) – custom cost function. Defaults to None.
min_size (int, optional) – minimum segment length. Defaults to 2 samples.
jump (int, optional) – subsample (one every jump points). Defaults to 5 samples.
params (dict, optional) – a dictionary of parameters for the cost instance.
- Returns
self
-
fit
(signal)[source]¶ Compute params to segment signal.
- Parameters
signal (array) – signal to segment. Shape (n_samples, n_features) or (n_samples,).
- Returns
self
-
fit_predict
(signal, n_bkps=None, pen=None, epsilon=None)[source]¶ Fit to the signal and return the optimal breakpoints.
Helper method to call fit and predict once
- Parameters
signal (array) – signal. Shape (n_samples, n_features) or (n_samples,).
n_bkps (int) – number of breakpoints.
penalty (float) – penalty value (>0)
epsilon (float) – reconstruction budget (>0)
- Returns
sorted list of breakpoints
- Return type
list
-
predict
(n_bkps=None, pen=None, epsilon=None)[source]¶ Return the optimal breakpoints.
Must be called after the fit method. The breakpoints are associated with the signal passed to fit(). The stopping rule depends on the parameter passed to the function.
- Parameters
n_bkps (int) – number of breakpoints to find before stopping.
penalty (float) – penalty value (>0)
epsilon (float) – reconstruction budget (>0)
- Returns
sorted list of breakpoints
- Return type
list
-
References
- BUFry07
Piotr Fryzlewicz. Unbalanced Haar Technique for Nonparametric Function Estimation. Journal of the American Statistical Association, 102(480):1318–1327, 2007. doi:10.1198/016214507000000860.
- BUKCHP01
E. Keogh, S. Chu, D. Hart, and M. Pazzani. An online algorithm for segmenting time series. In Proceedings of the IEEE International Conference on Data Mining (ICDM), 289–296. 2001.


High Score:
Why did the giant space chicken cross the GALAXY? Wo cares, welcome to
Far Out Fowl!
The cosmic destroyer, a fowl beast (get it?!?) has launched an all-out attack
on the Earth. It's up to you, chicken farmer Joe, to defeat this menace from
beyond the stars!

Control your basket by pressing the Q and P keys to move
left and right, or tap the left and right sides of the screen, or
tilt your device left and right.
You also have available to you a missile system with which to attack the
chicken. Hitting the chicken causes it to pause temporarily.
Fire your missile by pressing space bar or flicking your device forward.
Note that you can only have a single missile in flight at a time.
You get points for catching eggs and hitting the chicken.
Splattered eggs cost points and damage your basket. Too much damage
ends the game. Missile strikes on the chicken repairs your basket
a bit.
Click Start whenever you're ready Joe and get to saving the world!


# Structural Equation Modeling - Simple Linear Regression RAM Example {#simple-regression-ram-example}
In this example, we show how the Reticular Action Model (RAM) Matrices are specified using the simple linear regression model.
We also demonstrate how to use RAM notation functions in the jeksterslabRsem
package namely
In this hypothetical example, we assume that we have population data and we are interested in the association between wages and education. The regressor variable is years of education. The regressand variable is hourly wage in US dollars. The intercept is the predicted wage of an employee with 0 years of education. The slope is the increase in hourly wage in US dollars for one year increase in education.
The the following vectors represent the parameters of the simple linear regression model.
\[\begin{equation}
\boldsymbol{\theta}_{\text{mean structure}}
=
\begin{bmatrix}
\beta_1 \\
\mu_x
\end{bmatrix}
\end{equation}\]
\[\begin{equation}
\boldsymbol{\theta}_{\text{covariance structure}}
=
\begin{bmatrix}
\beta_2 \\
\sigma_{\varepsilon}^{2} \\
\sigma_{x}^{2}
\end{bmatrix}
\end{equation}\]
\(\boldsymbol{\theta}_{\text{mean structure}}\)
beta1 |
Intercept |
\(\beta_1\) |
-4.107586 |
mux |
Mean of \(x\)
|
\(\mu_x\) |
13.159813 |
\(\boldsymbol{\theta}_{\text{covariance structure}}\)
beta2 |
Slope |
\(\beta_2\) |
1.224860 |
sigma2epsilon |
Variance of \(\varepsilon\)
|
\(\sigma_{\varepsilon}^{2}\) |
47.887638 |
sigma2x |
Variance of \(x\)
|
\(\sigma_{x}^{2}\) |
7.765366 |
Parameterization Including the Error Variance
Variables are ordered as follows
-
\(y\),
-
\(x\), and
- \(\varepsilon\)
M Matrix
varnames <- c("y", "x", "epsilon")
k <- 2
q <- 1
p <- k + q
M <- matrix(
data = c(
beta1,
mux,
0
),
ncol = 1
)
rownames(M) <- varnames
colnames(M) <- "M"
| y |
-4.107586 |
| x |
13.159813 |
| epsilon |
0.000000 |
A Matrix
| y |
0 |
1.22486 |
1 |
| x |
0 |
0.00000 |
0 |
| epsilon |
0 |
0.00000 |
0 |
S Matrix
S <- matrix(
data = c(
0,
0,
0,
0,
sigma2x,
0,
0,
0,
sigma2epsilon
),
nrow = p
)
rownames(S) <- varnames
colnames(S) <- varnames
| y |
0 |
0.000000 |
0.00000 |
| x |
0 |
7.765366 |
0.00000 |
| epsilon |
0 |
0.000000 |
47.88764 |
I Matrix
| y |
1 |
0 |
0 |
| x |
0 |
1 |
0 |
| epsilon |
0 |
0 |
1 |
F Matrix
filter <- matrix(
data = c(
1,
0,
0,
1,
0,
0
),
nrow = 2,
ncol = 3
)
rownames(filter) <- c("y", "x")
colnames(filter) <- varnames
Model-Implied Mean Vector
result01_mutheta <- jeksterslabRsem::rammutheta(
M = M,
A = A,
filter = filter
)
Model-Implied Variance-Covariance Matrix
result01_Sigmatheta <- jeksterslabRsem::ramSigmatheta(
A = A,
S = S,
filter = filter
)
| y |
59.537875 |
9.511486 |
| x |
9.511486 |
7.765366 |
M Matrix from mutheta and A Matrix
result01_M <- jeksterslabRsem::ramM(
mu = mutheta,
A = A,
filter = filter
)
rownames(result01_M) <- varnames
colnames(result01_M) <- "M"
| y |
28.13027 |
| x |
13.15981 |
| epsilon |
0.00000 |
S Matrix from sigma2 and A Matrix
This should only be used when the off-diagonal elements of the \(\mathbf{S}\) matrix are all zeroes.
result01_S <- jeksterslabRsem::ramsigma2(
sigma2 = c(sigma2y, sigma2x, sigma2epsilon),
A = A,
start = FALSE
)
#> The off-diagonal elements of the S matrix are assumed to be zeroes.
rownames(result01_S) <- varnames
colnames(result01_S) <- varnames
| y |
0 |
0.000000 |
0.00000 |
| x |
0 |
7.765366 |
0.00000 |
| epsilon |
0 |
0.000000 |
47.88764 |
Parameterization Excluding the Error Variance
Variables are ordered as follows
M Matrix
varnames <- c("y", "x")
k <- 2
q <- 0
p <- k + q
M <- matrix(
data = c(
beta1,
mux
),
ncol = 1
)
rownames(M) <- varnames
colnames(M) <- "M"
S Matrix
| y |
47.88764 |
0.000000 |
| x |
0.00000 |
7.765366 |
Model-Implied Mean Vector
result02_mutheta <- jeksterslabRsem::rammutheta(
M = M,
A = A,
filter = filter
)
Model-Implied Variance-Covariance Matrix
result02_Sigmatheta <- jeksterslabRsem::ramSigmatheta(
A = A,
S = S,
filter = filter
)
| y |
59.537875 |
9.511486 |
| x |
9.511486 |
7.765366 |
M Matrix from mutheta and A Matrix
result02_M <- jeksterslabRsem::ramM(
mu = mutheta,
A = A,
filter = filter
)
rownames(result02_M) <- varnames
colnames(result02_M) <- "M"
S Matrix from sigma2 and A Matrix
This should only be used when the off-diagonal elements of the \(\mathbf{S}\) matrix are all zeroes.
result02_S <- jeksterslabRsem::ramsigma2(
sigma2 = c(sigma2y, sigma2x),
A = A,
start = FALSE
)
#> The off-diagonal elements of the S matrix are assumed to be zeroes.
rownames(result02_S) <- varnames
colnames(result02_S) <- varnames
| y |
47.88764 |
0.000000 |
| x |
0.00000 |
7.765366 |
Parameterization Excluding the Error Variance
Variables are ordered as follows
M Matrix
varnames <- c("x", "y")
k <- 2
q <- 0
p <- k + q
M <- matrix(
data = c(
mux,
beta1
),
ncol = 1
)
rownames(M) <- varnames
colnames(M) <- "M"
S Matrix
| x |
7.765366 |
0.00000 |
| y |
0.000000 |
47.88764 |
Model-Implied Mean Vector
result03_mutheta <- jeksterslabRsem::rammutheta(
M = M,
A = A,
filter = filter
)
Model-Implied Variance-Covariance Matrix
result03_Sigmatheta <- jeksterslabRsem::ramSigmatheta(
A = A,
S = S,
filter = filter
)
| x |
7.765366 |
9.511486 |
| y |
9.511486 |
59.537875 |
M Matrix from mutheta and A Matrix
result03_M <- jeksterslabRsem::ramM(
mu = mutheta,
A = A,
filter = filter
)
rownames(result03_M) <- varnames
colnames(result03_M) <- "M"
S Matrix from sigma2 and A Matrix
This should only be used when the off-diagonal elements of the \(\mathbf{S}\) matrix are all zeroes.
result03_S <- jeksterslabRsem::ramsigma2(
sigma2 = c(sigma2x, sigma2y),
A = A,
start = TRUE
)
#> The off-diagonal elements of the S matrix are assumed to be zeroes.
rownames(result03_S) <- varnames
colnames(result03_S) <- varnames
| x |
7.765366 |
0.00000 |
| y |
0.000000 |
47.88764 |


