PIZZA CRAFT
Let's design your perfect pizza together!
We'll need your name so that we can keep track of your creations
Start Your Design
Start by choosing your size, crust and sauce
Size
personal ($7)
medium ($12)
party ($18)
Sauce
marinara
alfredo
pesto
Crust
standard
thin $1.20
thick $1.20
Toppings
(first two are free, $1.50 for each additional)
Olives
Mushrooms
Pepperoni
Sausage
Pinapple
Peppers
Onions
Artichokes
Tomatos
Feta
Mozzarella
Goat Cheese
Blue Cheese
add your masterpiece
Here's your order so far:
Remove Pizza
Add Another Pizza
Complete Order
Thanks for placing your order!
Your total is $
You can pick it up in 20 minutes.


Artisan CLI
Introduction
Artisan is the name of the command-line interface included with Laravel. It provides a number of helpful commands for your use while developing your application. It is driven by the powerful Symfony Console component.
Usage
Listing All Available Commands
To view a list of all available Artisan commands, you may use the list
command:
php artisan list
Viewing The Help Screen For A Command
Every command also includes a "help" screen which displays and describes the command's available arguments and options. To view a help screen, simply precede the name of the command with help
:
php artisan help migrate
Specifying The Configuration Environment
You may specify the configuration environment that should be used while running a command using the --env
switch:
php artisan migrate --env=local
Displaying Your Current Laravel Version
You may also view the current version of your Laravel installation using the --version
option:
php artisan --version
Calling Commands Outside Of CLI
Sometimes you may wish to execute an Artisan command outside of the CLI. For example, you may wish to fire an Artisan command from an HTTP route. Just use the Artisan
facade:
Route::get('/foo', function()
{
$exitCode = Artisan::call('command:name', ['--option' => 'foo']);
//
});
You may even queue Artisan commands so they are processed in the background by your queue workers:
Route::get('/foo', function()
{
Artisan::queue('command:name', ['--option' => 'foo']);
//
});
Scheduling Artisan Commands
In the past, developers have generated a Cron entry for each console command they wished to schedule. However, this is a headache. Your console schedule is no longer in source control, and you must SSH into your server to add the Cron entries. Let's make our lives easier. The Laravel command scheduler allows you to fluently and expressively define your command schedule within Laravel itself, and only a single Cron entry is needed on your server.
Your command schedule is stored in the app/Console/Kernel.php
file. Within this class you will see a schedule
method. To help you get started, a simple example is included with the method. You are free to add as many scheduled jobs as you wish to the Schedule
object. The only Cron entry you need to add to your server is this:
* * * * * php /path/to/artisan schedule:run 1>> /dev/null 2>&1
This Cron will call the Laravel command scheduler every minute. Then, Laravel evaluates your scheduled jobs and runs the jobs that are due. It couldn't be easier!
More Scheduling Examples
Let's look at a few more scheduling examples:
Scheduling Closures
$schedule->call(function()
{
// Do some task...
})->hourly();
Scheduling Terminal Commands
$schedule->exec('composer self-update')->daily();
Manual Cron Expression
$schedule->command('foo')->cron('* * * * *');
Frequent Jobs
$schedule->command('foo')->everyFiveMinutes();
$schedule->command('foo')->everyTenMinutes();
$schedule->command('foo')->everyThirtyMinutes();
Daily Jobs
$schedule->command('foo')->daily();
Daily Jobs At A Specific Time (24 Hour Time)
$schedule->command('foo')->dailyAt('15:00');
Twice Daily Jobs
$schedule->command('foo')->twiceDaily();
Job That Runs Every Weekday
$schedule->command('foo')->weekdays();
Weekly Jobs
$schedule->command('foo')->weekly();
// Schedule weekly job for specific day (0-6) and time...
$schedule->command('foo')->weeklyOn(1, '8:00');
Monthly Jobs
$schedule->command('foo')->monthly();
Limit The Environment The Jobs Should Run In
$schedule->command('foo')->monthly()->environments('production');
Indicate The Job Should Run Even When Application Is In Maintenance Mode
$schedule->command('foo')->monthly()->evenInMaintenanceMode();
Only Allow Job To Run When Callback Is True
$schedule->command('foo')->monthly()->when(function()
{
return true;
});


Quick startThese are the basic steps that you need to take in order to get data using brandseyer2. First, ensure that you have authenticated yourself, then reference an account, then access that account’s mentions. See the Get started documentation for more details. |
|
|---|---|
Authenticate yourself with BrandsEye |
|
Access account information |
|
Fetch mentions from an account |
|
Update data stored on a mention |
|
Count mentions |
|
List the dashboards in an account |
|
AuthenticatingAuthentication is the first thing that you should do when using the library. It allows you to identify yourself with our API, and lets you know what accounts and brands you have access to. |
|
Authenticate yourself with BrandsEye |
|
Returns your current authentication credentials. |
|
Are you authorised as a BrandsEye staff member? |
|
AccountsThese functions allow you to determine what accounts you have access to, and to query information about them, such as the brands that you are tracking, and the tags and topics in each account. |
|
Access account information |
|
See if an object is a account object |
|
Get the version of the API the account uses |
|
Get an account's client code |
|
Get an account's code |
|
List all accounts available to you |
|
See your account manager |
|
Get an account's name |
|
Get an account's timezone |
|
Get an account's type |
|
Fetch brand information |
|
Find root brands in an account |
|
Fetch phrases for an accounts and mentions |
|
Fetch tags for accounts and mention data |
|
Fetch topics for an account or tibble of mentions |
|
List topic trees for an account |
|
Add tags to an account |
|
Determine the parent of tags given a particular tree |
|
Get users for an account |
|
Read account logs |
|
Read account rules |
|
Read online profiles for an account |
|
Accessing mentionsMentions make up the raw data that we store for you. Sentiment information and topic information are stored against individual mentions. These functions allow you to query for particular kinds of mentions using a filter language, to count mentions matching particular parameters, and to update the information stored on mentions. Important functions include mentions() to pull mentions in your account, and count_mentions(), to count mentions matching a particular filter. |
|
Fetch mentions from an account |
|
Fetch tags for accounts and mention data |
|
Fetch topics for an account or tibble of mentions |
|
Fetch brand information |
|
Fetch phrases for an accounts and mentions |
|
Count mentions |
|
Update data stored on a mention |
|
A query language: filtering and grouping your mentionsOften, when counting or fetching mentions, you may need to specify a filter, or the brands in the filter, and so on. The query language lets you easily define these things, as well as avoid specify brands in each and every filter. The Get started guide discusses this in more detail. |
|
Create a query for mentions or counts |
|
Add accounts to a query. |
|
Filter for only specific mentions |
|
Group mentions by shared criteria |
|
Compare mentions using sub-filters when counting them |
|
Select extra data when fetching or counting mentions |
|
Order results from fetching or counting mentions |
|
See if an object is a BrandsEyeR query |
|
Create a query object from another object |
|
See brands in a query |
|
See the accounts in a query. |
|
Working with account logsMost actions on user accounts are logged. brandseyer2 provides various functions to read and filter account logs, starting with the all encompassing logs() function, which returns all the logs in a time period for the account. |
|
Read account logs |
|
Finding retrosends for accounts |
|
Account dashboardsIn Analyse, BrandsEye’s analysis tool, people can set up dashboards to query our data. We have various functions that help you to read data from the dashboards, their various sub-sections, an the metrics that they contain. |
|
List the dashboards in an account |
|
Give section information for a dashboard |
|
See metrics in a dashboard |


| {{el.key}} | {{el.value}} |
| {{el.key}} | {{el.value}} |
For more information about this piece of software, please see this git repository
For simulating "spreading" on a public transport network, (i.e. to show the fastest ways for traveling, given a certain departure time), please click on the map first to set the spreading origin.
{{appdata.sim_time*1000 | date:'yyyy-MM-dd HH:mm:ss Z' : appdata.timezones[appdata.dbfname]}}
{{appdata.n_vehicles}} vehicles


{% extends "base.html" %} {% block title %}Loglet{% endblock %} {% block content %}
Loglet is a tiny tool for keeping tabs on long-running
processes. Send log messages to Loglet using a simple
POST
request and then view them in your browser or
subscribe to an Atom feed.
Hit the button to start a brand-new, empty log.
You’ll get a private, hard-to-guess URL like
{{ request.url_root }}2LNbYgNEAaezJduj
.
Send HTTP POST
requests to your log’s URL.
Include a message
parameter containing your log
string and, optionally, a level
parameter (which
should be an integer) indicating the severity of the message.
Try it out with cURL:
curl -d message=hello {{ request.url_root }}LOGID
Get
loglet.sh
for more convenient logging from the shell.
There’s also
a
handler available
for the
Python
logging
module
(you can install it using pip install loglet
):
import logging import loglet log = logging.getLogger('mylog') log.addHandler(loglet.LogletHandler('2LNbYgNEAaezJduj')) log.error('something horrible has happened')
You can read your log in a browser or get in plain text, JSON,
or Atom format by appending /txt
,
/json
, or /feed
to the end of your
URL.


