I live and work in Japan. My tango lessons are located in Tokio - roppongi.
Don’t miss the chance to learn the passion of Argentine Tango.
Feel free to email us to provide some feedback on our templates, give us suggestions for new templates and themes, or to just say hello!
Ezequiel Gomez
Dancer, teacher and choreographer dancing Tango Argentino
- It began in 1997 as a dancer Folkdance Argentinas, achieving first prize in competitions at the provincial level.
- It was developed as a teacher at the Institute of Folklore Luis Federico Leloir and Educational Institute in San Miguel (parish) in the period 2001 ~ 05.
- 2005 organized its first folk festival in the city of San Miguel (her native city), the event was named cultural interest by the Ministry of Culture of the City of San Miguel, province of Buenos Aires.
- In 2006 the month two of the first day was invited to join the staff of DNI Tango as a teacher and dancer. With the company DNI TANGO directed by Paul Villarraza and Dana Frígoli, he has participated in the 1st and 2nd International Tango Festival de la Plata, 3rd Festival of Dance and Cambalache theater show "The Collection, Los Tangos of a Life "at the theater on February 3rd city of Parana the November 1, 2007. 4th and 6th Tango dance and in the most important milongas of Buenos Aires World; The ideal, Salon Canning, Chip, The tile and Sunderland with the company DNI TANGO. 2nd Octopus Tango Week Festival. With Horacio Ferrer Garcia organized a fusion of tango, theater and poetry in the studio DNI TANGO.
- In 2009 the city of tokyo-japan is rooted to work in the studio Zero Hour directed by John Guida. Workshop conducted in major cities in Japan, Nagoya, Osaka, Kyoto, Kanagawa, Shizuoka. Participate in the bicentennial of Argentina organized by the Argentina Embassy in Japan. RED TANGO with Ezequiel Farfaro artist and theater director Francisco Leiva.
In the same añofue invited as teacher honor of St. Peters burg (Russia) to teach classes and do exhibitions.
- In 2010 TANGO BAR with Alejandra Gutty, Workshop folklore. It was the first Argentine tango workshop gave in Khabarovsk (Russia).
- During 2010 and 2013 he was invited to teach at the different countries of Asia, Singapore, Malaysia, Thailand and Indonesia.
- In 2013 he won the 2nd place in the competition held in Japan every year Asiato Argentine Tango Championship.
- In his present one he is working in Japan as a teacher, choreographer and dancer.
His dance character, strength and presence unfolds. In her dancing you can appreciate a traditional closed along with modern and contemporary.
Visit Download PageFeel free to email us to provide some feedback on our templates, give us suggestions for new templates and themes, or to just say hello!


jass.generators
Class OpenWebsterTube
java.lang.Object
jass.engine.Out
jass.engine.InOut
jass.generators.OpenWebsterTube
- All Implemented Interfaces:
- Sink, Source
public class OpenWebsterTube
- extends InOut
- Author:
- Kees van den Doel<EMAIL_ADDRESS>Implement Webster equation with open ends (pressure=0) and external fluid force applied on left end.
u,f on o grid, S (area) on both p on x grid, p=0 on boundaries
O x o x o x o x O
N grid points, N is odd. Let i= 0,...,N-1
S[i] p[i=0,2...,N-1], u[i=1,3,..,N-2], same for f[]
Physical parameters:
- pressure deviation PP = rho * p (rho = dmass dens. air)
- velocity UU = u/c
- force on fluid FF = (rho/c)*f
CFL conditions give delta_x = h > c/2*srate.
|
Field Summary |
protected double |
c
|
protected double[] |
d_pu
|
protected double |
h
|
protected double |
len
|
protected double |
minLen
|
protected int |
N
|
protected double[] |
outBuf
|
protected int |
overSamplingFactor
|
protected double[] |
pu
|
protected double[] |
S
|
protected double[] |
Sold
|
protected float |
srate
Sampling rate in Hertz. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
srate
protected float srate
- Sampling rate in Hertz.
minLen
protected double minLen
c
protected double c
len
protected double len
h
protected double h
overSamplingFactor
protected int overSamplingFactor
N
protected int N
S
protected double[] S
Sold
protected double[] Sold
pu
protected double[] pu
d_pu
protected double[] d_pu
outBuf
protected double[] outBuf
OpenWebsterTube
public OpenWebsterTube(float srate,
int bufferSize,
TubeModel tm)
- Create and initialize.
- Parameters:
srate
- sampling rate in Hertz.nTubeSection
- maximum number of sections (must be even)
changeTubeModel
public void changeTubeModel()
reset
public void reset()
setArea
public void setArea(int k,
double a)
- Set an individual segment area
- Parameters:
k
- index of segment (0,....N-1)a
- area to set
setAllAreas
public void setAllAreas(double[] a)
- Set all areas
- Parameters:
array
- of r areas
computeBuffer
protected void computeBuffer()
- Compute the next buffer and store in member float[] buf.
- Specified by:
computeBuffer
in class Out