Extending RestMicroservice
This example shows how the {@link oaj.microservice.RestMicroservice} class
can be extended to implement lifecycle listener methods or override existing methods.
We'll create a new class com.foo.SampleCustomRestMicroservice.
First, the manifest file needs to be modified to point to our new microservice:
Main-Class: com.foo.SampleCustomRestMicroservice
Then we define the following class:
/**
* Sample subclass of a RestMicroservice that provides customized behavior.
* This class must be specified in the Main-Class entry in the manifest file and optionally
* a Main-ConfigFile entry.
*/
public class SampleCustomRestMicroservice extends RestMicroservice {
/**
* Must implement a main method and call start()!
*/
public static void main(String[] args) throws Exception {
new SampleCustomRestMicroservice(args).start().join();
}
/**
* Must implement a constructor!
*
* @param args Command line arguments.
* @throws Exception
*/
public SampleCustomRestMicroservice(String[] args) throws Exception {
super(args);
}
The microservice APIs provide several useful methods that can be used or extended.
See Also:
- {@link oaj.microservice.Microservice}
- {@link oaj.microservice.RestMicroservice}


Paste or type your text in the box below. The character count/letter count will be visible instantly.
In many situations you need to know the letter count of your written text. To count all characters manually can be too dificult and takes too long.
Therefore we build our letter counter tool. With this online tool you can count all letters or characters in under 1 second wthout any mistakes. Safe your time and use Letter Counter Online.
To count the words of your text we recommend the online tool Word Counter. This is a free website that helps you to count all words and gives you more detailed statistics about your text.
Composed of 26 letters (consonants and vowels), the English alphabet evolved from the Latin alphabet while the English language developed from adopting bits and pieces from Germanic languages related to German, Frisian and Dutch. In addition, the construction of the English vocabulary is a fascinating amalgamation of words and letters borrowed from the French, Greek, Latin and dozens of other languages.
The English alphabet used today is actually a variation of the old Latin alphabet composed of 26 vowels and consonants. Although the English alphabet is easily recognizable by its ordered form–A, B, C, D–this alphabet also employs numerous diagraphs, or combinations of consonants to make sounds differing from the sound of a single consonant. For example, “c” and “h” are pronounced like “cee” and “aitch” but together they produce the sound “ch”, which forms words like “choke” and “cherry”.


That is seventeen five - your half of the thirty-five thousand. Plus there's an extra fifteen in there, it's all yours, you've earned it. We made a deal. That's right. Because I think that we can do business together - we came to an understanding. Take a look at the money in your hand. Now just imagine making that every week. That's right. Two pounds a week, thirty-five thousand a pound.Look... I feel like I'm running out of ways to explain this to you but once more, I shall try. This fly is a major problem for us. It will ruin our batch. And we need to destroy it and every trace of it, so we can cook. Failing that, we're dead. There's no more room for error. Not with these people.
That is seventeen five - your half of the thirty-five thousand. Plus there's an extra fifteen in there, it's all yours, you've earned it. We made a deal. That's right. Because I think that we can do business together - we came to an understanding. Take a look at the money in your hand. Now just imagine making that every week. That's right. Two pounds a week, thirty-five thousand a pound.Look... I feel like I'm running out of ways to explain this to you but once more, I shall try. This fly is a major problem for us. It will ruin our batch. And we need to destroy it and every trace of it, so we can cook. Failing that, we're dead. There's no more room for error. Not with these people.


Best Practices With Git/GitHub
Sagar Sapkota
/sgrspkt
A bit history & Introduction
- Developed by Linus Torvalds in 2005
- Was developed first to maintain version control of Linux kernel
GIT
- With distributed architecture, Git is an example of DVCS
GitHub
- Git repository hosting service
Why Version Control Needed ??
- Work together and collaborate on a single project
- Revert files to previous state
- Revert entire project back to previous state
- Compare changes over time
- See who modified what ? And much more….
Agendas
- Installing Git
- Introduction to Version Control
- Git Basics
- Creating a new local Git repository
- Cloning a Git repository
- Making use of Git commit history
- Reverting files to previous state
Agendas Cont..
- Creating a Github Repository
- Adding, Committing & Pushing changes
- Branching
- Merging Branches
- Sending Pull Request
- Stashing & Pop
- Github pages
Installing Git
git-scm.com/download/win
git-scm.com/mac
sudo apt-get install git (Ubuntu)
sudo yum install git (Fedora)
git --version
First Time Git Setup
git config --global user.name "Sagar Sapkota"
git config --global user.email<EMAIL_ADDRESS>git config --list
git config user.name
git help
Git: The Big Picture
Use Cases:
- Individual development
- Collaborative development
- Offline usage
Why Git ?
- Everything is local (full history tree available offline)
- Everything is fast
- Snapshots
- It is distributed not centralized
Git workflow: The three states
In a Git repository, file can reside in three main states:
- Modified
- Staged
- Committed
What does this mean ??
Git Workflow
Create a github repository
Basic Snapshotting
- To show summary of untracked, tracked, to be committed, etc. files
- Modify composer.json and type
git status
Add new files to staging area
git add (filename)
git add .
Commit your changes
git commit –m “commit message”
You can directly add and commit
git commit –a –m “commit message”
it's a standard git practice to add message as a command and not in past tense
If u want to remove the file from staging area but want to keep in working directory then use :
git rm --cached (file_name)
Rename file. Changes test.txt file to test
mv test.txt test
Commit Message
- Master :- You are in master branch [we see later on branching ]
- 4544ec71 :- Unique identifier that HEAD of commit holds
Get Git Commit History
You can study repository history using
git log
You can see last ‘n’ number of commits
git log –n
You can also see git history with affected files [detailed]
git log --stat
Commit History Cont..
Limit the commits to those made after the specified date.
git log --since 1.days
Git log --after 1.days
To see a compressed log where each commit is one line
git log –pretty=oneline
Compare Changes Over Time
See changes in a commit
git show commitID
Compare two commits by
git diff commit1ID commit2ID
Revert Files To Previous States
Reset staged file
git reset HEAD (file_name_that_was_staged)
Commit Recover
Wrong commit can be recovered, opens file in your configured text editor
git commit –amend
Pushing Changes
To send changes to your remote repository execute
git push origin master
You can also push to a branch other than ‘master’
git push origin branch_name
BUT WAIT
- What Is Origin ?
- What Is Master ?
- What Is Branch ?
Origin
When you clone a repository for the first time “Origin” is a default name given to the origin remote repository that you clone, from where you want to pull and push changes
git clone /path/to/repository
On initializing new repository
git init
git remote add origin /path/to/repository
git remote add (remote_name)
git remote remove (remote_name)
You can fetch from others remote and merge to your own
git fetch (remote_name)
Push skeleton
git push (remote_name)(branch_name)
View all remotes available
git remote -v
Want to know about remote
git remote show (remote_name)
Rename remote name
git remote rename (old) (new)
Branching
Branches are used to develop features isolated from each other. The master branch is the “default” branch when you create a repository. Use other branches for development and merge them back to master branch upon completion.
"Branches are just pointers to commits."
To create a new branch
git branch (new_branch_name)
Switch to a different branch
git checkout (other_branch_name)
To show all branches
git branch
To delete a branch (locally)
git branch –d (branch_name)
To delete a branch (remotely)
git push origin --delete (branchName)
Merging Branches
Checkout the branch you want to update
git checkout (branch_name)
Then merge branches by executing
git merge (this_branch_name) (that_branch_name)
Fork A Repo
A fork is a copy of a repository. Forking a repository allows you to freely experiment with changes without affecting the original project.
Purpose changes to someone else's project:
- Fork the repository.
- Make the fix.
- Submit a pull request to the project owner.
- If the project owner likes your work, they might pull your fix into the original repository!
You can now clone the repo and get all files and folders to your local directory.
Add Collaborators
Create Pull Request
Create a pull request to propose and collaborate on changes to a repository.
Steps involved :
- On GitHub, navigate to the main page of the repository.
- To the right of the Branch menu, click New pull request.
- The Compare page will automatically select the base and compare branches; to change these, click Edit.
- On the Compare page, click Create pull request.
- Type a title and description for your pull request.
- Click Create pull request.
git aliases
Git alias is used so that we may not type the entire text of each of the Git commands
git config --global alias.co checkout
git config --global alias.br branch
git config --global alias.ci commit
git config --global alias.st status
git stash
Why you use git stash ??
Save stash
git stash save “appropriate message what you have worked on”
Show stash list, Stash name is like this : stash@{0}
git stash list
This takes back to original but doesn’t delete stash
git stash apply (stash_name)
This deletes the stash
git stash drop (stash_name)
Stash list will delete and is back to original stage
git stash pop
GitHub Pages and Slides
Github pages can be created by creating github repo as.
username.github.io
index.html behaves as homepage
Eg : sgrspkt.github.io
THANK YOU !!
ANY QUESTIONS ??