--- title: Parallax header with blend background --- {% extends "layouts/default.html" %} {% block page %}


baidut
Baidu translator for NodeJS and Browser with Promise ready.
Installation
Install it via npm install baidut
.
Usage
You need to prepare Baidu environment first before making a call to translate.
Baidut.builder
.setAppId("2015063000000001")
.setKey("12345678")
.build();
Then translate it via
Baidut.translate('苹果')
.then((result) => {
console.log("translated text: " + result.trans_result[0].dst);
}, (e) => {
console.log(e.code + ": " + e.message);
});
There are options you can set when translate too.
Baidut.translate('สวัสดี', { http_request: true, from_lang: "th", to_lang: "en" })
.then((result) => {
console.log("translated text: " + result.trans_result[0].dst);
}, (e) => {
console.log(e.code + ": " + e.message);
});
Above set http_request=true
to force it to make a request via HTTP protocol, and set from_lang="th"
to mark that the source langauge is Thai, and to_lang="en"
to mark the result we want in English.
See list of language support at official document.
Note on Browser
As you likely to involve translating Unicode language as well thus defining that your HTML file has UTF-8 encoding is important to have conistency in displaying proper text for non-English language.
<head>
...
<meta charset="UTF-8">
...
</head>
Document
You can look here for document reference. Error code built-in for baidut can be seen here so you could have better error handling on your application by checking which code for which error.
License
MIT. See and understand license here.


Thanks For Securing Your Account!
if you need any help visit our Support Page at instagram.com/support


i
Spring
Summer
Fall
Winter
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Gradessup
Put your grades in with the help of the buttons!
A
B
C
D
F
Done
Reset
Delete last digit
Your current grades:
Your current average:


Start Farm
Stop Farm
Restart Farm
Id
CPU (%)
Process
Gomochi
Listen
Sycing
Solving
Height
Block
Diff
HPS
Solved


Place the .PPT/.PPTX files you want to merge into a folder and drag-drop that folder onto PPTmerge.vbs to launch PowerPoint and have the files merged into a file named after the folder (with _MERGED.pptx appended to the folder name [edit the script if you want to produce .ppt instead of .pptx]), that is placed inside the drag-dropped folder's parent folder. To enforce a certain merge order, name the files using an alphanumeric (sortable) prefix, for example "1 - Intro.ppt", "2 - Management.ppt", "3 - R&D.ppt" etc.
For the above to work you need PowerPoint installed (had tried previous version with Office97, Office2000, OfficeXP and Office2007 and the latest one with PowerPoint 2010) and Windows Script Host (pre-installed by default at WinXP and Windows Vista, can get it from http://msdn2.microsoft.com/en-us/library/ms950396.aspx)
Download: PPTmerge_20180202.zip


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5 things we all must do to reduce our carbon footprint
Making a carbon difference is easier than you think. The changes that you make in your day to day life add up. If everyone makes small changes it will make a big difference to our current climate crisis.
At The Virtual Training Team, we try to minimise our carbon output. From growing vegetables to composting, we strive to be carbon conscious; making mindful decisions on situations that impact our carbon output.
Here are the 5 things we do to live in a less carbon costly way
Growing Our Own Vegetables and FruitsWe grow as many vegetables as we can. Salad leaves, broad beans, onions, sweet potatoes, radish, sweetcorn, cucumbers, leeks and carrots to name a few. We also grow fruit; apples, pears, strawberries and peaches. Often, we freeze our surplus produce so that we can eat it all year round, in stews and soups.
Growing your own not only saves travelling to and from the supermarket but also mitigates the carbon miles of food being processed, packaged and brought to the store. When we must buy fruit and veg from a shop, we always buy it loose. Did you know, the bags that salad leaves come in can’t be recycled?
Recently, we did a video about growing our veg. Did you see it? If not, here’s the link and a snapshot below of our gardeners.
Composting
We compost all fruit and vegetable waste, eggshells and teabags. We tear up and compost smaller cardboard items like the inside of toilet and kitchen rolls, even our confidential waste! We add all of this to our garden waste; green cuttings and spent compost from plants, then use the compost when it has decomposed and put it back onto the garden.
Creating this cycle between growing our own vegetables and putting the remains back into the earth really makes a difference when it comes to our carbon impact. Studies show that the food wasted by United States and Europe alone could feed the world 3 times over! Just one ton of food waste prevented can save 4.2 tons of CO2 going into our atmosphere. That’s huge!
Going Paper-Less
As well as our fruits and vegetables, we also make conscious decisions about our paper usage. Going paper-less doesn’t mean paper free; but that we try to use as little as we can. When we must print, we use the low ink option and always print two sided.
We recycle all paper and cardboard, especially if it is sent in via deliveries. For our confidential waste, we shred all sensitive documents and use them on our compost heap.
We use e-handouts/interactive PDFs for our virtual training and F2F training, and we have committed to use whiteboards or smart boards if available. While we love mapping out processes with post-it notes, we now do this using the Post-It Plus app. It works the same but minus the paper use! This is how we plan out the design of our workshops and programmes and we use the TV screen to share it.
Travel
We don’t travel much physically, but virtually. We travel all over the world, working with clients in over 70 countries! That’s a lot of airmiles saved by taking business online.
Of course, we can’t avoid travel completely, but we endeavor to use other, less carbon-costly methods. We take the train rather driving, share taxis with others where possible and we are looking at replacing our vehicles with electric cars when the time comes to change them.
We have clients, coaches and colleagues all over the world who we have never physically been in the same room as us and that really excites us! Some new clients will ask us for F2F meetings, in order to get to know us, but a Zoom or Skype video call works just as well, if not better, for everyone.
The Little Things
It is impressive how much the little things can make a difference. Using LED lights in the office, avoiding single use plastic, having mugs and plates that can be washed and reused, repairing things rather than replacing them. Even just turning the lights and sockets off when not in use; that red standby light costs the planet more than many realise.
If it gets too hot, we open a window, rather than using the AC or a fan. If we get cold, we throw on another jumper before switching on the heaters. We have invested in the best insulation for our studio, so when we do have to heat the space, it warms up quickly and stays warm for longer.
You don’t have to turn your life upside down to make changes in the world. We can all change the way we interact with our environment and influencing little, but impactful, changes in your workplace is a great place to start.
What is your company doing to reduce their carbon usage? Do you do any of the things in our list?


Lili Reinhart Speaks Out Against Body Editing Phone Apps
Lili Reinhart is taking a stand against photography apps that allow body editing.
The 23-year-old Riverdale actress was browsing the app store when she came across an application that modified people’s bodies to look skinnier.
“This is not okay. This is why people develop eating disorders. This is why social media has become hazardous to our health. This is why people have unrealistic expectations of their bodies,” Lili wrote on her Instagram story.
Lili continued on to implore people to stop using similar apps and stop photoshopping their bodies.
“Looking ‘skinnier’ on Instagram is not worth the detrimental psychological effects that these photoshopping apps have given our generation…People who use these apps and alter their bodies are clearly suffering from low self esteem, body dysmorphia, or other mental health problems. My heart goes out to those people who feel like their bodies aren’t good enough,” Lili said.
She added, “Please don’t encourage this behavior. If we applaud and praise the men and women who photoshop themselves, it’s only encouraging them to continue. And to feel like the only way they will be accepted is if they keep altering themselves.”
We completely agree, Lili!
Make sure to check out what else Lili has said about body image.


HR Portal and Chatbot
Completed developing an MVP within 1 week. Continuously added new features and became the Architect and Lead Developer of this project. Currently, it is being used by a Startup Telecommunication Company in the Philippines as its HR Employee Portal.


Problem 😱
You want to process audio file into text.
Solution 🤓
We will use gapi which offers ability to transform audio files into text.
# GOOGLE_APPLICATION_CREDENTIALS=./gapi-auth.json
import json
import subprocess
from google.cloud import speech
from google.cloud.proto.speech.v1 import cloud_speech_pb2
speech_client = speech.SpeechClient()
path_to_file = './input.mp4'
output_path_to_file = './out.flac'
# command = 'ffmpeg -i {input_file} -ac 1 -c:a flac answer_11.flac'
subprocess.call(['ffmpeg', '-i', '{input_file}'.format(input_file=path_to_file), '-ac', '1', '-c:a', 'flac', output_path_to_file])
def parse_speech_recognition_result(speech_recognition_result):
data = {'data': []}
for result in speech_recognition_result.results:
for alternative in result.alternatives:
data['data'].append({
'transcript': alternative.transcript,
'confidence': alternative.confidence,
})
return data
with open(output_path_to_file, 'rb') as recording_file:
recording_bytes = recording_file.read()
audio = cloud_speech_pb2.RecognitionAudio(content=recording_bytes)
config = cloud_speech_pb2.RecognitionConfig(encoding="FLAC", sample_rate_hertz=44100, language_code="en-US")
speech_recognition_result = speech_client.recognize(config=config, audio=audio)
result = parse_speech_recognition_result(speech_recognition_result)
dump_result = json.dumps(result)
print(dump_result)


