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Police DPO kidnapped by gunmen in Adamawa
- Ahijo Mujeli, the Divisional Police Officer (DPO) for Mubi North LGA in Adamawa state has been abducted
- Mujeli was kidnapped on Tuesday, November 19, on his way from Yola, the capital of the state
- Adamawa police command has confirmed the incident, adding that the suspected kidnappers are already being trailed
As the spate of kidnapping in the country continues unabated, another senior police officer has been reportedly abducted by unknown gunmen.
Ahijo Mujeli, the kidnap victim, according to Premium Times, is the Divisional Police Officer (DPO) for Mubi North local government area in Adamawa state.
Mujeli was kidnapped on Tuesday, November 19, on his way from Yola, the capital of the state, according to residents who are aware of the incident.
One of the residents was quoted to have said: “The DPO was coming back from Yola and all of a sudden some yet to be identified gunmen wielding sophisticated rifles attacked him at Mararraba at about 8pm and whisked him away to an unknown destination.”
Breaking: Sad day for Nigeria as former minister of information dies after brief illness
The spokesperson of the Adamawa police command, Suleiman Nguroje, has also confirmed the abduction, the report added.
Nguroje said: “He was driving in his personal car around 6.30 p.m., along Mubi-Maraba road near the toll-gate when he was abducted.
“There were gunshots around the area. When our men arrived, they met an empty car identified as his.”
He added that the anti-kidnapping unit and Intelligence Response Team (IRT) of the Inspector-General of Police is already trailing the suspected kidnappers.
Nguroje also called on the members of the public with relevant security information on criminals to avail the command same for necessary action.
PAY ATTENTION: Install our latest app for Android, read best news on Nigeria’s #1 news app
Barely a month ago, an Assistant Commissioner of Police (ACP), Musa Rabo, was kidnapped.
The senior police officer was travelling in his civil dress, with his private vehicle and without escort or any special security at the time of the incident.
BREAKING: Four Immigration officers kidnapped in Rivers state
He has, however, regained his freedom. The Nigeria police in a thread of tweets posted on Twitter said ACP Rabo was rescued unhurt on Sunday, October 20.
NAIJ.com (naija.ng) -> Legit.ng. We have upgraded to serve you better.
NAF Officers make history as first female fighter, helicopter combat pilots | Legit TV


{% extends "hunt/layout_with_nav.tmpl" %}
{% load static %}
{% block title %}Credits — MIT Mystery Hunt 2021{% endblock %}
{% block content %}
Hunt Credits
Puzzles
- Editors in Chief: Jon Schneider and Rahul Sridhar
- Editors: Amon Ge, Andy Hauge, Anderson Wang, Ben Yang, Brian Chen, Chris Jones, Colin Lu, Danny Bulmash, Jakob Weisblat, Josh Alman, Lewis Chen, Nathan Pinsker, Sam Kim, Seth Mulhall, Zack Chroman
- Factchecking
- Factchecking Lead: Danny Bulmash
- Copy-editing Lead: Seth Mulhall
- Star Factcheckers: Anderson Wang, Curtis Chou, Danny Bulmash, Jenna Himawan, Lillian McKinley, Toomas Tennisberg, Xander Hughes, Yannick Yao
- Additional Factcheckers: Adam Barber, Amon Ge, CJ Quines, Connor Moss, Evan Chen, Herman Chau, Jakob Weisblat, Jingyi Zhao, Jon Schneider, Josh Alman, Leo Marchand, Leon Zhou, Qi Qi, Rahul Sridhar, Robert Tunney, Sammy Luo, Seth Mulhall, Zack Chroman
- Testsolving Coordinator: Robert Tunney
- Additional Testsolvers: Ashley Kim, Kevin Sun, Linus Strese, Patrick Yang, Wanlin Li
- Post Production: Alan Huang, Brian Chen, CJ Quines, Jakob Weisblat, Jon Schneider, Lewis Chen, Rahul Sridhar, Xander Hughes, and many puzzle authors
- Hunt Modeling: Jon Schneider, Robert Tunney, Yannick Yao
- Physical Puzzles
- Physical Puzzles Lead: Joanna Sands
- Assembly, Shipping, Sourcing and Transport: Charles Tam, Chris Jones, Jakob Weisblat, Jenna Himawan, Justine Jang, Lillian McKinley, Rahul Sridhar
- Writing Retreat Organizers: Cami Ramirez-Arau, Kat Fang
Projection Device
- Director: Ben Yang
- Client Development: Herman Chau, Nathan Pinsker, Kat Fang, Lennart Jansson, Evan Chen, DD Liu, Jakob Weisblat, Ben Yang, Anderson Wang, Josh Alman, Leon Zhou, Brian Chen, CJ Quines, Justine Jang, Toomas Tennisberg, Daniel Whatley, Steven Valdez, Jon Schneider, Mark Theng
- Server Development: Nathan Pinsker (lead), Damien Jiang, Herman Chau, Kat Fang
- Art Directors: DD Liu and Andy Hauge
- Artists: DD Liu, Cami Ramirez-Arau, Herman Chau, Lennart Jansson, Joie Chang, Jessica Xu, Amanda Liu, Justine Jang, Abigail Caron, Andy Hauge, Brian Chen, Kat Fang, Nathan Pinsker, Alan Huang, Rahul Sridhar, Connor Moss, Jakob Weisblat, Sammy Luo
- DevOps: Adam Barber, Damien Jiang
Story
- Story Director: Lillian McKinley
- Writers: Lillian McKinley (lead), Chris Jones (kickoff)
- Cast
- Prof. Yew: Theo Kunicki (main), Amanda Liu
- Robin: Kat Fang (main), Lillian McKinley
- Prof. Hemlock: Ian Osborn (main), Amanda Liu
- J: Cami Ramirez-Arau (main), Mitchell Lee, Jakob Weisblat
- Cameron: Josh Alman (main), Seth Mulhall
- Skylar: Connor Moss (main), Brian Chen
- Jenna Himawan, Chris Jones, Sammy Luo, Toomas Tennisberg, Jack Chen
- Endgame MIT Runners: CJ Quines, Mark Theng, Yannick Yao
- Stage Managers: Phillip Ai, Max Murin
- Props and Costumes: Cami Ramirez-Arau, Jenna Himawan
- Effects: Loren Sherman (portal), Jakob Weisblat (projection)
- Interactions
- Athletics: Anderson Wang, CJ Quines
- Clusters: Jakob Weisblat, Kat Fang
- Green Building: Josh Alman, Nathan Pinsker
- Infinite Corridor: Andy Hauge
- Stata Center: Curtis Chou
- Student Center: Lillian McKinley, Curtis Chou
- Tunnels: Colin Lu
- ⊥IW.nano: Cami Ramirez-Arau, Jakob Weisblat
- Coin Design: DD Liu
Website
- Tech Lead: Steven Valdez
- Additional Tech & Icons: Alan Huang
- Round Art: Lennart Jansson, DD Liu (Yew Labs)
- Basketball Infrastructure: Charles Tam, Mark Theng
- Zoom Infrastructure: Mitchell Gu
Events
- Events Leads: Colin Lu, Jingyi Zhao, Justine Jang
- Events Tech: Alan Huang, Charles Tam
- Events Runners: Amon Ge, Colin Lu, Dai Yang, Daniel Whatley, Danny Bulmash, Evan Chen, Justine Jang
- Event Authors: Alan Huang, Chris Jones, Colin Lu, Dai Yang, Jingyi Zhao, Justine Jang
- Events Finale: DD Liu, Kat Fang
During-Hunt Logistics
- Star Hint Answerers: Yannick Yao, Jason Lam, Jenna Himawan, Amon Ge, Curtis Chou, Daniel Whatley, Sam Kim
- Star Interaction Runners: Anderson Wang, Charles Tam, CJ Quines, Danny Bulmash, Jakob Weisblat, Jenna Himawan, Nathan Pinsker
- Star Team Check-in-ers: CJ Quines, Curtis Chou, Yannick Yao
- Interaction Managers: Abigail Caron, Andy Hauge, Brian Chen, Charles Tam, Chris Jones, Damien Jiang, Danny Bulmash, Jenna Himawan, Joanna Sands, Robert Tunney, Xander Hughes, Zack Chroman
- Stage Managers: Max Murin, Phillip Ai
- Zoomers: Adam Barber, CJ Quines, Daniel Whatley, Jenna Himawan, Joanna Sands, Mitchell Gu, Toomas Tennisberg, Yannick Yao
- Email & Phone Monitors: Alan Huang, Anderson Wang, Charles Tam, CJ Quines, Ixa Gani, Jakob Weisblat, Jenna Himawan, Josh Alman, Justine Jang, Lennart Jansson, Leon Zhou, Lewis Chen, Phillip Ai
Exec
- Benevolent Dictators: Jakob Weisblat and Josh Alman
- Editors in Chief: Jon Schneider and Rahul Sridhar
- Director: Ben Yang
- Archivist: Kat Fang
- Ombudsperson: Cami Ramirez-Arau
- Treasurer: Jenna Himawan
Committees
- Goals Committee: Ben Yang, Colin Lu, Ethan, Jingyi Zhao, Jon Schneider, Josh Alman, Lewis Chen, Rahul Sridhar, Steven Valdez, Yannick Yao
- Theme Proposal Feedback Committee: Ben Yang, Cami Ramirez-Arau, Colin Lu, Connor Moss, Ethan, Hailey, Ian Osborne, Ixa Gani, Jenna Himawan, Joshua Sloane, Rahul Sridhar, Steven Valdez, Theo Kunicki
- COVID Committee: Ben Yang, Cami Ramirez-Arau, Connor Moss, Jakob Weisblat, Jenna Himawan, Josh Alman, Justine Jang, Kat Fang, Lewis Chen, Lillian McKinley, Rahul Sridhar
Special Thanks
- MIT Puzzle Club, and especially Julia Wagner and Jenna Himawan
- Physical puzzle manufacture and delivery: Up In Pieces, Rocket Science, Deliverr
- For publishing parts of puzzles for us: The Tech, MIT Confessions, The Riddler (FiveThirtyEight), Kominers's Conundrums (Bloomberg Opinion)
- Video editing: Jake Gunter
- MIT SOLE, and especially Ethan Feuer
- Our sponsors: MIT Museum, MIT Large Event Fund, Jane Street, ClueKeeper, Hint Water
{% endblock %}


