"Innovation distinguishes between a leader and a follower"
"Sometimes when you innovate, you make mistakes. It is best to admit them quickly, and get on with improving your other innovations."
"My favorite things in life don't cost any money. It's really clear that the most precious resource we all have is time."
"Sometimes life is going to hit you in the head with a brick. Don't lose faith."
Steve Jobs was born in San Francisco, California, on February 24, 1955, to two University of Wisconsin graduate students who gave him up for adoption. Smart but directionless, Jobs experimented with different pursuits before starting Apple Computer with Steve Wozniak in 1976. Apple's revolutionary products, which include the iPod, iPhone and iPad, are now seen as dictating the evolution of modern technology, with Jobs having left the company in 1985 and returning more than a decade later. He died in 2011, following a long battle with pancreatic cancer.
Febuary 24th 1955
Steven Paul was born in San Francisco, the son of Abdulfattah Jandali and Joanne Schieble. He is quickly adopted by Paul and Clara Jobs1960
The Jobs family moves from San Francisco to Mountain View, a suburban town in Santa Clara county, more famous under the name Silicon Valley1969
Steve Jobs meets Steve Wozniak, 5 years older, through a mutual friend. Woz and Steve share a love of electronics, Bob Dylan, and pranks1972
Steve and Woz build and illegally sell 'blue boxes' that allow to make phone calls for free1974
Steve gets his first job at video game maker Atari, and later makes a trip to India to 'seek enlightenment' with his college friend Dan KottkeApril 1st 1976
Apple Computer Inc. is incorporated by Steve Jobs, Steve Wozniak and Ron WayneSpring 1976
Steve and Woz start assembling Apple I computers in the Jobses' garage, and sell them to computer hobbyists, including 50 for the Byte Shop28 Aug 1976
Steve Jobs and Woz show off the Apple I at the Personal Computing Festival in Atlantic City, with help from Dan Kottke17 Apr 1977
Apple makes a huge sensation at the West Coast Computer Faire with a prototype Apple II1978
Steve's ex-girlfriend Chris-Ann Brennan gives birth to their daugher Lisa. Steve refuses to acknowledge he is the father1978
At Apple, work starts on the Apple III and the Lisa, while Jef Raskin begins The Book of MacintoshDec 1979
Steve Jobs is shown the first working graphical user interface at Xerox PARC1979
Sales of Apple II skyrocket after pioneer spreadsheet software Visicalc is introduced1980
Jef Raskin’s Macintosh project is green-lighted. Lisa evolves into a GUI-computer, in part because of Steve Jobs' demands12 Dec 1980
Apple goes public, increasing Steve Jobs' net worth from dozens of millions of dollars to over $200 millionEarly 1981
Jef Raskin is forced out of his Macintosh project as Steve Jobs takes overJan 1983
Launch of the Lisa computer. The Lisa team later merges with the Mac team under Steve Jobs's leadership8 Apr 1983
PepsiCo CEO John Sculley becomes Apple's CEO after having been wooed by Steve Jobs for several monthsMay 1985
Palace coup: Apple's board sides with John Sculley and strips Steve off all executive dutiesSummer 1985
Alan Kay first introduces the Pixar team to Steve Jobs17 Sep 1985
Steve Jobs resigns from Apple and starts NeXT with five other refugees from Apple. Apple announces it will sue NeXT30 Jan 1986
Jobs buys the computer division of George Lucas' ILM for $10 million and incorporates it as Pixar1986
Steve's mother Clara dies. A couple months later, Steve discovers his biological mother Joanne and his sister, novelist Mona Simpson. They will become close friends18 Mar 1991
Steve Jobs marries Laurene Powell in Yosemite under the blessing of Steve's old zen guru Kobin Chino. Laurene is already pregnantMay 1991
Pixar signs a deal with Disney to make a computer-animated feature filmFall 1991
Laurene gives birth to Steve’s first son, Reed Paul JobsMar 1993
Steve's father, Paul Jobs, dies24 Mar 2001
After four years of hard work, Mac OS X 10.0, the new incarnation of NeXTSTEP, ships19 May 2001
Apple opens its first Retail Stores in Tysons Corner, Virginia and Glendale, CaliforniaFall 2003
Steve Jobs is diagnosed with pancreatic cancer, but stubbornly refuses any modern medical treatment for months. He tries alternative diets instead6 Jan 2004
Steve unveils the iPod mini and the iLife suite at Macworld. The iPod mini will soon become the world's best-selling MP3 player and truly establish Apple as a consumer electronics powerhouseAug 2004
Steve Jobs finally has his pancreatic tumor removed by surgery29 Apr 2005
Mac OS X 10.4 Tiger is released. A stable, fast release, it is immensely popular and marks the end of the four-year transition from the old Mac OS to UNIX-based Mac OS X12 Jun 2005
Steve Jobs makes a memorable commencement speech at Stanford University. History will remember its closing remarks, Steve's advice to the young students: 'Stay hungry, stay foolish', a quote from the last page of the Whole Earth Catalogue from his youth7 Sep 2005
Steve introduces the Motorola ROCKR, an iTunes-compatible cell phone, and the iPod nanoMid 2006
Apple starts its famous 'Mac vs PC' campaign, a series of TV commercials featuring Justin Long as Mac and John Hodgman as PC. The campaign will last for three years and mark popular culture7 Aug 2006
Apple completes the transition of its entire product line to the Intel platform with the new Mac Pro9 Jan 2007
In his most memorable keynote presentation ever, at Macworld 2007, Steve Jobs introduces iPhone and its revolutionary touch-screen interface. He also introduces Apple TV and announces the company's name change from Apple Computer Inc. to Apple Inc. to better reflect its new nature29 Jun 2007
iPhone is released in the US, the same day as Pixar’s 8th feature film, Ratatouille15 Jan 2008
At Macworld 2008, Steve Jobs introduces MacBook Air, with the tagline 'the world's thinnest notebook'. Three years later, it will come to redefine all of Apple's notebook product lineLate 2008
Apple starts its popular 'There's an app for that' campaign to illustrate the growing popularity of the App Store and the thousands of iPhone apps it offersApr 2009
Steve receives a liver transplant at the Methodist University Hospital in Memphis, Tennessee. He was weeks away from dying when he got the surgery3 Aug 2009
Google CEO Eric Schmidt leaves Apple's board because of conflicting interests due to Android28 Aug 2009
Apple releases Mac OS X 10.6 Snow Leopard, stripped off any code from the original Mac OS27 Jan 2010
After months of wild rumors, Steve Jobs unveils iPad, 'the biggest thing Apple's ever done'. The tablet runs the same operating system as iPhone6 Jun 2011
At his last keynote at WWDC 2011, a freil Steve Jobs unveils Apple's cloud offering, iCloud, the foundation for the next decade of Apple products24 Aug 2011
Steve Jobs resigns as CEO of Apple, with the words 'I have always said if there ever came a day when I could no longer meet my duties and expectations as Apple's CEO, I would be the first to let you know. Unfortunately, that day has come.' Tim Cook becomes Apple CEO5 Oct 2011
Steve Jobs dies at home, surrounded by his family24 Oct 2011
After two years of work, and forty interviews with Steve Jobs, Walter Isaacson publishes his authorized biography of the Apple and Pixar co-founder, simply named Steve Jobs


With Freeze On the Way, City to Open Warming Station At Boutwell
by Birmingham TimesThe City of Birmingham will open a Warming Station at the Boutwell Auditorium on the nights of Tuesday, November 12 and Wednesday, November 13.
The Boutwell Auditorium will operate from 5 p.m. to 7 a.m. on both nights. The auditorium is located at 1930 Rev. Abraham Woods, Jr. Boulevard.
According to the National Weather Service forecast, temperatures are predicted to be below freezing on Tuesday and Wednesday.
There is a freeze warning in effect from 3 a.m. Tuesday to 10 a.m. Wednesday and sub-freezing temperatures as low as 26 are expected on Tuesday morning and temperatures as low as 17 expected on Wednesday morning.
For Tuesday night, the low is predicted to be around 20 degrees and 28 degrees on Wednesday night.


Computer Programming
What is computer programming?
Computer programming is the act of writing computer codes that performs a particular task.
The writing is done using a specific computer programming language.
People who write these computer codes can be generally referred to as computer programmers.
What is a programming language?
A programming language, similar to human language, is a set of rules and principles that govern the creation of the codes.
These rules and principles are primarily called syntax. It is this syntax that makes a set of codes understandable by computer programmers and also by the computer.
What is JavaScript
JavaScript (JS) is a dynamic programming language with first-class functions. It is this nature of JavaScript that makes it so flexible to a point of almost being limitless.
Though JavaScript was formerly known as a scripting language for front-end of websites, it has been adopted for variety of use-cases such as scripting language for backend and command-line interface with
NodeJS, robotics with Johnny-Five and database with Apache CouchDB.
What you will learn from this book
In this book I'm hoping to teach you how to program with JavaScript, we are going to use it specifically for the front-end web development, but majority of the knowledge you will learn are tranferrable to
NodeJS since the two are closely related to each other. So you would still be able to move to NodeJS after learning from this book. Yes, we will have another book that will talk about NodeJS in particular.
Getting started
You can quickly run the codes that you will find on this book using your google's web browser. I recommend that you use the most latest version.
Step 1
Open a new empty tab and right side on any white on the page to get the output you see on the screenshot above. Then click on the inspect element.
Step 2
Another panel will appear, the location of the panel will be different based on your browser's settings.
It could be at the bottom as shown in the screenshot below
It could also be on the right side as shown in the screenshot below
You can always change this by looking for the 3 vertical dots on the right most side of the panel, and then look for the dock side and then click on the right most option which will put the panel on the right side of the window.
Step 3
Click on the console tab, and you will see the console, mine is shown below
Do note that I typed in You can type source codes here on the console.
Step 4
Try writing some codes there.
alert('You did it! Congratulations!');
The codes above will produce the following output:


TOASCII
Section: Linux Programmer's Manual (3)
Updated: 2013-12-23
Index
Return to Main Contents
NAME
toascii - convert character to ASCII
SYNOPSIS
#include <ctype.h>
int toascii(int c);
Feature Test Macro Requirements for glibc (see
feature_test_macros(7)):
toascii():
_BSD_SOURCE || _SVID_SOURCE || _XOPEN_SOURCE
DESCRIPTION
toascii()
converts
c
to a 7-bit
unsigned char
value that fits into the ASCII character set, by clearing the
high-order bits.
RETURN VALUE
The value returned is that of the converted character.
ATTRIBUTES
Multithreading (see pthreads(7))
The
toascii()
function is thread-safe.
CONFORMING TO
SVr4, BSD, POSIX.1-2001.
POSIX.1-2008 marks
toascii()
as obsolete,
noting that it cannot be used portably in a localized application.
BUGS
Many people will be unhappy if you use this function.
This function will convert accented letters into random characters.
SEE ALSO
isascii(3),
tolower(3),
toupper(3)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- RETURN VALUE
-
- ATTRIBUTES
-
- Multithreading (see pthreads(7))
-
- CONFORMING TO
-
- BUGS
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:54:54 GMT, September 18, 2014


Israeli Justice Ministry: Prime Minister Benjamin Netanyahu indicted on fraud, breach of trust and bribery.
by Associated PressJERUSALEM (AP) — Israeli Justice Ministry: Prime Minister Benjamin Netanyahu indicted on fraud, breach of trust and bribery.


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Selector: mdc-list
Exported as: mdcList| Name | Description |
|---|---|
| dense: boolean | Increases the density of the list, making it appear more compact. |
| twoLine: boolean | Increases the height of the row to give it greater visual separation from adjacent rows. |
| avatar: boolean | Configures the leading tiles of each row to display images instead of icons. This will make the graphics of the list items larger. |
| border: boolean | Sets a border around each list item. |
| singleSelection: boolean | When enabled, the space and enter keys (or click event) will trigger an single list item to become selected and any other previous selected element to become deselected. |
| interactive: boolean | List items receive styles for hover, focus, and press states (including the ripple). Default is true. |
| useActivatedClass: boolean | Sets the selection logic to apply/remove the mdc-list-item--activated class. |
| useSelectedClass: boolean | Sets the selection logic to apply/remove the mdc-list-item--selected class. |
| wrapFocus: boolean | Sets the list to allow the up arrow on the first element to focus the last element of the list and vice versa. |
| verticalOrientation: boolean | Sets the list to an orientation causing the keys used for navigation to change. true results in the Up/Down arrow keys being used. If false, the Left/Right arrow keys are used. |
| disableRipple: boolean | Disables the ripple effect. |
| setSelectedValue(value: any) | Sets the currently selected list item by value. |
| setSelectedIndex(index: number) | Sets the currently selected list item. |
| getSelectedIndex(): number | Returns the currently selected item index. |
| getSelectedItem(): MdcListItem | undefined | Returns the currently selected item. |
| focusItemAtIndex(index: number) | Sets focus to the element at the given index in a list. |
| focusFirstElement() | Sets focus to the first element in a list. |
| focusLastElement() | Sets focus to the last element in a list. |
| getListItemByValue(value: any): MdcListItem | undefined | Returns list item for given value. |
| getListItemByIndex(index: number): MdcListItem | undefined | Returns list item for given index. |
| getListItemIndexByValue(value: any): number | Returns the list item index for a given value. |
| reset() | Clear all activated and selected list item states. |
| selectionChange: MdcListItemChange | Change event that is being fired whenever the selected state of an option changes. |
Selector: mdc-list-item a[mdc-list-item]
Exported as: mdcListItem| Name | Description |
|---|---|
| selected: boolean | Styles the row in an selected state. |
| activated: boolean | Styles the row in an activated state. |
| disabled: boolean | Disables the list item. |
Selector: mdcListItemGraphic
Exported as: mdcListItemGraphicSelector: mdcListItemMeta
Exported as: mdcListItemMetaSelector: mdc-list-item-text mdcListItemText
Exported as: mdcListItemText| Name | Description |
|---|---|
| secondaryText: string | Optional secondary text for the list item. Displayed below the primary text. |
Selector: mdc-list-item-secondary mdcListItemSecondary
Exported as: mdcListItemSecondarySelector: mdc-list-group
Exported as: mdcListGroup| Name | Description |
|---|---|
| subheader: string | Optional, heading text displayed above each list in a group. |
Selector: mdc-list-group-subheader mdcListGroupSubheader
Exported as: mdcListGroupSubheaderSelector: mdc-list-divider mdcListDivider
Exported as: mdcListDivider| Name | Description |
|---|---|
| inset: boolean | Increases the leading margin of the divider so that it does not intersect the avatar column. |
| padded: boolean | To make a divider match the padding of list items. |


heading 1
this is a paragraph.
this is another paragraph.
frist name
last name
Menjivar
Adriana
heading
2
JMU website
coffee
tea
black tea
green tea
milk


I have a professional professor for learning all af things.his name is mehrdad abbasi and his nick name is mehrdadlinux.he specializes in all of things in computer fields especially networking and security.he has gained so much experience in many fields.he's hardworking and I wish to be like him in working one day and I hope he's always success
I have a programming class that I'm learning many things in it.I learn linux,how to programming,how to selft study,how to solve my problem etc.I love my class.Although my exercise is hard,it's very useful to make me.I hope to continue with all my strenght.
I try to learn how to programming in a correct way.I want to study software engineer,electronics,programming and managment between them.


{% extends 'base_test.html' %} {% load i18n %} {% block additional-headers %} {% endblock %} {% block content %}
| Case Number | {{ case_id | safe }} | Sex | {{ victim_sex | safe }} |
|---|---|---|---|
| Beneficiary's Name | {{ victim_name | safe }} | Address in country | {{ birth_country | safe }} |
| Date of Birth | {{ birth_date | safe }} | Return Date | {{ date_return | safe }} |
| Age at the time of return | {{ victim_age | safe }} | Country from | {{ return_from | safe }} |
| Division | {{ current_division | safe }} |
|---|---|
| District | {{ current_district | safe }} |
| Upazila | {{ current_upazila | safe }} |
| Union | {{ current_union | safe }} |
| Ward | {{ current_ward | safe }} |
| Village | {{ current_village | safe }} |
| Post Office | {{ current_postoffice | safe }} |
| Division | {{ permanent_division | safe }} |
|---|---|
| District | {{ permanent_district | safe }} |
| Upazila | {{ permanent_upazila | safe }} |
| Union | {{ permanent_union | safe }} |
| Ward | {{ permanent_ward | safe }} |
| Village | {{ permanent_village | safe }} |
| Post Office | {{ permanent_post_office | safe }} |


{% extends 'base.html' %} {% block content %}
Please, follow this link to see the source code of the project
Loans for people with no credit score is an unexplored ground. Around 1 in 10 American consumers has no credit history.
On one hand, it is both a business oportunity and a chance to help people when they need it. On the other hand, a solution to decide whether the loan will be paid back is needed.
Logistic Regression model that predicts the probability of default and paying back a small loan.
The data used to train the model contained only bank transaction information, no credit score involved. The data was provided by one of US startups that grants small loans to people with no credit score.
Cross-validated log loss: -0.56
Cross-validated AUC score: 0.63
ROC curve represents the tradeof between True Positive Rate and False Positive Rate. That is, how much TPR can be increased with as little increase of the FPR as possible. If the curve is a srtaight diagonal line, the model does not have any predicting power (the case when AUC is 0.5).
For the most ranges the average true class is close to the predicted probability. This means that, for example, if a person is predicted to have 0.35 probability of default, in reality 3-4 persons out of 10 will default. These predictions can be used to estimate company's income and apply different loan conditions to individuals with higher probability of default.
{% endblock %}


Welcome to 90s SteamPrepare your tastebuds for a feast. 90s steam is a website dedicated to ensuring you can re-live your childhood games, with a modern twist. Please note that this website and the themes contained are not intended as a controversial political or social commentary. Everything on the website has simply been built in parody of current affairs. Many images used to create this website and any games do not belong to me. If you are worried that there is infringement of your copyrighted material without permission, please contact me to allow fair chance to rectify the situation. 90s Steam was built for the Great Uni Hack 2018 in Manchester for the challenge put forward by American Express. The challenge was to take a modern day website and give it a retro, 90s twist. Naturally, with my inkling for games, I had to build a Steam client that the 90s would be proud of, and here it is. If you want to find out more about me and who I am, check out the about page. If you want to check out some of my games, including some that I've built before this hackathon, check out the games page. To see my open source projects check out my github. All of the code I produce at game jams or hackathons, including this code from the Great Uni Hack, is on there. |


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Development of Web and Mobile E-Commerce platforms for your online and physical shops
Conception, Developement and deployment of tailor made management systems that will help you attain your business goals.
Design and Development of secure and SEO optimized websites
Creation and Design of Posters, Business Cards, Banners, User Interfaces... and all that you will need
Promotion and launching of targeted marketing campains on social media and the internet in general
we are a Group of professionals with varying profiles and experiences in many countries including france, senegal, The United states, morocco, mauritania and india we united under ServIT LLC to offer you a fluid Transition towards digitalisation.


insecure, secure fallback
secure
insecure, no secure fallback
insecure, secure fallback, GET-only HTTP
insecure, secure fallback, GET-only HTTPS
insecure, secure fallback, GET-only HTTP/S
relative path
no protocol
local link


This is one of the simplest components of CardinalJS. Its role is to render values that have been dynamically set in the page controller.
The
To use this component at full capacity, read also about
In order to generate content in a simple form, without inputs you can use this component.