BIO_read_ex, BIO_write_ex, BIO_read, BIO_write, BIO_gets, BIO_get_line, BIO_puts - BIO I/O functions
#include <openssl/bio.h>
int BIO_read_ex(BIO *b, void *data, size_t dlen, size_t *readbytes);
int BIO_write_ex(BIO *b, const void *data, size_t dlen, size_t *written);
int BIO_read(BIO *b, void *data, int dlen);
int BIO_gets(BIO *b, char *buf, int size);
int BIO_get_line(BIO *b, char *buf, int size);
int BIO_write(BIO *b, const void *data, int dlen);
int BIO_puts(BIO *b, const char *buf);
BIO_read_ex() attempts to read dlen bytes from BIO b and places the data in data. If any bytes were successfully read then the number of bytes read is stored in *readbytes.
BIO_write_ex() attempts to write dlen bytes from data to BIO b. If successful then the number of bytes written is stored in *written unless written is NULL.
BIO_read() attempts to read len bytes from BIO b and places the data in buf.
BIO_gets() performs the BIOs "gets" operation and places the data in buf. Usually this operation will attempt to read a line of data from the BIO of maximum length size-1. There are exceptions to this, however; for example, BIO_gets() on a digest BIO will calculate and return the digest and other BIOs may not support BIO_gets() at all. The returned string is always NUL-terminated and the '\n' is preserved if present in the input data. On binary input there may be NUL characters within the string; in this case the return value (if nonnegative) may give an incorrect length.
BIO_get_line() attempts to read from BIO <b> a line of data up to the next '\n' or the maximum length size-1 is reached and places the data in buf. The returned string is always NUL-terminated and the '\n' is preserved if present in the input data. On binary input there may be NUL characters within the string; in this case the return value (if nonnegative) gives the actual length read. For implementing this, unfortunately the data needs to be read byte-by-byte.
BIO_write() attempts to write len bytes from buf to BIO b.
BIO_puts() attempts to write a NUL-terminated string buf to BIO b.
BIO_read_ex() returns 1 if data was successfully read, and 0 otherwise.
BIO_write_ex() returns 1 if no error was encountered writing data, 0 otherwise. Requesting to write 0 bytes is not considered an error.
BIO_write() returns -2 if the "write" operation is not implemented by the BIO or -1 on other errors. Otherwise it returns the number of bytes written. This may be 0 if the BIO b is NULL or dlen <= 0.
BIO_gets() returns -2 if the "gets" operation is not implemented by the BIO or -1 on other errors. Otherwise it typically returns the amount of data read, but depending on the implementation it may return only the length up to the first NUL character contained in the data read. In any case the trailing NUL that is added after the data read is not included in the length returned.
All other functions return either the amount of data successfully read or written (if the return value is positive) or that no data was successfully read or written if the result is 0 or -1. If the return value is -2 then the operation is not implemented in the specific BIO type.
A 0 or -1 return is not necessarily an indication of an error. In particular when the source/sink is nonblocking or of a certain type it may merely be an indication that no data is currently available and that the application should retry the operation later.
One technique sometimes used with blocking sockets is to use a system call (such as select(), poll() or equivalent) to determine when data is available and then call read() to read the data. The equivalent with BIOs (that is call select() on the underlying I/O structure and then call BIO_read() to read the data) should not be used because a single call to BIO_read() can cause several reads (and writes in the case of SSL BIOs) on the underlying I/O structure and may block as a result. Instead select() (or equivalent) should be combined with non blocking I/O so successive reads will request a retry instead of blocking.
See BIO_should_retry(3) for details of how to determine the cause of a retry and other I/O issues.
If the "gets" method is not supported by a BIO then BIO_get_line() can be used. It is also possible to make BIO_gets() usable even if the "gets" method is not supported by adding a buffering BIO BIO_f_buffer(3) to the chain.
BIO_gets() on 1.1.0 and older when called on BIO_fd() based BIO did not keep the '\n' at the end of the line in the buffer.
BIO_get_line() was added in OpenSSL 3.0.
BIO_write_ex() returns 1 if the size of the data to write is 0 and the written parameter of the function can be NULL since OpenSSL 3.0.
Copyright 2000-2021 The OpenSSL Project Authors. All Rights Reserved.
Licensed under the Apache License 2.0 (the "License"). You may not use this file except in compliance with the License. You can obtain a copy in the file LICENSE in the source distribution or at https://www.openssl.org/source/license.html.


Oman weather: Aqbat Baushar opened to traffic
by Times New ServiceMuscat: Aqbat Baushar road that was closed on Wednesday night following heavy rainfall is now opened to traffic, Royal Oman Police (ROP) has informed.
Also read: Heavy rains lash Oman, many rescued from overflowing wadis
“Aqbat Baushar road in Al Amerat is now opened after it was closed yesterday,” ROP said in a statement.
Read also: Several roads closed due to heavy rain


At the time you've implemented a generic set of functions for regular reuse you'd like to pack them into a module which can be called like a single page function. It would also be nice if the called function could return to the caller without actually knowing its name or arguments.
application.class provides such mechanisms: A function can be "called" (in matters of returning to the caller later on) by marking the arguments to the called function using view::set_caller().
In case you need sessions that live on outside modules, access the application's dbsession instance in member variable application::session.


This is a container for the player, auto-sized for testing purposes. It displays CSS values.
This demo showcases basic functionality of a player. It is a shell of an HTMLMediaElement with basic timing routines.
At 2 seconds, the phrase "2 seconds reached" will appear beside 'Event Output'


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Tilde commodo VHS leggings, art party ut skateboard keffiyeh ethical sunt you probably haven't heard of them ut flexitarian. Retro copper mug hoodie blue bottle elit street art polaroid tempor farm-to-table. Pitchfork selvage enim ut, slow-carb PBR&B do. Ut consectetur tote bag YOLO, chillwave excepteur id yr voluptate whatever cronut.
Ut occupy fixie literally roof party lomo kogi cupidatat thundercats do helvetica butcher nulla succulents pok pok. Aute pop-up trust fund master cleanse lyft schlitz whatever. Fugiat chambray blog crucifix scenester chillwave tousled readymade shaman ramps meh. Poke taxidermy pour-over, veniam et mumblecore hexagon YOLO live-edge. Oh. You need a little dummy text for your mockup? How quaint. I bet you’re still using Bootstrap too…


@(characterName : String, crew : String)
You should have known if it was that easy someone would have done it already. Your ship is hit by an iceberg. You and your crew fall into the sea and are attacked by giant coelacanths. Lucky for you a passing catfish chases away the coelacanths. You are all able to swim to the shore of a frozen island where you find nothing to eat but lettuce. Years later you are rescued by a ship named Titanic on its way to the New World. The end for @characterName.
Current crew size: @crew


--- title: dialga.dataset keywords: fastai sidebar: home_sidebar summary: "Contains commonly used dataset operations which include splitting and creation of validation sets based on various stratergies." ---
class
FilteredDataset
FilteredDataset(dataset, wanted_labels=[]) :: Dataset
dataset
wanted_labels
[]
Dataset
dataset : Any Pytorch Dataset wanted_labels : Iterable Containning the labels that are required
Eg: label_5_dataset = FilteredDataset(MNIST(root='',download=True), [5]) print(len(label_5_dataset))


this website is gone be about calculating the age and giving out the actual name of the user
to be pricise it will give out the Akan name
input details in the field below


ECB's Visco: European single currency needs to interact with a single fiscal policy
by Eren SengezerWhile speaking at a conference in Rome earlier in the day, "European single currency needs to interact with a single fiscal policy," European Central Bank governing council member Ignazio Visco argued, as reported by Reuters.
"The small and uncertain benefits of a debt restructuring mechanism must be weighed against the huge risk that the mere announcement of its introduction may trigger a perverse spiral of expectations of default, which may prove to be self-fulfilling," Visco further added in his prepared remarks, which were largely ignored by market participants.