One of the most frequent questions we get emailed is “how does Cappio work?” What powers Cappio is not some Wall Street wizardry, it’s tried and true methodology using fundamental analysis. As a quick refresher, fundamental analysis is the process of researching a company based on what they do, how they make money, and it’s financial data.
Usually this is a process that can take a couple hours, if not days for a novice. Luckily, an accounting professor by the name of Joseph Piotroski at the University of Chicago formalized many fundamental metrics that should be used when evaluating a company. We’ve added a few comparative metrics to the Cappio algorithm to make it more comphrensive and computational. Below is a video summarizing the methodology we will go over in this post:
Breaking it Down
Determining if a company is financially stable can seem subjective and daunting. Cappio breaks down the financial data into four domains to help analysze the company from the top down:
Industry
A healthy company should be growing faster and capturing the market it is in. To get a general idea, we can take a look at some common ratios and compare the company to industry averages.
If you need a refresher on any of these terms, Cappio has easy to understand tooltips for every one of these metrics. You can read them by hovering over the terms you are confused about in the app.
Valuation
Similar to the Industry section, Valuation helps compare a company to it’s top three competitors. Also like in the Industry section you can find explanations of the terms by highlighting them in the app. Generally, the first seven metrics should be higher than competitors and the last three should be lower.
Fundamentals
The fundamentals of a company involve diving into three major financial statements, the balance sheet, income statement, and cash flow statement. These three are used in tandem to find out if the company is making and retaining money.
Some specific metrics to look out for:
- Is the revenue growing?
- Is the net income growing?
- Is the current ratio (a measure of how able they are to pay off short term debt) not too far over 1.0?
- Is the free cash flow (money in the bank) increasing?
Management Effectiveness and Profitability
As a rule of thumb, you can gauge a company’s management effectiveness by taking a look at the Return on Assets (RoA) and Return on Equity (RoE.) It’s always a good sign to see them both increasing compared to the previous year and in well-ranked among its competitors.
To get an idea of the company’s profitability, take a look at the Operating and Profit Margins to make sure they are positive and increasing.
Bringing It All Together
As an overview, this methodology helps you get a good idea of how a potential investment stands against it’s industry, competitors, and past performance. It may not be the most fun thing to do but you can cover a lot of bases and reduce your risk if you following simple checklists like these. I’d also like to note that always helps to look at an investment holistically. Evaluating growth is subjective and the answer can’t be found in just numbers. This is where where keeping up with current events and formulating your own opinions comes into play.
Keep in mind there’s no company that is going to fulfill every criteria. However, a company that meets most of them is a solid investment. The reason is simple - a fundamentally strong company is making money and growing. As a company grows, so does the value of the company and therefore, the stock price increases over time.
Love to hear your thoughts on how you invest or any questions you have in the comments below!


Please connect your phone to the comptuer
test
Start using the App, and then stop profiling
Profiling Data :
Come Again


TCP Communication¶
TCP functionality is implemented in the common package. It is based on non blocking network IO model with Java NIO.
We support two modes of communication in the TCP implementation, namely Request/Response mode and generic messaging mode.
Edge is an important concept in TCP communication and helps to identify two message streams running through the same connections. Without edge number, we cannot identify the which message belongs to which communication.
Each TCP message is preceded by the following header.
Request Response Mode¶
In Request/Response model, we have a TCP Server and a TCP client which works using requests and responses. Requests and responses are always protocol buffer messages.
The servers always respond to requests and doesnt initiate any requests. Clients always send requests and expect responses.
This model is primarily used for control messages and is not recomended to be used for actual data processing messages.
When a message is received, a new byte buffer is allocated according to the length of the message and it is filled with the content of the message. This is returned to the application.
The request response mode should use RRServer and RRClient to do communications. In this mode, the application must first register protocol buffer message types with the transport. Name of the protocol buffer message type and a 32 bit request id is included additionally to the default message header.
So each message is preceded by
- ```bash
- 4 byte integer length 4 byte integer edge |4 byte request id length | request id | 4 byte message name length | message name
When we send a message, a callback is registered to receive the responses. The requests and responces are matched using the unique request id generated for each message.
Messaging Mode¶
In messaging mode, the tcp network sends data buffers in Java ByteBuffer objects. It uses set of fixed data buffers to transfer and reveive data.
Each TCP message is preceded by the following header.
In this mode, receiving buffers must be posted to the tranport and the when it receives a message, it will fill these posted buffers with content and return them. This means the size of the buffers are fixed for sending and receiving.
Here is a psuedo code of how to use the messaging mode.
- ```bash
TCPChannel channel = new TCPChannel(...) channel.startListening()
channel.startConnections()
// now post the required number of buffers TCPMessage rcv = channel.iRecv(recv_buffer, recv_edge)
// now send messages TCPMessage send = channel.iSend(send_buffer, send_edge)
// now we need to progress the select handler channel.progress()
// possibly it will return a message if (rcv.isComplete()) {
// recv message is complete}
// possibly it will return a message if (send.isComplete()) {
// send message is complete}
Because of the additional information carried in Request/Response mode and the use of protocol buffers, the general messaging mode is better performing than request / response mode.


