Heading 1
Heading 2
Heading 3
Heading 4
Heading 5
Heading 6
This is text which foes in many lines but the browser is ignoring them


Binary Search Tree is a node-based binary tree
data structure which has the following properties:
Explore
A new key is always inserted at the leaf. We
start searching a key from the root until we hit a leaf node. Once a leaf node is found, the
new node is added as a child of the leaf node.
The format to insert is: I (number)
Here I refers to insert command followed by a space and the number to be
inserted.
The video shows the Insertion in a BST.
The important thing to note is, inorder successor is needed only when the right child is not empty. In this particular case, inorder successor can be obtained by finding the minimum value in the right child of the node.
The format to delete is: D (number)
Here I refers to delete command followed by a space and the number to be
inserted.
The video shows the Deletion in a BST
Searching an element in the binary search
tree is basically this traversal in which at each step we will go either towards left or
right and hence in at each step we discard one of the sub-trees.If the key is found,
function returns saying KEY IS FOUND. Else KEY IS NOT
FOUND.
The format to search is: S (number)
Here S refers to
search command followed by a space and the number to be inserted.
The video shows the
Searching in a BST.
Post Order Traversal:
The format for postorder is: POS (number)
Here POS refers to
postorder command followed by a space and the number to be inserted.
Pre Order Traversal:
The format for postorder is: PRE (number)
Here PRE refers to preorder command followed by a space and the number to be inserted.
In Order Traversal:
The format for inorder is: IN (number)
Here IN refers to inorder command followed by a space and the number to be inserted.
Page was started with Mobirise


---
layout: post
title: Here's Dante to sing a song for you "Theme from 'Exodus'" - YouTube
date: '2011-08-30T16:03:00.000-05:00'
author: Tom Ray
tags:
- blog
modified_time: '2013-02-09T22:55:21.097-06:00'
thumbnail: https://img.youtube.com/vi/EKb7je5Hduc/default.jpg
blogger_id: tag:blogger.com,1999:blog-11319466.post-1932863219731280783
blogger_orig_url: http://www.lorenzosmusic.com/2011/08/here-dante-to-sing-song-for-you-from.html
---
Stairway To Stardom (1987) - Dante "Theme from 'Exodus'" - YouTube: ""