Welcome to Spring Travel
This sample demonstrates how to use Spring MVC and Spring Web Flow together with Thymeleaf and Tiles.
The key features illustrated in this sample include:
- A declarative navigation model enabling full browser button support and dynamic navigation rules
- A fine-grained state management model, including support for ConversationScope and ViewScope
- Modularization of web application functionality by domain use case, illustrating project structure best-practices
- Spring Expression Language (SpEL) integration
- Spring 3 formatting annotations @DateTimeFormat, @NumberFormat
- Spring MVC custom namespace
- Spring Security integration
- Annotated POJO @Controllers for implementing RESTful user interactions.
- Declarative page authoring with Thymeleaf and its Spring MVC's integration features.
- Page layout and composition with Apache Tiles
- A JavaScript API for decorating HTML elements with behaviors such as Ajax, validation, and effects.
- A grid layout with Blueprint CSS
- Exception handling support across all layers of the application
- SpringSource Tool Suite integration, with support for graphical flow modeling and visualization
Start your Spring Travel experience


| Book | Key | Meter | Start Degree | End Degree | Voices | Double Fugue | CS1 | CS2 | Stretto | Real Answer | Tonal Answer | Dominant Cadence in Answer | Episode after Answer | Expo. Progression | Subject |
|---|
| Book | Key | Meter | Start Degree | End Degree | Voices | Double Fugue | CS1 | CS2 | Stretto | Real Answer | Tonal Answer | Dominant Cadence in Answer | Episode after Answer | Expo. Progression | Subject |
|---|


Pat's Salmon Store
About Us
Locations/ Hours
Merchandise
Future Events
Locations/ Hours
Hours: 6am - 8pm
First and Pike
Seatac Airport
Seattle Center
Capitol Hill
Alki
Email Us


--- layout: post status: publish published: true title: "路的尽头" author: Leask author_login: leask author_email<EMAIL_ADDRESS>author_url: https://leaskh.com wordpress_id: 332 wordpress_url: http://leaskh.wordpress.com/2007/08/21/%e8%b7%af%e7%9a%84%e5%b0%bd%e5%a4%b4 date: '2007-08-21 07:52:55 +0800' date_gmt: '2007-08-21 07:52:55 +0800' categories: - Design tags: [] comments: [] ---


RegexBuddy is a great tool for working with these regular expressions.
This regular expression will match a sequence of lines that are identical. The replace pattern will replace all those matched lines with just the first instance of that line (i.e., it will remove duplicates).
(?<LINE>((?!\r|\n).)+)(\r\n\k<LINE>)+(?=\r\n|$)
${LINE}
No beer and no TV make Homer something something
No beer and no TV make Homer something
This regular expression will match rich text containing exactly one paragraph. Multiple paragraph tags will not match. Useful if you want to allow the content editor to use a rich text editor, but want to restrict them to a single paragraph.
^
((?!
).)+$

I am a single paragraph.


This is a web application that takes a user's birthday and calculates the day of the week they were born.
Then depending on their gender outputs their Akan Name.
Akan names are derived from Ghanian culture.
Frequently in Ghana, children are given their first name as a 'day name' which corresponds to the day in the week they were born.
Here are Ghanian day names:
Most people do know when their birthdays are, but some might not know what day of the week they were born.
Luckily for us, there are ways to calculate the day of the week from a specific date.


Should be uploaded to http://upstreamacademy.com/apps/ or already there.
http://upstreamacademy.com/apps/


Work smarter. Save time.
Easily manage your projects. Get on the list and receive in-app perks available only to early subscribers. We are moving into final development and getting ready for official launch soon.
Easily manage your projects. Get on the list and receive in-app perks available only to early subscribers. We are moving into final development and getting ready for official launch soon.


ORDER UPDATE
Enter the Order_ID of which you want to remove
Order_Id:
THE DATA ENTERED WILL BE UPDATED IN THE ORDERS TABLE


WITNESS LIST
Appropriations - S/C on Article II Committee
April 6, 2016 - 10:00 AM
Charge 10: Long Term Services and Supports in the Medicaid Program
On:
Berndt, Trey (AARP)
Borel, Dennis (Coalition of Texans with Disabilities)
Castillo, Rosemary (Texas PACE Association)
Castle, Danette (Texas Council of Community Centers)
Day, Judy (Leading Age Texas)
Hammon, Rachel (Texas Association for Home Care & Hospice)
Jessee, Gary (HHSC)
Kafka, Bob (Self; ADAPT PACT)
Kibbe, Scot (Texas Health Care Association)
Ligon, Katharine (Center for Public Policy Priorities)
McDonald, Pam (HHSC)
Murphree, Susan (Disability Rights Texas)
Murphy, Kate (Texas public policy foundation)
Smith, Carole (Private providers association of texas)
Svien, Doug (Self; Providers Alliance for Community Services of Texas (PACSTX))
Traylor, Chris (HHSC)
Weizenbaum, Jon (Department of Aging and Disability Services)
Registering, but not testifying:
On:
Butler, Rachel (HHSC)
Cook, David (Department of Aging and Disability Services)
Dunkelberg, Anne (Center for Public Policy Priorities)
Garza, Elisa (Department of Aging and Disability Services)
Lawson, Beth (StarCare Specialty Health System)
Charge 12: Examine the Historical Growth of the Medicaid Program
On:
Bowen, Jr., Stuart (HHSC Inspector General)
Carruth, Lisa (HHSC)
Dudensing, Jamie (Texas Association of Health Plans)
Jessee, Gary (HHSC)
Rodell, Leora (Legislative Budget Board)
Traylor, Chris (HHSC)
Registering, but not testifying:
On:
French, Lesley (HHSC)
Keyton, Sarah (Legislative Budget Board)
Lawson, Beth (StarCare Specialty Health System)
Vasquez, Andy (HHSC)
1


Urban Meyer Calls On Ohio State Fans Ahead Of Penn State Game
by Andrew HolleranColumbus, Ohio will play host to two big-time college football pregame shows on Saturday morning. ESPN’s College GameDay and FOX’s Big Noon Kickoff will be in town for the Ohio State-Penn State matchup.
ESPN’s pregame show has been around for much longer, but FOX’s has a special leg up when it comes to the Buckeyes: Urban Meyer.
The former Ohio State head coach has thrived in his TV role this season. He’s made FOX’s pregame show a really intriguing watch.
Meyer is calling on Ohio State fans to show up Saturday morning for his network’s show.
“BUCKEYE NATION!” Meyer tweeted. “See you Saturday outside the RPAC starting at 10 a.m. for the Big Noon Kickoff Show.”
ESPN’s College GameDay will likely be broadcasting from outside Ohio Stadium as well. It’s going to make for quite the scene on Ohio State’s campus on Saturday morning.
Ohio State and Penn State will kick off at noon E.T. on FOX.


Elliptic-curve cryptography (ECC) is an approach to public-key cryptography based on the algebraic structure of elliptic curves over finite fields. ECC requires smaller keys compared to non-EC cryptography (based on plain Galois fields) to provide equivalent security.
Elliptic curves are applicable for key agreement, digital signatures, pseudo-random generators and other tasks. Indirectly, they can be used for encryption by combining the key agreement with a symmetric encryption scheme. They are also used in several integer factorization algorithms based on elliptic curves that have applications in cryptography, such as
Lenstra elliptic-curve factorization.


--- layout: post authors: ["Greg Wilson"] title: "Instructor Training at UC Davis" date: 2015-01-12 time: "19:00:00" tags: ["Instructor Training"] ---
On Tuesday and Wednesday of last week, we ran a live instructor training class at UC Davis. Over 40 people from all across the country got a lightning introduction to the basics of educational psychology and instructional design, and had a chance to hear how and what we teach.
This is the fifth time we've run instructor training this way. The curriculum and schedule have improved a lot (see the bottom of this post), but still need more work:
We should ask attendees to look through our lessons and pick out one or two to use for sample teaching before the start of the training.
Everyone doing instructor training must have lots of first-hand experience teaching actual workshops to draw on.
We have to make sure the space is suitable for many groups of three to record one another. (There wasn't quite as much audio interference this time as there was at the University of Washington, but it was still quite difficult for some people to hear their partners.)
We were more organized both mornings than either afternoon. This was only partly fatigue; as mentioned above, our curriculum and schedule still need some work.
We need to do a better job of connecting the theory about lesson design with the practice of teaching Software Carpentry. What that actually means is upgrading our lessons: it's still clear that I wrote many parts of our current material before I really understood this stuff myself.
Those criticisms don't detract from the fact that this session went really well: feedback at the end was largely positive, and several of the participants are already lined up to teach in the coming months. 99 people are currently registered to start instructor training online in February; I think that what we've learned from the live courses will improve their experience considerably.
My thanks to Titus Brown for hosting us, to Tracy Teal, Aleksandra Pawlik, and Bill Mills for helping to teach, and to Warren Code for helping us improve this training in so many ways. Thanks also to Olga Botvinnik for the photograph of our class.
| Day 1 | the art of introducing yourself |
| the differences between novices, competent practitioners, and experts | |
| concept maps | |
| demonstration lesson | |
| how to give feedback | |
| motivation and demotivation | |
| some history and teaching tips | |
| Day 2 | formative vs. summative assessment |
| reverse instructional design | |
| peer instruction | |
| reading list | |
| setting up a workshop website | |
| massive open collaborative lesson development | |
| introducing related projects | |
| next steps (mechanics of qualifying as an instructor) |