We analyze the role of architecture in shaping the inductive bias of
deep classifiers, by introducing the concept of Neural
Anisotropy Directions (NADs)
Once you are given a finite number of samples, it is usually very easy to find many models that can perfectly classify them, but it is much harder to find a model which can also generalize. To find this model, a machine learning algorithm needs to exploit its so called inductive bias: a set of a priori assumptions about the world that allows it to identify the solution which is most likely to generalize. In deep learning, one of the main sources of inductive bias is the choice of a network's architecture. But, although we know which networks work well in practice, we still do not really understand how this bias influences their behaviour.
In this blog post, we try to give an intuitive explanation on how this happens, and illustrate how the interaction between architecture and data distribution shapes the inductive bias of a neural network. An interaction that allows a neural network to go from merely memorizing the training data to learning the perfect classifier.
We will show this through an embarrasingly simple experiment: training neural networks used in practice to classify a linearly separable distribution. Surprisingly, though, even in this setup the choice of architecture plays a fundamental role, and although all networks achieve good training accuracy, they do not always manage to generalize. We will see that the role of architecture can be summarized by a sequence of vectors, or neural anisotropy directions (NADs), that rank the preference of an architecture to select certain features of the input data. These NADs can be computed very efficiently, even without training, and we will explain why they are fundamental in generalization in more complex datasets, such as CIFAR-10. We hope that these new intuitions can improve your understanding of DNNs.
Deep learning is great! But because we mostly study it on complex problems, like classifying ImageNet, we barely know why it works so well in practice. A proof of how little we know about deep networks is how hard it is to to answer this simple question:
Can a modern deep neural network generalize on any linearly separable task?
If you are a true statistician, you would probably think that the answer is clearly "no": CNNs are very complex models, and therefore, they must overfit. However, if you are familiar with deep learning theory, you might be inclined towards a more affirmative answer, instead. Especially if you have read some works
Reality, though, is a bit more nuanced. And in practice, the answer for most CNN architectures seems to be "it depends". In particular, "it depends on the direction of the discriminative information in the dataset". We observe experimentally that each CNN cannot always solve the problem — they can do it only when the distribution is separable by certain hyperplanes. This is, CNNs have a strong directional inductive bias.
Testing this bias is quite easy. You just need to define a linearly separable distribution in which the data
As we can see, the test accuracy on this problem heavily
depends on the choice of direction
Now, note that the differences in performance cannot be due to a higher complexity of
the CNNs. If that was the case, then deep networks would
always show the same test accuracy, regardless of
The answer to why this happens is hidden in the architecture. Indeed, if we remove all pooling layers from these CNNs, and compensate the increase of dimensionality with a larger fully connected layer at the end, we will see that these networks generalize for all Fourier directions
But, of course, pooling cannot be the only source of directional inductive bias, as all these CNNs have pooling, yet they generalize on very different sets of distributions. In fact, it looks as if the set of distributions
Training and testing on different instances of
The key idea here is to stop worrying about training, and just focus on random neural networks, i.e.,
This bound is, actually, quite easy to interpret, as it only depends on the eigenvectors of the gradient covariance
What is very surprising is that the NADs fully encapsulate the directional inductive bias of an architecture: even if they are computed using random networks, repeating the same experiment as before but using NADs instead of Fourier vectors, yields a monotonic decay in accuracy of a neural network as we test on higher NAD indices
The diversity of NADs of the different networks is quite striking, and it highlights their role as a unique signature of each architecture. It suggests that each architecture uses a unique set of features to classify the data, and that the interactions between architectural elements can create very rich inductive biases. Besides, the fact that we computed NADs using random networks, but still they predict generalization performance after training hints towards a deeper connection between the optimization properties of a neural network and its functional properties. The link between the weight space and the input space on deep neural networks is largely unexplored, and we believe that NADs might give an exciting direction to better explore this connection.
So far, however, we have only talked about synthetic datasets and toy tasks. Sure, NADs are important quantities for linearly separable problems, but what about more complex tasks? Are NADs also important to learn more interesting datasets?
We do not have a definite answer, yet, but we believe they do. In fact, some of our most recent experiments suggest that the existence of NADs is necessary for generalization in complex datasets, such as CIFAR-10. We have two main pieces of evidence to believe so.
The first piece of evidence suggests that NADs determine the order in which a neural network looks for discriminative information in the training set. Or more informally, a CNN first tries to fit the data based on its projection on the lower NADs, and progressively grows the number of NADs to take into account depending on the training error.
We can actually test this hypothesis by modifying the CIFAR-10 training set. In particular, we can add a spurious feature in a certain NAD component of every CIFAR-10 training image and study what happens to its accuracy on the unmodified test images.
To poison the dataset we can introduce a very discriminative feature in all training samples in one NAD coefficient.
This feature is enough to separate the training set but does not generalize to the test set.
Note that poisoning CIFAR-10 renders its training set linearly separable. However, if a neural network tries to fit the training data using a hyperplane orthogonal to the spurious feature, it will not be able to generalize to the test set
Using this setup, we trained a ResNet-18 on multiple versions of the poisoned CIFAR-10 training set where the poisonous feature was placed at different NAD indices.
As we can see, when the dataset is poisoned on the first NAD index, the network fully overfits. But when the spurious feature is placed at the last NAD, the network can generalize. In between these two extremes we see a gradual increase in the test performance. This can only be explained if the network is progressively picking more features from the original CIFAR-10 data, before finding the poisonous signal. This clearly suggests to the existence of an ordered preference of features for CNNs, determined by the NAD basis.
NADs seem to determine the order of selected features in a dataset, but are they really necessary for generalization? We believe they are, and we actually think that their particular structure is what explains the good performance of most CNNs on image datasets
In order to support this hypothesis, we tried a funny experiment whose goal was to investigate the role of NADs as filters of non-generalizing solutions. In particular, we wanted to test the possible positive synergies arising from the alignment of NADs with the generalizing features of the training set.
To do so, we trained multiple CNNs using the same hyperparameters on two representations of CIFAR-10: the original representation, and a new one in which we flipped the representation of the data in the NAD basis
The result of these experiments shows that the performance of the networks trained on the flipped datasets is significantly lower than those on the original CIFAR-10. This demonstrates that misaligning the inductive bias of the networks with the dataset makes them prone to overfit.
We see these resutlts as strong supporting evidence that through the years the research community has managed to impose the right inductive biases in deep neural architectures. Letting them filter out spurious and noisy signals and hence being able to solve most standard vision benchmarks.
In this post, we have described a new type of model-driven inductive bias that controls generalization in deep neural networks: the directional inductive bias. We have seen that this bias is summarized by the NADs of an architecture, which seem to be responsible for the selection of discriminative features by a CNN.
The existence of NADs demonstrates that the full set of inductive biases in deep learning is much richer than it was previously believed. Prior to our work, some researchers highlighted that neural networks could memorize a dataset when there was no generalizable information present in the data
We think there are many possibilities to use NADs in future research and novel applications. For instance, we are very excited about the possibility of using NADs to comprehensively study architectures in deep learning. We have seen that pooling plays an important role in NADs for CNNs, but what about other layers or components in these architectures? And, can we think beyond image data, and use NADs to understand the misterious transformers or the promising GNNs? In general we see NADs as an interesting tool in AutoML, as they can give an explicit framework to align the inductive biases of an architecture, with our a priori knowledge of the task. This gives an exciting path towards the design of new architectures with richer invariances and more robust to adversarial and naturally occuring distribution shifts.
Finally, it is important to note that our results mostly apply to cases in which the data was fully separable, i.e. there was no label noise. And even more specifically, to the linearly separable case. In this sense, it still remains an open problem to understand how the directional inductive bias of deep learning influences neural networks trying to learn non-separable datasets.
This blog post is a short version of the NeurIPS 2020 article "Neural Anisotropy Directions". If you want to cite it, please cite the original paper
G. Ortiz-Jimenez, A. Modas, S.M. Moosavi-Dezfooli, and P. Frossard, “Neural Anisotropy Directions,” in Advances in Neural Information Processing Systems 34 (NeurIPS), Dec. 2020
To lighten this article and point the reader only to the most relevant papers, we cited only a subset of the relevant work that we built on. Please refer to the bibliography of the original paper for the complete list.
The article template is due to distill.pub and many formatting styles are inspired from other articles appearing on Distill.