In one line, RNN is a neural network with data input, input from the previous unit, and an output.
The following image is from [4]:
from IPython.display import Image
Image(filename='figures/RNN-rolled.png', width=200)
If we unroll this recurrence connection according to time $t$, then we get the following figure (from [4]):
from IPython.display import Image
Image(filename='figures/RNN-unrolled.png', width=800)
from IPython.display import Image
Image(filename='figures/RNN-longtermdependencies.png', width=600)
Tensorflow controls the turncation parameter through "num_steps" variable [7]. The following figure shows the Tensorflow implementation of BPTT with $num\_steps = 3$.
# Placeholder for the inputs in a given iteration.
import tensorflow as tf
batch_size = 1 # process each word at a time
num_steps = 3
words = tf.placeholder(tf.int32, [batch_size, num_steps])
from IPython.display import Image
Image(filename='figures/RNN_tf_truncated_backprop.png', width=600)
This style of BPTT have a problem of capturing dependencies beoyond "num_steps".
In Ilya Sutskever’s Ph.D. thesis [6] and [5], they talk about other variants of turncated back propagation.
from IPython.display import Image
Image(filename='figures/ilya_phd_thesis_bptt_truncate.png', width=600)
As mentioned in [5], the Tensorflow implementation is when $k_1 = k_2 = num\_steps$.
If you want to capture longer range dependencies than $num\_steps$, then one way is to set $k_2 << k_1$, for example $k_2 = 1$. The following figure is the case when $k_1 = 3$ and $k_2 = 1$ (which I borrowed from [5]):
from IPython.display import Image
Image(filename='figures/RNN_true_truncated_backprop.png', width=600)
I will borrow Ilya Sutskever's words from his PhD thesis [5]: "Consequently, its hidden states have been exposed to many timesteps and so may contain useful information about the far past, which would be opportunistically exploited."
Specifically, in this case, the 3rd RNN cell is exposed 2 more times than the Tensorflow implementaiton.
The other way to capture longer dependency is to simply increase $num\_steps$.
The chain rule for derivatives is applied when computing derivatives of a function composed by two or more functions (according to Wikipedia https://en.wikipedia.org/wiki/Chain_rule)
The chian rule is important when training a neural network because one nueron is dependent on another. This is the same case for RNNs. Let's be more precise and use a variable to define RNNs. Check out the following figure (from [1]) to get an intuitive idea of it.
from IPython.display import Image
Image(filename='figures/rnn-bptt1.png', width=600)
Where, $E_t$: is the error at time $t$. It is typically defined as categorical cross-entropy i.e. $$ E_t = -y_t \log(\hat{y_t}) $$ and we sum up the error and the total loss will be $$ E = \frac{1}{N}\sum_t^N -y_t \log(\hat{y_t}) $$
$s_2$ requires $s_1$, which is also dependent on a variable $s_0$.
Similarly, to compute the gradient for one example or one sentence, we sum up the gradient at all time $t$
$$ \frac{\partial E}{\partial{W}} = \sum_t \frac{\partial E_t}{\partial{W}} $$We will see in few minutes that using the chain rule or the computation graph of the chain rule, this is a product of partial derivatives on dependent variables.
The visualiztion of chain rules useful the intuitive understanding of the vanishing gradient problem. In my opinion, there are two difficulties in understanding the theory behing gradient vanishing problem:
To get ready to dive deep into to these two points, let's do a simple back propagation example on a simple function (folowing step by step presented in [1]).
Assume we are focusing on the following function (which corresponds to the cost function in the neural network training settings):
\begin{equation} f(c, d) = c d \\ c = a + b \\ d = b + 1 \\ \end{equation}The compuation graph of chain rules is the following (image ref [1]):
from IPython.display import Image
Image(filename='figures/computation_graph_1.png', width=300)
What is good about this graph? The answer is, you can now just compute the derivative of each node by summing up all the product of derivatives!
from IPython.display import Image
Image(filename='figures/computation_grpah_2_with_derivatives_edges.png', width=300)
The chain rule applied to $f(c, d)$ is \begin{align} \frac{\partial f(c, d)}{\partial a} &= \frac{\partial f}{\partial c} \frac{\partial c}{\partial a} \ &= d * b \end{align} and
\begin{align} \frac{\partial f(c, d)}{\partial b} &= \frac{\partial f}{\partial d} \frac{\partial d}{\partial b} + \frac{\partial f}{\partial c} \frac{\partial c}{\partial b} \\ &= c * 1 + d * a \end{align}Notice that it is simply following the all possible path in this graph (bottom to up) from input variables $a, b$.
Notice that when looking into the equations, there are derivatives that are shared among two equations, namely $\frac{\partial f}{\partial c} $? So if we naively compute the derivatives (or gradients in higher dimension) on each variable, it is computing the derivation on $\frac{\partial f}{\partial c} $ multiple times (well, twice in this case though).
Solution? Dynamic programming or in this particular application, there is a fancy name back propagation.
Now let's do the opposite i.e. going from up to bottom in this graph:
from IPython.display import Image
Image(filename='figures/computation_graph_3_back_prop.png', width=300)
CORRECTION: $\frac{\partial f}{\partial b} = c * 1 + d + a$.
So going from up to bottom and bottom to up is almost the same except that you traverse only once per edge for the top to bottom traversal. This is because the computational graph created from chain rule can have multiple children per node.
In the nueral network case, the bottom nodes (or the leaf nodes) are the weight parameters $W$ and the biases $b$. When the number of these parameters are large, it saves a LOT of time to compute the gradient according to each parameter.
How is the back propagation related to the vanishing gradient on the standard RNNs?
I am not going into mathematical details since I infer it will covered in Jordan's class, but I can explain the intuitive understanding.
The example I borrowed from [1] is the case of scalar or one-dimensional vector with a pretty shallow graph. What is the compuational graph created by chain rule for the simple RNN?
from IPython.display import Image
Image(filename='figures/rnn_cell.png', width=300)
When constructing the compuational graph for RNN, the key part is $s_{t-1} \rightarrow s_t$.
Image(filename='figures/computational_graph_4_rnn.png', width=600)
$J$ is a Jacobian matrix is the vector extension of first-order partial derivatives. (FYI, the second-order one is called Hessian matrix)
So remember from our fist example that when we backprogate from the top node to bottom node, we multiply the partial derivatives that corresponds to each edge. Also, notice that there is only one edge from $t-1$ nodes to $t$ nodes, namely $s_{t-1} \rightarrow s_{t}$.
In [3], there is a proof that the determinant of the Jacobian matrix betwen $s_{t-1}$ and $s_{t}$ is less than 1. In other words, every time we backpropagate through $s_{t-1} \leftarrow s_{t}$, the gradient value which we are currently holding will decrease!
$\therefore$ the standard RNN will not capture the long range dependencies.
If you want to know more about the ICML 2013 paper and cannot wait for Jodan's lecture in mid March, then I suggest you look into Richard Socher's slides [8] as a supplementary material for understanding what is going on.
Image(filename='figures/LSTM3-chain.png', width=600)
Image(filename='figures/lstm_cell.png', width=600)
where
where $\sigma$ is a sigmoid function.
The blue arrows represent the input filtered by either one of the following weighted matrices:
So notice that for example, if the input word vector $x_i$ is $300$ dimension, and $h_i$ is $100$ dimension. The dimesion of $W^{x\_input}$ is $300 \times 100 = 30,000$, so this single matrix has $30,000$ parameters to train. Furthermore, there are a total of $4$ similar matrices, so we have to train $30,000 \times 4 = 120,000$ parameters to train for the input word vector $x_i$.
Similarily, $W^{h\_input}$ has $100 \times 100 = 10,000$ parameters to train and there are total of $4$ such matrices, so we have $10,000 \times 4 = 40,000$ parameters to train for the hidden unit $h_i$.
To sum up, we have to trian $120,000 + 40,000 = 160,000$ parameters (plus the bias vector) to train when the input word vector is $300$ dimension and the hidden unit is in $100$ dimension.
This number of parameters is huge compated to the simple RNN such as
In case when the forget gate vector = $(1, 1, ..., 1)$, then it will simply copy your cell vector fro mthe previous time. This is different from the derivative edge between $s_t$ and $s_{t-1}$ having $< 1$.
"Most useful when you have lots and lots of data" [9]
So having $4$ times the number of parameters of the simple RNN seems to be a bit expensive.
The simpler version of LSTM and a bit complex than the simple RNN is the GRU (following image from [8]).
Image(filename='figures/gru_socher_slide.png', width=600)
Now, the weight matrices (or parameter matrices) are
Since there is one less matrix than LSTM, the parameters are less:


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| Packages that use SessionManager | |
|---|---|
| org.apache.shiro.guice | |
| org.apache.shiro.guice.web | |
| org.apache.shiro.mgt | Provides the master SecurityManager interface and a default implementation
hierarchy for managing all aspects of Shiro's functionality in an application. |
| org.apache.shiro.session.mgt | SessionManager components supporting enterprise session management. |
| org.apache.shiro.web.mgt | Components supporting web-specific
SecurityManager implementations. |
| org.apache.shiro.web.session.mgt |
| Uses of SessionManager in org.apache.shiro.guice |
|---|
| Method parameters in org.apache.shiro.guice with type arguments of type SessionManager | |
|---|---|
protected void |
ShiroModule.bindSessionManager(com.google.inject.binder.AnnotatedBindingBuilder<SessionManager> bind)
Binds the session manager. |
| Uses of SessionManager in org.apache.shiro.guice.web |
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| Method parameters in org.apache.shiro.guice.web with type arguments of type SessionManager | |
|---|---|
protected void |
ShiroWebModule.bindSessionManager(com.google.inject.binder.AnnotatedBindingBuilder<SessionManager> bind)
Binds the session manager. |
| Uses of SessionManager in org.apache.shiro.mgt |
|---|
| Subinterfaces of SessionManager in org.apache.shiro.mgt | |
|---|---|
interface |
SecurityManager
A SecurityManager executes all security operations for all Subjects (aka users) across a
single application. |
| Classes in org.apache.shiro.mgt that implement SessionManager | |
|---|---|
class |
AuthenticatingSecurityManager
Shiro support of a SecurityManager class hierarchy that delegates all
authentication operations to a wrapped Authenticator instance. |
class |
AuthorizingSecurityManager
Shiro support of a SecurityManager class hierarchy that delegates all
authorization (access control) operations to a wrapped Authorizer instance. |
class |
CachingSecurityManager
A very basic starting point for the SecurityManager interface that merely provides logging and caching support. |
class |
DefaultSecurityManager
The Shiro framework's default concrete implementation of the SecurityManager interface,
based around a collection of Realm s. |
class |
RealmSecurityManager
Shiro support of a SecurityManager class hierarchy based around a collection of
Realm s. |
class |
SessionsSecurityManager
Shiro support of a SecurityManager class hierarchy that delegates all
session operations to a wrapped
SessionManager instance. |
| Methods in org.apache.shiro.mgt that return SessionManager | |
|---|---|
SessionManager |
SessionsSecurityManager.getSessionManager()
Returns this security manager's internal delegate SessionManager . |
| Methods in org.apache.shiro.mgt with parameters of type SessionManager | |
|---|---|
void |
SessionsSecurityManager.setSessionManager(SessionManager sessionManager)
Sets the underlying delegate SessionManager instance that will be used to support this implementation's
SessionManager method calls. |
| Uses of SessionManager in org.apache.shiro.session.mgt |
|---|
| Subinterfaces of SessionManager in org.apache.shiro.session.mgt | |
|---|---|
interface |
NativeSessionManager
A Native session manager is one that manages sessions natively - that is, it is directly responsible
for the creation, persistence and removal of Session instances and their
lifecycles. |
interface |
ValidatingSessionManager
A ValidatingSessionManager is a SessionManager that can proactively validate any or all sessions that may be expired. |
| Classes in org.apache.shiro.session.mgt that implement SessionManager | |
|---|---|
class |
AbstractNativeSessionManager
Abstract implementation supporting the NativeSessionManager interface, supporting
SessionListener s and application of the
globalSessionTimeout . |
class |
AbstractSessionManager
Base abstract class of the SessionManager interface, enabling configuration of an
application-wide globalSessionTimeout . |
class |
AbstractValidatingSessionManager
Default business-tier implementation of the ValidatingSessionManager interface. |
class |
DefaultSessionManager
Default business-tier implementation of a ValidatingSessionManager . |
| Uses of SessionManager in org.apache.shiro.web.mgt |
|---|
| Subinterfaces of SessionManager in org.apache.shiro.web.mgt | |
|---|---|
interface |
WebSecurityManager
This interface represents a SecurityManager implementation that can used in web-enabled applications. |
| Classes in org.apache.shiro.web.mgt that implement SessionManager | |
|---|---|
class |
DefaultWebSecurityManager
Default WebSecurityManager implementation used in web-based applications or any
application that requires HTTP connectivity (SOAP, http remoting, etc). |
| Methods in org.apache.shiro.web.mgt that return SessionManager | |
|---|---|
protected SessionManager |
DefaultWebSecurityManager.createSessionManager(String sessionMode)
|
| Methods in org.apache.shiro.web.mgt with parameters of type SessionManager | |
|---|---|
void |
DefaultWebSecurityManager.setSessionManager(SessionManager sessionManager)
|
| Uses of SessionManager in org.apache.shiro.web.session.mgt |
|---|
| Subinterfaces of SessionManager in org.apache.shiro.web.session.mgt | |
|---|---|
interface |
WebSessionManager
SessionManager specific to web-enabled applications. |
| Classes in org.apache.shiro.web.session.mgt that implement SessionManager | |
|---|---|
class |
DefaultWebSessionManager
Web-application capable SessionManager implementation. |
class |
ServletContainerSessionManager
SessionManager implementation providing Session implementations that are merely wrappers for the
Servlet container's HttpSession . |
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Click on a Vent category below to message with a trained volunteer.
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AWS Amplify provides a declarative and easy-to-use interface across different categories of cloud operations. AWS Amplify goes well with any JavaScript based frontend workflow, and React Native for mobile developers.
Our default implementation works with Amazon Web Services (AWS), but AWS Amplify is designed to be open and pluggable for any custom backend or service.
