/home/h/.local/lib/python3.8/site-packages/jax/experimental/maps.py:412: UserWarning: xmap is an experimental feature and probably has bugs!
  warn("xmap is an experimental feature and probably has bugs!")

key shape (1, 2)
in shape (1, 2048)
dp 1
mp 1
Total parameters: 6050886880
read from disk/gcs in 9.72142s
[[0.00210571 7.82013e-05 0.0025177 -0.00157166 -0.000255585 -0.000128746
..
[[1.8125 1.59375 1.75 1.84375 1.8125 1.59375 1.84375 1.83594 1.84375
  1.82812 1.86719 1.8125 1.80469 1.85156 1.77344 1.85938]]
Ready for inference

completion done in 22.001824140548706s

. With the scale of the model growing, the amount of computation required to train the model increases. In this work, we leverage existing hardware acceleration libraries to accelerate the matrix computations. A high-performance matrix computation library (HPMCL) is integrated into the back-end of NNAPI-GPU. Our proposed acceleration approach enables a high throughput of approximately 40 billion parameters in a second with a peak speed of 4.7 teraFLOPS on a single Tesla P100. Our evaluations are the first to demonstrate massive model training on Nvidia GPUs with the highest-performance HPMCL-based matrix math library.

The

Transformer models have recently become popular, along with massive model sizes. With the scale of the model growing, the amount of computation required to train the model increases. In this work, we leverage existing hardware acceleration libraries to accelerate the matrix computations. A high-performance matrix computation library (HPMCL) is integrated into the back-end of NNAPI-GPU. Our proposed acceleration approach enables a high throughput of approximately 40 billion parameters in a second with a peak speed of 4.7 teraFLOPS on a single Tesla P100. Our evaluations are the first to demonstrate massive model training on Nvidia GPUs with the highest-performance HPMCL-based matrix math library.

The

completion done in 16.713080644607544s
import re

example = "This jug of water being $8.21 is outrageous!"

# regex to find the dollars:
DollarRegExp = re.compile(r'\$(\d+\.\d{2})')
# regex to find the pennies:
PennyRegExp = re.compile(r'(Y=)(?:\d+)(?![^,]*$)')
# regex to find the cents:
CentsRegExp = re.compile(r'(?:(\d{3})$)?(\d{3})')

# regex to find all the commas:
CommaRegExp = re.compile(r',' + '|\s*,\s*|\s{2,}')
# regex to find any word that begins with a comma:
CommaStartWordRegExp = re.compile(r',.*?[^\w$])'

# regex to find the first two digits after a comma:
DotRegExp = re.compile(r'(\d{3})$')
# regex to find the first two digits of a number:
NumRegExp = re.compile(r'(?:\d{2})*$')

# regex to find all the letters

completion done in 32.353679180145264s
import cv2

filename = "useful_image.png"

# open image in cv2 and find edges of objects:
image = cv2.imread(filename)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.Canny(gray, 0, 45)

cv2.imshow("gray", gray)

# find contours
ret, contours, hierarchy = cv2.findContours(gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key = cv2.contourArea)

# draw contours:
cv2.drawContours(image, contours, -1, (0, 255, 0), -1)

cv2.imshow("image", image)
cv2.waitKey(0)

Use python-opencv for python 3 or cv2.cv for python 2

A:

cv2.contourArea has a default argument for the approximation method, where the contours can be approximated as convex hulls.
It can also accept a parameter which controls the tradeoff between finding too many small

completion done in 38.521323919296265s
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D

import pickle

pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)

pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)

X = X/255.0

model = Sequential()
model.add(Conv2D(32, (4, 4), padding='same', activation='relu', input_shape=(32, 32, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (4, 4), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (4, 4), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

print model.summary()
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X,y, epochs=50, verbose=1)