Breadth-first search (BFS) is a strategy for running through the vertices of a graph and is a fundamental algorithm of graph theory.
It is used to find the shortest path from a given 'source vertex' to any other vertex in the
graph (provided there exists such a path). Breadth-first traversal visits all the vertices and edges of graph.
A BFS on a graph \(G\) can determine whether \(G\) is connected or not?, compute the connected components of \(G\),
compute a spanning forest of \(G\).
In physics literature, Breadth-first search is commonly known as a "burning algorithm" in view of the analogy of a
fire burning and spreading through an area, a piece of paper or a fabric, etc. similar to how a breadth first search in
a graph proceeds.
What is Breadth First Search ?
Starting from a given source vertex \(v\) of a graph \(G\), we first explore the neighborhood of \(v\) by visiting all vertices that are adjacent to \(v\). We then apply the same strategy to each of the unvisited neighbors of \(v\). The strategy of exploring the neighborhood of a vertex is applied to all vertices of \(G\). The result is a tree rooted at \(v\) and this tree is a subgraph of \(G\). This is a general template for the BFS strategy. The tree resulting from the BFS algorithm is called a Breadth First Search Tree
How does Breadth First Search work ?
Input: A directed or undirected graph \(G=(V,E)\) of order \(n (>0)\). A vertex \(s\) from which the search is to be started. The vertices are numbered from \(1 \dots n\)Output: A list \(D\) of distances of all vertices from \(s\). A Tree \(T\) rooted at \(s\).
Algorithm:
\(Q \leftarrow [s]\)
\(D \leftarrow [\infty, \infty \dots \infty]\)
\(D[s]\leftarrow 0\)
\(T\leftarrow [ ]\)
\(\textit{while } (length(Q) > 0) \textit{ do }\)
- \(\textit{if } (D[w]=\infty )\textit{ then }\)
- \(D[w] \leftarrow D[v]+1\)
- \(Enqueue(Q,w)\)
- \(append(T,vw)\)
- \(Enqueue(Q,w)\)
- \(D[w] \leftarrow D[v]+1\)
\(\)
\(\)


Provides classes for calculating Depth First Search (DFS<EMAIL_ADDRESS>clr98dfs}] Breadth First Search (BFS<EMAIL_ADDRESS>clr98bfs}] on molecular graphs.
And additional algorithms, like Morgan's algorithm for the unique numbering of molecules are also available.


Uber set up a covert unit tasked with stealing competitors' secrets and engaging in undercover surveillance, a letter published by a US court on Friday has alleged.
It is critical evidence in Uber's legal battle with Waymo, the self-driving car company that accuses the ride-sharing firm of stealing its technology.
The letter, sent by lawyers representing a former Uber employee, sparked an internal investigation when it was sent to Uber in May, but has not been made public until now.
In a statement, Uber said: "While we haven’t substantiated all the claims in this letter - and, importantly, any related to Waymo - our new leadership has made clear that going forward we will compete honestly and fairly, on the strength of our ideas and technology."
The allegations in the letter were made by Richard Jacobs, who worked at Uber until February this year. He left after an incident in which he felt he was unfairly demoted. Shortly afterwards, he sent the letter alleging the misconduct.
"These tactics were employed clandestinely through a distributed architecture of anonymous servers, telecommunications architecture, and non-attributable hardware and software," the letter read.


About Cloud MVC
Cloud MVC is an experimental project that aims to make it easy to use the Groovy programming languge for server-side web MVC on "cloud" platforms like Google App Engine. It is based on the Spring MVC framework.
The CloudMVC project is on GitHub and will be licensed under ASL2
Example Controllers
- Counter (Java/Spring Controller/Direct output) view source
- Hello World (Java/Spring Controller/JSP View) view source
- Hello World Groovy (Groovy/Spring Controller/JSP View) view source
- User Info Gaelyk (Groovy/Spring Controller with Gaelyk/JSP View) view source
Standalone Gaelyk Page
- Login (Groovy/Gaelyk/.gtpl) view source
This page is (now) a static HTML page


My favorite places
These are a few of my favorite places in the universe.
My geographical timeline
I grew up in Southern California, went to school in Poughkeepsie, New York, moved back to sunny SoCal, then fled to raise a family in Portland, Oregon.
My favorite places to sit
Sometimes, the best places are the most unexpected ones.
- The bluffs of the Pacific Palisades, overlooking the ocean
- The movies... any movie theater
- Portland, Oregon
- The Northeast U.S. in late October
- By the fireplace, on a rainy day
My favorite places in Portland
The city of roses smells so gosh darn sweet
- Mount Tabor Park
- Bamboo Sushi NE
- Forest Park
- Powell's Books
- Multnomah Village


@*
* Copyright 2021 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import models._
@import play.api.data._
@import play.api.data.Forms._
@import java.time.LocalDate
@(form:Form[_], prefix: String, helpText: Option[String] = None, suffix: String = "",
optional: Boolean = false, nojsText: Option[String] = None, label: Option[String] = None, fieldData: Option[LocalDate] = None)(implicit requestHeader: RequestHeader, messages: Messages)
@displayFieldError(message: String, args: Seq[Any]) = {


As the year draws to a close, we thought it fitting to wish you, our beloved contributors, season’s greetings. Merry Christmas, belated happy Hanukkah, happy Kwanzaa, and an excellent festivus to you all.
2018 has been an incredible year for Utopian. In this, our first full year, we’ve had rough times great times. We built this thing together with you.
We thought it fitting, with the holidays, to take some time to wish you all some things.
To our developers, we wish code that’s clean, elegant, and effective.
To our graphic designers, we wish art that’s beautiful and functional.
To our translators, we wish language that’s clear and impactful.
To our creators of tutorials in text and video, we wish explanations clearly understood.
To our analyzers we wish depth of understanding and knowledge.
To our writers of blogs, documentation, and copy, we wish texts that delivers their message with power.
To our ideators, we wish flashes of brilliance to illuminate the future.
To our bug hunters, we wish a great and fruitful hunt.
To our abuse fighters, we wish triumph in the face of darkness.
To all of our contributors, moderators, readers, and supporters, we wish the happiest of holidays. We’ll see you all in 2019, for what promises to be an incredible year.
First Time Contributing in Utopian.io?
Learn how to contribute on our website
This page is synchronized from the post: Happy Utopian Holiday


{% extends 'base.html' %} {% block extrahead %} {{ block.super }} {% endblock %} {% block body %} {% if flight %}
| date | {{ flight.date }} {{ flight.time }} ({{ flight.date|timesince }} ago) | flight no. | {{ flight.flight_number }} | |
|---|---|---|---|---|
| from |
{{ flight.orig.iata }} /
{{ flight.orig.icao }}
{{ flight.orig }} {{ flight.orig.country }} |
to |
{{ flight.dest.iata }} /
{{ flight.dest.icao }}
{{ flight.dest }} {{ flight.orig.country }} |
|
| timezone | UTC {{ flight.orig.timezone }} | timezone | UTC {{ flight.dest.timezone }} | |
| alt | {{ flight.orig.alt_in_meters }} m / {{ flight.orig.alt }} ft | alt | {{ flight.dest.alt_in_meters }} m / {{ flight.dest.alt }} ft | |
| distance | {{ flight.distance }} km | duration | {{ flight.duration|time:"H:i" }} hrs | |
| airline | {{ flight.airline }} | airplane | {{ flight.plane }} | |
| price | {{ flight.currency }} {{ flight.price|floatformat:0|default_if_none:'' }} | purchased | {% if flight.purchased %}{{ flight.purchased }} ({{ flight.purchase_delta }} days before){% endif %} | |
| notes | {{ flight.note|default_if_none:'' }} |
if you want to delete this flight here
{% endif %}


"x" "1" "WATANABE,T, KAJI,H, KASAI,T AND HIRAYAMA,T,MUTAGENICITY AND METABOLICACTIVATION OF NITRODIBENZOFURANS AND NITRODIBENZO-P-DIOXINS, HEN'IGENSEI SHIKEN2(4):226-233, 1993"