Notice:
<EMAIL_ADDRESS>has structural changes. For details please check Amplify Modularization.
AWS Amplify is available as aws-amplify
package on npm
Web
$ npm install aws-amplify --save
or you could install the module you want to use individually:
$ npm install @aws-amplify/auth --save
React
If you are developing a React app, you can install an additional package aws-amplify-react
containing Higher Order Components:
$ npm install aws-amplify --save
$ npm install aws-amplify-react --save
Angular
If you are developing an Angular app, you can install an additional package aws-amplify-angular
. This package contains an Angular module with a provider and components:
$ npm install aws-amplify --save
$ npm install aws-amplify-angular --save
Visit our Installation Guide for Web to start building your web app.
React Native
For React Native development, install aws-amplify
$ npm install aws-amplify --save
If you are developing a React Native app, you can install an additional package aws-amplify-react-native
containing Higher Order Components:
$ npm install aws-amplify-react-native --save
Visit our Installation Guide for React Native to start building your web app.
Somewhere in your app, preferably at the root level, configure Amplify with your resources.
Using AWS Resources
import Amplify from 'aws-amplify';
import aws_exports from './aws-exports';
Amplify.configure(aws_exports);
// or you don't want to install all the categories
import Amplify from '@aws-amplify/core';
import Auth from '@aws-amplify/auth';
import aws_exports from './aws-exports';
// in this way you are only importing Auth and configuring it.
Amplify.configure(aws_exports);
Without AWS
Amplify.configure({
API: {
graphql_endpoint: 'https://www.example.com/my-graphql-endpoint'
}
});
AWS Amplify supports many category scenarios such as Auth, Analytics, APIs and Storage as outlined in the Developer Guide. A couple of samples are below:
By default, AWS Amplify can collect user session tracking data with a few lines of code:
import Analytics from '@aws-amplify/analytics';
Analytics.record('myCustomEvent');
See our Analytics Developer Guide for detailed information.
Add user sign up and sign in using two of the many methods available to the Auth class:
import Auth from '@aws-amplify/auth';
Auth.signUp({
username: 'AmandaB',
password: 'MyCoolPassword1!',
attributes: {
email<EMAIL_ADDRESS>}
});
Auth.signIn(username, password)
.then(success => console.log('successful sign in'))
.catch(err => console.log(err));
See our Authentication Developer Guide for detailed information.
React / React Native
Adding authentication to your React or React Native app is as easy as wrapping your app's main component with our withAuthenticator
higher order component. AWS Amplify will provide you customizable UI for common use cases such as user registration and login.
// For React
import { withAuthenticator } from 'aws-amplify-react';
// For React Native
import { withAuthenticator } from 'aws-amplify-react-native';
export default withAuthenticator(App);
Angular
To add authentication to your Angular app you can also use the built-in service provider and components:
// app.component.ts
import { AmplifyService } from 'aws-amplify-angular';
...
constructor( public amplify:AmplifyService ) {
// handle auth state changes
this.amplify.authStateChange$
.subscribe(authState => {
this.authenticated = authState.state === 'signedIn';
if (!authState.user) {
this.user = null;
} else {
this.user = authState.user;
}
});
}
// app.component.html
<amplify-authenticator></amplify-authenticator>
See our Angular Guide for more details on Angular setup and usage.
AWS Amplify automatically signs your REST requests with AWS Signature Version 4 when using the API module :
import API from '@aws-amplify/api';
let apiName = 'MyApiName';
let path = '/path';
let options = {
headers: {...} // OPTIONAL
}
API.get(apiName, path, options).then(response => {
// Add your code here
});
See our API Developer Guide for detailed information.
To access a GraphQL API with your app, you need to make sure to configure the endpoint URL in your app’s configuration.
// configure a custom GraphQL endpoint
Amplify.configure({
API: {
graphql_endpoint: 'https://www.example.com/my-graphql-endpoint'
}
});
// Or configure an AWS AppSync endpoint.
let myAppConfig = {
// ...
'aws_appsync_graphqlEndpoint': 'https://xxxxxx.appsync-api.us-east-1.amazonaws.com/graphql',
'aws_appsync_region': 'us-east-1',
'aws_appsync_authenticationType': 'API_KEY',
'aws_appsync_apiKey': 'da2-xxxxxxxxxxxxxxxxxxxxxxxxxx',
// ...
};
Amplify.configure(myAppConfig);
queries
import API, { graphqlOperation } from "@aws-amplify/api";
const ListEvents = `query ListEvents {
listEvents {
items {
id
where
description
}
}
}`;
const allEvents = await API.graphql(graphqlOperation(ListEvents));
mutations
import API, { graphqlOperation } from "@aws-amplify/api";
const CreateEvent = `mutation CreateEvent($name: String!, $when: String!, $where: String!, $description: String!) {
createEvent(name: $name, when: $when, where: $where, description: $description) {
id
name
where
when
description
}
}`;
const eventDetails = {
name: 'Party tonight!',
when: '8:00pm',
where: 'Ballroom',
decription: 'Coming together as a team!'
};
const newEvent = await API.graphql(graphqlOperation(CreateEvent, eventDetails));
subscriptions
import API, { graphqlOperation } from "@aws-amplify/api";
const SubscribeToEventComments = `subscription subscribeToComments {
subscribeToComments {
commentId
content
}
}`;
const subscription = API.graphql(
graphqlOperation(SubscribeToEventComments)
).subscribe({
next: (eventData) => console.log(eventData)
});
See our GraphQL API Developer Guide for detailed information.
AWS Amplify provides an easy-to-use API to store and get content from public or private storage folders:
Storage.put(key, fileObj, {level: 'private'})
.then (result => console.log(result))
.catch(err => console.log(err));
// Stores data with specifying its MIME type
Storage.put(key, fileObj, {
level: 'private',
contentType: 'text/plain'
})
.then (result => console.log(result))
.catch(err => console.log(err));
See our Storage Developer Guide for detailed information.