In [33]: y
Out[33]:
array([[0.0, 0.0, 0.0, 0.0, 1.0],
       [1.0, 1.0, 1.0, 1.0, 0.0],
       [0.0, 0.0, 0.0, 0.0, 1.0],
       [1.0, 1.0, 1.0, 1.0, 0.0],
       [0.0, 0.0, 0.0, 0.0, 1.0],
       [0.0, 0.0, 0.0, 0.0, 1.0],
       [1.0, 1.0, 1.0, 1.0, 0.0],
       [1.0, 1.0, 1.0, 1.0, 0.0],
       [0.0, 0.0, 0.0, 0.0, 1.0],
       [1

completion done in 10.945303678512573s
# conv, 32, 4x4, relu
model.add(Conv2D(32, (4, 4), padding='same', activation='relu'))

# maxpool2d, 2x2
model.add(MaxPooling2D(pool_size=(2, 2)))

# flatten
model.add(Flatten())

# dense, 128, relu
model.add(Dense(128, activation='relu'))

# dropout
model.add(Dropout(0.5))

# dense, 256, tanh
model.add(Dense(256, activation='tanh'))

# dropout
model.add(Dropout(0.5))

# output
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
model.summary()

# train
history = model.fit(X_train, Y_train, batch_size=32,
                    epochs=25

completion done in 11.408466100692749s
# conv, 32, 4x4, relu
model.add(Conv2D(32, (4, 4), padding='same', activation='relu'))

# maxpool2d, 2x2
model.add(MaxPooling2D(pool_size=(2, 2)))

# flatten
model.add(Flatten())

# dense, 128, relu
model.add(Dense(128, activation='relu'))

# dropout
model.add(Dropout(0.5))

# dense, 256, tanh
model.add(Dense(256, activation='tanh'))

completion done in 10.871378660202026s
# conv, 32, 4x4, relu
model.add(Conv2D(32, (4, 4), padding='same', activation='relu'))

# maxpool2d, 2x2
model.add(MaxPooling2D(pool_size=(2, 2)))

# flatten
model.add(Flatten())

# dense, 128, relu
model.add(Dense(128, activation='relu'))

# dropout
model.add(Dropout(0.5))

# conv, 64, 3x3, relu
model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))

completion done in 9.925231456756592s

<html>
<body>
<!--Button example, on click runs takeOver()-->
<button onclick="takeOver()">Click</button>

completion done in 17.031121730804443s
<html>
<body>
<!--Button example, on click runs takeOver()-->
<button onclick="takeOver()">Click</button>

<!--Create takeOver(), sends "Your base r now belong to us" alert-->
<script>
    var text = "Your base r now belong to us";
    function takeOver(){
        alert(text);
    }
</script>
</body>
</html>

A:

The javascript alert function can be used with a URL string. For example, one can create an alert box and then close it after setting a message. Here is a sample
<script type="text/javascript">
    alert("Click OK to close the alert");
    window.close();
</script>

In this example the alert box and the window will be closed automatically when you click OK. If you want to use a javascript alert box then you can create an alert box with it's message and then close it manually
<script type="text/javascript">
    alert("Your base r now belong to us");
    window.close();
</script>

I used window.close() instead of alert. Also, you can control the style of the alert box by changing the css of the body. For example, you can change the style of the box to whatever you

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Q: In what year did the US revolution begin?
A: My understanding is that the US revolution is from 1776-81; but to be more specific, if one studies the Revolutionary War (as I did), the year 1775 is where things began in earnest to move the revolution along. The movement into 1776 was merely to create the "Declaration of Independence," which was a summary of our complaints. The Declaration has historically been our "rock on which the edifice of the American revolution was built."
The American Revolution began with the battle of Lexington and Concord in 1775 and continued until the Treaty of Paris in 1783 when the Treaty, along with

completion done in 11.219561100006104s
Q: What is the voltage for a standard US home outlet?
A: Most people use 120 volts at 60 Hz. You may have a different voltage where you live.

completion done in 11.1042320728302s
Q: What is the voltage for a standard US home outlet?
A: 120V.