Derek Eder
@derekeder
RT @largelots: Great story on yesterday's Englewood Large Lots closings http://abc7chicago.com/news/city-of-chicago-selling-vacant-lots-for-$1/443991/ by @ABC7Chicago
12/20/2014, 11:00:17 AM
Favs: 0
Retweets: 0
link← @derekeder Twitter archive
Derek Eder
@derekeder
RT @largelots: Great story on yesterday's Englewood Large Lots closings http://abc7chicago.com/news/city-of-chicago-selling-vacant-lots-for-$1/443991/ by @ABC7Chicago
12/20/2014, 11:00:17 AM
Favs: 0
Retweets: 0
link


@(accounts: Seq[String]) @main("Account"){ -------- Hedmark University of Applied Sciences Norwegian University of Life Sciences NTNU UiT The Arctic University of Norway UNINETT University of Bergen University of Oslo }


Please omit the protocol ("https" or "http")
Go to the 'Add URL' tab or use the pop up
to start tracking sites
For one reason or another, no matter how interesting of a project i'm working on, I always find a way to end up back on Reddit or similar sites. Having tried extensions that actively block these sites, I realized it benefited me more to have more of an odometer system to see exactly how many times I consciously left my work to look at photos of cats.
Additionally, certain productivity apps make a habit of sending browsing
activity to ad servers. TabTracker doesn't rely on any external data
processing and keeps all of your browsing activity local.
If for one reason or another, you end up not being a fan of TabTracker,
I personally recommend any of the following extensions which might be
more what you're looking for:
Stay Focused
Be Limitless


This page allows calculation of the meridional parts used in the
construction of Mercator charts and in Mercator sailing for each
minute of latitude from the equator to the poles.
Enter the latitude for which the meridional part is desired.
Latitudes may either be entered as decimal degrees by only
filling in the degrees field or as degrees and minutes by
entering the values in the appropriate fields.
|


Full Stack Web Developer with a certificate from UC Berkeley Extension’s Full Stack Flex Full Time Program. I am familiar with Javascript, HTML5, CSS3, SQL, C++, Max/MSP/Jitter , React, Express.js, and Bootstrap 4 . I earned a bachelor’s degree in Math and Music from Kenyon College, where I integrated my knowledge of the two subjects with Max Cycling `74 to create algorithms that explore artificial intelligence, virtual instruments, and digital synthesis of the human voice. I worked as an audio technician at Kenyon, collaborating on a team of six to plan and execute complete audio environments for major department-sponsored performances. I am looking to implement my analytical strategy and creative design in a collaborative workplace.
A MERN stack application that uses Megvii's Face++ API to determine a user's celebrity look-alikes. Client images hosted on Amazon S3. Developed a back-end server that communicates with S3 via AWS SDK for Js and leverages Megvii's facial recognition to search through a Mongo database of almost 1000 celebrities. Integrated authentication using Passport's local strategy.
A full-stack song forum application built with React. Scrapes top chart data from rollingstone.com using Cheerio to parse markup and traverse the resulting data structure. Uses mongoose ODM to communicate with MongoDB.
A news forum application built in React with MVC architecture. Single handedly developed a complete user authentication system that uses a local strategy. JWTs transfer from a MySQL database with Sequelize. Also developed a backend that stores and calls user comments mapped to an article database.
A full-stack React application that uses Google Books API to search for user-queried books. User-selected books can be stored in a Mongo Database. Utilizes Mongoose ODM for database communication and Axios for a Node-based HTTP client.
A modularized application that uses Express.js routing methods to communicate with a MySQL database. The client pairing survey is built with Chosen jQuery. Built an algorithm that receives the survey results, comparing them with other user data to find the optimal pair.
Music has always been an way for me to express my creativity and ambition. I have been playing piano since I was seven years old. I developed a love for jazz music in middle school, and ended up playing in jazz ensembles for the next eleven years in a row. I was introduced to Logic Pro my freshman year of high school. The program allowed me to expand my horizons in music recording and production. It also gave me a newfound appreciation for electronic music. I went to Kenyon College on a music scholarship, where continued to pursue my performance and composition skills, taking courses such as Advanced Computer Music and Jazz Theory and Composition. It was there that I discovered my love for Max Cycling '74. I graduated from the department with distinction, and had the honor of performing my jazz composition Balmy for legendary Lincoln Jazz center director and virtuoso trumpeter Wynton Marsalis.
Swimming has been an outlet for my drive and commitment. I began swimming competitively when I was nine, and continued the sport through my collegiate career. I was recruited by Kenyon College after my performances on both the Acalanes High Swim Team and Terapins Swim Team. I was a NCAA All American at Kenyon, earning a multiple top-ten finishes at the NCAA Men's Division III Swimming and Diving Championships. I have been out of the pool the past year, but continue to pursue athletics through rowing and weightlifting.


I study linguistics, TESOL, and computer science at Brigham Young University. I speak English, Mongolian, and Spanish. In my spare time I love to cook and read.


---
layout: gallery
images:
- image_path: "/gallery/img/gallery1/1.jpg"
image-name: Gallery1--示例相册
image-date: July 2015
- image_path: "/gallery/img/gallery1/2.jpg"
image-name: gallery2
image-date: June 2015
- image_path: "/gallery/img/gallery1/3.jpg"
image-name: gallery3
image-date: May 2015
- image_path: "/gallery/img/gallery1/4.jpg"
image-name: gallery4
image-date: April 2015
- image_path: "/gallery/img/gallery1/5.jpg"
image-name: gallery5
image-date: March 2015
- image_path: "/gallery/img/gallery1/6.jpg"
image-name: gallery6
image-date: February 2015
- image_path: "/gallery/img/gallery1/7.jpg"
image-name: gallery7
image-date: January 2015
- image_path: "/gallery/img/gallery1/8.jpg"
image-name: gallery8
image-date: December 2014
- image_path: "/gallery/img/gallery1/9.jpg"
image-name: gallery9
image-date: November 2014
- image_path: "/gallery/img/gallery1/10.jpg"
image-name: gallery9
image-date: November 2014
- image_path: "/gallery/img/gallery1/11.jpg"
image-name: gallery9
image-date: November 2014
- image_path: "/gallery/img/gallery1/12.jpg"
image-name: gallery9
image-date: November 2014
- image_path: "/gallery/img/gallery1/13.jpg"
image-name: gallery9
image-date: November 2014
- image_path: "/gallery/img/gallery1/14.jpg"
image-name: gallery9
image-date: November 2014
- image_path: "/gallery/img/gallery1/15.jpg"
image-name: gallery9
image-date: November 2014
---
{% for image in page.images %}
{% endfor %}


Access this site on your smartphone to watch the graphs below update in real-time based on sensor data readings.
Keep your phone active on this page and watch the magic happen on your computer monitor


{% extends "shop/base.html" %} {% load static %} {% load shop_extras %} {% block content %} {% with colors='bg-carolina-light bg-tangerine-light bg-indigo-light bg-chili-light' %} {% for book in books %} {% with next_element=colors.split|next:forloop.counter0 %} Paperback {{book.name}} {% for author in book.authors.all %} {{author.name}} {% endfor %} $41 ADD TO CART {% endwith %} {% endfor %} {% endwith %} Featured Books {% for book in books %} Paperback {{book.name}} {% for author in book.author.all %} {{author.name}} {% endfor %} ${{book.price}} ADD TO CART {% endfor %} Tranding Books View All {% for book in books|slice:":8" %} Paperback {{book.name}} {% for author in book.author.all %} {{author.name}} {% endfor %} ${{book.price}}29 ADD TO CART {% endfor %} New Arrivals {% for book in books|slice:":4" %} Paperback {{book.name}} {% for author in book.author.all %} {{author.name}} {% endfor %} ${{book.price}} ADD TO CART {% endfor %} {% for book in books|slice:"4:5" %} Kindle Edition {{book.name}} {% for author in book.author.all %} {{author.name}} {% endfor %} ${{book.price}} ADD TO CART {% endfor %} {% for book in books|slice:'5:9' %} Paperback {{book.name}} {% for author in book.author.all %} {{author.name}} {% endfor %} ${{book.price}} ADD TO CART {% endfor %} {% endblock content %}


The way you read will vary depending on what you are reading and what you need to get out of the reading! This resource provides a bank of quick and practical reading strategies that can help you build your own reading strategies toolbox.
Try out one or two of these reading strategies on your next reading assignment! Notice the strengths of the reading strategies and in what contexts they works best for you. You can always return here to try out a new reading strategy when your needs change later on!
Restate section headings as questions. For example, a section heading titled, “Youth Social Media Use” could be turned into, “How do youth in different communities use social media?
As you read and take notes, keep your question in mind. Write down information that answers the question.
In the end, you should be able to summarize the answer to your question.
You may need to revise your question if the content doesn't match up.
Slow down your reading when you encounter unfamiliar ideas or new information that seems particularly relevant to your class or research context.
Increase speed when you encounter ideas that you are familiar with or that don’t pertain to your research context or class.
You can use key phrases to identify when to change your speed. For example, if you already understand the concept, you can speed up when you see the phrase “for example.” But you might want to slow down when you see something like “Conversely” which indicates a shift or new perspective.
Designate a symbol, highlighter color, or other marker that you can use to make note of bits of text that you don’t understand or want more clarification on.
Make note of what you don’t understand… Then try to move on!
Don’t get bogged down by uncertainties—this can hinder your ability to comprehend the text as a whole.
It’s possible your question gets answered later on in the text! But if not, consider visiting your professor or TA to resolve your questions that you cannot answer using the text.