Basic fluid-type demo
This is fluid-type
This is fluid-type
This is fluid-type
This is fluid-type
This is fluid-type
This is fluid-type


This site will also have a link to my Youtube channel. The channel is still in progress and gaining more media before going live.
A fully function calendar will be loaded to this page to directly sync to your accound and mobile devices. Other local coupons and discounts will also be listed within this calendar.
Last section of this page is reserved for a fully functional Instant Messageing/Discussion board that will be optimized based on the user platfom accessing the site.


A request line. RFC 2616 Section 5.1 Request-Line.
HTTP request method. RFC 2616 Section 9 Method Definitions.
HTTP request. RFC 2616 Section 5 Request.
HTTP request headers. RFC 2616 Section 14 Header Field Definitions.
HTTP request URI. RFC 2616 Section 5.1.2 Request-URI.
A request line. RFC 2616 Section 5.1 Request-Line.
HTTP request method. RFC 2616 Section 9 Method Definitions.
HTTP request. RFC 2616 Section 5 Request.
HTTP request headers. RFC 2616 Section 14 Header Field Definitions.
A HTTP method whose name comes from toString().


How to count in robot!
Do you want to understand our robot overlords better?
Well nows your chance! Learn how to count in our basic robot to english lesson!
How high would you like to count?
Beep Boop
Here's your number in Robot speech!


3 Things You Didn't Know About Hello Kitty
Hello Kitty and many other Sanrio characters were designed without a mouth so that the character could take on whatever emotions the viewer needs them to have at the time. Some believe this is part of the reason they’re so popular across generations and cultures - anyone can relate to them. (It’s also one of the things that Kitty haters say creeps them out the most.)
Hello Kitty Online is a free massively multiplayer role playing game in the same vein as World of Warcraft. Players can adventure alone or join guilds to complete quests like finding all the ingredients for a special soup, delivering a pizza before it gets cold, or collecting wands that can be used to defeat monsters that guard treasure. There’s also the opportunity to build your own house, raise crops, adopt a pet, and customize your character’s wardrobe. For some of these perks, you’ll need to earn Sanrio Loyalty Points by posting videos, writing blog posts, and completing quizzes on Sanriotown.com. Or you can spend real money to buy Loyalty Points at an exchange rate of 80 points for a $1. To give you some idea of the price, a typical in-game house costs about $5, not including furniture, which is, of course, sold separately.
One story says that a controversial nuclear power plant hired Sanrio to create a cute corporate mascot that would help soften their image. Another legend tells of a married couple whose only daughter was sick with cancer. In exchange for her recovery, the parents made a pact with the Devil that they would create a character in Satan’s honor that would be adored worldwide. As you might have guessed, neither of these is true.


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threatTRANSFORM will take the confusion out of creating, compiling and publishing STIX datasets. Leverage form based, collector based and intelligent event creation to take control of your data.
Create A New Dataset


{% extends "base.html" %} {% block content %}
Non-profit organisation to bring together everyone interested in Research Software Engineer activities
We have seminars and conferences for you to network and develop your software skills.
Learn how your organisation can benefit from an RSE group
Learn how to get in touch
Join the association
Read what we have been up to
@nordic_rse@fosstodon.org


CSS3 Buttons
This is a small collection of buttons that show what is possible using CSS3, while also maintaining the simplest possible markup. They look best in WebKit-based browsers, almost as good in Firefox, with all other browsers falling back to a less-styled button. If you use these buttons in a project, or just want to say hi drop me a dime.
The latest code is located at Github


Welcome to the Bans.Page Stagelist creator! When you're happy with your selection, click "Create Stagelist"!Once viewing your Stagelist, you can share it with others by copying the URL.


Employee ID: 123
Email Address<EMAIL_ADDRESS>Office Phone:<PHONE_NUMBER>
Employee ID: 234
GitHub Account: dave123
Employee ID: 345
School Attending: Ohio University
Employee ID: 567
School Attending: UNCC


ACRONYM
Back for the new season, ACRONYM combine utilitarian silhouettes with unparalleled functionality and adaptability. Designed by the enigmatic Errolson Hugh, the brand has garnered a real cult following by way of their closely guarded releases and an alignment to a some of the outerwear world’s most innovative brands. This alignment has seen the Berlin-based brand team up with END. newcomer Nemen to produce an inspired capsule collection that channels the aesthetic of both brands.


{% extends 'mdfserver/base.html' %} {% load staticfiles %} {% block page_title %}English Questionnaires{% endblock %} {% block data_classes %}{{ block.super }} active{% endblock %} {% block content %}
Logan City neighborhoods
Other Cache County neighborhoods
Salt Lake City neighborhoods
Other Salt Lake County neighborhoods


The goal of this step is to extract information from the network (chain_id, block_num and ref_block_prefix), which will be needed in the subsequent step.
Copy or write down chain_id, block_num and ref_block_prefix. You will need those in the subsequent step to generate a valid transaction while being off-line.