Consultation outcome
Ref: Ofqual/15/5616 PDF , 1.66MB , 88 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Ref: Ofqual/15/5608 PDF , 327KB , 9 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Ref: Ofqual/15/5617 PDF , 715KB , 64 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Ref: Ofqual/15/5607 PDF , 363KB , 9 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
These decisions reflect the subject content requirements published by DfE .
Visit the assessment objectives for new GCSEs, AS and A levels page for more detailed information about the decisions.
This consultation ran from
to
New subjects are: citizenship studies, cooking and nutrition, design and technology, and drama for GCSE; drama and theatre AS and A level.
Ref: Ofqual/14/5520 PDF , 550KB , 57 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
This is a consultation about developing new GCSE, A and AS level qualifications that will be taught for the first time in September 2016. This is part of Ofqual’s work supporting the government’s changes to GCSE, AS and A levels .
We’re consulting on changes to the following subjects, which will be taught from the start of the 2016 school year:
We’re running consultation events to give you the chance to learn more about the proposed changes. See a list of dates and how to book for each subject event.
Published: 25 September 2014
Updated: 12 February 2015


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"The Big Hit" Is a Complete Miss
Here is a movie that sadly follows the Hong Kong-recipe of moviemaking and storytelling to the letter. These kinds of movies are marked by an eye-opening sequence that introduces us to the main characters; a life-and-death plot in which these characters become involved; the inclusion of a host of inconsequential characters; ridiculous subplots, and sunglass-wearing henchmen.
Observe the opening sequence. A band of hitmen are about to storm an apartment, led by Melvin (Mark Wahlberg) and Cisco (Lou Diamond Phillips). They are waiting for their partner to cut off the power so that they can storm the apartment with the help of infrared goggles. However, their partner is unsure of which cable to cut. He is undecided. This happens for about 10 seconds. The audience finds some humor in his foolishness. The lights suddenly go out. Cisco is surprised and says: "Whoa...let's go!" And they storm the apartment in a very nicely executed sequence.
The Big Hit of the title refers to a kidnapping that happens a quarter of the way through the film. Needing cash, Cisco devises a plan to kidnap the daughter (China Chow) of wealthy Japanese industrialist Jiro Nishi. However, the daughter is also the god-daughter of Paris (Avery Brooks), who is Cisco's and Melvin's boss. Infuriated and insufferable, Paris commands Cisco to uncover the mastermind of the kidnapping. During his 'investigation', Cisco singles out Melvin as the ringleader, and Paris orders his capture and execution. Melvin must now find a way to stay alive.
By the way, do you know how the investigation takes place? Cisco's partner makes the ransom phone call believing that his call will not be traced because of a Trace Buster that prevents tracing. But, Mr. Nishi has a Trace Buster Buster. To counter that, Cisco's partner has a Trace Buster Buster Buster. But Nishi has a Trace Buster Buster Buster Buster. And so on...
There is no doubt that this is an interesting world in which they live. And there are lots of issues that we'd like to know more about. How does Melvin justify his profession to his fiancee? What goes on among this clique? Why is Cisco at odds with Melvin? There is actually a lot of potential material that could have been explored, but it seems that no effort is made to go in that direction, and instead we are given a not-so-engrossing plot filled with one-liners, silly sight gags, extraneous sub-plots and tomfoolery.
And, I haven't even mentioned his financee's visiting parents who want them to separate, nor Melvin's mistress, nor the pimple-faced video clerk demanding the return of an overdue video and on and on and on. If The Big Hit signals the future of Hong Kong style movies that are made for American audiences, then shoot me now.
Grade: C-
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


This page is similar to the home page but uses WebSockets for real-time updates.
LED Control
| Uptime: | |
| Free heap: | |
| LED state: |
This demo uses 2 WebScokets. Status parameters are streamed by the server in JSON format every 2 seconds.
A websocket_task
is created each time a specific URI is requested.
ADC values are being continuously polled by the client (i.e. your browser).
Each time a WebSocket frame is received on the server side, websocket_cb
function is being called.


Building Digital Products, Brands & Experience.
A Product Design and Visual Designer in SF I”m Specialize in UI/UX, Responsive web design, and visual Development.
A Product Design and Visual Designer in SF I”m Specialize in UI/UX, Responsive web design, and visual Development.
I’ve created a user interface and user experience with some proccess and method.
I’ve created a user interface and user experience with some proccess and method.
I’ve created a user interface and user experience with some proccess and method.
I’ve created a user interface and user experience with some proccess and method.


We offer Non-Profit organisations access to sponsors that are committed to helping them reach their annual goals. We provide easy, fast and reliable access to corporate sponsors who are committed to giving back to their surrounding communities.
Whether your goal is to make a social impact or connect with Corporate funders our CSI management software creates an environment where Non-profit organisations find fast, easy and reliable access to funding and connect Corporate funders to relevant organisations at the same time.
Whether your goal is to make a social impact or connect with Corporate funders our CSI management software creates an environment where Non-profit organisations find fast, easy and reliable access to funding and connect Corporate funders to relevant organisations at the same time.
The CSI-Space is software that simplifies manual admin work for Corporate Social Investment departments by a system that regulates the information of your projects. CSI-Space eliminates the amount of time you spend on the net looking for information on organisations and provides the relevant details of organisations.
Companies want to ensure that your organisation is committed to its vision and mission.
It is easier to reach a common goal with a company that aligns with your organisations values, beliefs, mission, and vision.
Corporate sponsors often want a return on their investment. Consider how you can work together to create an outcome that benefits both of you.
It is important to keep in mind that corporates are often very busy running their companies. You will need to keep in touch.