Do you want to proceed?


pwm_hardware
Hardware PWM control
|
Author
|
Sebastien Lelong, Copyright © 2008-2009, all rights reserved.
|
|
Adapted-by
|
Albert Faber, Rob Hamerling.
|
|
Compiler
|
2.4l
|
Description
Include this library if you want to use the PWM feature of the CCP module(s).
This library uses conditional compile to selectively include underlying PWM
libraries and number of PWM channels, according to the target PIC configuration.
Ex: - 16f88 has 1 PWM channel: including pwm_hardware will prepare PWM
for 1 channel (using pwm_ccp1.jal)
- 16f877 has 2 PWM channels: including pwm_hardware will prepare PWM
for 2 channels (using pwm_ccp1.jal and pwm_ccp2.jal)
- etc. for PICs with more CCP or PWM modules
Note: pin_CCPx_directions or pin_PWMx_directions must be set to output
by the application.
.
With using the PWM hardware a design choice must be made between desired
PWM frequency and desired PWM resolution (number of possible PWM steps).
Choosing maximum PWM resolution implies a relative low PWM frequency and
vice versa: a high frequency limits the number of available PWM steps.
The figures are dependent on the target clock speed.
Refer to pwm_common, pwm_ccp1, pwm_ccp2 ... etc. documentation to
understand how to use these PWM libraries.
Notes
1. Because the PWM libraries use conditional compilation and the 'alias'
keyword, at least JalV2 compiler version 2.4l is required.
2. These libraries use the pin alias names as declared in Jallib device
files since revision 1171, so Jallib release 0.4 is a minimum requirement.
3. Some PICs have extended CCP modules. These will be handled by these
PWM libraries as 'classic' CCP modules.
4. Some of the newer PICs have PWM module(s) but no CCP module(s) and
the naming convention of registers and pins differs from the older
PICs. These PICS are supported as well, with same procedure names as
PICs with CCP modules.
Dependencies
Here are the list of samples which use this library:


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Took a 50 kilometres ride just to get your burger. It was totally worth it. Amazing burgers!
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| mandelbrot {future} | R Documentation |
Mandelbrot convergence counts
mandelbrot(...)
Z |
A complex matrix for which convergence counts should be calculated. |
xmid, ymid, side, resolution |
Alternative specification of
the complex plane |
maxIter |
Maximum number of iterations per bin. |
tau |
A threshold; the radius when calling divergence (Mod(z) > tau). |
Returns an integer matrix (of class Mandelbrot) with non-negative counts.
The internal Mandelbrot algorithm was inspired by and adopted from similar GPL code of Martin Maechler available from ftp://stat.ethz.ch/U/maechler/R/ on 2005-02-18 (sic!).
counts <- mandelbrot(xmid = -0.75, ymid = 0, side = 3) str(counts) ## Not run: plot(counts) ## End(Not run) ## Not run: demo("mandelbrot", package = "future", ask = FALSE) ## End(Not run)


SPLIT_PART Function
Splits a string on the specified delimiter and returns the part at the specified position.
Syntax
SPLIT_PART(string, delimiter, part)
Arguments
- string
-
The string to be split. The string can be CHAR or VARCHAR.
- delimiter
-
The delimiter string.
If delimiter is a literal, enclose it in single quotes.
- part
-
Position of the portion to return (counting from 1). Must be an integer greater than 0. If part is larger than the number of string portions, SPLIT_PART returns an empty string.
Return Type
A CHAR or VARCHAR string, the same as the string parameter.
Examples
The following example splits the time stamp field LISTTIME into year, month, and date components.
select listtime, split_part(listtime,'-',1) as year, split_part(listtime,'-',2) as month, split_part(split_part(listtime,'-',3),' ',1) as date from listing limit 5; listtime | year | month | date ---------------------+------+-------+------ 2008-03-05 12:25:29 | 2008 | 03 | 05 2008-09-09 08:03:36 | 2008 | 09 | 09 2008-09-26 05:43:12 | 2008 | 09 | 26 2008-10-04 02:00:30 | 2008 | 10 | 04 2008-01-06 08:33:11 | 2008 | 01 | 06 (5 rows)
The following example selects the LISTTIME time stamp field and splits it on the
'-'
character to get the month (the second part of the LISTTIME
string), then counts the number of entries for each month:
select split_part(listtime,'-',2) as month, count(*) from listing group by split_part(listtime,'-',2) order by 1, 2; month | count -------+------- 01 | 18543 02 | 16620 03 | 17594 04 | 16822 05 | 17618 06 | 17158 07 | 17626 08 | 17881 09 | 17378 10 | 17756 11 | 12912 12 | 4589 (12 rows)


“He hath a soul drowned in a lump of flesh, or is a piece of earth that Prometheus put not half his proportion of fire into … The Muses and the Graces are his hard mistresses; though he daily invocate them, though he sacrifice hecatombs, they still look asquint.  His jests are either old fled proverbs, or lean-starved hackney apophthegms, or poor verbal quips, outworn by serving-men, tapsters, and milkmaids.” — Sir Thomas Overbury, Characters; or Witty Descriptions of the Properties of Sundry Persons


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Teenage girl goes missing from Bristol home
If you see her, police say you should call 101
by Andrew McQuarrieA teenager has been missing from her Bristol home for three days.
Ellie Canning, 15, was reported missing on Thursday (November 21), but police are still trying to trace her.
She is described as white, 5ft 5ins tall and slim, with dark eyes, and shoulder-length dark hair.
Police say there was a possible sighting in the Frog Lane/Park Street area of Bristol at around 10.30pm last night (Saturday).
An Avon and Somerset Constabulary spokesperson said: "If you know where Ellie is now or have any information which could help to trace her, call 101 quoting reference<PHONE_NUMBER>."
They added: "Ellie, if you see this, please let us know you are safe - get in touch with your parents or ring 101."
For the latest news in and around Bristol, visit and bookmark Bristol Live's homepage .


Urban Planner need to keep track of information about building in a city. When people want to build something on a site, they have to make sure that they fit within the legal codes for that area --- they are called zoning codes.


1. Using HTML "table" tags, create the following tables:
2. Using the "table" tag frame and rules attributes, create the following tables:
3. Using the thead, tbody, and tfoot tag pairs, create a table similar to
the following, which assigns a different background color to each table section:
4. Using column groups, create the following table, which highlights each column.
5. Create one or more HTML tables that illustrate the use of cell padding and spacing.
6. Using nested HTML tables, create the following table:


for. T do. B end. for_xyz. T do. B end.The B block is evaluated once for each item of the array A that results from evaluating the T block. In the for_xyz. form, the local name xyz is set to the value of an item on each evaluation of the B block and xyz_index is set to the index of the item; results are not dependable if these names are re-assigned within the B block. break. goes to the end of the for. control structure, and continue. goes to the evaluation of B for the next item. |
f0=: 3 : 0 s=. 0 for. i. y do. s=.>:s end. ) (f0 = ])"0 ?5$100 1 1 1 1 1 f1=: 3 : 0 s=.0 for_j. i.y do. if. 2|j do. continue. end. s=.j+s end. ) (f1 = 2&!@>.&.-:)"0 ?5$100 1 1 1 1 1 comb=: 4 : 0 NB. All size x combinations of i.y k=. i.>:d=.y-x z=. (d$<i.0 0),<i.1 0 for. i.x do. z=. k ,.&.> ,&.>/\. >:&.> z end. ; z ) 3 comb 5 0 1 2 0 1 3 0 1 4 0 2 3 0 2 4 0 3 4 1 2 3 1 2 4 1 3 4 2 3 4 queens=: 3 : 0 NB. solves the n queens problem z=.i.n,*n=.y for. }.z do. b=. -. (i.n) e."1 ,. z +"1 _ ((-i.){:$z) */ _1 0 1 z=. ((+/"1 b)#z),.n|I.,b end. ) queens 5 0 2 4 1 3 0 3 1 4 2 1 3 0 2 4 1 4 2 0 3 2 0 3 1 4 2 4 1 3 0 3 0 2 4 1 3 1 4 2 0 4 1 3 0 2 4 2 0 3 1


R/bayesian_inference_functions.R
summarize_trunc_gauss_mix_sample.Rd
The input vector th parameterizes a Gaussian mixture, and tau_min / tau_max give the limits of truncation. Summarize the sample by identifying growth / decay periods and the peak value using the following procedure.
(1) Calculate the derivative, f'(t), at the points t = seq(tau_min,tau_max,len=N), where N is 1000 by default.
(2) Identify points where f'(t) changes sign, then numerically estimate the crossing point between the two t values where there was a sign change.
(3) Create a vector of critical points, t_crit, which includes tau_min / tau_max as well as the crossing points found in the preceding step.
(4) Calculate the density at the critical points to identify the peak value, f_peak, and corresponding calendar date, t_peak, as well as the index of the peak in t_crit, ind_peak.
(5) For each time period (the length(t_peak)-1 durations defined by t_peak) determine the sign of the density function, f(t), and create a character vector, slope, that has the value 'pos' if f(t) is positive and 'neg' if f(t) is negative.
(6) Finally, create a character vector, pattern, that appends the index of the peak in t_crit (converted to a character) to the character vector slope. This defines a unique pattern of the sample that takes into account periods of growth / decline and the relative location of the peak.
summarize_trunc_gauss_mix_sample(th, tau_min, tau_max, N = 1000)
| th | The Gaussian mixture parameterization |
|---|---|
| tau_min | The lower truncation value |
| tau_max | The upper truncation value |
| N | The number of points use for identifying slope changes (default: 1000) |
A list consisting of:
periods
A data-frame where the columns t_lo/t_hi indicate the starting and
ending calendars dates of periods and slope is negative if the growth
rate is negative over that time period and positive if it is positive.
ind_peak
The index of the period in the data-frame periods
with the peak
value of the density.
t_peak
The calendar date of the peak value of the density.
f_peak
The value of the density function at the peak calendar date.
pattern
A unique pattern that summaries the periods of growth/decary and
relative locaiton of the peak (see Description).