webcomponents.js
A suite of polyfills supporting the Web Components specs:
Custom Elements: allows authors to define their own custom tags (spec).
HTML Imports: a way to include and reuse HTML documents via other HTML documents (spec).
Shadow DOM: provides encapsulation by hiding DOM subtrees under shadow roots (spec).
This also folds in polyfills for MutationObserver
and WeakMap
.
Releases
Pre-built (concatenated & minified) versions of the polyfills are maintained in the tagged versions of this repo. There are two variants:
webcomponents.js
includes all of the polyfills.
webcomponents-lite.js
includes all polyfills except for shadow DOM.
Browser Support
Our polyfills are intended to work in the latest versions of evergreen browsers. See below for our complete browser support matrix:
| Polyfill | IE10 | IE11+ | Chrome* | Firefox* | Safari 7+* | Chrome Android* | Mobile Safari* |
|---|---|---|---|---|---|---|---|
| Custom Elements | ~ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| HTML Imports | ~ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Shadow DOM | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Templates | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
*Indicates the current version of the browser
~Indicates support may be flaky. If using Custom Elements or HTML Imports with Shadow DOM, you will get the non-flaky Mutation Observer polyfill that Shadow DOM includes.
The polyfills may work in older browsers, however require additional polyfills (such as classList) to be used. We cannot guarantee support for browsers outside of our compatibility matrix.
Manually Building
If you wish to build the polyfills yourself, you'll need node
and gulp
on your system:
- install node.js using the instructions on their website
- use
npm
to install gulp.js:npm install -g gulp
Now you are ready to build the polyfills with:
# install dependencies
npm install
# build
gulp build
The builds will be placed into the dist/
directory.
Contribute
See the contributing guide
License
Everything in this repository is BSD style license unless otherwise specified.
Copyright (c) 2015 The Polymer Authors. All rights reserved.
Helper utilities
WebComponentsReady
Under native HTML Imports, <script>
tags in the main document block the loading of such imports. This is to ensure the imports have loaded and any registered elements in them have been upgraded.
The webcomponents.js and webcomponents-lite.js polyfills parse element definitions and handle their upgrade asynchronously. If prematurely fetching the element from the DOM before it has an opportunity to upgrade, you'll be working with an HTMLUnknownElement
.
For these situations (or when you need an approximate replacement for the Polymer 0.5 polymer-ready
behavior), you can use the WebComponentsReady
event as a signal before interacting with the element. The criteria for this event to fire is all Custom Elements with definitions registered by the time HTML Imports available at load time have loaded have upgraded.
window.addEventListener('WebComponentsReady', function(e) {
// imports are loaded and elements have been registered
console.log('Components are ready');
});
Known Issues
- Limited CSS encapsulation
- Element wrapping / unwrapping limitations
- Custom element's constructor property is unreliable
- Contenteditable elements do not trigger MutationObserver
- ShadowCSS: :host-context(...):host(...) doesn't work
- ShadowCSS: :host(.zot:not(.bar:nth-child(2))) doesn't work
- HTML imports: document.currentScript doesn't work as expected
- execCommand isn't supported under Shadow DOM
Limited CSS encapsulation
Under native Shadow DOM, CSS selectors cannot cross the shadow boundary. This means document level styles don't apply to shadow roots, and styles defined within a shadow root don't apply outside of that shadow root. Several selectors are provided to be able to deal with the shadow boundary.
The Shadow DOM polyfill can't prevent document styles from leaking into shadow roots. It can, however, encapsulate styles within shadow roots to some extent. This behavior isn't automatically emulated by the Shadow DOM polyfill, but it can be achieved by manually using the included ShadowCSS shim:
WebComponents.ShadowCSS.shimStyling( shadowRoot, scope );
... where shadowRoot
is the shadow root of a DOM element, and scope
is the name of the scope used to prefix the selectors. This removes all <style>
elements from the shadow root, rewrites it rules using the given scope and reinserts the style as a document level stylesheet. Note that the :host
and :host-context
pseudo classes are also rewritten.
For a full explanation on the implementation and both the possibilities and the limitations of ShadowCSS please view the documentation in the ShadowCSS source.
Element wrapping / unwrapping limitations
The Shadow DOM polyfill is implemented by wrapping DOM elements whenever possible. It does this by wrapping methods like document.querySelector
to return wrapped DOM elements. This has a few caveats:
- Not everything can be wrapped. For example, elements like
document
,window
,document.body
,document.fullscreenElement
and others are non-configurable and thus cannot be overridden. - Wrappers don't support live NodeLists like
HTMLElement.childNodes
andHTMLFormElement.elements
. All NodeLists are snapshotted upon read. See #217 for an explanation.
In order to work around these limitations the polyfill provides the ShadowDOMPolyfill.wrap
and ShadowDOMPolyfill.unwrap
methods to respectively wrap and unwrap DOM elements manually.
Custom element's constructor property is unreliable
See #215 for background.
In Safari and IE, instances of Custom Elements have a constructor
property of HTMLUnknownElementConstructor
and HTMLUnknownElement
, respectively. It's unsafe to rely on this property for checking element types.
It's worth noting that customElement.__proto__.__proto__.constructor
is HTMLElementPrototype
and that the prototype chain isn't modified by the polyfills(onto ElementPrototype
, etc.)
Contenteditable elements do not trigger MutationObserver
Using the MutationObserver polyfill, it isn't possible to monitor mutations of an element marked contenteditable
.
See the mailing list
ShadowCSS: :host-context(...):host(...) doesn't work
See #16 for background.
Under the shadow DOM polyfill, rules like:
:host-context(.foo):host(.bar) {...}
don't work, despite working under native Shadow DOM. The solution is to use polyfill-next-selector
like:
polyfill-next-selector { content: '.foo :host.bar, :host.foo.bar'; }
ShadowCSS: :host(.zot:not(.bar:nth-child(2))) doesn't work
ShadowCSS :host()
rules can only have (at most) 1-level of nested parentheses in its argument selector under ShadowCSS. For example, :host(.zot)
and :host(.zot:not(.bar))
both work, but :host(.zot:not(.bar:nth-child(2)))
does not.
HTML imports: document.currentScript doesn't work as expected
In native HTML Imports, document.currentScript.ownerDocument references the import document itself. In the polyfill use document._currentScript.ownerDocument (note the underscore).
execCommand and contenteditable isn't supported under Shadow DOM
See #212
execCommand
, and contenteditable
aren't supported under the ShadowDOM polyfill, with commands that insert or remove nodes being especially prone to failure.


We can't find page you're looking for. The specified file was not found on this website. Please check the URL for mistakes and try again.
Go Back to HomeThis page was generated by the Firebase Command-Line Interface.
modify it, edit the 404.html file in your project's configured public directory.


Preparation time
less than 30 mins
Cooking time
30 mins to 1 hour
Serves
Serves 8-10
Dietary
This simple and utterly delicious baked pudding works best with freshly picked raspberries.
Preheat the oven to 200C/400F/Gas 6 and grease a 20cm/8in springform tin with butter.
To make the base and topping of the pudding, use your fingers to rub the butter into the flour. Stir in the sugar.
Place half the mixture in the bottom of the tin and press it down firmly with your knuckles.
Place all the fruit evenly on top of the firm base.
Pour the remaining mixture over the fruit and again press down to firm.
Bake for 30 minutes.
Remove from the oven and further compress the pudding by placing a plate or pan on top filled with weights. A couple of tins would do the trick. Set aside to cool.
When cooled, remove the pudding from the tin and dust with icing sugar. Serve with fresh raspberries and double cream.