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This documentation relates to Opera's now deprecated .oex Extension API framework for Opera versions <= 12.15 and also provided by our OEX2NEX shim library.
For the latest Opera Extensions API documentation for Opera versions > 12.15 please consult the latest Opera Extensions API documentation online.
An array of zero or more domains on which to apply a given URL Filter rule.
This property is passed within a RuleOptions
object as a parameter for URL Filter methods.
DOMString[] includeDomains
The following example blocks any content with /ads/
in the URL except from ||huffpost.com/images/ads/*
and when included in pages in the huffingtonpost.com domain.
<!--
The configuration file ('config.xml').
-->
<?xml version='1.0' encoding='utf-8'?>
<widget xmlns="http://www.w3.org/ns/widgets">
...
<feature name="opera:urlfilter"/>
</widget>
//
// The background process (e.g. index.html)
//
// Check that the URL Filter API exists
if (typeof opera.extension.urlfilter != 'undefined') {
// Assign the URLFilter object to a variable for efficiency
var filter = opera.extension.urlfilter;
filter.block.add('*/ads/*');
// The following is the same as this Adblock syntax for whitelisting:
// @@||huffpost.com/images/ads/$domain=huffingtonpost.com
var ruleOptions = {
includeDomains: ['huffingtonpost.com']
}
filter.allow.add('||huffpost.com/images/ads/*', ruleOptions);
}
Note the use of ||
which is a hostname mark. This indicates that the subsequent characters should begin matching from any host name in the URL. For example, ||example.com*
matches http://example.com/
, https://www.example.com/
, or similar. However, it will not match http://badexample.com/
.