|
STEP 2: Adding the Framework For Sonification
|
Now that we have designed the HTML layout, there is some basic legwork that must first be done to enable the navbar to be sonified.
Sonifying a Web page using the interactive scripting capability of Beatnik requires that you 'talk' to Beatnik through the Music Object API. The Music Object API comes in the form of a separate JavaScript file that is sourced (linked) into the current page. It then becomes part of the page, just as if the JavaScript code was copied-and-pasted into the page itself. The benefit of sourcing it in is that you don't need to see all this messy JavaScript code in your pages, and the file can also be shared between all pages that need to be sonified. The alternative would be to copy-and-paste the JavaScript code into every page you wanted to sonify - a lot of tedious work that would also bump up the file size of all your pages. The Music Object Javascript file is sourced into your page with the line...
 |
In this example, it is assumed that the JavaScript file called "music-object.js" is located in the same folder as the HTML file being sonified. If this file is to be located in a different resource directory for your Web site, then the correct path to the file must be specified in front of the actual filename. If the path name is incorrectly specified, the page will not function correctly and no sound will be heard.
When you wish to embed a Beatnik Player instance in your HTML page so that you can send sonification 'instructions' to it - for example, telling it to play notes or Groovoids - you need to create a Music Object instance to accompany that Beatnik Player instance. In fact, it is necessary to create a unique Music Object instance for each Beatnik Player instance that you wish to embed. Once the Music Object instance has been created and you have embedded a Beatnik Player instance for that Music Object instance, you can then use all of the callable methods of the Music Object API. The method that we're most interested in right now is the playNote method.
Now, the Music Object instance in this example is created with the statement...
notePlayer = new Music (); |
You can also see this code in its full context further down, where the complete source code is shown. The reserved keyword 'new' is the constructor keyword in JavaScript that allows you to create a new instance of an object. In this case, we are creating a new instance of the object called 'Music' which is defined in the "music-object.js" file that we've already sourced into the HTML document. All the playNote method calls used in the final sonified navbar will be targeted to the Music Object instance named "notePlayer". You will see the exact syntax for these method calls in further steps of this tutorial.
Now that the "music-object.js" file has been sourced into the HTML document and the Music Object instance "notePlayer" has been created, it is necessary to embed a Beatnik Player instance for that Music Object instance. We do this using the stubEmbed instance method.
The stubEmbed method embeds a Beatnik Player instance with certain preset properties that are convenient for embedding a hidden player instance that is going to be used specifically for UI sonification techniques that involve the playing of notes. For instance, the player that is embedded by stubEmbed will have a size of 0 pixels by 0 pixels and will be hidden on the page. It will also start playing automatically and will loop continuously, both of which are requirements for playing notes through the Beatnik Player. The only parameter that gets passed to the stubEmbed method is a string representing the URL of the stub file that should be loaded by the player. The stub file, itself, is no more than a very small blank RMF file that loops only after a very long time. It is required to instantiate the Beatnik Player using a source file, and so a stub file is used in this case because we don't want to have any music playing in the background.
The Beatnik Player instance is embedded for the "notePlayer" Music Object instance with the statement...
notePlayer.stubEmbed ('stub.rmf'); |
You can also see this code in its full context further down, where the complete source code is shown.
In this example, it is assumed that the RMF stub file called "stub.rmf" is located in the same folder as the HTML file being sonified. If this file is to be located in a different resource directory for your Web site, then the correct path to the file must be specified in front of the actual filename. If the path name is incorrectly specified, the page will not function correctly and no sound will be heard.
So, this is what we have so far...
Let's take a look at the HTML for this sample. The table border has now been set to 0 and so you can see how the table layout has arranged these graphics to seamlessly connect to each other, forming what appears to be just one graphic. The highlighted text indicates the code that has been added since the previous step.

|
It is customary to place the code that sources in the "music-object.js" file and sets up the Music Object instance, ahead of all the HTML in the document. This is because HTML in the document may call methods of the Music Object instances that you set up towards the head of your document. If the Music Object instances are not created by the time the HTML calls methods of them, users will be bombarded with JavaScript error alerts and the sonification will fail.
It is also, of course, important that you source in the "music-object.js" file before you try creating new Music Object instances, since it is the "music-object.js" file that defines the Music Object object class. A failure to have "music-object.js" loaded in before trying to create new Music Object instances will result in JavaScript error messages - not a pleasant experience for users of a Web site.


Turn your heartbeat up while you dance to the latest music hits!
Release adrenaline and gain strength with this Japanese sport. The king of all contact sports.
Do you dare with military training? Fit cross, your high intensity activity that adapts to your physical condition.
Step is low-impact physical training to improve resistance, strength and flexibility, a wonderful grease burner.
Are those group activities that are aimed to improve the cardiorespiratory system and result in increased aerobic capacity and decreased body fat.
Are those group activities that are aimed to improve the cardiorespiratory system and result in increased aerobic capacity and decreased body fat.


sftp —
secure
file transfer program
| sftp |
[-46aCfpqrv]
[-B buffer_size]
[-b batchfile]
[-c cipher]
[-D sftp_server_path]
[-F ssh_config]
[-i identity_file]
[-l limit]
[-o ssh_option]
[-P port]
[-R num_requests]
[-S program]
[-s subsystem | sftp_server] host |
| sftp |
[user@]host[:file
...] |
| sftp |
[user@]host[:dir[/]] |
| sftp |
-b
batchfile
[user@]host |
sftp is an interactive file transfer program,
similar to
ftp(1), which
performs all operations over an encrypted
ssh(1) transport. It may also
use many features of ssh, such as public key authentication and compression.
sftp connects and logs into the specified
host, then enters an interactive command
mode.
The second usage format will retrieve files automatically if a non-interactive
authentication method is used; otherwise it will do so after successful
interactive authentication.
The third usage format allows
sftp to start in a
remote directory.
The final usage format allows for automated sessions using the
-b option. In such cases, it is necessary to
configure non-interactive authentication to obviate the need to enter a
password at connection time (see
sshd(8) and
ssh-keygen(1) for
details).
Since some usage formats use colon characters to delimit host names from path
names, IPv6 addresses must be enclosed in square brackets to avoid ambiguity.
The options are as follows:
- -4
- Forces sftp to use IPv4
addresses only.
- -6
- Forces sftp to use IPv6
addresses only.
- -a
- Attempt to continue interrupted transfers rather than
overwriting existing partial or complete copies of files. If the partial
contents differ from those being transferred, then the resultant file is
likely to be corrupt.
- -B
buffer_size
- Specify the size of the buffer that
sftp uses when transferring files. Larger
buffers require fewer round trips at the cost of higher memory
consumption. The default is 32768 bytes.
- -b
batchfile
- Batch mode reads a series of commands from an input
batchfile instead of
stdin. Since it lacks user interaction it
should be used in conjunction with non-interactive authentication. A
batchfile of ‘-’ may be
used to indicate standard input. sftp will
abort if any of the following commands fail:
get, put,
reget, reput,
rename, ln,
rm, mkdir,
chdir, ls,
lchdir, chmod,
chown, chgrp,
lpwd, df,
symlink, and
lmkdir. Termination on error can be
suppressed on a command by command basis by prefixing the command with a
‘-’ character (for example, -rm
/tmp/blah*).
- -C
- Enables compression (via ssh's
-C flag).
- -c
cipher
- Selects the cipher to use for encrypting the data
transfers. This option is directly passed to
ssh(1).
- -D
sftp_server_path
- Connect directly to a local sftp server (rather than via
ssh(1)). This option may be
useful in debugging the client and server.
- -F
ssh_config
- Specifies an alternative per-user configuration file for
ssh(1). This option is
directly passed to
ssh(1).
- -f
- Requests that files be flushed to disk immediately after
transfer. When uploading files, this feature is only enabled if the server
implements the<EMAIL_ADDRESS>extension.
- -i
identity_file
- Selects the file from which the identity (private key) for
public key authentication is read. This option is directly passed to
ssh(1).
- -l
limit
- Limits the used bandwidth, specified in Kbit/s.
- -o
ssh_option
- Can be used to pass options to
ssh in the format used in
ssh_config(5). This
is useful for specifying options for which there is no separate
sftp command-line flag. For example, to
specify an alternate port use: sftp
-oPort=24. For full details of the options listed below, and their
possible values, see
ssh_config(5).

- -R
num_requests
- Specify how many requests may be outstanding at any one
time. Increasing this may slightly improve file transfer speed but will
increase memory usage. The default is 64 outstanding requests.
- -r
- Recursively copy entire directories when uploading and
downloading. Note that sftp does not follow
symbolic links encountered in the tree traversal.
- -S
program
- Name of the program to use
for the encrypted connection. The program must understand
ssh(1) options.
- -s
subsystem |
sftp_server
- Specifies the SSH2 subsystem or the path for an sftp server
on the remote host. A path is useful when the remote
sshd(8) does not have an
sftp subsystem configured.
- -v
- Raise logging level. This option is also passed to
ssh.
Once in interactive mode,
sftp understands a set of
commands similar to those of
ftp(1). Commands are case
insensitive. Pathnames that contain spaces must be enclosed in quotes. Any
special characters contained within pathnames that are recognized by
glob(3) must be escaped with
backslashes (‘\’).
- bye
- Quit sftp.
- cd
path
- Change remote directory to
path.
- chgrp
grp
path
- Change group of file path
to grp.
path may contain
glob(3) characters and may
match multiple files. grp must be a
numeric GID.
- chmod
mode
path
- Change permissions of file
path to
mode.
path may contain
glob(3) characters and may
match multiple files.
- chown
own
path
- Change owner of file path
to own.
path may contain
glob(3) characters and may
match multiple files. own must be a
numeric UID.
- df
[-hi]
[path]
- Display usage information for the filesystem holding the
current directory (or path if specified).
If the -h flag is specified, the capacity
information will be displayed using "human-readable" suffixes.
The -i flag requests display of inode
information in addition to capacity information. This command is only
supported on servers that implement the
<EMAIL_ADDRESS>extension.
- exit
- Quit sftp.
- get
[-afPpr]
remote-path
[local-path]
- Retrieve the remote-path
and store it on the local machine. If the local path name is not
specified, it is given the same name it has on the remote machine.
remote-path may contain
glob(3) characters and may
match multiple files. If it does and
local-path is specified, then
local-path must specify a directory.
If the -a flag is specified, then attempt to
resume partial transfers of existing files. Note that resumption assumes
that any partial copy of the local file matches the remote copy. If the
remote file contents differ from the partial local copy then the resultant
file is likely to be corrupt.
If the -f flag is specified, then
fsync(2) will be called
after the file transfer has completed to flush the file to disk.
If either the -P or
-p flag is specified, then full file
permissions and access times are copied too.
If the -r flag is specified then directories
will be copied recursively. Note that sftp
does not follow symbolic links when performing recursive transfers.
- help
- Display help text.
- lcd
path
- Change local directory to
path.
- lls
[ls-options
[path]]
- Display local directory listing of either
path or current directory if
path is not specified.
ls-options may contain any flags
supported by the local system's
ls(1) command.
path may contain
glob(3) characters and may
match multiple files.
- lmkdir
path
- Create local directory specified by
path.
- ln
[-s]
oldpath
newpath
- Create a link from oldpath
to newpath. If the
-s flag is specified the created link is a
symbolic link, otherwise it is a hard link.
- lpwd
- Print local working directory.
- ls
[-1afhlnrSt]
[path]
- Display a remote directory listing of either
path or the current directory if
path is not specified.
path may contain
glob(3) characters and may
match multiple files.
The following flags are recognized and alter the behaviour of
ls accordingly:
- -1
- Produce single columnar output.
- -a
- List files beginning with a dot
(‘.’).
- -f
- Do not sort the listing. The default sort order is
lexicographical.
- -h
- When used with a long format option, use unit suffixes:
Byte, Kilobyte, Megabyte, Gigabyte, Terabyte, Petabyte, and Exabyte in
order to reduce the number of digits to four or fewer using powers of
2 for sizes (K=1024, M=1048576, etc.).
- -l
- Display additional details including permissions and
ownership information.
- -n
- Produce a long listing with user and group information
presented numerically.
- -r
- Reverse the sort order of the listing.
- -S
- Sort the listing by file size.
- -t
- Sort the listing by last modification time.
- lumask
umask
- Set local umask to
umask.
- mkdir
path
- Create remote directory specified by
path.
- progress
- Toggle display of progress meter.
- put
[-afPpr]
local-path
[remote-path]
- Upload local-path and
store it on the remote machine. If the remote path name is not specified,
it is given the same name it has on the local machine.
local-path may contain
glob(3) characters and may
match multiple files. If it does and
remote-path is specified, then
remote-path must specify a directory.
If the -a flag is specified, then attempt to
resume partial transfers of existing files. Note that resumption assumes
that any partial copy of the remote file matches the local copy. If the
local file contents differ from the remote local copy then the resultant
file is likely to be corrupt.
If the -f flag is specified, then a request
will be sent to the server to call
fsync(2) after the file
has been transferred. Note that this is only supported by servers that
implement the<EMAIL_ADDRESS>extension.
If either the -P or
-p flag is specified, then full file
permissions and access times are copied too.
If the -r flag is specified then directories
will be copied recursively. Note that sftp
does not follow symbolic links when performing recursive transfers.
- pwd
- Display remote working directory.
- quit
- Quit sftp.
- reget
[-Ppr]
remote-path
[local-path]
- Resume download of
remote-path. Equivalent to
get with the -a
flag set.
- reput
[-Ppr]
[local-path]
remote-path
- Resume upload of
[local-path].
Equivalent to put with the
-a flag set.
- rename
oldpath
newpath
- Rename remote file from
oldpath to
newpath.
- rm
path
- Delete remote file specified by
path.
- rmdir
path
- Remove remote directory specified by
path.
- symlink
oldpath
newpath
- Create a symbolic link from
oldpath to
newpath.
- version
- Display the sftp protocol
version.
- !command
- Execute command in local
shell.
- !
- Escape to local shell.
- ?
- Synonym for help.
ftp(1),
ls(1),
scp(1),
ssh(1),
ssh-add(1),
ssh-keygen(1),
glob(3),
ssh_config(5),
sftp-server(8),
sshd(8)
T. Ylonen and
S. Lehtinen, SSH File Transfer
Protocol, draft-ietf-secsh-filexfer-00.txt,
January 2001, work in progress
material.