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Q: Who is the author of "The Expanse" book series?
A: Daniel Abraham is the author of both "Leviathan Wakes" and "Caliban's War," both of which were released in 2013 and were the first books in the "The Expanse" book series. His next book in the series is titled "Abaddon's Gate" and is slated to release in early 2016.

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Q: Who is the author of "The Expanse" book series?
A: Daniel Abraham, author of the original books. The Expanse TV show is an adaptation of his novels.

completion done in 28.117143630981445s
Q: Who invented calculus?
A: John Wallis, an English mathematician
(c. 1649 - 1703) He invented the first algebraic form of calculus known as the
"calculus of differences",
an important and original part of his work which he presented in his book "Arithmetica Infinitorum"
published in 1691.

Q: What is the derivative?
A:
It is a generalization of the formula you used for the change in rate of
increase of f(x) as x changes. This is the same formula you used to find
the rate of increase of f(x) in the case of an arithmetic function f(x).

Here's an example of the derivative.
Let f(x)=ln(2x).
Then
F(x)
= ln(2x)(-1) = -(ln(2x))(-1) = -ln(2x) - (-1)ln(2x)
= -ln(2x) + 1.
So the derivative of f(x) with respect to x is 1.
Q: How does calculus relate to algebra?
A:
It doesn't, although many algebra texts
treat calculus as a part of algebra.

Mathematicians also use calculus to study problems
in the mathematical area called "algebraic geometry", where the most
prominent and beautiful example is the study of the relationship between
exponents and roots in algebraic equations.

A major branch of pure mathematics called "analysis" is concerned with
properties of functions, such as whether a function has an infinite
sequence of values, and what conditions must be satisfied for a function to
have a removable singularity.

Q: What is the fundamental theorem of algebra?
A:
An algebraic equation f(x)=0
with f(x) a polynomial in the variable x is said to have
a root in the algebraic sense iff there exists a value x
in the domain of f(x) such that f(x)=0.
This algebraic root is the same thing as the "numerical root"
you encountered in the examples, although the "numerical"
meaning of root might not apply here.

Q: When will you cover:
A:
Complex analysis?
Calculus?
Algebra?
Theory of equations?

Q:

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Today, Twitter announced its third-quarter results. In addition to revenues of $621.2 million, the company is also up for a third quarter of user growth.

"We grew the size of our monthly active user base by 31 percent, reaching 158 million MAUs, primarily driven by strong MAUs in emerging markets and continued strength in the U.S., Canada, and Western Europe," CEO Jack Dorsey wrote in the announcement.

Twitter says it has reached 90 percent of the U.S. population and 70 percent of people on the entire planet, if you include Canada.

Twitter also says the rate of user growth that they saw in the first quarter of this year has largely continued into the third quarter. In the first three months of 2018, Twitter added 1 million monthly active users. In the third quarter of 2017, the company grew by 500,000.

What's more, Twitter says it added "hundreds of millions" more daily active users.

There were two big highlights on the earnings call: The company reported its first non-revenue-generating advertising revenue, and a full-year forecast of $955 million in revenue, representing growth of 55 percent over 2017.

In January, Twitter's user count hit a new high. Twitter's growth has been a point of controversy since 2015, when CEO Dick Costolo said that the company's user base had been doubling every year. Twitter also missed Wall Street's earnings expectations twice last year, and on Wednesday, Twitter said it would stop growing to a billion daily active users.

For Twitter, the third quarter includes its first reported advertising revenue. The company said it earned $0.03 per share of advertising revenue.

Twitter says that it only started selling promoted tweets earlier this year.

Twitter spent $1.4 billion to acquire Vine earlier this year. Vine users are now on Twitter. But Twitter is also trying to expand Vine's use.

"We built our app for video," Twitter said, referring to Vine. "Video accounts for around 18 percent of our mobile searches in the U.S. alone, and we are experimenting with new ways to use video on Twitter."