Namespace PlayGen.SUGAR.Common
Classes
APIVersion
This class is used to see which version of the API exists in consuming projects to facillitate checking compatibility.
Major versions should increment when API Breaking changes are added.
Minor version should increment for Fixes and Additions that won't cause existing clients with the same Major version to break.
Build version should increment for every build.
RegexUtil
Enums
ActorType
Enum for selecting the target type of actor.
ComparisonType
Enum for selecting target comparison between current value and target value.
CriteriaQueryType
Enum for selecting what values will be used to query if criteria is met.
CriteriaScope
Enum for selecting if EvaluationData is to checked against just the actor in question or other actors relating to them (i.e. group members)
EvaluationDataCategory
Enum for the type of data being stored into/loaded from EvlautionData.
EvaluationDataType
Enum for selecting the type of data being stored or looked for.
EvaluationType
Enum for selecting the category of evaluation being stored or looked for.
LeaderboardFilterType
Enum for selecting subsection of users for current standings of a leaderboard.
LeaderboardType
Enum for selecting the type and sorting order of the leaderboard being created.


In the case , player 1 put a , player 2 a .
So, you the game.
We now invite you to test our two different strategy, classic optimal and quantum optimal to try it out.
Your final grid:
{{ items1.0 }}
{{ items1.1 }}
{{ items1.2 }}
{{ items1.3 }}
{{ items1.4 }}
If you click here, the grid will be replaced with a classical optimal strategy. (You can switch to your inputs by clicking again.)
If you click here, we will propose you an answer which used a quantum optimal strategy
Player 1 could have put on line {{ items1.1 }} these following numbers: {{ ma }}, player 2 could have used on column {{ items1.3 }} these inputs: {{ mb }}.


This post is me riding the coattails of the GPT-2 hype train. Now for those that don’t know, GPT-2 is a machine learning model that is very effective at text generation. It can maintain context over a long period of text making it more ‘human’ than previous algorithms.
For those that are interested read this article on transformers and of course openai’s site
Now for the memes. To get GPT-2 talking like Alex Jones, we need text and a lot of it. Unfortunately, Alex Jones was largely purged from social media in 2019 so we’ll have to get creative. First, wikiquotes was a decent starting point. I used python w/ requests and bs4 to scrape the data (your smart, do it yourself).
I’ve looked for transcripts of the Alex Jones show to no avail. But what I did find was Alex Jones’s archived twitter on webrecorder.io. Not sure what kind of wizardry those guys are running for this web app, but I couldn’t get the HTML of his twitter feed for the life of me. So instead I just selected all text and wrote it to a file. Genius. There was some manual formatting I did to group them into tweets and then clean up the actual contents, but once that’s done it was somewhat useable for my purposes.
Now that we’ve collected around 900 lines of the good stuff we can train a model. Here’s the code I used (stolen from some medium post, probably):
#!/usr/bin/python3
import gpt_2_simple as gpt2
import os
model_name = "124M"
# model is saved into current directory under /models/124M/
if not os.path.isdir(os.path.join("models", model_name)):
gpt2.download_gpt2(model_name=model_name)
file_name = "./src/alex_jones.txt"
sess = gpt2.start_tf_sess()
gpt2.finetune(sess,
file_name,
model_name=model_name,
run_name='run2',
steps=1000,
save_every=50,
print_every=5,
sample_every=10,
learning_rate = 0.0001
)
gpt2.generate_to_file(sess)
I’ve let this train in my homelab for a few hours while I slept, and checked back when I thought it was ready.
Here are some of the results:
Organizer: Planned Parenthood Ships Guns To The US View all other
Open book censorship represents a poison pill for the people and a soulless dollop of
propaganda for the corporate media. #FreeInfowars #1A
These people are the future. They're such zombies that you can not only read the news,
but look at these people! They're so freaking fastidious they have time organs in their
knees that they can hardly walk!  That's evil! The Alex Jones Show, "HILLARY CLINTON IS A GOD DAMN
DEMON", June 2016.
 It just so happens, is gonna be really bad. I'm thinking ABOUT
WHATEVER I'm thinking about today. Yeah, I'm thinking
A vocal minority of Trump hating registered voters has called off an election for fear of
repercussions from his supporters. #Trump2020 #MAGA #1a #USA
Video: left wing extremists targeting DC political events with machetes this weekend
The coordinated corporate attack on Infowars has exposed a deceptic by any measure, but
the silencing of the truth is even more apparent.
Now you need to find a way to stop the Chinese from harvesting our blood.
The Rise Of Alex Jones As A Fake News Crikey, Tune in M F 11am 3pm central at: <<
#RealNews #WednesdayWisdom #1a #ThursdayMotivation #FreeInfowars
These are just a few of the hundreds of little gems GPT-2 spat out for us. There were a few artifacts that I didn’t like from the outputs; so in the alex_jones.txt, I went back and cleaned it up a bit more. The rest of the text files are relatively unmodified if you’d like to do the scrubbing yourself. Unlike GPT-2, all the code, output, and finetuning text I used can all be found here. Have fun. And don’t let the globalists win.