--- layout: page title: About header-img: "img/about-bg.png" ---
I love writing clean, simple code, and writing about code and life. I approach JavaScript with fear and trembling. Oh, and tacos. I love tacos. This website is my blog for the Flatiron School community. You can find my main blog here, though much of the content will be the same, and my main website here.


Problem Statement
- Develop a software system to use the Pozyx positioning hardware for physics labs.
- Create abstraction about the system to make it as easy as possible for educators and students to use.
Motivation
Figure 1: Two Pozyx tags in operation
Design Criteria
- Uniform software solution for interfacing with Pozyx and using its hardware for physics education.
- Commands to the hardware must implement a Pozyx library written in Python, so most code will be in Python.
- Graphical user interface (GUI) needed for easy interaction in the classroom, and Java using the JavaFX package chosen for portability and simple development.
- Means to analyze and visualize data both while it is being collected and after it is collected are required.
- Must create a simple, cross-platform installation with streamlined usage patterns so educators and students can quickly and easily start using the software.
Figure 2: An example of debugging some Python code
Software Solution
Figure 3: The GUI prototype with features labeled
Testing
Figure 4: The materials involved in my inclined plane acceleration lab
Figure 5: Test of cart displacement (mm) over time (s)
Evaluation
My research shows that my implementation of a software abstraction over the Pozyx positioning hardware has been successful in creating a tool to easily and effectively improve physics labs.
- I successfully created working data collection and user interface software for the system.
- My design of the software shows plenty of potential for further improvement and tailoring towards education.
- When tested in a physics lab, I was able to easily collect data that had an outstanding <1% error.
- Cross-platform development is prone to bugs and edge cases, so my software is most stable on Windows.
- The Pozyx Python library is still being developed by Pozyx Labs, so both breaking changes and beneficial improvements must be considered in the future.
- Currently, the price of each Pozyx device at around $150 USD could be rather steep for many educators, but prices of Pozyx or similar hardware solutions will likely drop with greater production and implementation.
Implementation Plans
Figure 6: The university team and I are investigating professional sports applications
- Develop stable distributions for Windows, OS X, and Linux.
- Create 3-dimensional real time graphing visualizations.
- Implement a data-replay system to allow the "real-time" visualization of data collected in the past stored in .csv files.
- Distribute my software to physics educators and test its efficacy in high-school and college classrooms through redesigned kinematic physics labs.
- Investigate other applications of a software abstraction for Pozyx, such as in professional sports, robotics, or behavioral studies.
Acknowledgements
Special thanks go to the university team with whom I collaborated. All pictures were taken or created by researcher.