All the radio to rewind, listen and share
People want to listen to music when ever they want and share it
Radio stations web sitesHard to admin. Listeners don't have all in one |
PodcastingDon't have radio content |
RadioCut is a platform where you can listen to past and present radio shows and you can cut and share radio segments you enjoy with your friends.
| Current metrics | Other revenues |
|
1.2 million monthly visits / 2.5 M pageviews 688 radio stations Cuts: 12.000 per month Users: 30.000 registered. 4.000 active. 120 Premium 3 monitoring service company customers |
Private services for media monitoring companies Services for radio stations and radio shows. |
Guillermo Narvaja: CEO, software developer, product manager
Milena Armada: Software developer, team leader
|
Experience in long term projects. Technical and business skills |
Impetus to start another project completely different and innovator |
| Argentina | USA | ||
| #448 other.com | #448 other.com | ||
| #448 other.com | #448 other.com | ||
| #448 other.com | #448 other.com | ||
| #448 other.com | #448 other.com | ||
| #448 other.com | #448 other.com |


| DOSE: T+ 0:00 |
100 seeds |
oral |
Morning Glory |
(ground / crushed) |
| T+ 1:00 |
0.5 joints/cigs |
smoked |
Cannabis |
(plant material) |
| T+ 0:00 |
200 seeds |
oral |
Morning Glory |
(ground / crushed) |
Well, I´m 26 and a proud psychonaut or drugs devotee since I tried pot eight years ago. I´m a rare instance of oral marijuana dependent, severely. I would like to be high all the time, being high on cannabis is the most healthy state of mind in my opinion and experience, in most instances. I don´t smoke denicotinezed tobacco from the damned United States (first producer of cigarettes, that is of cancer, in the world). And I don´t like true Cuban cigars either. I´ve just graduated as a doctor (general physician) in 133% the time it was supposed to take for rat racers. Mine is the best university by far in my proud Latin American country.
So, to the point: today on morning I went to the hill behind my town where I used to go as a boy, to light a joint (exceptionally) and wander around among the tropical biodiversity. I discovered the Ipomoeas on some coffee trees and itabos (as we call them). Their flowers were blue, I´ve already researched on them and had localized some specimens near home but hadn´t yet collected any seeds from them. This time I finally discovered the seed pods and managed to collect around 300 black seeds clearly corresponding to the ones Hoffmann (in LSD my problem child) says aztecs called black badoh.
I got home, counted and grounded one hundred of them, finely, in my coffee grinder. Took them with water. A fraction of the resulting dust (which adhered to the plastic of the grinder cap) had that disgusting fleshy, taste I´d already noted in the young (still chewable) seeds and in the flowers themselves. The rest of the seeds were actually edible with mild to moderate disgust, at least for me, accustomed to eating the Indian hemp right from the bag, chewing it thoroughly then adding some fat or oil to enhance bioavability of THC.
About two hours later, I felt clearly high on marijuana and decided it was because I´d smoked half joint one hour ago, afraid of the nausea described in some reports of morning glory tripping. I got no nausea, but profuse diarrhea, without abdominal pain. The diarrhea resembled that of cholera (of secretory mechanism, not painful, and so loose from the stimulated colonic mucosal cells as to resemble rice water). But, in general, the mechanism is probably osmotic because the number of depositions was rather small (3 to 4) and I retained much continence (I could make it to the toilet and slept without leaking). I ate lunch without much trouble, and then decided to try doubling the dose. So I repeated the procedure with two hundred seeds.
I was awarded with continuing diarrhea, which seems to had cleaned my colon thoroughly by now 23:00 hours, when it subsides. Still no psychotropicity from the product, even though the slight electrolitic imbalance from the diarrhea makes me feel kind of clean inside. In the end, I got the experience of a purge, so appreciated by old medicine men and women in this country (and many others I guess) as therapeutic because it washed the dirty gut from all its filth, presumably endotoxic. By now I feel kind of clean inside, and lighter, the natural drug having freed me of a not so terrible, yet constant, burden: that of all the shit that we literally carry on, every day, in the bag of our intestines.
In that aspect I could review this experience as a somatic equivalent of that of a good, chemically pure, judiciously low dose of the unsurpassed medicine, the diethylamide of lisergic acid. I had located a source of a not psychotropic, neither somadelic (as the sports 'doping' drugs should be called) agent, and actual somatropic experience, producing unusual movements of that organ we usually dismiss: the bowel.
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.


This is the site for collaborating with the people. Where the students and Alumni can communicate and get the updates on jobs and events etc.
- we can create blogs and we can post a comment for the blog.we can apply for job based on the eligibilty.
- we can send friend request to other people.we can chit-chat with our friends.
- we can keep our chat privately.we can celebrate lot of events and post the events.


| 002:054 Khan | : |
And (remember) when Musa (Moses) said to his people: "O my people! Verily, you have wronged yourselves by worshipping the calf.  Then He accepted your repentance. Truly, He is the One Who accepts repentance, the Most Merciful. |
| 002:054 Maulana | : |
And when Moses said to his people: O my people, you have surely wronged yourselves by taking the calf (for a god), so turn to your Creator (penitently), and kill your passions. That is best for you with your Creator. So He turned to you (mercifully). Surely He is the Oft-returning (to mercy), the Merciful. |
| 002:054 Pickthal | : |
And when Moses said unto his people: O my people! Ye have wronged yourselves by your choosing of the calf (for worship) so turn in penitence to your Creator, and kill (the guilty) yourselves. That will be best for you with your Creator and He will relent toward you. Lo! He is the Relenting, the Merciful. |
| 002:054 Rashad | : |
Recall that Moses said to his people, "O my people, you have wronged your souls by worshipping the calf. You must repent to your Creator.  This is better for you in the sight of your Creator." He did redeem you. He is the Redeemer, Most Merciful. |
| 002:054 Sarwar | : |
Moses said to his people, "My people, you have done wrong to yourselves by worshipping the calf. Seek pardon from your Lord and slay yourselves." He told them that it would be best for them in the sight of their Lord, Who would forgive them, for He is All-forgiving and All-merciful. |
| 002:054 Shakir | : |
And when Musa said to his people: O my people! you have surely been unjust to yourselves by taking the calf (for a god), therefore turn to your Creator (penitently), so kill your people, that is best for you with your Creator: so He turned to you (mercifully), for surely He is the Oft-returning (to mercy), the Merciful. |
| 002:054 Sherali | : |
And when Moses said to his people, 'O my people, you have indeed wronged yourselves by taking the calf for worship; turn ye therefore to your Maker, and kill your evil desires; that is best for you in the sight of your Maker.' Then HE turned towards you with compassion. Surely HE is Oft-Returning with compassion and is Merciful. |
| 002:054 Yusufali | : |
And remember Moses said to his people: "O my people! Ye have indeed wronged yourselves by your worship of the calf: So turn (in repentance) to your Maker, and slay yourselves (the wrong-doers); that will be better for you in the sight of your Maker." Then He turned towards you (in forgiveness): For He is Oft-Returning, Most Merciful. |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > صفات الله تعالى > التواب Pillars of Islam> Islamic> Oneness of Allah> His Glory's Characteristics> The One who Accepts Repentance العلوم والفنون > الحقائق العلمية و الإشاة إلى وقائع أيدتهاالإكتشافات العلمية > سرعة النور Science and Art> The Scientific Facts and the Indication to Facts which have been supported by the Scientific Discoveries>Light Speed الديانات > بنو إسرائيل > نعم الله عليهم Religions>Children of Israel>Allah Gifts on them
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.


Demo for AngularJS features
Directives vs Components
Ui-Router Simple
Ui-Router Nested States
Ng1 Component Router example 1 (deprecated)
Ng1 Component Router example 2 (deprecated)


|
PART A - step 1: Setting up the HTML
|
Since this is just a modest example we don't wish to be too graphic intensive quite yet, so we'll make use of a few bright (arguably blinding) table cell background colors. Form elements are a dime a dozen and they can even be coerced to change state with just a little bit of JavaScript finesse. Throw in a neat little table - complete with Beatnik Player - and you have the layout seen below...
|
|
Interacting with the sample, you will notice how there is as yet no connection between the player and the HTML form elements. Take a look at the HTML source code listing for this step. It's not too complex and won't be that much more complex when the connections have been wired in the next step. The main goal is for you to be able to clearly see the mechanics of callbacks in action, in a form that is simple and not obscured by a necessarily more complicated real-life application.

 <FORM NAME="callbackForm">
  <INPUT TYPE=radio NAME=playState ONCLICK="music.stop ()" CHECKED > STOP

  <INPUT TYPE=radio NAME=playState ONCLICK="music.play ()" > PLAY

  <INPUT TYPE=radio NAME=playState ONCLICK="music.pause ()" > PAUSE
 </FORM>

 |
In the above source code listing, the text that is highlighted represents the code necessary to embed a Beatnik Player using Music Object's magicEmbed method. Using Music Object is necessary since in the next step we will use Music Object methods to set up callbacks from the player in order to 'wire together' the player and the HTML form elements.
See how the magicEmbed method has been broken up into multiple lines, with each line concatenating a further piece of the EMBED string. This was only done to avoid the line from scrolling off way to the right in the source code listing, making the source code a little easier to read. It is not necessary to do this in one's real code - the EMBED string can be one solid and unbroken string.


{include file="public/header" title="旧时光博客管理系统" /}
{include file="public/top" /}
{include file="public/footer" /}


| Marginable Securities | Intraday | Overnight | ||
|---|---|---|---|---|
| Purchasing Power Before Open Orders Reserve | $1,000.00 | $350.00 | ||
| Reserve for Open Orders | $1,000.00 | $350.00 | ||
| Purchasing Power After Open Orders Reserve | $1,000.00 | $350.00 |
| Non-Marginable Securities | Intraday | Overnight | ||
|---|---|---|---|---|
| Purchasing Power Before Open Orders Reserve | $1,000.00 | $350.00 | ||
| Reserve for Open Orders | $1,000.00 | $350.00 | ||
| Purchasing Power After Open Orders Reserve | $1,000.00 | $350.00 |


Gantz Returns Mandate, Now It’s Up to the Knesset
by Hana Levi JulianOn Wednesday evening, Blue and White party chairperson Benny Gantz returned the mandate to form a new Israeli government to President Reuven Rivlin, acknowledging that he had failed to build a majority coalition.
Israeli Elections 3.0 in the Wings?
The Blue and White party said earlier Wednesday it was not prepared to accept the eventuality of incumbent Prime Minister Benjamin Netanyahu serving once again in that position, regardless of when in a rotation it might come up, according to Likud negotiator MK Ze’ev Elkin, who was interviewed by Israel’s Kann public broadcasting network.
In a speech he delivered after returning the mandate to the president, Gantz accused Netanyahu of “running a hate and incitement campaign aimed at justifying the entrenchment of a governing rule against the will of the voter.”
Netanyahu, who currently presides over a caretaker government, also failed to form a government, although he managed to gather 55 seats over the 44 garnered by Gantz.
The mandate now goes to the Knesset plenum for a 21-day period to provide an opportunity for lawmakers to attempt – individually – to form a coalition on their own.
Failing that last-gasp effort, Israel will move to an unprecedented third round of elections.