Tutor
Time Limit: 2000/1000 MS (Java/Others) Memory Limit: 65535/65535 K (Java/Others)
Total Submission(s): 48 Accepted Submission(s): 27
Problem Description
Lilin was a student of Tonghua Normal University. She is studying at University of Chicago now. Besides studying, she worked as a tutor teaching Chinese to Americans. So, she can earn some money per month. At the end of the year,
Lilin wants to know his average monthly money to decide whether continue or not. But she is not good at calculation, so she ask for your help. Please write a program to help Lilin to calculate the average money her earned per month.
Lilin wants to know his average monthly money to decide whether continue or not. But she is not good at calculation, so she ask for your help. Please write a program to help Lilin to calculate the average money her earned per month.
Input
The first line contain one integer T, means the total number of cases.
Every case will be twelve lines. Each line will contain the money she earned per month. Each number will be positive and displayed to the penny. No dollar sign will be included.
Every case will be twelve lines. Each line will contain the money she earned per month. Each number will be positive and displayed to the penny. No dollar sign will be included.
Output
The output will be a single number, the average of money she earned for the twelve months. It will be rounded to the nearest penny, preceded immediately by a dollar sign without tail zero. There will be no other spaces or characters
in the output.
in the output.
Sample Input
2 100.00 489.12 12454.12 1234.10 823.05 109.20 5.27 1542.25 839.18 83.99 1295.01 1.75 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
Sample Output
$1581.42 $100
Source
大坑题啊，不知道错了多少次了！
#include <iostream> #include <stdio.h> #include <string.h> using namespace std; int main() { int n,i,m; double temp,sum; scanf("%d",&n); while(n--) { sum=0; for(i=0;i<12;i++) { scanf("%lf",&temp); sum+=temp; } sum=sum/12.0; int m=(int)(sum*100); int sum0=(int)(sum*1000); if(sum0%10>=5) m+=1; printf("$%d",m/100); m=m%100; if(m%100==0) { printf("\n"); } else if(m%10==0) { printf(".%d\n",m/10); } else { if(m<10) printf(".0%d\n",m); else printf(".%d\n",m); } } return 0; }
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Optional Retirement Program
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On:
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Teacher Retirement System
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hexagonal is an LMS (library management system), which is built as a first year
bachelors project at Innopolis
University.
Its name originates from "The
Library of Babel",
a book by Jorge Luis Borges.
To login, you must first be registered by a librarian


In some applications, you may want Serial Logger Service to create a new log file at unpredictable times whenever you need the data. For instance, you may want to run a simple program or batch file (.CMD or .BAT) that will import the current log file and then have Serial Logger Service start using a new blank log file.
The batch file below is an example that will move the latest Serial Logger Service log file (called pbx.txt) to an import directory that another appliication (such as a PBX call analysis program) will import from. It is expected that this other application will delete or move the file from the import directory after it has successfully imported the file.
Note: This batch file assumes that Serial Logger Service is configured to NOT use periodic log rotation. The Log Rotation setting should be set to None.
This batch file performs the following steps:
@echo off if exist c:\ToImport\pbx.txt goto :alreadyexists net pause seriallogger1 move "c:\Program Files\Serial Logger Service\pbx.txt" c:\ToImport net continue seriallogger1 goto :end :alreadyexists echo. echo Cannot copy latest data because the import file already exists echo in the import directory. echo. :end