In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing the code template and writeup template will cover all of the rubric points for this project.
The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.
# Load pickled data
import pickle
# TODO: Fill this in based on where you saved the training and testing data
training_file = "traffic-signs-data/train.p"
validation_file= "traffic-signs-data/valid.p"
testing_file = "traffic-signs-data/test.p"
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(validation_file, mode='rb') as f:
valid = pickle.load(f)
with open(testing_file, mode='rb') as f:
test = pickle.load(f)
X_train, y_train = train['features'], train['labels']
X_valid, y_valid = valid['features'], valid['labels']
X_test, y_test = test['features'], test['labels']
The pickled data is a dictionary with 4 key/value pairs:
'features'
is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).'labels'
is a 1D array containing the label/class id of the traffic sign. The file signnames.csv
contains id -> name mappings for each id.'sizes'
is a list containing tuples, (width, height) representing the the original width and height the image.'coords'
is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGESComplete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the pandas shape method might be useful for calculating some of the summary results.
### Replace each question mark with the appropriate value.
### Use python, pandas or numpy methods rather than hard coding the results
import numpy as np
import matplotlib.pyplot as plt
# Number of training examples
n_train = len(y_train)
n_test = len(y_test)
n_valid = len(y_valid)
# What's the shape of an traffic sign image?
image_shape = X_train[0].shape
# How many unique classes/labels there are in the dataset.
n_classes = len(np.unique(y_test))
print("Number of training examples =", n_train)
print("Number of validation examples =", n_valid)
print("Number of testing examples =", n_test)
print("X_train data shape =", X_train.shape)
print("Image data shape =", image_shape)
print("Number of classes =", n_classes)
# Scatter plot to show uneven class distribution
uniq_label, ucount = np.unique(y_train, return_counts=True)
plt.scatter(uniq_label, ucount)
plt.show()
Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.
The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.
NOTE: It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections.
### Data exploration visualization code goes here.
### Feel free to use as many code cells as needed.
import matplotlib.pyplot as plt
import random
# Visualizations will be shown in the notebook.
%matplotlib inline
index = random.randint(0, n_train)
image = X_train[index].squeeze()
plt.figure(figsize=(1,1))
plt.imshow(image)
print(y_train[index])
Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.
There are various aspects to consider when thinking about this problem:
Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.
NOTE: The LeNet-5 implementation shown in the classroom at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!
Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.
### Preprocess the data here. Preprocessing steps could include normalization, converting to grayscale, etc.
### Feel free to use as many code cells as needed.
from sklearn.utils import shuffle
X_train, y_train = shuffle(X_train, y_train)
def plot_img_sample(data, y):
for i in range(0, 9):
plt.subplot(330 + 1 + i)
plt.title(y[i])
plt.imshow(data[i])
plt.show()
# after shuffle, recheck image data in a 3x3 grid
plot_img_sample(X_train, y_train)
# normaliztion seems to speed up training
def normalize_data(X):
return (X - 127.5)/255
X_train = normalize_data(X_train)
X_valid = normalize_data(X_valid)
X_test = normalize_data(X_test)
### Define your architecture here.
### Feel free to use as many code cells as needed.
import tensorflow as tf
from tensorflow.contrib.layers import flatten
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
mu = 0
sigma = 0.05
hidden_1 = 240
hidden_2 = 86
print("total params approx: ", 400*hidden_1+hidden_1*hidden_2+hidden_2*n_classes)
weights = {
'wc1': tf.Variable(tf.truncated_normal([5, 5, 3, 6], mean = mu, stddev = sigma)),
'wc2': tf.Variable(tf.truncated_normal([5, 5, 6, 16], mean = mu, stddev = sigma)),
'wd1': tf.Variable(tf.random_normal([5*5*16, hidden_1], mean = mu, stddev = sigma)),
'wd2': tf.Variable(tf.random_normal([hidden_1, hidden_2], mean = mu, stddev = sigma)),
'out': tf.Variable(tf.random_normal([hidden_2, n_classes]))}
biases = {
'bc1': tf.Variable(tf.zeros([6])),
'bc2': tf.Variable(tf.zeros([16])),
'bd1': tf.Variable(tf.zeros([hidden_1])),
'bd2': tf.Variable(tf.zeros([hidden_2])),
'out': tf.Variable(tf.zeros([n_classes]))}
def conv2d(x, W, b, strides=1):
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='VALID')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='VALID')
keep_prob = tf.placeholder(tf.float32)
def LeNet(x):
# Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
keep_prob = 0.5
# TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
# TODO: Activation.
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# TODO: Pooling. Input = 28x28x6. Output = 14x14x6.
conv1 = maxpool2d(conv1, k=2)
#print("conv1 shape should be 14x14x6: ", conv1.get_shape())
# TODO: Layer 2: Convolutional. Output = 10x10x16.
# TODO: Activation.
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
#print("conv2 shape: ", conv2.get_shape())
# TODO: Pooling. Input = 10x10x16. Output = 5x5x16.
conv2 = maxpool2d(conv2, k=2)
#print("conv2 shape should be 5x5x16: ", conv2.get_shape())
# TODO: Flatten. Input = 5x5x16. Output = 400.
flat_x = tf.contrib.layers.flatten(conv2)
#print("flat_x shape: ", flat_x.get_shape())
# TODO: Layer 3: Fully Connected. Input = 400. Output = 120.
# TODO: Activation.
fc1 = tf.add(tf.matmul(flat_x, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
fc1 = tf.nn.dropout(fc1, keep_prob)
# TODO: Layer 4: Fully Connected. Input = 120. Output = 84.
# TODO: Activation.
fc2 = tf.add(tf.matmul(fc1, weights['wd2']), biases['bd2'])
fc2 = tf.nn.relu(fc2)
fc2 = tf.nn.dropout(fc2, keep_prob)
# TODO: Layer 5: Fully Connected. Input = 84. Output = n_classes.
logits = tf.add(tf.matmul(fc2, weights['out']), biases['out'])
return logits
A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation sets imply underfitting. A high accuracy on the test set but low accuracy on the validation set implies overfitting.
### Train your model here.
### Calculate and report the accuracy on the training and validation set.
### Once a final model architecture is selected,
### the accuracy on the test set should be calculated and reported as well.
### Feel free to use as many code cells as needed.
import tensorflow as tf
rate = 0.001
x = tf.placeholder(tf.float32, (None, 32, 32, 3))
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, n_classes)
logits = LeNet(x)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits, one_hot_y)
loss_operation = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate = rate)
training_operation = optimizer.minimize(loss_operation)
### Evaluation
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
print("X_train image shape: ", X_train[0].shape)
print("x input tensor shape: ", x.get_shape())
def evaluate(X_data, y_data):
num_examples = len(X_data)
total_accuracy = 0
sess = tf.get_default_session()
for offset in range(0, num_examples, BATCH_SIZE):
batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob : 1.0})
total_accuracy += (accuracy * len(batch_x))
return total_accuracy / num_examples
import time
start = time.clock()
EPOCHS = 40
BATCH_SIZE = 128
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
num_examples = len(X_train)
print("Training...")
print()
for i in range(EPOCHS):
X_train, y_train = shuffle(X_train, y_train)
for offset in range(0, num_examples, BATCH_SIZE):
end = offset + BATCH_SIZE
batch_x, batch_y = X_train[offset:end], y_train[offset:end]
sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob : 0.5})
training_accuracy = evaluate(X_train, y_train)
validation_accuracy = evaluate(X_valid, y_valid)
print("EPOCH {} ...".format(i+1))
print("Training Accuracy = {:.3f}".format(training_accuracy))
print("Validation Accuracy = {:.3f}".format(validation_accuracy))
print()
saver.save(sess, './trafficsigns')
print("Model saved")
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
test_accuracy = evaluate(X_test, y_test)
print("Test Accuracy = {:.3f}".format(test_accuracy))
To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.
You may find signnames.csv
useful as it contains mappings from the class id (integer) to the actual sign name.
from IPython.display import display, Image
from scipy import ndimage
import os
### Load the images and plot them here.
### Feel free to use as many code cells as needed.
def display_train_img(folder, numImage):
listOfImageNames = os.listdir(folder)
n = 0;
for imageName in listOfImageNames:
image_file = os.path.join(folder, imageName)
print(image_file)
display(Image(filename=image_file))
n += 1;
if(n>=numImage):
break
display_train_img("testimages", 6)
### Load image into array (using code from Tensorflow notmist tutorial)
image_size = 32
image_channels = 3
def load_tests(folder):
image_files = os.listdir(folder)
dataset = np.ndarray(shape=(len(image_files), image_size, image_size, image_channels),
dtype=np.uint8)
print("Test image folder:", folder)
num_images = 0
for image in image_files:
image_file = os.path.join(folder, image)
try:
image_data = ndimage.imread(image_file, mode="RGB")
if image_data.shape != (image_size, image_size, image_channels):
raise Exception('Unexpected image shape: %s' % str(image_data.shape))
dataset[num_images, :, :] = image_data
num_images = num_images + 1
except IOError as e:
print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.')
dataset = dataset[0:num_images, :, :]
print('Full dataset tensor:', dataset.shape)
print('Mean:', np.mean(dataset))
print('Standard deviation:', np.std(dataset))
return dataset
test_dataset = load_tests("testimages")
def plot_test_imgs(data):
numImg = len(data)
for i in range(0, numImg):
plt.subplot(330 + 1 + i)
plt.title(y[i])
plt.imshow(data[i])
plt.show()
plot_test_imgs(test_dataset)
### Run the predictions here and use the model to output the prediction for each image.
### Make sure to pre-process the images with the same pre-processing pipeline used earlier.
### Feel free to use as many code cells as needed.
test_dataset = normalize_data(test_dataset)
print(test_dataset.shape)
test_logits = LeNet(x)
test_prob = tf.nn.softmax(test_logits)
test_predict = tf.argmax(test_logits, 1)
test_target_classes = np.array([22, 28, 17, 25, 1, 2]);
top_probs, top_indices = tf.nn.top_k(test_prob, k=5)
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
test_prob_val = sess.run(test_prob, feed_dict={x: test_dataset, keep_prob: 1.0})
test_predict_val = sess.run(test_predict, feed_dict={x: test_dataset, keep_prob: 1.0})
top_five_probs, top_five_indices = sess.run([top_probs, top_indices], feed_dict={x: test_dataset, keep_prob: 1.0})
test_accuracy = evaluate(test_dataset, test_target_classes)
print("Test Accuracy = {:.3f}".format(test_accuracy))
print("Prediction: ", test_predict_val)
### Calculate the accuracy for these 5 new images.
### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.
## 3 of 6 images were predicted correctly
print("Test Accuracy = {:.3f}".format(test_accuracy))
For each of the new images, print out the model's softmax probabilities to show the certainty of the model's predictions (limit the output to the top 5 probabilities for each image). tf.nn.top_k
could prove helpful here.
The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.
tf.nn.top_k
will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.
Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. tk.nn.top_k
is used to choose the three classes with the highest probability:
# (5, 6) array
a = np.array([[ 0.24879643, 0.07032244, 0.12641572, 0.34763842, 0.07893497,
0.12789202],
[ 0.28086119, 0.27569815, 0.08594638, 0.0178669 , 0.18063401,
0.15899337],
[ 0.26076848, 0.23664738, 0.08020603, 0.07001922, 0.1134371 ,
0.23892179],
[ 0.11943333, 0.29198961, 0.02605103, 0.26234032, 0.1351348 ,
0.16505091],
[ 0.09561176, 0.34396535, 0.0643941 , 0.16240774, 0.24206137,
0.09155967]])
Running it through sess.run(tf.nn.top_k(tf.constant(a), k=3))
produces:
TopKV2(values=array([[ 0.34763842, 0.24879643, 0.12789202],
[ 0.28086119, 0.27569815, 0.18063401],
[ 0.26076848, 0.23892179, 0.23664738],
[ 0.29198961, 0.26234032, 0.16505091],
[ 0.34396535, 0.24206137, 0.16240774]]), indices=array([[3, 0, 5],
[0, 1, 4],
[0, 5, 1],
[1, 3, 5],
[1, 4, 3]], dtype=int32))
Looking just at the first row we get [ 0.34763842, 0.24879643, 0.12789202]
, you can confirm these are the 3 largest probabilities in a
. You'll also notice [3, 0, 5]
are the corresponding indices.
### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web.
### Feel free to use as many code cells as needed.
print(top_five_probs)
print(top_five_indices)
## Visualize softmax probabilities of each prediction
def plot_probability(data):
numImg = len(data)
for i in range(0, numImg):
plt.subplot(330 + 1 + i)
ind = np.arange(43)
plt.bar(ind, data[i])
plt.show()
plot_probability(test_prob_val)
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the IPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.


The graph uses hourly weather data from ERCOT and other data sources to produce an accurate model within the industry standard of 3%.
The model is tested using actual demands from past recordings up to the present. The graph displays comparisons of predictions and actuals for the selected time period.
The map is drillable, includes overlay capabilities, and allows for the user to see detailed information at any given point in the graph when clicked on.
The map has intuitive colors and is drillable to a state, region, and county level. Clicking on an area provides summary information about it.
The energy prediction of this software has not been continued for further use. The values you will see are from the period of November 2015 to March 2016.


- Supported students in three courses in game programming and software development techniques under Prof. Richard McKenna, with a fourth in progress.
- Held tutorials, review sessions, and technology workshops with an average of 30 students in attendance, using materials I personally prepared.
- Also helped students with other courses on an unofficial basis.
- Arranged a discount on a game design tool, exclusively for students in the game programming course.
- Added features and fixed bugs in course-provided code (mostly porting it from DirectX to SFML).


Here are some sample services that can illustrate how JAX-RS works with Apache Abdera Atom. In all cases, the client servlet(JAX-RS 2.0 Client API) is making requests to the server. Click a link, server return the Atom Object back to the client, and the client servlet will print the output in the browser window.
By watching the output of the server (the console window of WDT, or standard out or console.log), you can see: AtomEntryProvider--writable------------ AtomFeedProvider--writable------------ AtomFeedProvider--write------------


#include <BalanceOperations.hpp>
◆ WithdrawContractOperation() [1/2]
| hsrcore::blockchain::WithdrawContractOperation::WithdrawContractOperation |
( |
| ) |
|
|
inline |
◆ WithdrawContractOperation() [2/2]
◆ evaluate()
◆ amount
| ShareType hsrcore::blockchain::WithdrawContractOperation::amount |
◆ balance_id
| BalanceIdType hsrcore::blockchain::WithdrawContractOperation::balance_id |
◆ contract
| ContractIdType hsrcore::blockchain::WithdrawContractOperation::contract |
◆ type
The documentation for this struct was generated from the following files:


name is required
name contains only letters and spaces
name must contain 6 characters at least
email is required
Invalid email
Password is required
password must contain 8 characters at least
password can contain 30 characters maximum
password must contain at least one uppercase letter and one lowercase letter
password must contain at least one number and one special character
Repeat Password is required
The passwords entered do not match
website is required
Invalid website
fax is required
Invalid fax make sure you enter 9 digits at least after +
fax can contain 15 characters maximum
Phone is required
Invalid Phone
Phone must contain a minimum of 11 characters
Phone must contain a maximum of 15 characters
address is required
Description is required
Description can contain 250 characters maximum