MicroPython documentation and references¶
- MicroPython libraries
- Python standard libraries and micro-libraries
- Builtin functions and exceptions
cmath
– mathematical functions for complex numbersgc
– control the garbage collectormath
– mathematical functionssys
– system specific functionsuarray
– arrays of numeric dataubinascii
– binary/ASCII conversionsucollections
– collection and container typesuerrno
– system error codesuhashlib
– hashing algorithmsuheapq
– heap queue algorithmuio
– input/output streamsujson
– JSON encoding and decodinguos
– basic “operating system” servicesure
– simple regular expressionsuselect
– wait for events on a set of streamsusocket
– socket moduleussl
– SSL/TLS moduleustruct
– pack and unpack primitive data typesutime
– time related functionsuzlib
– zlib decompression_thread
– multithreading support
- MicroPython-specific libraries
btree
– simple BTree databaseframebuf
— frame buffer manipulationmachine
— functions related to the hardware- Reset related functions
- Interrupt related functions
- Power related functions
- Miscellaneous functions
- Constants
- Classes
- class Pin – control I/O pins
- class Signal – control and sense external I/O devices
- class ADC – analog to digital conversion
- class UART – duplex serial communication bus
- class SPI – a Serial Peripheral Interface bus protocol (master side)
- class I2C – a two-wire serial protocol
- class RTC – real time clock
- class Timer – control hardware timers
- class WDT – watchdog timer
- class SD – secure digital memory card (cc3200 port only)
- class SDCard – secure digital memory card
micropython
– access and control MicroPython internalsnetwork
— network configurationubluetooth
— low-level Bluetoothucryptolib
– cryptographic ciphersuctypes
– access binary data in a structured way
- Port-specific libraries
- Libraries specific to the pyboard
pyb
— functions related to the board- Time related functions
- Reset related functions
- Interrupt related functions
- Power related functions
- Miscellaneous functions
- Classes
- class Accel – accelerometer control
- class ADC – analog to digital conversion
- class CAN – controller area network communication bus
- class DAC – digital to analog conversion
- class ExtInt – configure I/O pins to interrupt on external events
- class Flash – access to built-in flash storage
- class I2C – a two-wire serial protocol
- class LCD – LCD control for the LCD touch-sensor pyskin
- class LED – LED object
- class Pin – control I/O pins
- class PinAF – Pin Alternate Functions
- class RTC – real time clock
- class Servo – 3-wire hobby servo driver
- class SPI – a master-driven serial protocol
- class Switch – switch object
- class Timer – control internal timers
- class TimerChannel — setup a channel for a timer
- class UART – duplex serial communication bus
- class USB_HID – USB Human Interface Device (HID)
- class USB_VCP – USB virtual comm port
lcd160cr
— control of LCD160CR display
- Libraries specific to the WiPy
- Libraries specific to the ESP8266 and ESP32
- Python standard libraries and micro-libraries
- MicroPython language and implementation
- Glossary
- The MicroPython Interactive Interpreter Mode (aka REPL)
- MicroPython .mpy files
- Writing interrupt handlers
- Maximising MicroPython speed
- MicroPython on microcontrollers
- Distribution packages, package management, and deploying applications
- Inline assembler for Thumb2 architectures
- Document conventions
- Instruction categories
- 1. Register move instructions
- 2. Load register from memory
- 3. Store register to memory
- 4. Logical & bitwise instructions
- 5. Arithmetic instructions
- 6. Comparison instructions
- 7. Branch instructions
- 8. Stack push and pop
- 9. Miscellaneous instructions
- 10. Floating point instructions
- 11. Assembler directives
- Usage examples
- References
- Working with filesystems
- The pyboard.py tool
- MicroPython differences from CPython
- Syntax
- Core language
- Builtin types
- Exception
- bytearray
- bytes
- dict
- float
- int
- list
- str
- Start/end indices such as str.endswith(s, start) not implemented
- Attributes/subscr not implemented
- str(…) with keywords not implemented
- str.ljust() and str.rjust() not implemented
- None as first argument for rsplit such as str.rsplit(None, n) not implemented
- Subscript with step != 1 is not yet implemented
- tuple
- Modules
- Developing and building MicroPython
- MicroPython license information
- Quick reference for the pyboard
- General information about the pyboard
- MicroPython tutorial for the pyboard
- 1. Introduction to the pyboard
- 2. Running your first script
- 3. Getting a MicroPython REPL prompt
- 4. Turning on LEDs and basic Python concepts
- 5. Switches, callbacks and interrupts
- 6. The accelerometer
- 7. Safe mode and factory reset
- 8. Making the pyboard act as a USB mouse
- 9. The Timers
- 10. Inline assembler
- 11. Power control
- Tutorials requiring extra components
- Tips, tricks and useful things to know
- General board control
- Delay and timing
- Internal LEDs
- Internal switch
- Pins and GPIO
- Servo control
- External interrupts
- Timers
- RTC (real time clock)
- PWM (pulse width modulation)
- ADC (analog to digital conversion)
- DAC (digital to analog conversion)
- UART (serial bus)
- SPI bus
- I2C bus
- CAN bus (controller area network)
- Internal accelerometer
- Quick reference for the ESP8266
- General information about the ESP8266 port
- MicroPython tutorial for ESP8266
- 1. Getting started with MicroPython on the ESP8266
- 2. Getting a MicroPython REPL prompt
- 3. The internal filesystem
- 4. Network basics
- 5. Network - TCP sockets
- 6. GPIO Pins
- 7. Pulse Width Modulation
- 8. Analog to Digital Conversion
- 9. Power control
- 10. Controlling 1-wire devices
- 11. Controlling NeoPixels
- 12. Controlling APA102 LEDs
- 13. Temperature and Humidity
- 14. Next steps
- Installing MicroPython
- General board control
- Networking
- Delay and timing
- Timers
- Pins and GPIO
- PWM (pulse width modulation)
- ADC (analog to digital conversion)
- Software SPI bus
- Hardware SPI bus
- I2C bus
- Real time clock (RTC)
- Deep-sleep mode
- OneWire driver
- NeoPixel driver
- APA102 driver
- DHT driver
- WebREPL (web browser interactive prompt)
- Quick reference for the ESP32
- General information about the ESP32 port
- Getting started with MicroPython on the ESP32
- Installing MicroPython
- General board control
- Networking
- Delay and timing
- Timers
- Pins and GPIO
- PWM (pulse width modulation)
- ADC (analog to digital conversion)
- Software SPI bus
- Hardware SPI bus
- I2C bus
- Real time clock (RTC)
- Deep-sleep mode
- RMT
- OneWire driver
- NeoPixel driver
- Capacitive touch
- DHT driver
- WebREPL (web browser interactive prompt)
- Quick reference for the WiPy
- General information about the WiPy
- No floating point support
- Before applying power
- WLAN default behaviour
- Telnet REPL
- Local file system and FTP access
- FileZilla settings
- Upgrading the firmware Over The Air
- Boot modes and safe boot
- The heartbeat LED
- Details on sleep modes
- Additional details for machine.Pin
- Additional details for machine.I2C
- Known issues
- WiPy tutorials and examples
- General board control (including sleep modes)
- Pins and GPIO
- Timers
- PWM (pulse width modulation)
- ADC (analog to digital conversion)
- UART (serial bus)
- SPI bus
- I2C bus
- Watchdog timer (WDT)
- Real time clock (RTC)
- SD card
- WLAN (WiFi)
- Telnet and FTP server
- Heart beat LED
- General information about the WiPy
- Quick reference for the UNIX and Windows ports