--
Transparency
Playback Speed
Current Time
Revisit
To get started try one of the following
Drag a file onto the screen
Specify log file using URL query
Type the file path location into your URL


<%productsGender%>’s Clothing
{{ name }}
${{ price }}
%%include('../partials/copyright.html')
%%include('../partials/head.html', { "pageType": "product-listing" }) %%include('../partials/header.html') %%include('../partials/menu.html')


Onion Assistance
You can now create a container tab which uses Tor to browse the web privately.
Clear all browsing data
Get the latest Tor News


Example Application
Included with the download of DMZ is a simple bug-tracking application. It is not complete (nor is it intended to be), but it has examples of a lot of DMZ functionality.
Setting up the Example Application
Follow these instructions to get the examples running on your server:
- Get a fully-functional version of CodeIgniter running, connected to a blank MySQL or PostgreSQL database.
- Complete the Installation Instructions. Make sure you include the application/datamapper and application/views folders.
- Copy everything in the examples folder into the new server. This includes views, controllers, and styling elements. If you haven't moved the CodeIgniter Application folder to the root of your website, you'll want to copy that into the correct place.
- Connect to your CI installation. It should automatically take you to the database setup screen.
Example Highlights
Example Application
Look for these boxes throughout the documentation for working examples in the example application.
These are some highlights from the examples.
- Handling Logins
Shows processing login attempts by users. - HTML Form Usage
Shows how the model and controller can use the HTML Form extension to generate a form. Also shows using the Array Utilities extension to process a submitted form. - More to come, but you should look through the example application for more examples!


[1]
never struggled to find a space , if you did there is on surrounding side roads .
[2]
few parking spaces out the front , and we never struggled to find a space .


Computation Class
Item Index
Methods
- addEventListenersToPool
- allFound
- counterCompleteHandler
- createFactories
- createThreadPool
- createWorkers
- isComplete
- jobCounterCompleteHandler
- pause
- poolFileFoundHandler
- poolFileRequiredHandler
- poolJobFoundHandler
- poolJobRequiredHandler
- poolResultFoundHandler
- poolResultRequiredHandler
- pushJobToAwaitingWorker
- removeEventListenersFromPool
- resultCounterCompleteHandler
- resume
- start
- stop
- stopFactories
- stopWorkers
Methods
addEventListenersToPool
()
private
allFound
(
Promise
private
-
type
-
expected
Parameters:
-
type
Object -
expected
Object
Returns:
Promise:
counterCompleteHandler
(
private
-
e
Parameters:
-
e
Object
createFactories
()
Promise
private
Create jobFactory
Returns:
Promise:
createThreadPool
(
Promise
private
-
type
-
url
Parameters:
-
type
Object -
url
Object
Returns:
Promise:
createWorkers
()
Promise
private
Create worker instances
Returns:
Promise:
isComplete
()
Promise
Returns:
Promise:
jobCounterCompleteHandler
(
private
-
e
Parameters:
-
e
Object
pause
()
poolFileFoundHandler
()
private
Event-Listener, listens for file:push from Thread-Pool
poolFileRequiredHandler
()
private
Event-Listener, listens for file:pull from Thread-Pool
poolJobFoundHandler
()
private
Event-Listener, listens for job:push from Thread-Pool
poolJobRequiredHandler
()
private
Event-Listener, listens for job:pull from Thread-Pool
poolResultFoundHandler
()
private
Event-Listener, listens for result:push from Thread-Pool
poolResultRequiredHandler
()
private
Event-Listener, listens for result:pull from Thread-Pool
pushJobToAwaitingWorker
()
Array
Returns:
Array:
removeEventListenersFromPool
()
private
resultCounterCompleteHandler
(
private
-
e
Parameters:
-
e
Object
resume
()
start
()
stop
()
stopFactories
()
stopWorkers
()
Properties
isPaused
Boolean
Default: false
isRunning
Boolean
Default: false


The predictor package contains code for different PNG predictor algorithms that are present in PDF documents. These classes are used internally by PDFBox.