Feedback from C to Java can be difficult. In Drawgram and Drawtree it is fairly easy, as the plotting is done (to the file JavaPreview.ps in case you need to know) and the program returns. The Java interface waits until the C code completes and returns, then reads JavaPreview.ps and displays the preview. In cases where one needs progress indicators, one needs to multithread the Java code and display a continually updating progress file. Phylip 3.695 has no need of multithreading but it will be implemented in Phylip 4.0.
This set of Frequently Asked Questions, and their answers, is from the PHYLIP web site. A more up-to-date version can be found there, at:
| (a) | In Mix, make up an extra character with states 0 for all the outgroups
and 1 for all the ingroups. If using Dnapars the ingroup can have (say) G and the outgroup A. |
| (b) | Assign this character an enormous weight
(such as Z for 35) using the W
option, all other characters getting weight 1, or whatever weight they had before. |
| (c) | If it is available, Use the A (Ancestral
states) option to designate that
for that new character the state found in the outgroup is the ancestral state. |
| (d) | In Mix do not use the O (Outgroup) option. |
| (e) | After the tree is found, the designated
ingroup should have been held
together by the fake character. The tree will be rooted somewhere in the outgroup (the program may or may not have a preference for one place in the outgroup over another). Make sure that you subtract from the total number of steps on the tree all steps in the new character. |
| In programs like Dnapars, you cannot use this method as weights of sites cannot be greater than 1. But you do an analogous trick, by adding a largish number of extra sites to the data, with one nucleotide state ("A") for the ingroup and another ("G") for the outgroup. You will then have to use Retree to manually reroot the tree in the desired place. |
At the upper-undergraduate level:
and as graduate-level texts:
For more mathematically-oriented readers, there is the book
Best of all is of course my own book on phylogenies, which
covers the subject for many data types, at a
graduate course level:
There are also some recent books that take a more practical hands-on approach, and give some detailed information on how to use programs, including PHYLIP programs. These include:
In addition, one of these three review articles may help:
A useful article introducing the inference of phylogenies at a more elementary level is:
I have already mentioned above that there is an excellent guide to using PHYLIP 3.6 for molecular analyses available. It is by Jarno Tuimala:
Felsenstein, J. 2009. PHYLIP (Phylogeny Inference Package) version 3.7a. Distributed by the author. Department of Genome Sciences, University of Washington, Seattle.
or if the editor for whom you are writing insists that the citation must be to a printed publication, you could cite a notice for version 3.2 published in Cladistics:
Felsenstein, J. 1989. PHYLIP - Phylogeny Inference Package (Version 3.2). Cladistics 5: 164-166.
(This citation has been so commonly made that this is the most-cited paper ever in the journal Cladistics, I am the most-cited author ever in that journal, and these citations are responsible for more than 15% of the impact factor of that journal!).
For a while a printed version of the PHYLIP documentation was available and one could cite that. This is no longer true. Other than that, this is difficult, because I have never written a paper announcing PHYLIP! My 1985b paper in Evolution on the bootstrap method contains a one-paragraph Appendix describing the availability of this package, and that can also be cited as a reference for the package, although it was distributed since 1980 while the bootstrap paper is 1985. A paper on PHYLIP is needed mostly to give people something to cite, as word-of-mouth, references in other people's papers, and electronic newsgroup postings have spread the word about PHYLIP's existence quite effectively.
(The following four questions, once common, have finally disappeared, I am pleased to report. I include them to give you some idea of what kinds of requests I had to cope with.)
Version 3.6 has many new features:
There are many more, lesser features added as well.
Version 3.7 has some new features:
There are some obvious deficiencies in this version. Some of these holes will be filled in the next few releases (leading to version 4.0). They include:
There will also be many future developments in the programs that treat continuously-measured data (quantitative characters) and morphological or behavioral data with discrete states, as I have new ideas for analyzing these data in ways that connect to within-species quantitative genetic analyses. This will compete with parsimony analysis.
Here are some comments people have made in print about PHYLIP. Explanatory material in square brackets is my own. They fall naturally into three groups:
"Under no circumstances can we recommend PHYLIP/WAG [their name for the Wagner parsimony option of Mix]."Luckow, M. and R. A. Pimentel (1985)
"PHYLIP has not proven very effective in implementing parsimony (Luckow and Pimentel, 1985)."J. Carpenter (1987a)
"... PHYLIP. This is the computer program where every newsletter concerning it is mostly bug-catching, some of which have been put there by previous corrections. As Platnick (1987) documents, through dint of much labor useful results may be attained with this program, but I would suggest an easier way: FORMAT b:"J. Carpenter (1987b)
"PHYLIP is bug-infested and both less effective and orders of magnitude slower than other programs ....""T. N. Nayenizgani" [J. S. Farris] (1990)
"Hennig86 [by J. S. Farris] provides such substantial improvements over previously available programs (for both mainframes and microcomputers) that it should now become the tool of choice for practising systematists."N. Platnick (1989)
"The availability, within PHYLIP of distance, compatibility, maximum likelihood, and generalized `invariants' algorithms (Cavender and Felsenstein, 1987) sets it apart from other packages .... One of the strengths of PHYLIP is its documentation ...."Michael J. Sanderson (1990)(Sanderson also criticizes PHYLIP for slowness and inflexibility of its parsimony algorithms, and compliments other packages on their strengths).
"This package of programs has gradually become a basic necessity to anyone working seriously on various aspects of phylogenetic inference .... The package includes more programs than any other known phylogeny package. But it is not just a collection of cladistic and related programs. The package has great value added to the whole, and for this it is unique and of extreme importance .... its various strengths are in the great array of methods provided ...."Bernard R. Baum (1989)(note also W. Fink's critical remarks (1986) on version 2.8 of PHYLIP).
"a program on phylogeny -- PHYLOGENY INTERFERENCE PACKAGE (PHYLIP). We would therefore like to ask ..."
"I am struglling with your clever programs."
"I'm famously computer illiterate - I look forward to many frustrating hours trying to run this program"
"I am a brave man. PHYLIP is a brave program. We'll do fine together."
"The Mahabarata of phylogenetics looks better than ever."
"I love phylip. Tastes great and less filling!"
In the documentation files that follow I frequently refer to papers in the literature. In order to centralize the references they are given in this section. If you want to find further papers beyond these, my book (Felsenstein, 2004) lists more than 1,000 further references.
Adams, E. N. 1972. Consensus techniques and the comparison of taxonomic trees. Systematic Zoology 21: 390-397.
Adams, E. N. 1986. N-trees as nestings: complexity, similarity, and consensus. Journal of Classification 3: 299-317.
Archie, J. W. 1989. A randomization test for phylogenetic information in systematic data. Systematic Zoology 38: 239-252.
Backeljau, T., L. De Bruyn, H. De Wolf, K. Jordaens, S. Van Dongen, and B. Winnepenninckx. 1996. Multiple UPGMA and neighbor-joining trees and the performance of some computer packages. Molecular Biology and Evolution 13: 309–313.
Barry, D., and J. A. Hartigan. 1987. Statistical analysis of hominoid molecular evolution. Statistical Science 2: 191-210.
Baum, B. R. 1989. PHYLIP: Phylogeny Inference Package. Version 3.2. (Software review). Quarterly Review of Biology 64: 539-541.
Bourque, M. 1978. Arbres de Steiner et reseaux dont certains sommets sont à localisation variable. Ph. D. Dissertation, Université de Montréal, Quebec.
Bron, C., and J. Kerbosch. 1973. Algorithm 457: Finding all cliques of an undirected graph. Communications of the Association for Computing Machinery 16: 575-577.
Camin, J. H., and R. R. Sokal. 1965. A method for deducing branching sequences in phylogeny. Evolution 19: 311-326.
Carpenter, J. 1987a. A report on the Society for the Study of Evolution workshop "Computer Programs for Inferring Phylogenies". Cladistics 3: 363-375.
Carpenter, J. 1987b. Cladistics of cladists. Cladistics 3: 363-375.
Cavalli-Sforza, L. L., and A. W. F. Edwards. 1967. Phylogenetic analysis: models and estimation procedures. Evolution 32: 550-570 (also American Journal of Human Genetics 19: 233-257).
Cavender, J. A. and J. Felsenstein. 1987. Invariants of phylogenies in a simple case with discrete states. Journal of Classification 4: 57-71.
Churchill, G.A. 1989. Stochastic models for heterogeneous DNA sequences. Bulletin of Mathematical Biology 51: 79-94.
Conn, E. E. and P. K. Stumpf. 1963. Outlines of Biochemistry. John Wiley and Sons, New York.
Day, W. H. E. 1983. Computationally difficult parsimony problems in phylogenetic systematics. Journal of Theoretical Biology 103: 429-438.
Dayhoff, M. O. and R. V. Eck. 1968. Atlas of Protein Sequence and Structure 1967-1968. National Biomedical Research Foundation, Silver Spring, Maryland.
Dayhoff, M. O., R. M. Schwartz, and B. C. Orcutt. 1979. A model of evolutionary change in proteins. pp. 345-352 in Atlas of Protein Sequence and Structure, volume 5, supplement 3, 1978, ed. M. O. Dayhoff. National Biomedical Research Foundation, Silver Spring, Maryland .
Dayhoff, M. O. 1979. Atlas of Protein Sequence and Structure, Volume 5, Supplement 3, 1978. National Biomedical Research Foundation, Washington, D.C.
DeBry, R. W. and N. A. Slade. 1985. Cladistic analysis of restriction endonuclease cleavage maps within a maximum-likelihood framework. Systematic Zoology 34: 21-34.
Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39: 1-38.
Eck, R. V., and M. O. Dayhoff. 1966. Atlas of Protein Sequence and Structure 1966. National Biomedical Research Foundation, Silver Spring, Maryland.
Edwards, A. W. F., and L. L. Cavalli-Sforza. 1964. Reconstruction of evolutionary trees. pp. 67-76 in Phenetic and Phylogenetic Classification, ed. V. H. Heywood and J. McNeill. Systematics Association Volume No. 6. Systematics Association, London.
Estabrook, G. F., C. S. Johnson, Jr., and F. R. McMorris. 1976a. A mathematical foundation for the analysis of character compatibility. Mathematical Biosciences 23: 181-187.
Estabrook, G. F., C. S. Johnson, Jr., and F. R. McMorris. 1976b. An algebraic analysis of cladistic characters. Discrete Mathematics 16: 141-147.
Estabrook, G. F., F. R. McMorris, and C. A. Meacham. 1985. Comparison of undirected phylogenetic trees based on subtrees of four evolutionary units. Systematic Zoology 34: 193-200.
Faith, D. P. 1990. Chance marsupial relationships. Nature 345: 393-394.
Faith, D. P. and P. S. Cranston. 1991. Could a cladogram this short have arisen by chance alone?: On permutation tests for cladistic structure. Cladistics 7: 1-28.
Farris, J. S. 1977. Phylogenetic analysis under Dollo's Law. Systematic Zoology 26: 77-88.
Farris, J. S. 1978a. Inferring phylogenetic trees from chromosome inversion data. Systematic Zoology 27: 275-284.
Farris, J. S. 1981. Distance data in phylogenetic analysis. pp. 3-23 in Advances in Cladistics: Proceedings of the first meeting of the Willi Hennig Society, ed. V. A. Funk and D. R. Brooks. New York Botanical Garden, Bronx, New York.
Farris, J. S. 1983. The logical basis of phylogenetic analysis. pp. 1-47 in Advances in Cladistics, Volume 2, Proceedings of the Second Meeting of the Willi Hennig Society. ed. Norman I. Platnick and V. A. Funk. Columbia University Press, New York.
Farris, J. S. 1985. Distance data revisited. Cladistics 1: 67-85.
Farris, J. S. 1986. Distances and statistics. Cladistics 2: 144-157.
Farris, J. S. [“T. N. Nayenizgani”]. 1990. The systematics association enters its golden years (review of Prospects in Systematics, ed. D. Hawksworth). Cladistics 6: 307-314.
Farris, J. S., V. A. Albert, M. K&aauml;llersj&oauml;, D. Lipscomb, and A. G. Kluge. 1996. Parsimony jackknifing outperforms neighbor-joining. Cladistics 12: 99-124.
Felsenstein, J. 1973a. Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. Systematic Zoology 22: 240-249.
Felsenstein, J. 1973b. Maximum-likelihood estimation of evolutionary trees from continuous characters. American Journal of Human Genetics 25: 471-492.
Felsenstein, J. 1978a. The number of evolutionary trees. Systematic Zoology 27: 27-33.
Felsenstein, J. 1978b. Cases in which parsimony and compatibility methods will be positively misleading. Systematic Zoology 27: 401-410.
Felsenstein, J. 1979. Alternative methods of phylogenetic inference and their interrelationship. Systematic Zoology 28: 49-62.
Felsenstein, J. 1981a. Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evolution 17: 368-376.
Felsenstein, J. 1981b. A likelihood approach to character weighting and what it tells us about parsimony and compatibility. Biological Journal of the Linnean Society 16: 183-196.
Felsenstein, J. 1981c. Evolutionary trees from gene frequencies and quantitative characters: finding maximum likelihood estimates. Evolution 35: 1229-1242.
Felsenstein, J. 1982. Numerical methods for inferring evolutionary trees. Quarterly Review of Biology 57: 379-404.
Felsenstein, J. 1983b. Parsimony in systematics: biological and statistical issues. Annual Review of Ecology and Systematics 14: 313-333.
Felsenstein, J. 1984a. Distance methods for inferring phylogenies: a justification. Evolution 38: 16-24.
Felsenstein, J. 1984b. The statistical approach to inferring evolutionary trees and what it tells us about parsimony and compatibility. pp. 169-191 in: Cladistics: Perspectives in the Reconstruction of Evolutionary History, edited by T. Duncan and T. F. Stuessy. Columbia University Press, New York.
Felsenstein, J. 1985a. Confidence limits on phylogenies with a molecular clock. Systematic Zoology 34: 152-161.
Felsenstein, J. 1985b. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39: 783-791.
Felsenstein, J. 1985c. Phylogenies from gene frequencies: a statistical problem. Systematic Zoology 34: 300-311.
Felsenstein, J. 1985d. Phylogenies and the comparative method. American Naturalist 125: 1-12.
Felsenstein, J. 1986. Distance methods: a reply to Farris. Cladistics 2: 130-144.
Felsenstein, J. and E. Sober. 1986. Parsimony and likelihood: an exchange. Systematic Zoology 35: 617-626.
Felsenstein, J. 1988a. Phylogenies and quantitative characters. Annual Review of Ecology and Systematics 19: 445-471.
Felsenstein, J. 1988b. Phylogenies from molecular sequences: inference and reliability. Annual Review of Genetics 22: 521-565.
Felsenstein, J. 1992. Phylogenies from restriction sites, a maximum likelihood approach. Evolution 46: 159-173.
Felsenstein, J. and G. A. Churchill. 1996. A hidden Markov model approach to variation among sites in rate of evolution Molecular Biology and Evolution 13: 93-104.
Felsenstein, J. 2004. Inferring Phylogenies. Sinauer Associates, Sunderland, Massachusetts.
Felsenstein, J. 2005. Using the threshold model of quantitative genetics for inferences within and between species. Philosophical Transactions of the Royal Society of London, Series B 360 1427-1434.
Felsenstein, J. 2008. Comparative methods with sampling error and within-species variation: contrasts revisited and revised. American Naturalist 171: 713-725.
Fink, W. L. 1986. Microcomputers and phylogenetic analysis. Science 234: 1135-1139.
Fitch, W. M., and E. Markowitz. 1970. An improved method for determining codon variability in a gene and its application to the rate of fixation of mutations in evolution. Biochemical Genetics 4: 579-593.
Fitch, W. M., and E. Margoliash. 1967. Construction of phylogenetic trees. Science 155: 279-284.
Fitch, W. M. 1971. Toward defining the course of evolution: minimum change for a specified tree topology. Systematic Zoology 20: 406-416.
Fitch, W. M. 1975. Toward finding the tree of maximum parsimony. pp. 189-230 in Proceedings of the Eighth International Conference on Numerical Taxonomy, ed. G. F. Estabrook. W. H. Freeman, San Francisco.
Fitch, W. M. and E. Markowitz. 1970. An improved method for determining codon variability and its application to the rate of fixation of mutations in evolution. Biochemical Genetics 4: 579-593.