The Group 7 story started in 2019 when we launched operations with just two aircraft. Today, we fly the world’s biggest fleets of K-C45, AJ360, XL89 and SY-u34, offering our customers the comforts of the latest and most efficient wide-body aircraft in the skies. We inspire travelers around the world with our growing network of worldwide destinations, industry leading inflight entertainment, regionally inspired cuisine, and world-class service. Read on to find out more.
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--- title: "Transfer learning with tfhub" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Tutorial: Text Classification} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} type: docs repo: https://github.com/rstudio/keras menu: main: name: "Transfer learning with tfhub" identifier: "keras-tutorial-text-classification-tfhub" parent: "tutorials-beginners-basic-ml-top" weight: 35 ---
This tutorial classifies movie reviews as positive or negative using the text of the review. This is an example of binary — or two-class — classification, an important and widely applicable kind of machine learning problem.
We’ll use the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are balanced, meaning they contain an equal number of positive and negative reviews.
We will use Keras to build and train the model and tfhub for Transfer Learning. We will also use tfds to load the IMDB dataset.
Let’s start and load the required libraries.
The IMDB dataset is available on imdb reviews or on tfds. The one that comes packaged with Keras is already pre-processed so it’s not useful for this tutorial.
The following code downloads the IMDB dataset to your machine:
imdb <- tfds_load(
"imdb_reviews:1.0.0",
split = list("train[:60%]", "train[-40%:]", "test"),
as_supervised = TRUE
)
summary(imdb)
## ── Large Movie Review Dataset.
## This is a dataset for binary sentiment classification containing
## ❯ Name: imdb_reviews
## ❯ Version: 1.0.0
## ❯ URLs: http://ai.stanford.edu/~amaas/data/sentiment/
## Registered S3 method overwritten by 'R.oo':
## method from
## throw.default R.methodsS3
## ❯ Size: 80.2 MiB
## ❯ Splits:
## — test (25000 examples)
## — train (25000 examples)
## — unsupervised (50000 examples)
## ❯ Schema:
## — label [] INT
## — text [] BYTES
tfds_load
returns a TensorFlow Dataset, an abstraction that represents a sequence of elements, in which each element consists of one or more components.
To access individual elements, of a Dataset you can use:
first <- imdb[[1]] %>%
dataset_batch(1) %>% # Used to get only the first example
reticulate::as_iterator() %>%
reticulate::iter_next()
str(first)
## List of 2
## $ :tf.Tensor([b"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions.  I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it."], shape=(1,), dtype=string)
## $ :tf.Tensor([0], shape=(1,), dtype=int64)
We will see next that Keras knows how to extract elements from TensorFlow Datasets automatically making it a much more memory efficient alterantive than loading the entire dataset to RAM before passing to Keras.
The neural network is created by stacking layers—this requires three main architectural decisions:
In this example, the input data consists of sentences. The labels to predict are either 0 or 1.
One way to represent the text is to convert sentences into embeddings vectors. We can use a pre-trained text embedding as the first layer, which will have three advantages: * we don’t have to worry about text preprocessing, * we can benefit from transfer learning, * the embedding has a fixed size, so it’s simpler to process.
For this example we will use a pre-trained text embedding model from TensorFlow Hub called google/tf2-preview/gnews-swivel-20dim/1.
There are three other pre-trained models to test for the sake of this tutorial:
Let’s first create a Keras layer that uses a TensorFlow Hub model to embed the sentences, and try it out on a couple of input examples. Note that no matter the length of the input text, the output shape of the embeddings is: (num_examples, embedding_dimension)
.
embedding_layer <- layer_hub(handle = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1")
embedding_layer(first[[1]])
## tf.Tensor(
## [[ 1.765786 -3.882232 3.9134233 -1.5557289 -3.3362343 -1.7357955
## -1.9954445 1.2989551 5.081598 -1.1041286 -2.0503852 -0.72675157
## -0.65675956 0.24436149 -3.7208383 2.0954835 2.2969332 -2.0689783
## -2.9489717 -1.1315987 ]], shape=(1, 20), dtype=float32)
Let’s now build the full model:
model <- keras_model_sequential() %>%
layer_hub(
handle = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1",