AWS::Batch::JobDefinition ResourceRequirement
The type and amount of a resource to assign to a container. The supported resources include
GPU
, MEMORY
, and VCPU
.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
Properties
Type
-
The type of resource to assign to a container. The supported resources include
GPU
,MEMORY
, andVCPU
.Required: No
Type: String
Allowed values:
GPU | MEMORY | VCPU
Update requires: No interruption
Value
-
The quantity of the specified resource to reserve for the container. The values vary based on the
type
specified.- type="GPU"
-
The number of physical GPUs to reserve for the container. Make sure that the number of GPUs reserved for all containers in a job doesn't exceed the number of available GPUs on the compute resource that the job is launched on.
Note GPUs aren't available for jobs that are running on Fargate resources.
- type="MEMORY"
-
The memory hard limit (in MiB) present to the container. This parameter is supported for jobs that are running on EC2 resources. If your container attempts to exceed the memory specified, the container is terminated. This parameter maps to
Memory
in the Create a containersection of the Docker Remote API and the --memory
option to docker run. You must specify at least 4 MiB of memory for a job. This is required but can be specified in several places for multi-node parallel (MNP) jobs. It must be specified for each node at least once. This parameter maps to Memory
in the Create a containersection of the Docker Remote API and the --memory
option to docker run. Note If you're trying to maximize your resource utilization by providing your jobs as much memory as possible for a particular instance type, see Memory management in the AWS Batch User Guide.
For jobs that are running on Fargate resources, then
value
is the hard limit (in MiB), and must match one of the supported values and theVCPU
values must be one of the values supported for that memory value.- value = 512
-
VCPU
= 0.25 - value = 1024
-
VCPU
= 0.25 or 0.5 - value = 2048
-
VCPU
= 0.25, 0.5, or 1 - value = 3072
-
VCPU
= 0.5, or 1 - value = 4096
-
VCPU
= 0.5, 1, or 2 - value = 5120, 6144, or 7168
-
VCPU
= 1 or 2 - value = 8192
-
VCPU
= 1, 2, or 4 - value = 9216, 10240, 11264, 12288, 13312, 14336, or 15360
-
VCPU
= 2 or 4 - value = 16384
-
VCPU
= 2, 4, or 8 - value = 17408, 18432, 19456, 21504, 22528, 23552, 25600, 26624, 27648, 29696, or 30720
-
VCPU
= 4 - value = 20480, 24576, or 28672
-
VCPU
= 4 or 8 - value = 36864, 45056, 53248, or 61440
-
VCPU
= 8 - value = 32768, 40960, 49152, or 57344
-
VCPU
= 8 or 16 - value = 65536, 73728, 81920, 90112, 98304, 106496, 114688, or 122880
-
VCPU
= 16
- type="VCPU"
-
The number of vCPUs reserved for the container. This parameter maps to
CpuShares
in the Create a containersection of the Docker Remote API and the --cpu-shares
option to docker run. Each vCPU is equivalent to 1,024 CPU shares. For EC2 resources, you must specify at least one vCPU. This is required but can be specified in several places; it must be specified for each node at least once. The default for the Fargate On-Demand vCPU resource count quota is 6 vCPUs. For more information about Fargate quotas, see AWS Fargate quotas in the AWS General Reference.
For jobs that are running on Fargate resources, then
value
must match one of the supported values and theMEMORY
values must be one of the values supported for thatVCPU
value. The supported values are 0.25, 0.5, 1, 2, 4, 8, and 16- value = 0.25
-
MEMORY
= 512, 1024, or 2048 - value = 0.5
-
MEMORY
= 1024, 2048, 3072, or 4096 - value = 1
-
MEMORY
= 2048, 3072, 4096, 5120, 6144, 7168, or 8192 - value = 2
-
MEMORY
= 4096, 5120, 6144, 7168, 8192, 9216, 10240, 11264, 12288, 13312, 14336, 15360, or 16384 - value = 4
-
MEMORY
= 8192, 9216, 10240, 11264, 12288, 13312, 14336, 15360, 16384, 17408, 18432, 19456, 20480, 21504, 22528, 23552, 24576, 25600, 26624, 27648, 28672, 29696, or 30720 - value = 8
-
MEMORY
= 16384, 20480, 24576, 28672, 32768, 36864, 40960, 45056, 49152, 53248, 57344, or 61440 - value = 16
-
MEMORY
= 32768, 40960, 49152, 57344, 65536, 73728, 81920, 90112, 98304, 106496, 114688, or 122880
Required: No
Type: String
Update requires: No interruption
Examples
Resource requirements
This example specifies 4096 MiB of memory and 2 vCPUs.
JSON
"ResourceRequirements": [ { "Type": "MEMORY", "Value": "4096" }, { "Type": "VCPU", "Value": "2" } ]
YAML
ResourceRequirements: - Type: MEMORY Value: '4096' - Type: VCPU Value: '2'


How did the 'process over product' concept affect the way you tackled the site redesign and rebuild?
At the beginning I didn't get the point of 'process over product',Then i read again of the material for the concept.I kept 'Process over product' in mind while i was redesigning and building the blog site. So i can try to impelement as many features as i can, just play and explore, rather than persue perfect products at this stage. Eventually there will be a good product.
What did you think about meditation before reading chapter 2?
Before I read chapter 2, I thought meditation is just we sit quietly and think about things, figure out what's happenning and why it happens. Never know there are so many stuff to talk about meditation.
What new things have you learnt about mediation?
Meditation could be trainned, It could change the quality of our lifes.And I didn't know there are so many posture to meditate as long as we are alert and relaxed.What is more,Happy is the default state of mind.
Did any of the suggested mediation techniques stand out to you?
I picked up some techniques that sounds good for me. Find the comfortable posture,Keep awake, be alert and relaxed. Also let things go, not be distracted too much, maintain the concentration when my mind wanders away.
Any other musings?
Sometime when i get bored,I think about what is the meanning of living in this world.Why do we come here, what do we want? My friend told me, life is how we make up of it. Yes,I think i should just concentrate on what i am doing, and enjoy life, being who i am, and be a happy person.


| DOSE: T+ 0:00 |
6.0 g |
oral |
Kratom |
(capsule) |
| T+ 1:15 |
6.0 g |
oral |
Kratom |
(capsule) |
I am age 24, 65kg in weight, intellectually I am a INTP (geek).
I had been reading up and experimenting with a range of entheogens and other mind altering substances for the last few months and decided to give kratom a try. The kratom was just dried leaf from an online vendor (not extract or super powder). I had tried smoking it in a joint before, but with only very mild, almost unnoticeable effects.
It was a Wednesday evening at home after work, living with my girlfriend. I had not taken any other drugs (bar caffeine) for week and a half. My main activity for the evening was taking a variety of dried leaves, grinding them in a coffee grinder and placing them into empty vitamin capsules using a filling tool. By weight I could fit roughly 0.4g (400mg) of ground leaf per capsule (this seems the same for most ground plant material), with the capsule itself (size 0) weighing about 0.1g.
This was also my first time taking herbs in capsule form, so was unfamiliar with the timescales involved for capsules as a delivery mechanism, but I expected so see some effects within 30-45 mins. I thought 3g was roughly a threshold dose, 15g a medium dose and 30g a strong dose. I had not eaten since lunch at 3pm.
19:30 (T+0:00) - Take 15 Kratom capsules (6g) with some orange juice. I decide to keep a paper log of the effects.
20:00 (T+0:30) - No effects so far
20:10 (T+0:40) - Starting to feel a little more energized and slightly hyperactive, not sure if this is the Kratom or just a placebo effect.
20:40 (T+1:10) - The initial buzz has worn off a little, I feel a just a little bit in the background but nothing significant. My thoughts at the time where 'is this it, another herb with little effect, lets take some more to make sure I experience some effect before giving up on kratom'.
20:45 (T+1:15 | T+0:00) - I take an additional 15 Kratom capsules (6g) with some orange juice.
21:10 (T+1:40 | T+0:25) - I start to feel very slightly light headed.
21:25 (T+1:55 | T+0:40) - Feeling a slight buzz but nothing major
21:40 (T+2:10 | T+0:55) - I had put some music on a while earlier, and now I feel like standing up and dancing to it, and am definitely in a houseparty type mood.
21:50 (T+2:20 | T+1:05) - I'm feeling a little less focused and more in the moment, still in a party mood but its feels like being slightly drunk.
22:10 (T+2:40 | T+1:25) - I'm talking to a friend who has popped round, I feeling excited about the things I am talking to him about and also fairly hyperactive inside.
22:25 (T+2:55 | T+1:40) - Feeling a bit of a buzz now, very much in a partymood. Hyperactive and can't sit still easily (I'm still trying to fill more capsules), and a little less focused.
22:50 (T+3:20 | T+2:05) - I'm still feeling the same effects as half an hour ago.
23:00 (T+3:30 | T+2:15) - I'm feeling a little dizzy and somewhat intoxicated.
23:25 (T+3:55 | T+2:40) - My head is spinning slightly and I feel a little queezy. I check the mirror for the first time and my pupils are very small. I can still think clearly but I am a little unfocused and am easily distracted. Physically (but not mentally), I feel like I have been to the pub, had just a few to many to drink and am now back home.
23:45 (T+4:15 | T+3:00) - The effects are starting to wear off slightly, but are still present. I start eating dinner.
00:05 (T+4:35 | T+3:20) - I finish eating dinner. I stand up and start walking but have a sudden wave of nausea, but don't throw up.
00:45 (T+5:15 | T+4:00) - Intoxicating effects have mostly worn off, but not fully.
01:10 (T+5:40 | T+4:25) - I'm looking though my log notes and trying calculate, in my head, the lengths of time between the various effects and the first and second dose, trying to work out how long the effects would take to kick in if I had just taken a single dose. I'm finding it hard to focus on the math's and keep all the numbers in my head at the same time, and I keep losing track of what I am trying to work out. However it is late and I am somewhat tired, so this is definitely a contributing factor.
01:45 (T+6:15 | T+5:00) - I'm still feeling slightly lightheaded.
01:55 (T+6:25 | T+5:10) - I finally go to bed.
------ Next Day -----
09:00 - I wake up, less than 7 hours sleep (I prefer more), my pupils are still very small and I am feeling a quite tired (a bit more than usual in the morning).
10:00 - I arrive at work and my normal morning coffee.
12:00 - At work, I'm feeling a little hazy, but can still work normally. Also feel a little queezy, almost felt I needed to throw up, but didn't.
12:30 - Feeling very sleepy, I close my eyes and half-nod off while sitting (but don't fall asleep).
13:05 - I stand up and walk to another room, this wakes me up a little, but a few moments later I get another wave of nausea but rush to the loo and this time I manage to throw up properly. After this I go an get some lunch.
17:00 - I'm still feeling a little unwell, slightly queezy and my head spinning a little. Its like having a hangover that simply won't go away.
18:30 - I get home, take two ibuprofen headache tablets and lie down for an hour before a friend pops round and spend the rest of the evening talking, but still not feeling great.
23:30 - I'm feeling more normal now, and the side effects of the kratom have now gone.
In retrospect, I should have waited longer before taking the second dose and not taken so much the second time. At lower doses, krakom has stimulating effects, which I enjoyed, but as my body starting absorbing more and more of the kratom, its effects more intoxicating and this part I did not enjoy so much. The price I paid the next day was essentially an all day hangover that didn't respond well to my usual treatment of a strong sweet coffee in the morning. It was fun while it lasted, and an interesting experience and lesson learnt, but not worth the after effects late in the evening and the whole of the next day. The closest thing I can describe it to is a slightly modified version of getting drunk on alcohol. I won't be taking it again in the near future, but if I do, I'll stick to a lower dosage and wait at least 2-3 hours before deciding if I want to take a second dose.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