George, D. G., L. T. Hunt, and W. C. Barker. 1988. Current methods in sequence comparison and analysis. pp. 127-149 in Macromolecular Sequencing and Synthesis, ed. D. H. Schlesinger. Alan R. Liss, New York.
Gilmour, R. 2000. Taxonomic markup language: applying XML to systematic data. Bioinformatics 16: 406-407.
Goldman, N., and Z. Yang. 1994. A codon-based model of nucleotide substitution for protein-coding DNA sequences. Molecular Biology and Evolution 11: 725-736.
Goldstein, D. B., A. Ru&iiacute;z-Linares, M. Feldman, and L. L. Cavalli-Sforza. 1995. Genetic absolute dating based on microsatellites and the origin of modern humans. Proceedings of the National Academy of Sciences USA 92: 6720-6727.
Gomberg, D. 1968. "Bayesian" post-diction in an evolution process. unpublished manuscript, University of Pavia, Italy.
Graham, R. L., and L. R. Foulds. 1982. Unlikelihood that minimal phylogenies for a realistic biological study can be constructed in reasonable computational time. Mathematical Biosciences 60: 133-142.
Hasegawa, M. and T. Yano. 1984a. Maximum likelihood method of phylogenetic inference from DNA sequence data. Bulletin of the Biometric Society of Japan No. 5: 1-7.
Hasegawa, M. and T. Yano. 1984b. Phylogeny and classification of Hominoidea as inferred from DNA sequence data. Proceedings of the Japan Academy 60 B: 389-392.
Hasegawa, M., Y. Iida, T. Yano, F. Takaiwa, and M. Iwabuchi. 1985a. Phylogenetic relationships among eukaryotic kingdoms as inferred from ribosomal RNA sequences. Journal of Molecular Evolution 22: 32-38.
Hasegawa, M., H. Kishino, and T. Yano. 1985b. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. Journal of Molecular Evolution 22: 160-174.
Hendy, M. D., and D. Penny. 1982. Branch and bound algorithms to determine minimal evolutionary trees. Mathematical Biosciences 59: 277-290.
Higgins, D. G. and P. M. Sharp. 1989. Fast and sensitive multiple sequence alignments on a microcomputer. Computer Applications in the Biological Sciences (CABIOS) 5: 151-153.
Hochbaum, D. S. and A. Pathria. 1997. Path costs in evolutionary tree reconstruction. Journal of Computational Biology 4: 163-175.
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Over the years various granting agencies have contributed to the support of the PHYLIP project (at first without knowing it). They are:
| Years | Agency | Grant or Contract Number |
| 2005-2009 | NIH NIGMS | R01 GM071639 |
| 2003-2007 | NIH NIGMS | R01 GM51929-05 (PI: Mary Kuhner) |
| 1999-2003 | NSF | BIR-9527687 |
| 1999-2002 | NIH NIGMS | R01 GM51929-04 |
| 1999-2001 | NIH NIMH | R01 HG01989-01 |
| 1995-1999 | NIH NIGMS | R01 GM51929-01 |
| 1992-1995 | National Science Foundation | DEB-9207558 |
| 1992-1994 | NIH NIGMS Shannon Award | 2 R55 GM41716-04 |
| 1989-1992 | NIH NIGMS | 1 R01-GM41716-01 |
| 1990-1992 | National Science Foundation | BSR-8918333 |
| 1987-1990 | National Science Foundation | BSR-8614807 |
| 1979-1987 | U.S. Department of Energy | DE-AM06-76RLO2225 TA DE-AT06-76EV71005 |
However, starting in April, 2009 there is no grant support for PHYLIP.
I am particularly grateful to past program administrators William Moore, Irene Eckstrand, Peter Arzberger, and Conrad Istock, who have gone beyond the call of duty to make sure that PHYLIP continued.
Booby prizes for funding are awarded to:
The original Camin-Sokal parsimony program and the polymorphism parsimony program were written by me in 1977 and 1978. They were Pascal versions of earlier FORTRAN programs I wrote in 1966 and 1967 using the same algorithm to infer phylogenies under the Camin-Sokal and polymorphism parsimony criteria. Harvey Motulsky worked for me as a programmer in 1971 and wrote FORTRAN programs to carry out the Camin-Sokal, Dollo, and polymorphism methods (he is better-known these days as the author of the scientific data analysis package GraphPad). But most of the early work on PHYLIP other than my own was by Jerry Shurman and Mark Moehring. Jerry Shurman worked for me in the summers of 1979 and 1980, and Mark Moehring worked for me in the summers of 1980 and 1981. Both wrote original versions of many of the other programs, based on the original versions of my Camin-Sokal parsimony program and my polymorphism parsimony program. These formed the basis of Version 1 of the Package, first distributed in October, 1980.
Version 2, released in the spring of 1982, involved a fairly complete rewrite by me of many of those programs. Hisashi Horino for version 3.3 reworked some parts of the programs Clique and Consense to make their output more comprehensible, and has added some code to the tree-drawing programs Drawgram and Drawtree as well. He also worked on some of the Drawtree and Drawgram driver code.
Later programmers Akiko Fuseki, Sean Lamont, Andrew Keeffe, Daniel Yek, Dan Fineman, Patrick Colacurcio, Mike Palczewski, Doug Buxton, Ian Robertson, Marissa LaMadrid, Eric Rynes, and Elizabeth Walkup gave me substantial help with the 3.6 releases, and their excellent work is greatly appreciated. Akiko, in over 10 years of excellent work, did much of the hard work of adding new features and changing old ones in the 3.4 and 3.5 releases, centralized many of the C routines in support files, and is responsible for the new versions of Dnapars and Pars. Andrew prepared the Macintosh version, wrote Retree, added the ray-tracing and PICT code to the Draw... programs and has since done much other work. Sean was central to the conversion to C, and tested it extensively. Mike Palczewski reorganized the code and centralized routines, bringing us closer to object-oriented structure. My (then) postdoctoral fellow Mary Kuhner and her associate Jon Yamato created Neighbor, the neighbor-joining and UPGMA program, for the current release, for which I am also grateful (Naruya Saitou and Li Jin kindly encouraged us to use some of the code from their own implementation of this method). Lucas Mix created the protein likelihood programs Protml and Protmlk. Elisabeth Tillier provided the code for her PMB amino acid model. My current programmers Jim McGill and Bob Giansiracusa have made a great contribution to getting the current version working.
I am very grateful to over 400
users for algorithmic suggestions, complaints about features (or lack of
features), and information about the behavior of their operating systems
and compilers. A list of some of their names will be found at
the
credits page on the PHYLIP web site which is at
http://evolution.gs.washington.edu/phylip/credits.html
A major contribution to this package has been made by others writing programs or parts of programs. Chris Meacham contributed the important program Factor, long demanded by users, and the even more important ones PLOTREE and PLOTGRAM. Important parts of the code in Drawgram and Drawtree were taken over from those two programs. Kent Fiala wrote function "reroot" to do outgroup-rooting, which was an essential part of many programs in earlier versions. Someone at the Western Australia Institute of Technology suggested the name PHYLIP (by writing it the label on the outside of a magnetic tape). Probably it was the late Julian Ford (I've lost the relevant letter).
The distribution of the package also owes much to Buz Wilson and Willem Ellis, who put a lot of effort into the early distributions of the PCDOS and Macintosh versions respectively. Christopher Meacham and Tom Duncan for three versions distributed a printed version of these documentation files (they could not continue to do so), and I am very grateful to them for those efforts. William H.E. Day and F. James Rohlf were very helpful in setting up the listserver news bulletin service which succeeded the PHYLIP newsletter for a time.
I also wish to thank the people who have made computer resources available to me, mostly in the loan of use of microcomputers. These include Jeremy Field, Clem Furlong, Rick Garber, Dan Jacobson, Rochelle Kochin, Monty Slatkin, Jim Archie, Jim Thomas, and George Gilchrist.
I should also note the computers used to develop this package: These include a CDC 6400, two DECSystem 1090s, my trusty old SOL-20, my old Osborne-1, a VAX 11/780, a VAX 8600, a MicroVAX I, a DECstation 3100, my old Toshiba 1100+, my DECstation 5000/200, a DECstation 5000/125, a Compudyne 486DX/33, a Trinity Genesis 386SX, a Zenith Z386, a Mac Classic, a DEC Alphastation 400 4/233, a Pentium 120, a Pentium 200, a PowerMac 6100, and a Macintosh G3. (One of the reasons we have been successful in achieving compatibility between different computer systems is that I have had to run them myself under so many different operating systems and compilers).
A comprehensive list of phylogeny programs is maintained at the PHYLIP web site on the Phylogeny Programs pages:
Here we will simply mention some of the major general-purpose programs. For many more and much more, see those web pages.
PAUP* A comprehensive program with parsimony, likelihood, and distance matrix methods. It competes with PHYLIP to be responsible for the most trees published. Written by David Swofford, now of Duke University and distributed by Sinauer Associates of Sunderland, Massachusetts. It is described in a web page. at http://www.sinauer.com/detail.php?id=8060. Current prices are $100 for the Macintosh version, $85 for the Windows version, and $150 for Unix versions for many kinds of workstations.
MrBayes The leading program for Bayesian inference of
phylogenies. It uses Markov Chain Monte Carlo inference to assess
support for clades and to infer posterior distrubutions of parameters.
Produced by John Huelsenbeck and Fredrik Ronquist, it is available at
its web site at
http://mrbayes.net
as a Mac OS X or Windows
executable, or in source code in C.
MEGA A program by Sudhir Kumar of Arizona State University (written together with Koichiro Tamura, Joel Dudley and Masatoshi Nei). It can carry out parsimony and distance matrix methods for DNA sequence data. Version 4 for Windows, Macintosh, and Linux can be downloaded from the MEGA web site at http://www.megasoftware.net.
PAML Ziheng Yang of the Department of Genetics and Biometry at University College, London has written this package of programs to carry out likelihood analysis of DNA and protein sequence data. It is one of the only packages able to use the codon model for protein sequence data which takes the genetic code reasonably fully into account. PAML is particularly strong in the options for coping with variability of rates of evolution from site to site, though it is less able than some other packages to search effectively for the best tree. It is available as C source code and as Mac OS X and Windows executables from its web site at http://abacus.gene.ucl.ac.uk/software/paml.html.
Phyml Stephane Guindon, currently of the University of Auckland, New Zealand, has written Phyml, a fast likelihood program for molecular sequence data It is available as binaries from its web page at the ATGC site at the Université de Montpellier in France. Source code for Phyml, including later developments of the program, are available at its site at Google Code.
RAxML Alexandros Stamatakis, of the Exelexis Lab at the Technische Universität München has written RAxML, a very fast likelihood program for molecular sequences. It is available from the Exelexis Lab software web page. Source code is available too. RAxML seems to be the fastest implementation of likelihood for molecular data.
TNT This program, by Pablo Goloboff, J. S. Farris, and Kevin Nixon, is for searching large data sets for most parsimonious trees. The authors are respectively at the Instituto Miguel Lillo in Tucumán, Argentina, the Naturhistoriska Riksmuseet in Stockholm, Sweden, and the Hortorium, Cornell University, Ithaca, New York. TNT is described as faster than other methods, though not faster than NONA for small to medium data sets. It is distributed as Windows, Linux, and Mac OS X executables (the latter two require the PVM Parallel Virtual Machine library to be installed). The program and some support files including documentation are available from its download area at http://www.zmuc.dk/public/phylogeny/tnt (see the ReadMe! web page there). It is free, provided you agree to a license with some reasonable limitations.
DAMBE A package written by Xuhua Xia of the
Department of Biology of the University of Ottawa.
Its initials stand for Data Analysis in Molecular Biology and Evolution.
DAMBE is a general-purpose package for DNA and protein sequence phylogenies.
It can read and
convert a number of file formats, and has many features for
descriptive statistics, and can compute a number of commonly-used
distance matrix measures and infer phylogenies by parsimony, distance,
or likelihood methods, including bootstrapping and jackknifing. There are
a number of kinds of statistical tests of trees available and it
can also display phylogenies. DAMBE includes a copy of ClustalW as well;
DAMBE consists of Windows executables. It is available from its
web site at
http://dambe.bio.uottawa.ca/dambe.asp
.
These are only a few of the over 380 different phylogeny packages that are now available (as of July, 2010 - the number keeps increasing). The others are described (and web links provided) at my Phylogeny Programs web pages at the address given above.
Simply let me know of any problems you have had adapting the programs to your computer. I can often make "transparent" changes that, by making the code avoid the wilder, woolier, and less standard parts of C, not only help others who have your machine but even improve the chance of the programs functioning on new machines. I would like fairly detailed information on what gave trouble, on what operating system, machine, and (if relevant) compiler, and what had to be done to make the programs work. I am sometimes able to do some over-the-telephone trouble-shooting, particularly if I don't have to pay for the call, but electronic mail is a the best way for me to be asked about problems, as you can include your input and output files so I can see what is going on (please do not send them as Attachments, but as part of the body of a message). I'd really like these programs to be able to run with only routine changes on absolutely everything, down to and possibly including the Amana Touchmatic Radarange Microwave Oven which was an Intel 8080 system (in fact, early versions of this package did run successfully on Intel 8080 systems running the CP/M operating system). A PalmPilot version was contemplated too.
I would also like to know timings of programs from the package, when run on the three test input files provided above, for various computer and compiler combinations, so that I can provide this information in the section on speeds of this document.
For the phylogeny plotting programs Drawgram and Drawtree, I am particularly interested in knowing what has to be done to adapt them for other graphic file formats.
You can also be helpful to PHYLIP users in your part of the world by helping them get the latest version of PHYLIP from our web site and by helping them with any problems they may have in getting PHYLIP working on their data.
Your help is appreciated. I am always happy to hear suggestions for features and programs that ought to be incorporated in the package, but please do not be upset if I turn out to have already considered the particular possibility you suggest and decided against it.
Read The (documentation) Files Meticulously ("RTFM"). If that doesn't solve the problem, please check the Frequently Asked Questions web page at the PHYLIP web site:
http://evolution.gs.washington.edu/phylip/faq.html
and the PHYLIP Bugs web page at that site:
http://evolution.gs.washington.edu/phylip/bugs.html
If none of these answers your question, get in touch with me. My email address is given below. If you do ask about a problem, please specify the program name, version of the package, computer operating system, and send me your data file so I can test the problem. Also it will help if you have the relevant output and documentation files so that you can refer to them in any correspondence. I can also be reached by telephone by calling me in my office: +1-(206)-543-0150, or at home: +1-(206)-526-9057 (how's that for user support!). If I cannot be reached at either place, a message can be left at the office of the Department of Genome Sciences, +1-(206)-221-7377 but I prefer strongly that I not call you, as in any phone consultation the least you can do is pay the phone bill. Better yet, use email.
Particularly if you are in a part of the world distant from me, you may also want to try to get in touch with other users of PHYLIP nearby. I can also, if requested, provide a list of nearby users.
|
Joe Felsenstein Department of Genome Sciences University of Washington Box 355065 Seattle, Washington 98195-5065, U.S.A. |
Electronic mail addresses: joe (at) gs.washington.edu