程序包 ride
类 User
- java.lang.Object
-
- ride.User
-
-
方法概要
所有方法 实例方法 具体方法 修饰符和类型 方法 说明 void
addCard(Card card)
to set a card to the uservoid
displayStat()
to display user's information(like toString)Card
getCard()
java.lang.String
getName()
double
getTotalCharge()
int
getTotalRides()
double
getTotalRidingTimeInMIn()
int
getUserId()
boolean
isRiding()
boolean
isWithCard()
void
payFor(OngoingRide ride)
user pay for a ride and get related information noted downvoid
setRiding(boolean riding)
void
setTotalRides(int totalRides)
java.lang.String
toString()
override toString method of uservoid
update(Station station)
observer pattern, user is an observer to be notified with returnability of told end stationvoid
updateStatistic(Record record)
to record ride times and duration of the user after a riding, and give 5 minutes time credit to him if bike is returned to a plus station
-
-
-
方法详细资料
-
getUserId
public int getUserId()
-
getName
public java.lang.String getName()
-
isRiding
public boolean isRiding()
-
setRiding
public void setRiding(boolean riding)
-
isWithCard
public boolean isWithCard()
-
getCard
public Card getCard()
-
getTotalRides
public int getTotalRides()
-
setTotalRides
public void setTotalRides(int totalRides)
-
getTotalRidingTimeInMIn
public double getTotalRidingTimeInMIn()
-
getTotalCharge
public double getTotalCharge()
-
toString
public java.lang.String toString()
override toString method of user- 覆盖:
toString
在类中java.lang.Object
-
addCard
public void addCard(Card card)
to set a card to the user- 参数:
card
- a card to be set
-
updateStatistic
public void updateStatistic(Record record)
to record ride times and duration of the user after a riding, and give 5 minutes time credit to him if bike is returned to a plus station- 参数:
record
- a riding record
-
update
public void update(Station station)
observer pattern, user is an observer to be notified with returnability of told end station
-
displayStat
public void displayStat()
to display user's information(like toString)
-
payFor
public void payFor(OngoingRide ride)
user pay for a ride and get related information noted down- 参数:
ride
- a ride to be charged
-
-


%include file="header.html" args="page='/config/authorize', subtitle='Authorization'"/>
These settings allow granting giving permissions to users based only on their DN, so no VO extensions are required.
This is particularly handy precisely for the web configuration.


I’ve continued to stay heartbroken since my favorite pizza place in Dubai shut down almost two years ago, back when my photo skills were absolute bullshevic (thanks, Madagascar 3!). The photos are only marginally better now, granted, but I shudder at those woeful pictures, ew.
 It’s what defines a real pizza for me. I’ll typically take a look at a picture (or the real thing) and almost instinctively decide whether I’ll like the pizza or not. I had searched frantically for a shot when Jamie’s Italian introduced pizzas at their Dubai outlet a few months ago before I agreed to try them out. Speaking of which, I need to blog about that experience soon eventually.
So it was little surprise that Foodiva’s post about Rossovivo caught my eye. The review was definitely positive and the pictures got me excited. Could it be, could it? Alas, I had to hold my hopes when the dinner being organized there a few weeks ago was moved after the guest list expanded to a size beyond Rossovivo’s current capacity.
But let it be known that I pestered incessantly waited patiently until another visit was scheduled last weekend. And while I want to let the pictures speak for themselves, we all know that’s not really going to happen.
Pic 1: The Bufalina. A thing of beautiy. I wish I’d realized that the menu was not out of the shot enough, but this is where you thank me for revealing 87.5% of the delivery number before the end.
Pic 2: The Raffaele. Who knew pistachio on a pizza could work?
Pic 3: The Marinara
The star of the show — for me, anyway — was the Bufalina: Buffalo cheese, tomato, basil. You could taste the difference in the quality of the cheese instantly and, coupled with the crust, it made for the perfect minimalist pizza I enjoy. I jumped at the tomato and garlic Marinara — my mind forgetting that it also meant there was no cheese on it — but the sauce was the right amount of fresh and tangy that I ate it all up anyway.
The surprise of the show — was the Raffaele: pizza with, wait for it, pistachio! Named after the Chef himself. Frankly I thought it was outright bizarre but I did try a little piece — we all live for bizarre, don’t we? — and I’ll admit the chef gets points for creativity. Pistachio and cheese do seem to go together despite my initial objections, and the women at the table absolutely loved it.
Love the fact that you can watch your pizzas being made at the table.
So finally, a pizza place I can rave about again. It helped that someone used me as a landmark with a taxi-driver to spot the place while I stood outside frantically calling everyone waiting quietly for everyone to arrive; nothing like an ego boost just before you’re treated to amazing buffalo cheese pizza.
Rossovivo
Millenium Tower, Business Bay, just off the service road.
www.rossovivo.ae | +9714 380 5833


responsible for
in charge of
the
its
establishment
inception
[vocabulary]
decides
decided
to
unify
synergize
[vocabulary]
development
of
Orion
directly employs
its (last-known) workforce consisted of
approximately


Captain Mathias:
Good job today.
You were a great help. I know you're busy, but please help me again next time. The Elmore Kingdom will always welcome help from brave adventurers such as yourself.
(This quest can only be undertaken once a day. This quest gets initialized everyday at 6:30 am.)


{% for house in manage_houses %}
{% endfor %}
Manage {{ house }} members
{% for member_type in house_members[house] %}
{% set member_type_name = member_type['pos_name'].lower() %}
{% endfor %}
Add a {{ member_type_name }}
Current {{ member_type_name }}s
| Name | Remove |
|---|---|
| {{ member['full_name'] }} |


Mother in law has all the answers.
Last update: 03-Sep-2010 21:56 UTC
This is an index for a set of troubleshooting notes contained in individual text files in the ./hints directory. They were supplied by various volunteers in the form of mail messages, patches or just plain word of mouth. Each note applies to a specific computer and operating system and gives information found useful in setting up the NTP distribution or site configuration. The notes are very informal and subject to errors; no attempt has been made to verify the accuracy of the information contained in them.
Additions or corrections to this list or the information contained in the notes is solicited. The most useful submissions include the name of the computer manufacturer (and model numbers where appropriate), operating system (specific version(s) where appropriate), problem description, problem solution and submitter's name and electric address. If the submitter is willing to continue debate on the problem, please so advise. See the directory listing.