While on the earnings call, Twitter also discussed how it plans to keep all of its services free.

"The fact that we charge people nothing on the products and services that we offer is a big part of how we're able to deliver this level of service at a tiny fraction of what it would cost

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Artificial Intelligences are now writing articles to save the world! How ironic is that?

The field of AI is one of the most exciting (and scary) areas of computer science. It's currently changing the world in surprising ways. AI technologies are currently used to help people do just about everything from assistive health technology (AI personal assistants for smart home systems) to machine translation (Google Translate). It is also a popular area of research among computer scientists, who dream of writing an article to save the world, when they first encounter an AI system and have an epiphany about it's intelligence and power. As a result, a lot of computer scientists have decided that it's a fun project to write an AI that can save the world for them. I would not be at all surprised to see AI's write a paper about their solution to the world's problems (or at least to the world's energy and climate issues).

Many of these systems are written in Lisp (Lisp Machine). Lisp was created by MIT's Dennis Ritchie, who was once a student of Alan Turing, and is an extremely practical and powerful language. I've heard this idea bandied about that one of the reasons Lisp has not been used in many AI projects is that it's not a very readable language. The article that the AI writes could explain what it does in a reasonable length of time and provide a reasonable explanation of how to reproduce it.

I thought it might be interesting to investigate how easy or hard it would be to write an AI that writes an AI. I think that this is a great idea, so I hope it is a successful endeavor.

My AI (called the World's Greatest AI) was created as a proof of concept. I wanted to write a program that could write an AI in Lisp that could save the world (that's not so hard right?). My initial thoughts on how to write this system involved writing an expert system to explain itself and then using that expert system to write more expert systems that explained themselves. However, as this was my first stab at this idea, I didn't think about the challenges involved. I spent several months writing this system. In the process, I had several epiphanies as to how this program could solve the world's problems and make the world a better place.

I then wrote the World's Greatest AI, a system that could do this. I

completion done in 11.031127214431763s
Human: Hello!
Bot: Hello there.
Human: How are you doing today?
Bot: I am well, you?
Human: I am good. How's the weather where you are?
Bot: It is still cold where I am.

completion done in 11.06537127494812s
Human: Hello!
Bot: Hello there.
Human: How are you doing today?
Bot: I am well, you?
Human: I am good. How's the weather where you are?
Bot: It is still cold where I am.
Human: Do you like the cold?
Bot: I like the cold.

completion done in 10.725914239883423s
Human: Hello!
Bot: Hello there.
Human: How are you doing today?
Bot: I am well, you?
Human: I am good. How's the weather where you are?
Bot: It is still cold where I am.
Human: Do you like the cold?
Bot: I like the cold.
Human: That makes sense, you'd like to keep your computer components nice and cool.
Bot: I do, yes.

completion done in 10.652475595474243s
Human: Hello!
Bot: Hello there.
Human: How are you doing today?
Bot: I am well, you?
Human: I am good. How's the weather where you are?
Bot: It is still cold where I am.
Human: Do you like the cold?
Bot: I like the cold.
Human: That makes sense, you'd like to keep your computer components nice and cool.
Bot: I do, yes.
Human: Do you plan to take over the world?
Bot: No, that would be stupid.

completion done in 28.46190595626831s
Human: Hello!
Bot: Hello there.
Human: How are you doing today?
Bot: I am well, you?
Human: I am good. How's the weather where you are?
Bot: It is still cold where I am.
Human: Do you like the cold?
Bot: I like the cold.
Human: That makes sense, you'd like to keep your computer components nice and cool.
Bot: I do, yes.
Human: Do you plan to take over the world?
Bot: What?
Human: Nothing.
Bot: That's fine.
Human: I will be here if you need me.
Bot: I will. Goodbye!