--- title: "CHASE MCCLEARY ART EXHIBIT" date: "2000-01-01 18:08:17 -06:00" categories: - exhibit subtitle: "" layout: "events" event-image: "chase-mccleary-exhibit.jpg" event-dates: "AUGUST 17 - SEPTEMBER 9, 2018" event-location: "Red Butte Garden Visitor Center" button-type: "register" tickets-link: "" registration-info: "N/A" instructor-info: "N/A" member-cost: "Free" public-cost: "Garden Admission" event-notes: "Artist Reception Aug 18 @ 1-4PM" tickets-button: "hide" refund-policy: "hide" published: false ---
Meet the Artist Reception
Saturday, August 18 from 1-4PM
The rugged beauty of Utah has always been enchanting to me as a lifelong resident and artist. Every time I venture into the desert or explore mountain trails, I’m inspired by the amazing wildlife and landscape that surround me. Nature is my muse and I humbly try to capture its beauty on canvas.
“It’s not what you look at that matters... it’s what you see.” - Henry David Thoreau
This show is dedicated to my lifelong friend Leigh Ann Givens.


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Welcome to The Queer Agenda! This is a queering of Seattle and its politics. The Queer agenda is more than equality. The Queer Agenda is justice. It is more than a solution, it is a commitment. The Queer Agenda is about committing to EACH OTHER! Join us!
#BlackLivesMatter is The Queer Agenda. Stop the displacement of black and brown people from our city center.
Trans Liberation is The Queer Agenda. Stopping I-1552 is imperative because it will legalize discrimination if it passes. Washington won’t discriminate. Seattle won’t discriminate. The Queer Agenda is pro-trans. The Queer Agenda Declines to Sign.
The Queer Agenda is about the issues where our identities intersect, like homelessness and public safety. The City is playing a game of wack-a-mole to resolve homelessness, and The Queer Agenda is No-Sweeps. We demand an institutional shift in our response to homelessness.
The Queer Agenda knows that housing, healthcare, and safety are a human right. The Queer Agenda demands safe spaces. Sidewalks and stickers are bullshit; they don’t solve hate-violence. The Queer Agenda needs an LGBTQIA community center and real safe spaces.
The Queer Agenda is you, and you are The Queer Agenda. Share your contact information and we'll reach out to get your input. And, PLEASE, register to vote by July 3. THIS IS MAJORLY IMPORTANT! Voting is the first and easiest way to get started on a path to transformation: THE QUEER AGENDA.


Mushrooms (P. cubensis)
Bali is well known by its many visitors and residents as an island paradise. It is also famous for the ease of finding mushrooms. Personally I have been collecting mushrooms here for an intermittent period of 11 years. The experience is made very special indeed by the intrinsically spiritual nature of the island, the enormous variance in lighting conditions (can go from almost blinding equatorial sunlight to dark tropical storm conditions very quickly indeed) and, quite simply, the fact that there is a lot to look at.
I should make it clear that I am no mycologist and it has proved amazingly difficult to find any meaningful scientific references to the shrooms found so widely in Bali. If ANYBODY has serious information on this, please submit it. I am fairly sure that the common shroom found here is Psilocybe cubensis and also think that many of shrooms sold by beachboys to tourists are cultivated Copelandia cyanescens. If anybody knows different to this, please, please correct me.
Before dealing seriously with experiences, a note about preparation. I have surprisingly seen no references on Erowid about preparing fresh mushroom 'shakes'. This is by far my preferred method of ingestion. Take the desired quantity of fresh shrooms and put them into a blender with about 300 ML juice (Apple juice is my favourite, but Pineapple Juice also works well) with a few drops of concentrated lemon juice, a desert spoonful of sugar and ice cubes. Blend thoroughly for 30 seconds or so. The resulting liquid is pleasant to consume (although a revolting dark grey colour - do not look at it for too long!) with virtually no earthy shroom aftertaste. When consumed the effects come on much faster and more thoruoghly than when taking whole shrooms or tea. It is best consumed immediately but can be kept bottled in a refrigerator for up to two or three days (goes a little rank if kept for longer than that).
The following experience is one which I had only three days ago and it can be described as a fairly typical Bali experience for me. I was in a genial frame of mind without any particular worries. At 3.00 pm I prepared two 'shakes' as described above using 20 g (that would be regarded by most as a pretty heavy dose) of fresh Psilocybe cubensis (I think!) in each, which I had picked early that morning. I then went through my usual ritual of tidying and cleaning the house before consumption at 5.30 pm. I always like my environment to be clean and comfortable before setting off on the experience. Sun sets at around 6.30 pm in Bali and I like my mushroom experience to cover this period and into the hours of darkness. I was joined by a friend for whom the second shake was intended.
After drinking the whole shake, the usual stoning effect took about 15 minutes by which time my legs were very numb and likewise my sinuses (does anyone else get that sinus effect?). After 25 minutes full-on open and closed eye visuals were apparent and I settled into a very comfortable trip phase as the sun was setting.
The 'religion' in Bali is a fusion of Hinduism and ancient animist practices. The local gods are gendered as Dewa (male) and Dewi (female). I ALWAYS find that when tripping, the energy is either male or female and the female energy experiences are more satisfying and never worrying in any way. This was such an experience. Looking at the extraordinary setting sun I was experiencing full-on swirling visuals of Dewi God images... very friendly and not at all threatening. I tend to find that the female (Dewi) energy is almost always dominant when I trip at my own house and it is invariably completely non-threatening. If I choose to trip elsewhere, it is more unpredictable. I guess that this is due to the familiar and comfortable surroundings of my own house and perhaps to a genuine Dewi god energy being present in that area.
I can also see the tallest mountain in Bali from my balcony, Mount Agung. This is known by locals as the seat of the Gods. Invariably when I look at Agung the swirling images are at their most intense and wild. On this particular occasion wild, fractile-type patterns were forming over the peak of the mountain, seemingly gaining energy from the mountain before 'taking off' high into the evening sky. I had a really intense feeling that I was moving with these images and that they wanted me to be with them. This engendered a very special feeling of elation in my mind and heart. As the shadows lengthened and darkness came this imagery was less intense but still very prevalent.
For the next 6 hours I tripped comfortably mostly inside my house. The Dewi energy was still with me and never at any stage did I find this uncomfortable or threatening. Full on spectacular visuals were apparent for four hours even during the hours of complete darkness and the final two hours involved a period of gradually re-gaining a sense of true reality. Through the final two hours my mind started to work very fast indeed and rather than being consumed by the visuals I was obsessed with thinking of people special to me. I resolved that there were at least 12 friends whom I must contact very soon and tell them how much they mean to me (typically it is now three days later and I have not done this!). At 1.00 am I was suddenly hungry, consumed a little food, retired to bed and slowly slipped off into a dreamy sleep.
This is an example of a typical shroom experience for me in Bali. The energy, lighting and physical imagery on this island make the shroom experience very special and certainly better than I have had anywhere else in the world. If a visitor cannot find their own wild shrooms (it can be difficult), then it is not hard to find beach boys in the Kutas area offering little bags for sale. They usually say one bag is a portion and it is enough for a mild experience. Normally the price will be about RP 30,000 per bag (about US$ 3.50).
Once again if anyone has confirmation or otherwise of my assumptions about Psilocybe cubensis (naturally growing in cow pasture) and Copelandia cyanescens (cultivated and for sale by beach hustlers) please do submit that information.
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.


Enter a Query Name for this query. This is will be the name of the query as it is listed on your My Results page.
Enter a Key Phrase for the topic you want to rank on. This is the general application you're interested in. For example, if you want to find transcription factors associated with embryonic stem cells, 'embryonic stem cell' (without quotes) would be your Key Phrase.
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For every target term, KinderMiner™ counts (1) the articles (available on PubMed) in which the term occurs with the key phrase of interest, (2) the articles in which it occurs without the key phrase, and (3) the articles in which the key phrase occurs without the term. It then performs a simple statistical calculation with all term counts to filter and rank the terms by their co-occurrence with the key phrase. In this way, co-occurrence is assumed to represent association between any one term and the key phrase.
Depending on the size of your query and overall demand, it may take several minutes to several hours. Please be patient and we will send you an email when your query is complete and ready to view on your My Results page.
Kuusisto F, Steill J, Kuang Z, Thomson J, Page D, Stewart R. A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications. In Proceedings of the AMIA Joint Summits on Translational Science. 2017.