| DOSE: |
500 mg |
oral |
DXM |
(capsule) |
I am 17 years old, and am a chronic cannibis smoker. My friend had been telling me that he had been trying DXM in the form of Robitussin DM for a few months, and that he was getting 30 grams of pure DXM powder from a mail order site he found on the Internet. He convinced me to try it for the first time, in the form of a 500mg capsule. I purchased that from him for $5, and decided I would take it that night before I went to bed. I really had no idea how strong of a dose this was, and what it would do to me. At the most, I thought it would make me feel drunk, and maybe a little tripped out.
I usually smoke a joint before I go to sleep, but I had decided against that this night, because I wanted a pure DXM experience. I had eaten chinese food about 2 hours before, so I did not have any empty stomach. I took the pill around 10:00pm, thinking i could just go to sleep if I didn't like what happened to me. I then played around on my computer for 45 minutes, thinking that nothing was going to happen to me and that perhaps my friend had tricked me into buying a capsule of powdered sugar, or some other substance.
Then, at about 10:50 I was hit very hard by sensations that everything was moving really slow and was pulsating. I no longer had interest in my computer, and I decided to lay down and try to go to sleep. I called another friend of mine and told him that I thought DXM was pretty cool, and he ought to try it.  I noticed that all my limbs felt very heavy, and that It was very difficult to move them. When I did manage to move a limb or especially my head, my stomach felt very sick. Whenever I opened my eyes, my room spun around me in an array of flashing, pulsating, gold, yellow, green, and magenta lights. The whole time I was hearing deep, garbled human speech, and a consistent thud, thud, thud of drums beating. The combination of these effects was freaking me out, and I wanted it to all end right then.
I considered waking my dad, who is the head of the local posion control center, and telling him what had I had done. I probbably would have, if i could have gotten out of bed to tell him. I had no idea how long the trip would last, but every time I managed to open my eyes long enough to read my digital clock, time had only moved ahead 5 minutes. I thought that I had overdosed and that I was going to die, and I could feel my organs in my stomach grinding against each other.
At About 12:30am, I must have fallen asleep or went into a deep trip I dont recall, because I 'woke up' at 2:30am feeling that I needed to go take a dump. The crazy noises were still there, but the flashing lights were all gone. When I got out of bed and tried to open my door, the door apperead to lean backwards to a 45 degree angle, and the sprung back at me. I managed to make it to the toilet, and I took a dump, and right after I finished i vomited repeatedly. Because my whole face and the rest of my body felt numb, I couldn't taste the vomit, so I felt much better after this. Walking back to my room, I felt very good, and was extremely happy that I could finnaly go to sleep.
When I woke up the next morning to my alarm clock, I felt wired. My eyes were wide open, and I felt as though low amounts of electricty were pusling through me, causing me to walk very fast with stiff legs. I went to school, and I made it through all my classes ok. My friends told me that I was shaking and staring at nothing for most of the day.  At 10:00pm, i smoked a joint with a friend, and this helped tremendously to calm my nerves, and It rid my of the other symtoms as well.
I learned from this experience that I should not have done such a high dosage my first time, and that I should have learned more about what I was doing before I did it.  I think if I was more aware of what was going to happen, I could have controlled the trip better and enjoyed it alot more.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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FTP is used to transfer files over a network
An FTP server is a server software that implements the FTP protocol (example: PureFTPd, bftpd, proFTPd, Filezilla server, ...). It is used to enable the sharing of files between different parties over a network.
An FTP client is a software that can be used to access an FTP server. Example: Filezilla FTP, Filestash, Cyberduck, ...
FTP was invented by Abhay Bhushan while he was a student at MIT.
The original specification of FTP was published the 16 April 1971 and was revisited several times in the 1980s, 1990s and 2000s.
FTP is a means by which two machines can transfer files over a network.
No, FTP is alive and well. If we account only for the visible side of the iceberg, there are about as many FTP servers exposed to the internet as the entire population of Switzerland.
While it is true that FTP wasn't initially created with security in mind, the protocol got refined in 2005 to support TLS with RFC4217
Being invented 18 years before Time Berners Lee invented HTTP, FTP has passed the test of time. In 2020, there's still millions of FTP servers exposed to the internet and all popular FTP servers are still being actively maintained
No. In fact, FTP was created at a time where nobody could afford the hardware to run anything bulky.
Not necessarily. FTP is a protocol, not an end-user application. On the many software available for FTP, some are harder to use than other ones. Filestash is quite easy to use
Out of the millions of FTP server exposed to the internet, the most popular server software are:
FTP clients are available in 2 flavors:


{% extends "accounts/account_form.html" %} {% load i18n static mezzanine_tags social_auth_filter %} {% block breadcrumb_menu %}{% endblock %} {% block body_id %}account signup{% endblock %} {% block main_content %} {% if request.user.is_authenticated %}
{% trans "You're already logged in. If you'd like to log in as a different user, you'll need to log out first." %}


Review changes
There are mismatches between the data in the apprenticeship service and what your provider has told us.
| Name | Mel O'Connor |
| Date of birth | 28 February 1997 |
| Apprenticeship service | Data from provider | |
| From date | 01 September 2017 | 01 August 2017 |
| Cost | £2,000 | £3,000 |
| Apprenticeship service | Data from provider | |
| From date | 01 September 2017 | 01 September 2017 |
| Cost | £2,000 | £2,000 |


Lecture 15 by Nikolai Gad
München, 5. February 2020
(This is literally just a copyt of the slide from last week)
Send your questions by Friday 12.00, so I can prepare answers.
But feel free to bring questions on the day too.
So a sparse matrix has a sparsity above 0.5, which is the same as a density below 0,5.
The density of a document-feature-matrix can be interpreted as the proportion of text documents from the corpus in which each feature/token on average occur.
So in a document-feature-matrix with a sparsity of 82%, each feature/token on average occur in 18% of the text documents.
Or it can also be interpreted as the proportion of all the features/tokens in the corpus that occure in each text document.
So in a document-feature-matrix with a sparsity of 82%, on average 18% of all the features/tokens in the whole corpus are used in each text document.
But by their nature most document-feature-matrices will be sparse.
A data release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the data.
Altogether the dataset has 1-anonymity because each tweet consist of a unique text, so none of the tweets have exactly the same values for all variables.
| Name | Age | Gender | State of domicile | Religion | Disease |
|---|---|---|---|---|---|
| Ramsha | 30 | Female | Tamil Nadu | Hindu | Cancer |
| Yadu | 24 | Female | Kerala | Hindu | Viral infection |
| Salima | 28 | Female | Tamil Nadu | Muslim | TB |
| Sunny | 27 | Male | Karnataka | Parsi | No illness |
| Joan | 24 | Female | Kerala | Christian | Heart-related |
| Bahuksana | 23 | Male | Karnataka | Buddhist | TB |
| Rambha | 19 | Male | Kerala | Hindu | Cancer |
| Kishor | 29 | Male | Karnataka | Hindu | Heart-related |
| Johnson | 17 | Male | Kerala | Christian | Heart-related |
| John | 19 | Male | Kerala | Christian | Viral infection |
Dataset with 2-anonymity with respect to "age", "gender" and "state of domicile":
| Name | Age | Gender | State of domicile | Religion | Disease |
|---|---|---|---|---|---|
| * | 20 < Age ≤ 30 | Female | Tamil Nadu | * | Cancer |
| * | 20 < Age ≤ 30 | Female | Kerala | * | Viral infection |
| * | 20 < Age ≤ 30 | Female | Tamil Nadu | * | TB |
| * | 20 < Age ≤ 30 | Male | Karnataka | * | No illness |
| * | 20 < Age ≤ 30 | Female | Kerala | * | Heart-related |
| * | 20 < Age ≤ 30 | Male | Karnataka | * | TB |
| * | Age ≤ 20 | Male | Kerala | * | Cancer |
| * | 20 < Age ≤ 30 | Male | Karnataka | * | Heart-related |
| * | Age ≤ 20 | Male | Kerala | * | Heart-related |
| * | Age ≤ 20 | Male | Kerala | * | Viral infection |
A data release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the data.
So essentially we need to find all the unique combinations of values in our dataset and calculate how many times each of these combinations occur.
The uniqe combination of all answers that occurs the least times are then our k-anonymity.
This is the minimum number of records/respondents that have exactly identical values to each other.
> unique(df)
Age Gender SofD Disease
1 20-Age-30 Female Tamil Nadu Cancer
2 20-Age-30 Female Kerala Viral Infection
3 20-Age-30 Female Tamil Nadu TB
4 20-Age-30 Male Karnataka No illness
5 20-Age-30 Female Kerala Heart-related
6 20-Age-30 Male Karnataka TB
7 Age-20 Male Kerala Cancer
8 20-Age-30 Male Karnataka Heart-related
9 Age-20 Male Kerala Heart-related
10 Age-20 Male Kerala Viral Infection
> unique(df[,1:3])
Age Gender SofD
1 20-Age-30 Female Tamil Nadu
2 20-Age-30 Female Kerala
4 20-Age-30 Male Karnataka
7 Age-20 Male Kerala
Can be done with the methods you know, but much easier with the "plyr" package. So if you do not already have it you need to install this first:
> install.packages("plyr")
> library(plyr)
> count(df[,1:3])
Age Gender SofD freq
1 20-Age-30 Female Kerala 2
2 20-Age-30 Female Tamil Nadu 2
3 20-Age-30 Male Karnataka 3
4 Age-20 Male Kerala 3
> dfUnique <- count(df[,1:3])
> min(dfUnique$freq)
[1] 2
Using the dfm_group function, we can easily merge text documents that has the same value on one docvar. For instance, we can group together all speeches by the same president:
dfmspeeches <- dfm(data_corpus_inaugural)
dfmbypresident = dfm_group(dfmspeeches, groups = "FirstName")
This can make it easy to define target texts in a keyness analysis in some cases:
> Obama_Keyness = textstat_keyness(dfmbypresident, "Barack")
> head(Obama_Keyness)
feature chi2 p n_target n_reference
1 we 90.98875 0.000000e+00 130 1609
2 journey 88.76702 0.000000e+00 9 12
3 our 68.94311 1.110223e-16 143 2038
4 creed 66.42421 3.330669e-16 6 6
5 storms 58.88511 1.676437e-14 3 0
6 founding 53.20145 3.009815e-13 5 5
> Obama_Keyness2 <- textstat_keyness(dfmspeeches, docvars(inaugDfm, "President") == "Obama")
> head(Obama_Keyness2)
feature chi2 p n_target n_reference
1 we 90.98875 0.000000e+00 130 1609
2 journey 88.76702 0.000000e+00 9 12
3 our 68.94311 1.110223e-16 143 2038
4 creed 66.42421 3.330669e-16 6 6
5 storms 58.88511 1.676437e-14 3 0
6 founding 53.20145 3.009815e-13 5 5
But it gives the same results and it adds an extra step to the procedure.
We have used several functions from the apply family in R:
And then there is also...
Overview also uploaded to Moodle.