For most applications, I/O with character streams is no more complicated than I/O with byte streams. Input and output done with stream classes automatically translates to and from the local character set. A program that uses character streams in place of byte streams automatically adapts to the local character set and is ready for internationalization — all without extra effort by the programmer.
If internationalization isn't a priority, you can simply use the character stream classes without paying much attention to character set issues. Later, if internationalization becomes a priority, your program can be adapted without extensive recoding. See the Internationalization trail for more information.
Reader
and
Writer
. As with byte streams, there are character stream classes that
specialize in file I/O:
FileReader
and
FileWriter
.
The
CopyCharacters
example illustrates these classes.
import java.io.FileReader; import java.io.FileWriter; import java.io.IOException; public class CopyCharacters { public static void main(String[] args) throws IOException { FileReader inputStream = null; FileWriter outputStream = null; try { inputStream = new FileReader("xanadu.txt"); outputStream = new FileWriter("characteroutput.txt"); int c; while ((c = inputStream.read()) != -1) { outputStream.write(c); } } finally { if (inputStream != null) { inputStream.close(); } if (outputStream != null) { outputStream.close(); } } } }
CopyCharacters
is very similar to
CopyBytes
. The most important difference is that
CopyCharacters
uses FileReader
and
FileWriter
for input and output in place of
FileInputStream
and FileOutputStream
.
Notice that both CopyBytes
and CopyCharacters
use an int
variable to read to and write from. However, in
CopyCharacters
, the int
variable holds a
character value in its last 16 bits; in CopyBytes
, the
int
variable holds a byte
value in its last
8 bits.
FileReader
, for example, uses
FileInputStream
, while
FileWriter
uses FileOutputStream
.
There are two general-purpose byte-to-character "bridge" streams:
InputStreamReader
and
OutputStreamWriter
. Use them to create character streams when there are no prepackaged
character stream classes that meet your needs. The
sockets lesson
in the
networking trail
shows how to create character streams from the byte streams provided
by socket classes.
"\r\n"
), a single
carriage-return ("\r"
), or a single line-feed
("\n"
). Supporting all possible line terminators allows
programs to read text files created on any of the widely used
operating systems.
Let's modify the CopyCharacters
example to use
line-oriented I/O. To do this, we have to use two classes we haven't
seen before,
BufferedReader
and
PrintWriter
.
We'll explore these classes in greater depth in Buffered I/O and Formatting. Right now, we're just
interested in their support for line-oriented I/O.
The
CopyLines
example invokes BufferedReader.readLine
and
PrintWriter.println
to do input and output one line at a
time.
import java.io.FileReader; import java.io.FileWriter; import java.io.BufferedReader; import java.io.PrintWriter; import java.io.IOException; public class CopyLines { public static void main(String[] args) throws IOException { BufferedReader inputStream = null; PrintWriter outputStream = null; try { inputStream = new BufferedReader(new FileReader("xanadu.txt")); outputStream = new PrintWriter(new FileWriter("characteroutput.txt")); String l; while ((l = inputStream.readLine()) != null) { outputStream.println(l); } } finally { if (inputStream != null) { inputStream.close(); } if (outputStream != null) { outputStream.close(); } } } }
readLine
returns a line of text with the line.
CopyLines
outputs each line using println
,
which appends the line terminator for the current operating system.
This might not be the same line terminator that was used in the input
file.
There are many ways to structure text input and output beyond characters and lines. For more information, see Scanning and Formatting.


Azheekal
Ooty
Maunting
Humpi
Kovalam
Kuttanad
Thenmala
Beach
Munnar
Punaloor
Sites the world
Shopping
Photography
Find


{{ctrl.lastQuery}}
Format as Table: - Can return any set of columns. Format as Time series: - Requires exactly one column of Kusto type datetime. (tip: can use Kusto's project-away operator to remove columns). - Requires at least one value column of a number type. Each value column is considered a metric. - May optionally have one or more string columns. Each string column is considered as a key=value tag pair (where the key is the column name, and the value is the value of the column for each row). - A time series is returned for each value column + unique set of string column values. Each series has name of valueColumnName { stringColumnName=columnValue, ... }. If there are no string columns in the request, the name will just be valueColumnName. - Example Time Series Query: AzureActivity | where $__timeFilter() | summarize count() by Category, bin(TimeGenerated, 60min) | order by TimeGenerated asc Format as ADX Time series: - Used for queries that return Kusto's "time series" type, such as the make-series operator - Must have a datetime column named "Timestamp" - Example ADX Time series query: let T = range Timestamp from $__timeFrom to $__timeTo step $__timeInterval * 4 | extend Person = dynamic(["Torkel", "Daniel", "Kyle", "Sofia"]) | extend Place = dynamic(["EU", "EU", "US", "EU"]) | mvexpand Person, Place | extend HatInventory = rand(5) | project Timestamp, tostring(Person), tostring(Place), HatInventory; T | make-series avg(HatInventory) on Timestamp from $__timeFrom to $__timeTo step $__timeInterval * 4 by Person, Place; Time Macros: - $__timeFilter -> TimeGenerated ≥ datetime(2018-06-05T18:09:58.907Z) and TimeGenerated ≤ datetime(2018-06-05T20:09:58.907Z) - $__timeFilter(datetimeColumn) -> datetimeColumn ≥ datetime(2018-06-05T18:09:58.907Z) and datetimeColumn ≤ datetime(2018-06-05T20:09:58.907Z) - $__timeFrom -> datetime(2018-06-05T18:09:58.907Z) [the start time of the query] - $__timeTo -> datetime(2018-06-05T20:09:58.907Z) [the end time of the query] - $__timeInterval -> 5000ms [Grafana's recommended bin size based on the timespan of the query, in ms] Templating Macros: - $__escapeMulti($myTemplateVar) -> $myTemplateVar should be a multi-value template variables that contains illegal characters - $__contains(aColumn, $myTemplateVar) -> aColumn in ($myTemplateVar) If using the All option, then check the Include All Option checkbox and in the Custom all value field type in: all. If All is chosen -> 1 == 1 Macro Examples: - ¡ where $__timeFilter() - | where TimeGenerated ≥ $__timeFrom() and TimeGenerated ≤ $__timeTo() - | summarize count() by Category, bin(TimeGenerated, $__timeInterval)
{{ctrl.lastQueryError}}
Data is not sorted ascending by Time. Recommend adding an order by clause.


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saveBinds()
- Static method in class call.game.input.keyboard.
KeyBind
sendEventTo(ModEntry, Event)
- Static method in class call.game.mod.event.
EventHelper
sendEventToAll(Event)
- Static method in class call.game.mod.event.
EventHelper
setAngle(double)
- Method in class call.game.image.
BaseSprite
setCameraScale(double)
- Method in class call.game.main.
Camera
setCameraX(double)
- Method in class call.game.main.
Camera
setCameraY(double)
- Method in class call.game.main.
Camera
setDisplaySettings(int)
- Method in class call.game.main.
GameSettings
setFlipType(int)
- Method in class call.game.image.
Image
setFps(int)
- Method in class call.game.main.
GameSettings
setFPS(int)
- Static method in class call.game.main.
Unknown
setGL(GL2)
- Static method in class call.game.main.
Unknown
setHealth(double)
- Method in class call.game.entitys.
BaseEntity
setHealth(double)
- Method in class call.game.entitys.
HealthEntity
setHealth(double)
- Method in class call.game.entitys.
TwoStateEntity
setHeight(int)
- Method in class call.game.main.
GameSettings
setLooping(boolean)
- Method in interface call.game.sound.
IPlayable
setLooping(boolean)
- Method in class call.game.sound.
Music
setLooping(boolean)
- Method in class call.game.sound.
Sound
setLooping(boolean)
- Method in class call.game.sound.
SoundEffect
setRealKey(int)
- Method in class call.game.input.keyboard.
KeyBind
setScale(float)
- Method in class call.game.image.
Image
setSprite(BaseSprite)
- Method in class call.game.entitys.
BaseEntity
setState(int)
- Method in class call.game.state.
StateEngine
Sets the current state with no inner-state data
setState(int, StateInfo)
- Method in class call.game.state.
StateEngine
Sets the current state with inner-state data
setTitle(String)
- Method in class call.game.main.
GameSettings
setTps(int)
- Method in class call.game.main.
GameSettings
setTPS(int)
- Static method in class call.game.main.
Unknown
setUniformFloat(String, float)
- Method in class call.game.shader.
Shader
setUniformVec4(String, float, float, float, float)
- Method in class call.game.shader.
Shader
setWidth(int)
- Method in class call.game.main.
GameSettings
setX(double)
- Method in class call.game.image.
BaseSprite
setX(double)
- Method in class call.game.image.
GroupedSprite
setX(double)
- Method in class call.game.utils.
Vec2Double
setY(double)
- Method in class call.game.image.
BaseSprite
setY(double)
- Method in class call.game.image.
GroupedSprite
setY(double)
- Method in class call.game.utils.
Vec2Double
Shader
- Class in
call.game.shader
Shader()
- Constructor for class call.game.shader.
Shader
Sound
- Class in
call.game.sound
Sound()
- Constructor for class call.game.sound.
Sound
SoundEffect
- Class in
call.game.sound
SoundEffect(String)
- Constructor for class call.game.sound.
SoundEffect
spawnParticle(BaseSprite, int)
- Static method in class call.game.entitys.particle.
ParticleHandler
spawnParticle(BaseParticle)
- Static method in class call.game.entitys.particle.
ParticleHandler
sprite
- Variable in class call.game.entitys.
BaseEntity
Sprite
- Class in
call.game.image
Sprite(Image)
- Constructor for class call.game.image.
Sprite
Sprite(double, double)
- Constructor for class call.game.image.
Sprite
Sprite(double, double, Image)
- Constructor for class call.game.image.
Sprite
SpriteSheet
- Class in
call.game.image
SpriteSheet(String, String, int)
- Constructor for class call.game.image.
SpriteSheet
square(double, double, double)
- Static method in class call.game.geom.
UI2D
square(double, double, double, int)
- Static method in class call.game.geom.
UI2D
STATE_CLICKED
- Static variable in class call.game.input.mouse.
Mouse
STATE_MOUSE_DOWN
- Static variable in class call.game.input.mouse.
Mouse
STATE_MOUSE_UP
- Static variable in class call.game.input.mouse.
Mouse
StateData
- Annotation Type in
call.game.state
StateEngine
- Class in
call.game.state
A state system for managing states and passing inner-state data
StateEngine(EnumCallTime)
- Constructor for class call.game.state.
StateEngine
StateInfo
- Class in
call.game.state
Used for storing extra data to pass from state to state
StateInfo()
- Constructor for class call.game.state.
StateInfo
stop()
- Method in interface call.game.sound.
IPlayable
stop()
- Method in class call.game.sound.
Music
stop()
- Method in class call.game.sound.
Sound
stop()
- Method in class call.game.sound.
SoundEffect
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Overview
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Class
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When you work with us you are part of commited collaborators.All aspects of our software developement process are intergrated,interactive and agile. Our approach unifies design, development and product management to create exceptional products.We start each project by understanding your business goals, the user's needs and the product requirements.Then we help translate them into effective and elegant solutions.
Before we sign a contract or write a line of code our team will spend the necessary time needed to understand your product vision. The outcome of this scoping session is a proposal or high-level statement of work. Based on your requirements, we can provide a complete offering or just the pieces you need.
Our design practice offers a full range of services including brand strategy, interaction and visual design and user experience testing. Throughout your project, our designers create and implement visual design and workflows, solicit user feedback and work with you to make sure what gets built is what is needed.
All engeneers are fluent in the latest All engineers are fluent in the latest enterprise, mobile and web development technologies. They collaborate with your team to write, and improve code on a daily basis, using proven practices such as test-driven development and pair programming.
Planning and development is iterative. Because we are constantly coding and testing,the products we build are always ready to go live. This iterative process allows for changes as business requirements evolve.