input_shape = list(),
dtype = tf$string,
trainable = TRUE
) %>%
layer_dense(units = 16, activation = "relu") %>%
layer_dense(units = 1, activation = "sigmoid")
summary(model)
## Model: "sequential"
## ___________________________________________________________________________
## Layer (type) Output Shape Param #
## ===========================================================================
## keras_layer_1 (KerasLayer) (None, 20) 400020
## ___________________________________________________________________________
## dense (Dense) (None, 16) 336
## ___________________________________________________________________________
## dense_1 (Dense) (None, 1) 17
## ===========================================================================
## Total params: 400,373
## Trainable params: 400,373
## Non-trainable params: 0
## ___________________________________________________________________________
The layers are stacked sequentially to build the classifier:
Let’s compile the model.
A model needs a loss function and an optimizer for training. Since this is a binary classification problem and the model outputs a probability (a single-unit layer with a sigmoid activation), we’ll use the binary_crossentropy
loss function.
This isn’t the only choice for a loss function, you could, for instance, choose mean_squared_error. But, generally, binary_crossentropy
is better for dealing with probabilities—it measures the “distance” between probability distributions, or in our case, between the ground-truth distribution and the predictions.
Later, when we are exploring regression problems (say, to predict the price of a house), we will see how to use another loss function called mean squared error.
Now, configure the model to use an optimizer and a loss function:
Train the model for 20 epochs in mini-batches of 512 samples. This is 20 iterations over all samples in the dataset. While training, monitor the model’s loss and accuracy on the 10,000 samples from the validation set:
model %>%
fit(
imdb[[1]] %>% dataset_shuffle(10000) %>% dataset_batch(512),
epochs = 20,
validation_data = imdb[[2]] %>% dataset_batch(512),
verbose = 2
)
## Epoch 1/20
## 30/30 - 3s - loss: 0.8245 - accuracy: 0.5371 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00
## Epoch 2/20
## 30/30 - 2s - loss: 0.6803 - accuracy: 0.5983 - val_loss: 0.6597 - val_accuracy: 0.6131
## Epoch 3/20
## 30/30 - 2s - loss: 0.6277 - accuracy: 0.6553 - val_loss: 0.6107 - val_accuracy: 0.6708
## Epoch 4/20
## 30/30 - 3s - loss: 0.5771 - accuracy: 0.7061 - val_loss: 0.5655 - val_accuracy: 0.7175
## Epoch 5/20
## 30/30 - 3s - loss: 0.5298 - accuracy: 0.7503 - val_loss: 0.5250 - val_accuracy: 0.7506
## Epoch 6/20
## 30/30 - 3s - loss: 0.4901 - accuracy: 0.7849 - val_loss: 0.4887 - val_accuracy: 0.7748
## Epoch 7/20
## 30/30 - 3s - loss: 0.4484 - accuracy: 0.8079 - val_loss: 0.4583 - val_accuracy: 0.7945
## Epoch 8/20
## 30/30 - 4s - loss: 0.4121 - accuracy: 0.8288 - val_loss: 0.4290 - val_accuracy: 0.8144
## Epoch 9/20
## 30/30 - 3s - loss: 0.3824 - accuracy: 0.8455 - val_loss: 0.4071 - val_accuracy: 0.8246
## Epoch 10/20
## 30/30 - 3s - loss: 0.3523 - accuracy: 0.8587 - val_loss: 0.3852 - val_accuracy: 0.8376
## Epoch 11/20
## 30/30 - 3s - loss: 0.3263 - accuracy: 0.8709 - val_loss: 0.3682 - val_accuracy: 0.8442
## Epoch 12/20
## 30/30 - 3s - loss: 0.3019 - accuracy: 0.8829 - val_loss: 0.3539 - val_accuracy: 0.8496
## Epoch 13/20
## 30/30 - 3s - loss: 0.2847 - accuracy: 0.8923 - val_loss: 0.3458 - val_accuracy: 0.8512
## Epoch 14/20
## 30/30 - 3s - loss: 0.2622 - accuracy: 0.9007 - val_loss: 0.3320 - val_accuracy: 0.8591
## Epoch 15/20
## 30/30 - 3s - loss: 0.2476 - accuracy: 0.9111 - val_loss: 0.3236 - val_accuracy: 0.8625
## Epoch 16/20
## 30/30 - 3s - loss: 0.2309 - accuracy: 0.9167 - val_loss: 0.3175 - val_accuracy: 0.8663
## Epoch 17/20
## 30/30 - 3s - loss: 0.2145 - accuracy: 0.9230 - val_loss: 0.3130 - val_accuracy: 0.8677
## Epoch 18/20
## 30/30 - 3s - loss: 0.2023 - accuracy: 0.9288 - val_loss: 0.3086 - val_accuracy: 0.8698
## Epoch 19/20
## 30/30 - 3s - loss: 0.1894 - accuracy: 0.9349 - val_loss: 0.3057 - val_accuracy: 0.8711
## Epoch 20/20
## 30/30 - 3s - loss: 0.1804 - accuracy: 0.9382 - val_loss: 0.3037 - val_accuracy: 0.8724
And let’s see how the model performs. Two values will be returned. Loss (a number which represents our error, lower values are better), and accuracy.
## $loss
## [1] 0.3144187
##
## $accuracy
## [1] 0.86488
This fairly naive approach achieves an accuracy of about 87%. With more advanced approaches, the model should get closer to 95%.