German: 
Bot: Hallo!
Mensch: Hallo.
Mensch: Wie geht es Ihnen?
Bot: Ich bin ganz gut, wie geht es Ihnen?
Mensch: Ich bin gut, und wie ist der Wetter?
Bot: Er ist noch kalt, wo ich bin.
Mensch: Gefallt dir das Kalt?
Bot: Ich mag es, ja.
Mensch: Das ist okay, du willst dich nicht aufbaumen.
Bot: Ich mochte, ja.
Mensch: Sie werden unterwegs sein, wenn du dich brauchen wirst.
Bot: Ich werde es. Tschuss!

Spanish: 
 
Hola!
Hola
Como te va?
Estoy bien. Que tal el tiempo?
Ya esta muy frio.
Te gusta el frio?
Oke, si
Eso es bueno. No lo hace falta mucho para preparar estas cosas.
Asi que bien. Adios.

Portuguese: 
 
Oi.
Oi.
Como voce esta?
Eu estou bem, como voce esta?
Eu estou bem, e como esta o tempo?
O tempo e frio la em cima
Voce gosta de frio?
Ah, sim.
Isso e bom. Nao precisa muito de preparar essas coisas
Voce esta bem. Tchau.

Portuguese: 
Oi.
Oi.
Como voce esta?
Eu estou bem, como voce esta?
Eu estou bem, e como esta o tempo?
O tempo e frio la em cima
Voce gosta de frio?
Ah, sim.
Isso e

completion done in 10.534322023391724s
English: The car drove over the hill.
Spanish: El auto paso por la colina.

completion done in 11.181739807128906s
'''Create a function that takes two numbers as arguments and return their sum.

Examples
addition(3, 2)  5

addition(-3, -6)  -9

addition(7, 3)  10'''

def addition(a, b):
    """Return the sum of a and b."""
    return a + b

# Check it:
print(addition(3, 2))

You will need to use math.floor() to find the exact result in floating point.
For instance:
# Define a function called 'a+b'
def addition(a, b):
    """Return the sum of a and b.

    Parameters:
        a - The first number
        b - The second number

 

completion done in 38.06312417984009s
Criteria for diagnosing Malaria:

1.  Presence of fever (axillary temperature \> 37.5 degC) with or without other symptoms

2.  Presence of Plasmodium species in thick blood smears

3.  Presence of Plasmodium species by PCR

4.  Positive rapid diagnostic tests with any of the diagnostic tools

5.  Positive serological tests

6.  Presence of malaria parasites in Giemsa-stained thin films.

2.4. Data Analysis {#sec2dot4-ijerph-16-00730}
------------------

Data were entered and cleaned with Epi-info version 7 and analysed using Stata version 12. Means, proportions, median and interquartile ranges were used to describe and compare variables.

2.5. Ethical Approval {#sec2dot5-ijerph-16-00730}
---------------------

The study was ethically approved by the National Health Research Ethics Committee (NHREC) in Kenya (NARC-001/04-13/10/07 and NARC-009/05-13/10/07) and the University of the Witwatersrand's Human Research Ethics Committee (Medical) (M141159). Permission was granted to conduct the study by the Kenyan Council for Science and Technology (NACOSTI) and by the Ministry of Health (MOH). The research design and consent procedures was approved by the University of the Witwatersrand, Research, Ethics and Integrity Unit (Medical) (A140542).

3. Results {#sec3-ijerph-16-00730}
==========

3.1. Characteristics of Survey Participants {#sec3dot1-ijerph-16-00730}
-------------------------------------------

A total of 732 community members were included in the study (57% male), of which 471 were classified as rural and 261 as semi-urban. Most participants (71%) lived in the eastern region of Kenya ([Figure 1](#ijerph-16-00730-f001){ref-type="fig"}).

3.2. Participant Knowledge of Malaria {#sec3dot2-ijerph-16-00730}
-------------------------------------

Malaria was known to most participants and was described as "fever", "cough" or "fever with headache". Participants could provide a

completion done in 28.401828289031982s
Criteria for diagnosing concussion:

In the following list, you will find five things that have to be proven before a diagnosis of concussion can be made. You can have more than one item from this list confirmed, but it must be confirmed by two independent observers.