| 046:010 Khan | : |
Say: "Tell me! If this (Qur'an) is from Allah, and you deny it, and a witness from among the Children of Israel ('Abdullah bin Salam radhiallahu'anhu) testifies that this Qur'an is from Allah [like the Taurat (Torah)], so he believed (embraced Islam) while you are too proud (to believe)." Verily! Allah guides not the people who are Zalimun (polytheists, disbelievers and wrong-doing). |
| 046:010 Maulana | : |
Say: See you if it is from Allah, and you disbelieve in it, and a witness from among the Children of Israel has borne witness of one like him, so he believed, while you are big with pride. Surely Allah guides not the iniquitous ones. |
| 046:010 Pickthal | : |
Bethink you: If it is from Allah and ye disbelieve therein, and a witness of the Children of Israel hath already testified to the like thereof and hath believed, and ye are too proud (what plight is yours)? Lo! Allah guideth not wrong-doing folk. |
| 046:010 Rashad | : |
Say, "What if it is from GOD and you disbelieved in it? A witness from the Children of Israel has borne witness to a similar phenomenon, and he has believed, while you have turned arrogant. Surely, GOD does not guide the wicked people." |
| 046:010 Sarwar | : |
Say, "What do you think will happen if this Quran is from God and you have rejected it? Besides, a witness from among the Israelites has testified to the divinity of a Book like it and believed in it (Quran) while you have arrogantly denied it. God does not guide the unjust. |
| 046:010 Shakir | : |
Say: Have you considered if it is from Allah, and you disbelieve in it, and a witness from among the children of Israel has borne witness of one like it, so he believed, while you are big with pride; surely Allah does not guide the unjust people. |
| 046:010 Sherali | : |
Say, 'Tell me, if this Qur'an is from ALLAH and you disbelieve therein, and a witness from among the Children of Israel bears witness to the advent of one like him, and he believed, but you are too proud to believe, how should you fare?' Verily ALLAH guides not the wrongdoing people. |
| 046:010 Yusufali | : |
Say: "See ye? If (this teaching) be from Allah, and ye reject it, and a witness from among the Children of Israel testifies to its similarity (with earlier scripture), and has believed while ye are arrogant, (how unjust ye are!) truly, Allah guides not a people unjust." |
Concepts/Themes Covered: القرآن الكريم > حقيقته وتصديقه للكتب الأوائل The Holy Quran> The Quran's Reality and its confirmation of the Previous Books
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


| IV(SSRMS) |
EV1(Drew) |
EV2(Taz) |
-
Record PET start time ___:___ (Pwr to Batt)
-
Start WVS Recorders
|
Initial Configuration
-
✓ All gates closed & hooks locked
-
❏ R Waist Tether to EV2 Blank hook
-
❏ Red hook on L D-ring ext
-
❏ Yellow hook on Green ERCM
-
❏ Green hook on Red ERCM
-
❏ Blank hook on MWS
|
Initial Configuration
-
✓ All gates closed & hooks locked
-
❏ All gates closed & hooks locked
-
❏ R Waist Tether to A/L D-ring ext
-
❏ Red hook on L D-ring ext
-
❏ Yellow hook on Green ERCM
-
❏ Green hook on Red ERCM
-
❏ Blank hook to EV1 R Waist Tether
|
-
Start Hatch thermal cover clock PET (30 min) ___:___
|
EGRESS/SETUP (00:25)
-
Open hatch thermal cover
-
Egress A/L
|
|
|
|
-
Receive EWC ORU bag from EV3, stow on BRT w/RET
|
-
Transfer EWC ORU Bag to EV1
|
-
Post crew egress, WVS Software:
-
select page - RF camera.
-
Sel 'Advanced Controls'
-
S-Band Level (two) - Max
|
|
-
Perform buddy checks
-
❏ MWS tabs up
-
❏ BRT tab up
-
❏ tether configs
-
Verify SAFER config
-
❏WVS - Green LED
|
|
|
-
Perform buddy checks
-
❏ MWS tabs up
-
❏ BRT tab up
-
❏ tether configs
-
Verify SAFER config
-
❏WVS - Green LED
|
-
Perform buddy checks
-
❏ MWS tabs up
-
❏ BRT tab up
-
❏ tether configs
-
Verify SAFER config
-
❏WVS - Green LED
|
|
|
-
Translate to S0 Port Struts
-
Attach EV1 BLANK hook to port-outboard strut
-
Attach EV2 BLANK hook to port-inboard strut
-
Release EV1 R Waist Tether from EV2 BLANK hook
|
-
Retrieve CETA ORU bag, stow on BRT with RET
|
|
|
-
Give EV3 GO to release Waist Tether
|
-
On EV1 GO, EV2 release right Waist Tether from Airlock internal D-Ring
|
|
|
-
Translate forward along Lab.
-
Stow EWC ORU bag on Lab HR 0260, 0259 with hinge ISS aft
-
Translate to S1 Face 1, zenith - MISSE 7 Retrieve
|
-
Close hatch thermal cover
-
Perform translation adaptation
-
Translate to S1 Face 1, nadir - MISSE 7 Retrieve
|
-
Stop Hatch Thermal Cover clock PET (30 min) ___:___
|
|
|
| IV(SSRMS) |
EV1(Drew) |
EV2(Taz) |
-
Verify inhibits in place
-
❏ Stbd SARJ locked
-
❏ MISSE/ELC-2 inhibits
|
|
|
|
|
WARNING
Do not touch the hinged side while closing the MISSE PECs (Pinch Point)
CAUTION
Avoid inadverntent contat with the deployed MISSE PECs, which have shatterable materials, and the silver avionics boxes atop the ExPA
CAUTION
There isn't a second caution in this block, but I wanted to add one here to show that there could be (also for "warning" and "note" blocks).
|
WARNING
Do not touch the hinged side while closing the MISSE PECs (Pinch Point)
CAUTION
Avoid inadverntent contat with the deployed MISSE PECs, which have shatterable materials, and the silver avionics boxes atop the ExPA
CAUTION
There isn't a second caution in this block, but I wanted to add one here to show that there could be (also for "warning" and "note" blocks).
|
|
|
-
Translate to S1 Face 2, zenith route
-
Attach GREEN hook on S1 HR 3208 (bay 17)
-
Translate zenith to ELC-2
-
Photograph deployed PECs
-
PEC 7B (❏ Ram/Fwd side, ❏ Aft side)
-
PEC 7A (❏ Zenith side, ❏ Nadir side)
-
❏❏ Overall View (2 angles)
|
-
Translate to S1 Face 6, nadir route
-
Attach GREEN hook on S1 HR 3217 or Xo 5790 (bay 17)
-
Translate outbd to bay 19
-
Stow CETA ORU bag on S3 HRs 3046, 3038
-
Translate zenith to ELC2
|
|
|
|
-
If EV1 complete with photos of PEC 7B, close PEC 7B
-
Disconnect connector from PEC 7B
-
❏ RET, remove cap from dummy connector J7B
-
❏ Demate connector from PEC
-
Mate connector to dummy connector J7B (❏ no FOD, pins)
-
❏ Install cap onto PEC 7B
|
|
|
-
Close PEC 7A
|
-
Disconnect connector from PEC 7A
-
❏ RET, remove cap from dummy connector J7A
-
❏ Demate connector from PEC
-
Mate connector to dummy connector J7A (❏ no FOD, pins)
-
❏ Install cap onto PEC 7A
|
|
|
|
-
Release PEC 7B (2 PIP pins), stow on BRT w/RET (nadir HR)
-
|
|
|
CAUTION
When translating around fwd edge of Node 2 and PMA2, avoid contact with Shuttle
|
CAUTION
When translating around fwd edge of Node 2 and PMA2, avoid contact with Shuttle
|
|
|
-
Retrieve GREEN hook from S1 HR 3208
-
Translate to Node 2, along port/zenith
STOW MISSE 7A IN PLB (00:10)
-
Attach GREEN hook on Node 2 HR 0344
-
Translate to Orbiter, port, bay 4
-
Remove SWC latch PIP pins (2)
-
Release latch (2) from stow position
-
Stow PEC 7A in carrier (HRs out, Probe up)
-
Lock fwd latch in landing config
-
Retrieve RET
-
Lock aft latch in landing config
|
-
Retrieve GREEN hook from S1 HR 3217 or Xo 5790
-
Translate to Node 2, stbd/zenith route
-
❏ Fairlead on Lab HR 0247 with wrist Adj
-
❏ Fairlead on Node 2 HR 0314
-
❏ Face toward Col and Node 2
STOW MISSE 7B IN PLB (00:15)
WARNING
Stay 2 ft from exposed EFGF connector when OBSS berthed, powered, and EFGF not grappled
-
Translate to Orbiter, stbd, bay 4
-
Remove SWC latch PIP pins (2)
-
Release latch (2) from stow position
-
Stow PEC 7B in carrier (HRs out, Probe up)
-
Lock aft latch in landing config
-
Retrieve RET
-
Lock fwd latch in landing config
|
|
|
-
Glove and Gauntlet check
-
Translate aft to Bay 9 - MISSE 8 Retrieve
|
-
Glove and Gauntlet check
-
Translate aft to S3 Face 1 - S3 CETA Light Install
|