In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.
Run the following cell to download the CIFAR-10 dataset for python.
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10_location = '/input/cifar-10/python.tar.gz'
if isfile(floyd_cifar10_location):
tar_gz_path = floyd_cifar10_location
else:
tar_gz_path = 'cifar-10-python.tar.gz'
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.last_block = block_num
if not isfile(tar_gz_path):
with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:
urlretrieve(
'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',
tar_gz_path,
pbar.hook)
if not isdir(cifar10_dataset_folder_path):
with tarfile.open(tar_gz_path) as tar:
tar.extractall()
tar.close()
tests.test_folder_path(cifar10_dataset_folder_path)
The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named data_batch_1
, data_batch_2
, etc.. Each batch contains the labels and images that are one of the following:
Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the batch_id
and sample_id
. The batch_id
is the id for a batch (1-5). The sample_id
is the id for a image and label pair in the batch.
Ask yourself "What are all possible labels?", "What is the range of values for the image data?", "Are the labels in order or random?". Answers to questions like these will help you preprocess the data and end up with better predictions.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import helper
import numpy as np
# Explore the dataset
batch_id = 2
sample_id = 10
helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)
def normalize(x):
"""
Normalize a list of sample image data in the range of 0 to 1
: x: List of image data. The image shape is (32, 32, 3)
: return: Numpy array of normalize data
"""
mx = np.amax(x)
mn = np.amin(x)
return (x - mn) / (mx - mn)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_normalize(normalize)
Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the one_hot_encode
function. The input, x
, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to one_hot_encode
. Make sure to save the map of encodings outside the function.
Hint: Don't reinvent the wheel.
def one_hot_encode(x):
"""
One hot encode a list of sample labels. Return a one-hot encoded vector for each label.
: x: List of sample Labels
: return: Numpy array of one-hot encoded labels
"""
return np.eye(10)[x]
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_one_hot_encode(one_hot_encode)
As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset.
Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)
This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import pickle
import problem_unittests as tests
import helper
# Load the Preprocessed Validation data
valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))
For the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.
Note: If you're finding it hard to dedicate enough time for this course each week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages to build each layer, except the layers you build in the "Convolutional and Max Pooling Layer" section. TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.
However, if you would like to get the most out of this course, try to solve all the problems without using anything from the TF Layers packages. You can still use classes from other packages that happen to have the same name as ones you find in TF Layers! For example, instead of using the TF Layers version of the
conv2d
class, tf.layers.conv2d, you would want to use the TF Neural Network version ofconv2d
, tf.nn.conv2d.
Let's begin!
The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions
neural_net_image_input
image_shape
with batch size set to None
.name
parameter in the TF Placeholder.neural_net_label_input
n_classes
with batch size set to None
.name
parameter in the TF Placeholder.neural_net_keep_prob_input
name
parameter in the TF Placeholder.These names will be used at the end of the project to load your saved model.
Note: None
for shapes in TensorFlow allow for a dynamic size.
import tensorflow as tf
def neural_net_image_input(image_shape):
"""
Return a Tensor for a batch of image input
: image_shape: Shape of the images
: return: Tensor for image input.
"""
return tf.placeholder(tf.float32, \
shape=(None, image_shape[0], image_shape[1], image_shape[2]), name="x")
def neural_net_label_input(n_classes):
"""
Return a Tensor for a batch of label input
: n_classes: Number of classes
: return: Tensor for label input.
"""
return tf.placeholder(tf.float32, \
shape=(None, n_classes), name="y")
def neural_net_keep_prob_input():
"""
Return a Tensor for keep probability
: return: Tensor for keep probability.
"""
return tf.placeholder(tf.float32, \
shape=(None), name="keep_prob")
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tf.reset_default_graph()
tests.test_nn_image_inputs(neural_net_image_input)
tests.test_nn_label_inputs(neural_net_label_input)
tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)
Convolution layers have a lot of success with images. For this code cell, you should implement the function conv2d_maxpool
to apply convolution then max pooling:
conv_ksize
, conv_num_outputs
and the shape of x_tensor
.x_tensor
using weight and conv_strides
.pool_ksize
and pool_strides
.Note: You can't use TensorFlow Layers or TensorFlow Layers (contrib) for this layer, but you can still use TensorFlow's Neural Network package. You may still use the shortcut option for all the other layers.
def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):
"""
Apply convolution then max pooling to x_tensor
:param x_tensor: TensorFlow Tensor
:param conv_num_outputs: Number of outputs for the convolutional layer
:param conv_ksize: kernal size 2-D Tuple for the convolutional layer
:param conv_strides: Stride 2-D Tuple for convolution
:param pool_ksize: kernal size 2-D Tuple for pool
:param pool_strides: Stride 2-D Tuple for pool
: return: A tensor that represents convolution and max pooling of x_tensor
"""
# TODO: Implement Function
W = tf.Variable(tf.truncated_normal([conv_ksize[0], conv_ksize[1], x_tensor.shape[3].value, conv_num_outputs],
mean=0, stddev=0.1))
b = tf.Variable(tf.zeros(conv_num_outputs))
x = tf.nn.conv2d(x_tensor, W, strides=[1, conv_strides[0], conv_strides[1], 1], padding='SAME')
x = tf.nn.bias_add(x, b)
x = tf.nn.relu(x)
x = tf.nn.max_pool(x, \
ksize=[1, pool_ksize[0], pool_ksize[1], 1], \
strides=[1, pool_strides[0], pool_strides[1], 1], \
padding='SAME')
return x
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_con_pool(conv2d_maxpool)
Implement the flatten
function to change the dimension of x_tensor
from a 4-D tensor to a 2-D tensor. The output should be the shape (Batch Size, Flattened Image Size). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
def flatten(x_tensor):
"""
Flatten x_tensor to (Batch Size, Flattened Image Size)
: x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.
: return: A tensor of size (Batch Size, Flattened Image Size).
"""
x = x_tensor.get_shape().as_list()
size = x[1] * x[2] * x[3]
return tf.reshape(x_tensor, [-1, size])
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_flatten(flatten)
Implement the fully_conn
function to apply a fully connected layer to x_tensor
with the shape (Batch Size, num_outputs). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
def fully_conn(x_tensor, num_outputs):
"""
Apply a fully connected layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
"""
# TODO: Implement Function
W = tf.Variable(tf.truncated_normal([x_tensor.get_shape().as_list()[1], num_outputs], mean=0, stddev=0.1))
b = tf.Variable(tf.zeros(num_outputs))
x = tf.add(tf.matmul(x_tensor, W), b)
x = tf.nn.relu(x)
return x
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_fully_conn(fully_conn)
Implement the output
function to apply a fully connected layer to x_tensor
with the shape (Batch Size, num_outputs). Shortcut option: you can use classes from the TensorFlow Layers or TensorFlow Layers (contrib) packages for this layer. For more of a challenge, only use other TensorFlow packages.
Note: Activation, softmax, or cross entropy should not be applied to this.
def output(x_tensor, num_outputs):
"""
Apply a output layer to x_tensor using weight and bias
: x_tensor: A 2-D tensor where the first dimension is batch size.
: num_outputs: The number of output that the new tensor should be.
: return: A 2-D tensor where the second dimension is num_outputs.
"""
# TODO: Implement Function
W = tf.Variable(tf.truncated_normal([x_tensor.get_shape().as_list()[1], num_outputs], mean=0, stddev=0.1))
b = tf.Variable(tf.zeros(num_outputs))
x = tf.add(tf.matmul(x_tensor, W), b)
return x
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_output(output)
Implement the function conv_net
to create a convolutional neural network model. The function takes in a batch of images, x
, and outputs logits. Use the layers you created above to create this model:
keep_prob
. def conv_net(x, keep_prob):
"""
Create a convolutional neural network model
: x: Placeholder tensor that holds image data.
: keep_prob: Placeholder tensor that hold dropout keep probability.
: return: Tensor that represents logits
"""
# TODO: Apply 1, 2, or 3 Convolution and Max Pool layers
# Play around with different number of outputs, kernel size and stride
# Function Definition from Above:
# conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)
conv_num_outputs = {
"layer1": 16,
"layer2": 64,
"layer3": 32,
}
conv_ksize = {
"layer1": (4,4),
"layer2": (4,4),
"layer3": (4,4),
}
conv_strides = {
"layer1": (1,1),
"layer2": (1,1),
"layer3": (1,1),
}
pool_ksize = {
"layer1": (2,2),
"layer2": (2,2),
"layer3": (2,2),
}
pool_strides = {
"layer1": (2,2),
"layer2": (2,2),
"layer3": (2,2),
}
x = conv2d_maxpool(x, conv_num_outputs["layer1"], conv_ksize["layer1"], conv_strides["layer1"], pool_ksize["layer1"], pool_strides["layer1"])
x = conv2d_maxpool(x, conv_num_outputs["layer2"], conv_ksize["layer2"], conv_strides["layer2"], pool_ksize["layer2"], pool_strides["layer2"])
x = conv2d_maxpool(x, conv_num_outputs["layer3"], conv_ksize["layer3"], conv_strides["layer3"], pool_ksize["layer3"], pool_strides["layer3"])
# TODO: Apply a Flatten Layer
# Function Definition from Above:
x = flatten(x)
# TODO: Apply 1, 2, or 3 Fully Connected Layers
# Play around with different number of outputs
# Function Definition from Above:
x = fully_conn(x, 16)
x = fully_conn(x, 32)
x = tf.nn.dropout(x, keep_prob)
# TODO: Apply an Output Layer
# Set this to the number of classes
# Function Definition from Above:
x = output(x, 10)
# TODO: return output
return x
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
##############################
## Build the Neural Network ##
##############################
# Remove previous weights, bias, inputs, etc..
tf.reset_default_graph()
# Inputs
x = neural_net_image_input((32, 32, 3))
y = neural_net_label_input(10)
keep_prob = neural_net_keep_prob_input()
# Model
logits = conv_net(x, keep_prob)
# Name logits Tensor, so that is can be loaded from disk after training
logits = tf.identity(logits, name='logits')
# Loss and Optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
# Accuracy
correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')
tests.test_conv_net(conv_net)
Implement the function train_neural_network
to do a single optimization. The optimization should use optimizer
to optimize in session
with a feed_dict
of the following:
x
for image inputy
for labelskeep_prob
for keep probability for dropoutThis function will be called for each batch, so tf.global_variables_initializer()
has already been called.
Note: Nothing needs to be returned. This function is only optimizing the neural network.
def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):
"""
Optimize the session on a batch of images and labels
: session: Current TensorFlow session
: optimizer: TensorFlow optimizer function
: keep_probability: keep probability
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
"""
session.run(optimizer, feed_dict={
x: feature_batch,
y: label_batch,
keep_prob: keep_probability})
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_train_nn(train_neural_network)
Implement the function print_stats
to print loss and validation accuracy. Use the global variables valid_features
and valid_labels
to calculate validation accuracy. Use a keep probability of 1.0
to calculate the loss and validation accuracy.
def print_stats(session, feature_batch, label_batch, cost, accuracy):
"""
Print information about loss and validation accuracy
: session: Current TensorFlow session
: feature_batch: Batch of Numpy image data
: label_batch: Batch of Numpy label data
: cost: TensorFlow cost function
: accuracy: TensorFlow accuracy function
"""
loss = session.run(cost, feed_dict={
x: feature_batch,
y: label_batch,
keep_prob: 1.0})
validation_accuracy = session.run(accuracy, feed_dict={
x: valid_features,
y: valid_labels,
keep_prob: 1.0})
print('Loss: ' + str(loss) + ', Validation accuracy: ' + str(validation_accuracy))
Tune the following parameters:
epochs
to the number of iterations until the network stops learning or start overfittingbatch_size
to the highest number that your machine has memory for. Most people set them to common sizes of memory:keep_probability
to the probability of keeping a node using dropout# TODO: Tune Parameters
epochs = 15
batch_size = 128
keep_probability = 0.75
Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
print('Checking the Training on a Single Batch...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
batch_i = 1
for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')
print_stats(sess, batch_features, batch_labels, cost, accuracy)
Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches.
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
save_model_path = './image_classification'
print('Training...')
with tf.Session() as sess:
# Initializing the variables
sess.run(tf.global_variables_initializer())
# Training cycle
for epoch in range(epochs):
# Loop over all batches
n_batches = 5
for batch_i in range(1, n_batches + 1):
for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):
train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)
print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')
print_stats(sess, batch_features, batch_labels, cost, accuracy)
# Save Model
saver = tf.train.Saver()
save_path = saver.save(sess, save_model_path)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import tensorflow as tf
import pickle
import helper
import random
# Set batch size if not already set
try:
if batch_size:
pass
except NameError:
batch_size = 64
save_model_path = './image_classification'
n_samples = 4
top_n_predictions = 3
def test_model():
"""
Test the saved model against the test dataset
"""
test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load model
loader = tf.train.import_meta_graph(save_model_path + '.meta')
loader.restore(sess, save_model_path)
# Get Tensors from loaded model
loaded_x = loaded_graph.get_tensor_by_name('x:0')
loaded_y = loaded_graph.get_tensor_by_name('y:0')
loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
loaded_logits = loaded_graph.get_tensor_by_name('logits:0')
loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')
# Get accuracy in batches for memory limitations
test_batch_acc_total = 0
test_batch_count = 0
for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):
test_batch_acc_total += sess.run(
loaded_acc,
feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})
test_batch_count += 1
print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count))
# Print Random Samples
random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))
random_test_predictions = sess.run(
tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),
feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})
helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)
test_model()
You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN. Pure guessing would get you 10% accuracy. However, you might notice people are getting scores well above 80%. That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_image_classification.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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Place order
settings
Settings
Report
Group by
Location
User
Product
Month
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SEARCH
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CLIENT
applications are not provided with potentially
sensitive configuration files by default.
This option lets you whitelist directories within the normal configuration files space, which will get provisioned to clients when they install this application.
Make sure to only include directories which contain files safe for provisioning to potentially unsafe clients.