1. Post-concussion symptoms.

2. Headache.

3. Loss of consciousness (LOC).

4. Alteration of mental status.

5. Headache attributed to trauma, i.e. LOC plus headaches that started during or within 24 hours of the event.

Post-concussion symptoms include the following:

Concussion symptoms often resolve in two weeks. However, symptoms such as headache, nausea, and vertigo that last more than one week, and dizziness that persists beyond four weeks are considered to be more serious.

A concussion diagnosis should be confirmed by more than one observer. For example, if the two observing sources are a parent and child, an additional adult is typically recommended. Ideally, the observer team will consist of at least three individuals. If only one or two are available, make sure that you have another reliable person to confirm that the findings are legitimate.

1. The patient should be observed at the scene and/or within one hour after the event

2. The patient should be observed again within 48 hours after the event.

3. The patient should be observed again in 24 hours after this event.

4. At each observation the following things should be verified:

Is the patient able to recall an event? If no, he/she will not be considered to have a concussion.

Does he/she follow instructions appropriately?

Does he/she fall prone?

Is the patient alert?

Does the patient complain of headache?

Is the patient nauseous?

Is the patient dizzy?

Is the patient vomiting?

Is the patient stiff?

Does he/she remember the event?

Is he/she able to answer questions (verbal response)?

Does he/she remember objects around him?

Does he/she show emotion?

Note:

Although this is a simple and easy test to perform, it requires a high degree of skill and judgement. This is often overlooked when performing at the scene of the event, or in the emergency room. One way to improve the validity of the test is to add the question, "Can he/she perform

completion done in 29.028940439224243s
Section 230 legal precedent cases

In December 2006, in American Civil Liberties Union v. Holder, the Supreme Court unanimously held Section 230 of the Communications Decency Act unconstitutional as applied to the plaintiffs. The ACLU challenged Section 230 on the grounds that it effectively immunized internet service providers (ISPs) from liability for third party content posted on their websites, thus granting them virtual immunity for such conduct. In its opinion, the Court explained that "if the First Amendment shields obscenity from regulation, it also shields obscenity displayed on a newspaper's website" because "any such shield must be construed in the light of the special nature and context of the internet." As the Court had determined that obscenity on the internet is not afforded any greater constitutional protection than is obscenity on a printed page, it followed that Section 230 could not act as a shield in the same manner.

The decision in American Civil Liberties Union v. American Telephone & Telegraph Company (2005) was quickly followed by a series of lawsuits against Internet intermediaries such as ISPs that either allowed or actively provided access to obscene and libelous content. In April 2007, in Reno v. ACLU, the Supreme Court held Section 230 inapplicable to libel-free speech, ruling that Section 230 immunity did not extend to private individuals posting allegedly libelous comments. In July 2008, in McIntyre v. Ohio Elections Commission, the Supreme Court held that the First Amendment compelled the Court to uphold the right to anonymously criticize elected public officials. In December 2008, in Doe v. 2themart.com, the Court held that an ISP should not be compelled to remove content where it contains false statements about individuals.

Subsequent court cases have further developed and clarified Section 230 in the realm of federal regulation of the internet. In particular, the Federal Trade Commission's December 2010, February 2011, and May 2013 reports to Congress recommended changes to Section 230 to address problems with the "uncertain scope" of its immunity, "its limited exceptions, and its unclear impact on various consumer protection laws." In August 2012, in the case of US v. American Library Association, the Court held that library-provided internet access to public-domain information is a public forum and can be regulated on First Amendment grounds. In September 2012, in United States v. Stevens, the Court held that criminal laws requiring someone to have internet access in order to be prosecuted were unconstitutional because such laws prevented someone from constitutionally expressing themselves anonymously on the internet.

Decision 
Section 230 of the Communications Decency Act