| DOSE: |
|
|
Cannabis |
(plant material) |
| |
2500 ml |
oral |
Various |
(liquid) |
A few years ago I decided to try brewing some Pilsenkraut using the recipe available at this site.
The only ingredient I wasn't willing to pay for was the henbane plant matter its self, so I grew some from seeds I acquired from a now defunct ethnobotanical supplier. I managed to grow the plants rather successfully and come harvest day I reaped, separated the flowers and leaves and left the seed pods to open on their own. Unfortunately once the flower and leaf matter had dried, I was only left with around 30g of material, of which the recipe required 40g, so I decided to add a handful of seeds when the pods opened (I remember reading somewhere that some monks used to add a handful of henbane seeds to their regular beer batches for `extra effect`).
After a week I syphoned off the brew into bottles with a spoonful of honey added to each, and left those for a further two weeks. The resultant pilsenkraut tasted much like traditional flat cider but with a touch of fizz. I was so excited at successfully producing my first batch of tasty pilsenkraut that most of it was given away to friends or taken down to the pub I worked in at the time for people to sample. It was when I was down to my last 2 or 3 litres that I decided to consume the rest on my own to try the pilsenkraut `experience`
At the time I used to smoke a lot of weed (cannabis, in this case resin) and so was probably fair stoned before beginning the endevour. I didn't particularly have too many worries at the time and was fairly contented with life (well as much as a skint student with child can be). I drunk the remaining brew over the space of around 3 hours (I had one 1 litre bottle and three 750ml bottles each 3/4 filled with pilsenkraut) in my home with my then partner not taking part. I think I also had a few bowls of weed during the experience.
After the first bottle (the litre one) I started to feel a little bit drunk, as though I had just drunk a litre of 5% beer. After the second bottle the regular drunk feeling started to abate and a sort of `drunk on shrooms` feeling begun to take over. After the remaining two bottles I no longer felt drunk like on alcohol but a sort of wired drunk, like that from mushrooms. Colours were a little more vivid and everything started to take on a surreal quality. Unfortunately this is where I ran out of the beer and although the surreality intensified a little more after half an hour or so after my last bottle, no other effects seemed to.
The next day saw me with just a little groggy feeling but no other untoward side effects, although I do remember wishing that I hadn't had any weed so that I could have properly discerned the effects of the pilsenkraut more clearly.
My circumstances changed soon after that experience and I wasn't able to brew any more of the loverly beer (I did have another batch on the go about a year later from the seeds I grew, but that spoiled1:<, and my circumstances changed yet again). Overall, I associated that experience as being similar to what I experienced (well from what I remember of it anyway) of when a friend and my self demolished a bottle of absinthe each, except the absinthe was a hell of a lot more intense (and a good 10 hours disappeared from our lives forever!).>
I have recently though purchased some henbane seeds and will soon be growing again with a view to brew!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


public class SlimeMold_CellState extends AbstractDiscrete_CellState<SlimeMold_CellState,SlimeMold_CellState.SlimeMoldState>
| Modifier and Type | Class and Description |
|---|---|
static class |
SlimeMold_CellState.SlimeMoldState |
| Modifier and Type | Field and Description |
|---|---|
static double |
DEFAULT_CHEMICAL |
static double |
DEFAULT_DIFF |
static double |
DEFAULT_EVAP |
static double |
DEFAULT_THRESHOLD |
| Constructor and Description |
|---|
SlimeMold_CellState(SlimeMold_CellState.SlimeMoldState state)
constructs new cellstate according to state, but with defautl values
|
SlimeMold_CellState(SlimeMold_CellState.SlimeMoldState state,
java.util.Collection<Turtle> turtles)
Uses default values except for turtles and state
|
SlimeMold_CellState(SlimeMold_CellState.SlimeMoldState state,
java.util.Collection<Turtle> turtles,
double chem,
double evaporation,
double diffusion,
double threshold,
java.util.Collection<Turtle> nextTurts)
Constructs the cellstate with the values given, not the default values
|
SlimeMold_CellState(java.lang.String... params)
constructs a cell state with default values and name of param
|
| Modifier and Type | Method and Description |
|---|---|
void |
addNextChemical(double addChem)
Adds amount of chemical to the next state's chemical
|
void |
addNextTurtle(Turtle t)
Adds turtle to the next state
|
void |
clearNextTurtles()
clears all turtles in the next state
|
void |
evaporate()
reduces chemical in cell according to evaporation rate
|
double |
getChemical() |
double |
getDiffusionAmount() |
javafx.scene.paint.Color |
getFill()
Colors according to how much chemical there is.
|
java.util.Collection<Turtle> |
getNextTurtle()
returns collection of turtles in the next state
|
SlimeMold_CellState |
getSuccessorState()
Gets successorState according to the current values
|
java.util.Collection<Turtle> |
getTurtles() |
boolean |
hasNextTurtles() |
boolean |
hasTurtle() |
void |
setNextChemical(double nextChem)
sets chemical of the next state of this cell
|
compareTo, equals, getDistinctCellStates, randomState
getState, toString
public static final double DEFAULT_CHEMICAL
public static final double DEFAULT_EVAP
public static final double DEFAULT_DIFF
public static final double DEFAULT_THRESHOLD
public SlimeMold_CellState(SlimeMold_CellState.SlimeMoldState state)
state
- public SlimeMold_CellState(SlimeMold_CellState.SlimeMoldState state, java.util.Collection<Turtle> turtles)
state
- turtles
- public SlimeMold_CellState(SlimeMold_CellState.SlimeMoldState state, java.util.Collection<Turtle> turtles, double chem, double evaporation, double diffusion, double threshold, java.util.Collection<Turtle> nextTurts)
state
- turtles
- chem
- evaporation
- diffusion
- threshold
- nextTurts
- public SlimeMold_CellState(java.lang.String... params)
params
- public javafx.scene.paint.Color getFill()
getFill
in class Abstract_CellState<SlimeMold_CellState,SlimeMold_CellState.SlimeMoldState>
Abstract_CellState.getFill()
public SlimeMold_CellState getSuccessorState()
getSuccessorState
in class Abstract_CellState<SlimeMold_CellState,SlimeMold_CellState.SlimeMoldState>
Abstract_CellState.getSuccessorState()
public boolean hasTurtle()
public java.util.Collection<Turtle> getTurtles()
public double getChemical()
public void setNextChemical(double nextChem)
nextChem
- public void evaporate()
public double getDiffusionAmount()
public void addNextChemical(double addChem)
addChem
- public void addNextTurtle(Turtle t)
t
- public java.util.Collection<Turtle> getNextTurtle()
public boolean hasNextTurtles()
public void clearNextTurtles()


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