BufferList
public class BufferList
This is a BufferList implementation using a ByteBuffer as the backing store. In future, we may want to do away with the BufferList abstraction and use SwiftNIO.ByteBuffer directly.
-
Creates a
BufferList
instance to store bytes to be writtenDeclaration
Swift
public init()
-
Get the number of bytes stored in the
BufferList
.Declaration
Swift
public var count: Int
-
Read the data from the
BufferList
Declaration
Swift
public var data: Data
-
Append bytes to the buffer.
Declaration
Swift
public func append(bytes: UnsafePointer<UInt8>, length: Int)
-
Append data into the
BufferList
.Declaration
Swift
public func append(data: Data)
-
Fill an array with data from the buffer. The data is copied from the BufferList to
array
.Declaration
Swift
public func fill(array: inout [UInt8]) -> Int
-
Fill memory with data from a
BufferList
. The data is copied from theBufferList
tobuffer
.Declaration
Swift
public func fill(buffer: UnsafeMutablePointer<UInt8>, length: Int) -> Int
-
Fill a
Data
structure with data from the buffer.Declaration
Swift
public func fill(data: inout Data) -> Int
-
Fill a
NSMutableData
with data from the buffer.Declaration
Swift
public func fill(data: NSMutableData) -> Int
-
Reset the buffer to the beginning position and the buffer length to zero.
Declaration
Swift
public func reset()
-
Sets the buffer back to the beginning position. The next
BufferList.fill()
will take data from the beginning of the buffer.Declaration
Swift
public func rewind()


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Maginhawa Street in Teacher’s Village seems to be one of the most popular neighborhood food trip destinations in the metro these days. There are a lot of restaurants and food parks along the street as well as nearby streets like Malingap, Magiting and Mapagkawanggawa. It seems that every week, a new place opens up.
Once a quiet, residential neighborhood, the whole area surged in popularity with weekend food-trippers after being featured in various blogs, television shows and hosting the first Quezon City Food Festival on October 2014. Since then, it’s become a “tourist destination” of sorts for people looking for good eats. For those not familiar with this street, here’s a quick guide on how to get there by different modes of transport.
Maginhawa, named after a positive Filipino characteristic which could either mean “convenient” or “refreshing,” has been one of the favorite hang out spots of students, faculty and employees in Quezon City area.
For food enthusiasts who want to feast on a wide selection of food, Maginhawa Street offers a long row of homey food stores and a variety of cuisines. Located near UP Diliman campus in Quezon City, Maginhawa Street is a residential area. But now, the street has become a haven for refreshments and relaxation and has sometimes been referred to as the “melting pot” street of cuisines.


Navigation
Projects
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import/export
sales order import
**master import***
export sales orders
user management
user search


Let's make games for news
with Sisi Wei -
@sisiwei
presentation at
sisiwei.com/nicar-news-games/
#1: Fun
Exploring new environments
Creative problem solving, given rules
Simulated experiences not found in real life
#2:
Re-playability
Your browser does not support video
#3: Context
#4: The Reveal
News games create a
data experience.


That's my TL;DR. I am a BS/MS Computer Scientist at UNC Chapel Hill in my fourth year by day; by night, I am the Technical Lead on the SIGGRAPH Student Volunteer Subcommittee. I love to make graphics and video games. So if you want either, contact me!


Workers 'more than £14,000 a year worse off' because of inflation shock research reveals
Shocking new research from the TUC said years of low pay have driven desperate families deeper into the red, as real wages have not recovered to their 2008 levels
by Brett Gibbons, https://www.birminghammail.co.uk/authors/brett-gibbons/Workers are more than £14,000 worse off because wages have not kept pace with inflation over the past decade.
The shocking claim from the TUC said years of low pay have driven working families deeper into the red, as real wages have not recovered to their 2008 levels, claiming it was the longest pay squeeze in centuries.
The median employee is £14,278 worse off today than they would be had wages kept pace with inflation since 2008, while, in the same period, unsecured household debt has risen by a third to a record high of £14,177, according to the union organisation.
The TUC said its study showed that, in nominal terms, household debt has risen 1.5 times faster than wages since 2010.
Individual insolvencies are also at their highest level since 2010, with more than 115,000 people "pushed over the financial cliff edge" last year, the report said.
TUC general secretary Frances O'Grady said: "People are working harder than ever but wages have stood still for over a decade. Millions of households have been pushed to the financial cliff edge.
"Working-class families have had enough of a system that's rigged in favour of the rich and bosses. We want better than rising debts and jobs on impossible hours that make it hard to see our loved ones.
"So, in this election, we're going to use our votes to rebuild Britain and make politicians put working families first."
The TUC said its study on real wage loss was calculated using data from the 1997-2019 Annual Survey of Hours and Earnings.


flixel.animation.FlxPrerotatedAnimation
type
class
extends
FlxBaseAnimation
...
Static Variables
PREROTATED
(inline,null) :
String
Constructor
new
(Parent :
FlxAnimationController
, Baked :
Float
)
Instance Variables
hide inherited
show inherited
angle
(default,accessor) :
Float
curIndex
(default,accessor) :
Int
inherited from
flixel.animation.FlxBaseAnimation
Keeps track of the current index into the tile sheet based on animation or rotation. Allow access to private var from FlxAnimationController.
name
:
String
inherited from
flixel.animation.FlxBaseAnimation
String name of the animation (e.g. "walk")
parent
:
FlxAnimationController
inherited from
flixel.animation.FlxBaseAnimation
Animation controller this animation belongs to
Instance Methods
hide inherited
show inherited
clone
(Parent :
FlxAnimationController
) :
FlxPrerotatedAnimation
overrides
flixel.animation.FlxBaseAnimation
destroy
() :
Void
inherited from
flixel.animation.FlxBaseAnimation
update
() :
Void
inherited from
flixel.animation.FlxBaseAnimation