So that is why it is a bit messy here right now..
BEng in Computer Engineering, BBA in Business Management and MEng in Welfare Technology
Also, vocational teachers qualification (WIP). Completed around 650 ECTS in 9 different universities and worked in the HEI sector for the last five years now. I have a keen interest in continuous learning and digital transformation.
..is a bootstrap theme that I swapped with my minimal theme.
I have a good general knowledge of IT, I guess?
Been around the globe, some IB studies and international tutoring.
I have my flaws, nobody is perfect.


{% extends "layout.html" %} {% block content %}
This application was created by Michael Ghen and it is designed to be used as a teaching aid for a Data Engineering course.
The application is designed to mirror the early version of NetFlix.
A very minimalistic set of features is implemented because only the data is of interest.


-
- All Implemented Interfaces:
-
java.io.Serializable
,kotlin.Comparable
,kotlin.sequences.Sequence
public final class SingleRange extends BigIntegerRange
The class represents BigIntegerProgression, sequences.BigIntegerRange with exact 1 element.All methods here are designed to specify return type (EmptyRange) statically.Method toString works as it is defined in BigIntegerProgression.Step equals to 1 for all instances of SingleRange.
-
-
Field Summary
Fields Modifier and Type Field Description private final BigInteger
only
private final BigInteger
last
private final BigInteger
first
private final BigInteger
step
-
Constructor Summary
Constructors Constructor Description SingleRange(Number only)
SingleRange(BigInteger only)
-
Method Summary
Modifier and Type Method Description String
toString()
SingleRange
shl(Number number)
Makes number steps backward for each item. SingleRange
shr(Number number)
Makes number steps forward for each item. SingleRange
step(Number step)
Changes step not changing direction of progression. final SingleRange
copy(BigInteger only)
final BigInteger
getOnly()
final BigInteger
getLast()
The last element when possible. final BigInteger
getFirst()
final BigInteger
getStep()
-
Methods inherited from class sequences.SingleRange
compareTo, drop, extend, intersect, intersect, intersect, intersect, minus, minus, minus, minus, minus, subtract, subtract, subtract, take
-
Methods inherited from class sequences.BigIntegerRange
average, checkStepIsPositive, component1, component2, component3, component4, component5, contains, contains, contains, contains, contains, count, distinct, drop, elementAt, elementAtOrElse, elementAtOrNull, elementAtOrNull, equals, extend, flip, hashCode, indexOf, indexOf, infiniteConversation, isEmpty, isFinite, isInfinite, isNotEmpty, isSingle, iterator, last, lastIndexOf, lastOrNull, max, min, plus, plus, plus, plus, plusElement, reversed, sum, take, toCollection, toHashSet, toList, toMutableList, toMutableSet, toSet, toSortedSet, toSortedSet
-
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
-
-
Constructor Detail
-
SingleRange
SingleRange(Number only)
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SingleRange
SingleRange(BigInteger only)
-
-
Method Detail
-
shl
SingleRange shl(Number number)
Makes number steps backward for each item.
-
shr
SingleRange shr(Number number)
Makes number steps forward for each item.
-
step
SingleRange step(Number step)
Changes step not changing direction of progression.- Parameters:
step
- positive number to be new property step
-
copy
final SingleRange copy(BigInteger only)
-
getOnly
final BigInteger getOnly()
-
getLast
final BigInteger getLast()
The last element when possible.
-
getFirst
final BigInteger getFirst()
-
getStep
final BigInteger getStep()
-
-
-
-


 I talked to the main developer with jl777, he said something about the timing of one week - but it seems to me that it will be longer! What does everyone think about this?
[telegram] <henkyyyy> They do as fast as they can, I'm sure (re @xarux: I wonder how long the refund will take? I talked to the main developer with jl777, he said something about the timing of one week - but it seems to me that it will be longer! What does everyone think about this?)
[telegram] <xarux> Yes - the team works well - they will not leave anyone without attention! The main developer is a pity - the guy has not slept for more than 40 hours (re @henkyyyy: They do as fast as they can, I'm sure)
[telegram] <xarux> but do not forget that there are more than 8 million coins! It is not yet known how many addresses are affected - and the processing is most likely in manual mode by the developers themselves - therefore the deadlines can be very long! (re @henkyyyy: They do as fast as they can, I'm sure)
[telegram] <xarux> it's interesting that the price on the markets has even grown - as long as there is no negative reaction to the vulnerability - it is strange
[telegram] <Hg022> this happens all the time to every coin, every slip found builds the tech stronger and in the long run is better for the entire world
[telegram] <Hg022> there will always be evil ppl out there tryin to take advantage of others, luckily the team hear has our backs and will pull us to the future of saftey and userbility in the near future
[telegram] <xarux> it's not about the coin - the komodo network is flawless! Blockchain is very stable. - Deal in infrastructure vulnerability - namely, the wallet! (re @Hg022: this happens all the time to every coin, every slip found builds the tech stronger and in the long run is better for the entire world)
[telegram] <Seko1900> Attention! Scam Alert!  Please be careful! There will be more fake profiles reported who want to use yesterday's problem sustainably.
Should an admin write to you, turn to real admins about this channel.
[telegram] <Hg022> then why you think its strange that the price still increase?
[telegram] <Hg022> its best tech around by far
[telegram] <xarux> because price manipulators on exchanges often use negative news to arrange panic sales and buy on bottoms! (re @Hg022: then why you think its strange that the price still increase?)
[telegram] <taurados> theres a scammer aron goldberg wanted my seed
[telegram] <torsten1> I think people understand crypto is bleeding-edge and shit happens. Price is stable because of how Komodo is handling it. You can be damn certain this team is not going to exit scam on you haha
[telegram] <xarux> I agree - technology is cool! But infrastructure - such as wallets are still far from ideal! ( (re @Hg022: then why you think its strange that the price still increase?)
[telegram] <Hg022> should prove kmd isnt minipulated atm, true price prevails
[telegram] <Hg022> wait till dex release
[telegram] <Hg022> and mobile
[telegram] <taurados> my kmd was transferred to unkwown adress help me please
[telegram] <xarux> the team is great!  Price is stable because of how Komodo is handling it.


Prerequisites
The Virgo Tomcat Server, or VTS for short, requires
Java SE 6
or later to be installed, and is available as a zip file from
http://www.eclipse.org/virgo/download/


Starter Page source code
Styled with Bulma
Hero components styled with Bulma
Contact components styled with Bulma
Admin components styled with Bulma
Call to action components styled with Bulma
Decentralized, secure, private. The PiperNet is on it's way to revolutionize every smartphone, PC, and smart-fridge near you.
Join the Revolution!cards styled with Bulma
We are the guys that made this whole thing possible
CEO
When I'm not obsessively stressing about the fate of Pied Piper, I sometimes give lectures to school kids. Spend most of my time coding.
CTO
I am the absolute best programmer at Pied Piper (especially better than Gilfoyle). My genius shines and guides the Pied Piper team.
Chief Systems Architect
I work on Pied Piper only for the advanced technology. I don't really like most people I work with, but they need me to put out their fires, so I stay.


--- layout: default published: true ---
Census API key:
Specify location by:
lat:
lng:
Number - The latitude of the requested location (North). Also supported: latitude, y
Number - The longitude of the requested location (East). Also supported: longitude, x
zip:
String - The 5-digit zip code of the location. Note that USPS zip code areas do not align precisely with Census geographies, so when high accuracy is required it is recommended to use latitude and longitude. Specified as a string because certain zip codes begin with zeroes.
state:
String - The 2-letter USPS state code. It will be converted into the latitude and longitude of that state's capital.
Street:
City:
State:
Object - Represents the address of the requested location.
variables:
Array - Optional - An array of strings specifying which variables to query. One can specify an aliased variable (see variable aliases) or a specific ACS variable code (e.g. "B01003_001E"). If this array is not specified, the SDK will simply geocode the location into Census FIPS codes. A list of all ACS variables is available on the Census's developer portal or via the SDK's getACSVariableDictionary method.
level:
String - At what level to request the data. These are based on census geographies. Supported options are: "blockGroup", "tract", "county", "state", "us", and "place". Note that the "place" tag currently only supports incorporated places.
sublevel:
Boolean - Optional Whether or not to return based upon sublevels. See sublevel requests for more information. Defaults to false.
container:
String - Optional, GeoJSON request only - Specifies a level which serves as a boundary for a GeoJSON request. For instance, if your level is "tract" and your container is set as "place" with sublevel enabled, it the SDK will return all tracts which fall within or touch that place's geographical boundaries. The container tag says "get all level within container", a place is the census word for an incorporated city, and sublevel says "get all of that level"
CitySDK Census Module Request:
Output:


2AM's Jinwoon & Nine Muses's Kyung Li confirm their dating news!
by beansssShortly after dating news between 2AM member Jinwoon (28) and Nine Muses member Kyung Li (29) garnered attention online, Jinwoon's label Mystic Story stepped up to confirm the reports to be true!
A representative from Mystic Story told various media outlets on November 13, "Jinwoon and Kyung Li maintained a close relationship after working together on various broadcast programs, until the end of 2017 when they began to see each other with good feelings. Please watch over them with warmth, and cheer them on as Jinwoon continues his mandatory military service duties diligently, and Kyung Li greets viewers through various programs."
Meanwhile, Jinwoon debuted as a member of 2AM in 2008, before enlisting in March of this year in order to serve as a member of the military music corps. Kyung Li debuted as a member of Nine Muses in 2012, and chose not to renew her contract with her label Star Empire after 7-years this past July.
Congratulations to the new idol couple!