Welcome to the inventive world of a maximalist from the walled city of Lahore who is flashily flamboyant with purposeful Aesthetics in her early twenties
خوبصورت شہر لاہور سے تعلق رکھنے والی ایک ایستھیٹک ڈیزائنر
More About meI was born and raised in Lahore, a city full of aesthetics. I listned to my innervoice and chosed to be a designer. I believe that creativity is something you cannot learn but it is in your blood. My work is the result of my inspirations and imaginations.
Know some moreThe artwork is made from an assemblage of different forms
Image editing encompasses the processes of altering images
Collage of images, text, & objects in a composition
something that everyone would claim to know nowadays :)
Want me to design for you?
Check my Instagram or send me an email
Remember me as a time of the day...


What size dog breed would you like to find? Small Medium Large
How would you describe your lifestyle? I'm a couch potato Somewhat Active Active
Do you have a history of pet allergies? Yes No
Is this dog going to be utilized as a service dog? Yes No
In terms of barking, how much noise can you tolerate? Very little, I prefer a dog that doesn't bark much. Some barking doesn't bother me too much. Barking is not an issue for me!
 As little fur as possible. Furry is not a problem! It doesn't matter!


Contact: Christian Grimaldi Bookings Name: Subject: Email: Message: Blog Ideay? 14 December 2014 QLEH! QDAV! SI QUERES! 13 December 2014 Solo Verg Still


You have completed your journey through The Solar Swiftem and are now a Swift expert!
You may now return home to keep practicing your skills!
Don't forget there is always space in the Solar Swiftem for you to brush up on any forgotten knowledge :)


Introduction
- Studies have shown relationships of financial behavior with external factors such as financial literacy and education methods as well as internal factors like self-esteem and sense of coherence with other people.
- Financial literacy influences financial behavior, and an individual’s perceived financial literacy may be as important as actual financial literacy (Allgood et al., 2016).
- In a more psychological dimension, studies have shown that self-esteem significantly relates to financial behavior positively, subject to varying financial knowledge (Tang et al., 2016).
- This suggests that there are certain correlations between a person’s psychological tendencies and their economic health, but it has not been studied extensively.
Research Question
- Is there an association between one’s level of optimism and their financial well-being?
- Does this association vary across different economic classes?
Methodology
Sample
- Data from National Financial Well-Being Survey (NFWBS) conducted by the Consumer Financial Protection Bureau (CFPB) in 2016.
- Survey sample drawn from GfK KnowledgePanel
- Population: Sample represents the noninstitutionalized adult (18+) population in the United States.
- Overall sample size was 6394 participants.
Measures
- Financial well-being scale score constructed using Item Response Theory methods developed by the CFPB.
- Represents individual’s overall financial health and ability to manage money.
- Financial well-being scale constructed by a 10-item survey each with 5 response options ranging from “Never” to “Always” or “Does not describe me at all” to “Describes me completely”.
- NFWBS optimism survey question “I am optimistic about my future” has response options from a scale to 1 to 7, with 1 being “Strongly disagree” and 7 being “Strongly agree”.
Results
Univariate
- Average financial well-being scale score was 56, with the lowest being 14 and the highest being 95.
- A large proportion of people responded as being optimistic towards their future, with 26% “strongly agree” and 29% “agree”.
- But only 2.1% “strongly disagree” and 2.2% “disagree”.
Bivariate
- Linear regression analysis showed that people who responded positively to the question of optimism were significantly more likely to have a higher financial well-being score.
- Compared to “strongly disagree”, “strongly agree” responders on average had a higher FWB score of 17.2. “Agree” responders were 16.2 points higher, “Somewhat agree” were 9.8 points higher, and “Neither” were 6.5 points higher. For all of these results, at least p < 0.000001 (Figure 1).
- “Somewhat disagree” and “Disagree” responders had no statistically significant difference with “Strongly disagree”.
Multivariate
- Positive responses to optimism maintained its significance of at least p < 0.00001 after controlling for other possible confounding variables such as education, ethnicity, gender, generation, and poverty level.
- FWB scores were lower for individuals in lower poverty levels (as expected), but the positive association between optimism and financial well-being was still present with a significance of p < 0.000001 (Figure 2).
Conclusion
- Individuals that are more optimistic about their future are more likely to have higher financial well-being scores, but there is no significant difference between negative responses themselves.
- Poverty level is significantly correlated with financial well-being, and the association between optimism and financial well-being holds true within poverty levels.
- Such a result suggests that going forwards efforts in improving financial well-being may need to incorporate one’s psychological values in addition to other external factors such as financial literacy.
- Further research is required into determining causality, and it also opens the discussion of what other psychological or philosophical ideals influence one’s financial well-being and health.
References
Allgood, S. and W. B. Walstad (2016). "The Effects of Perceived and Actual Financial Literacy on Financial Behaviors." Economic Inquiry 54(1): 675-697.
Tang, N. and A. Baker (2016). "Self-Esteem, Financial Knowledge and Financial Behavior." Journal of Economic Psychology 54: 164-176.
Wesleyan University | Spring 2019 | Applied Data Analysis | Professor Valerie Nazzaro


Before Change
if not hasattr(settings,"COMIC_PUBLIC_FOLDER_NAME"):
raise ImproperlyConfigured("Don"t know from which folder serving files is allowed. Please add a setting \
"COMIC_PUBLIC_FOLDER_NAME = \"public_html\" to your .conf file." )
if not filepath.startswith(settings.COMIC_PUBLIC_FOLDER_NAME):
//FIXME: throw 403 or proper permission denied here
raise Http404("Permission Denied. Only objects in folder named "%s" can be served without credentials\
FIXME: throw proper 403 or permission denied here." % settings.COMIC_PUBLIC_FOLDER_NAME )
After Change
filename = path.join(settings.DROPBOX_ROOT,site_short_name,filepath)
if can_access(request.user,
filename,
site_short_name,
override_permission=ComicSiteModel.REGISTERED_ONLY):
try:
file = open(filename,"rb")


{% include 'notifications.html' %}
{% include 'download_container.html' %}
Tip Assess the performance of the selected features using a machine learning (ML) model. However, if doing so, make sure to perform feature selection using a training set in order to produce an unbiased predictive performance on a test set.