Welcome to Lorenz Attractor Visualizer a.k.a LAVis ver 3.0 .
The main AIM of this web application is to visualize the chaotic property of Lorenz Attractor and the effect of parameters to the solution.
First, select Initialize from the OPTION menu. Note down the values of state variables x, y, z and the parameters σ, β, and ρ as the inital set of values. After noting down, select Visualization from the OPTION menu to simulate the Solutions and the Attractor.
If you have any query or need any assistance regarding this project or the application, please feel free to click info and help button from top right corner of this app.
Thank you.
Please use 1080p [1920x1080] Monitor for best viewing experience.
Name : Lorenz Attractor Visualizer [LAVis]
Version : 3.0
Author : Amlan Saha Kundu
License : Apache 2.0
Source-code : Visit Repository
Project : Download PDF
Contact : Send Email
What's NEW in v3.0?
Updation
First, select Initialize from the OPTION menu. Note down the values of state
variables x, y, z and the parameters σ, β, and ρ as the inital set of values.
After noting down, select Visualization from the OPTION menu to
simulate the Solutions and the Attractor.
You can use following keys to control the visualization.
| MOUSE & KEYBOARD | |
|---|---|
| ( left-click + drag ) | Rotate X-Y axes |
| ( middle-click + drag ) | Translate X-Y axes |
| ( shift + click + drag ) | Rotate / Translate Z axis |
| ( mousewheel ) | Zoom in / Zoom out |
| ( spacebar ) | Pause / Play |
| KEYBOARD | |
| a | Add solution with random initial value |
| x | Clear all solutions |
| F5 | Reset to Default |
| h | Toggle head (point) visibility |
| [ | Decrease tail length |
| ] | Increase tail length |
The Controls on the Graph are self-explanatory.


Manufacturer Name: Volvo
Model: V_1344
Price: $ 1,000
Car Condition: New
Description: This is a description point for the reported car....
Manufacturer Name: Volvo
Model: V_1344
Price: $ 1,000
Car Condition: New
Description: This is a description point for the reported car....
Manufacturer Name: Volvo
Model: V_1344
Price: $ 1,000
Car Condition: New
Description: This is a description point for the reported car....
Manufacturer Name: Volvo
Model: V_1344
Price: $ 1,000
Car Condition: New
Description: This is a description point for the reported car....


{% extends 'backstage/root/manage_settings/base.html' %} {% from 'bootstrap/form.html' import render_form %} {% block settings_content %}


5.3 Dual /-w
Dual kin terms are of the type 'his two Fa-s', as opposed to Dyadic forms of the type 'Fa-and-Ch pair'.
The regular Du suffix for nouns is /-w2a:/ §4.15), and this is used with VOC/PROP1 and PROP2 kin terms:
/na-baba-wa:/ 'my/our two Fa-s'
/na-ni-nyara-wa:/ 'your two Fa-s'
However, a special suffix /-w2i:yung/ is used in PROP3 forms (i.e., following suffix /-yung/ or /-nyung/):
/na-ni-nyara-yum-bi:yung/ 'his/her/their two Fa-s' These exx. show M NCinfl prefix /na-/.


---
layout: default
title: Home
---
{% include intro.html %}
Public Speaking
- OKR: No Hype | CodeFest. Novosibirsk, 2019. Slides.
- OKR: Instruction Manual | TeamLead Conf. Moscow, 2019. Video, slides.
- Avito Mobile: State of the Union | Kolesa Meetup. Alma-Aty, 2018. Video, slides.
- Developer Experience: The Art of Building Spaceships 🇬🇧 | FrenchKit. Paris, 2018. Video, slides.
- Developer Experience: The Art of Building Spaceships 🇬🇧 | SwiftConf. Cologne, 2018. Video, slides.
- Speak | CocoaHeads. Moscow, 2018. Video, slides.
- Performance Review in Avito | CodeFest. Novosibirsk, 2018. Video, slides.
- Improving Performance Review | TeamleadConf. Moscow, 2018. Video, slides.
- May the Code Review be with you 🇬🇧 | FrenchKit. Paris, 2017. Video, slides.
- May the Code Review be with you | Mobius. St. Petersburg, 2017. Video, slides.
- Five Stories about LiveJournal | TК° Conf. Rostov-on-Don, 2016. Video, slides.
- Clean Unit Tests | Rambler.iOS #8. Moscow, 2016. Video, slides.
- Typhoon Explained | Mobius. St. Petersburg, 2016. Video, slides.
- Pagination Demystified | Rambler.iOS #6. Moscow, 2016. Video, slides.
- Generamba and Code Generation | Rambler.iOS #5. Moscow, 2015. Video, slides, code.
- The Secrets of VIPER | #{MBLT}Dev'15. Moscow, 2015. Video, slides.
- Synchronization Problems | Rambler.iOS #4. Moscow, 2015. Video, slides, code.
- iOS Application Exploitation | PHDays V. Moscow, 2015. Video, slides.
- Dependency Injection in iOS | Rambler.iOS #3. Moscow, 2015. Video, slides.


Oh The Places You'll Go...
Place Name(Country, City, etc):
Date Visited(MM/DD/YYYY):
Length of Visit(In days):
Submit!
Oh the Places You've been!