Larkist.com is an AI powered application that generates recommended Twitter lists to either follow or block.
This 5 week team project integrated web developers and data scientists, implementing Twitter's API to execute.
Create your own list here.
Men's minimalist lifestyle brand & designer of the original "barefoot dress shoe."
Responsible for:
Created Successful Kickstarter Campaign:
Managed productions runs in:
Changing the core SEO focus from "minimalist dress shoes" to "zero drop dress shoes" resulted in +300% increase in monthly traffic + first page ranking.
Conducted a 30 Day Social Media Sprint Challenge that resulted in over 1k qualified email leads, +2k Instagram followers and +50% ROI on ad spends & blank apparel.


Check if a path exists
Because fs.exists()
is being deprecated, but there’s still a genuine use-case of being able to check if a path exists for other purposes than doing IO with it.
Never use this before handling a file though:
In particular, checking if a file exists before opening it is an anti-pattern that leaves you vulnerable to race conditions: another process may remove the file between the calls to
fs.exists()
andfs.open()
. Just open the file and handle the error when it’s not there.
$ npm install --save path-exists
```js // foo.js const pathExists = require(‘path-exists’);
pathExists(‘foo.js’).then(exists => { console.log(exists); //=> true }); ```
Returns a promise for a boolean of whether the path exists.
Returns a boolean of whether the path exists.
MIT © Sindre Sorhus


Requirements
Recent version
- Firefox + firebug
- Google chrome / chromium
Primitive data-types
- Number
- String
- Boolean
- Undefined
- Null
Everything else are objects
- Function
- Array
- User defined objects
- etc.
Value type vs Reference type
Object access
Dot notation
Square notation
Numbers
All numbers are double precision numbers
Integers are accurate when inside 1053
Special numbers
Scoping
Types
- Lexical
- Dynamic
- Unscoped
Function scope vs block scope
Reduce global scope pollution
These create global scopes
Equality
Don't use == or !=
Instead use === or !==
Automatic semicolon insertions
Fragile parsing in IE
Trailing comma
Lint your JavaScript
Use JSLint or JSHint
Use ECMAScript 5 strict mode
Versus
new
When new foo(), is executed the following things happen
- A new object inherited from foo.prototype is created
- The constructor function foo is called with the given arguments - "this" bound to above object
- The object returned by constructor function is returned (if no explicit return present)
Global namespace pollution
new agnostic constructor
Prototype inheritance
prototype
Object.getPrototypeOf / __proto__
Object.create()
Move all declerations to top
More about functions
arguments
caller
callee
console.stacktrace()
Exceptions
Include property
forEach, map, filter, reduce
Immediately executing function
←
→
/
#


Capture of Psychological Bio Markers
BrainVu analyzes a spectrum of unique eye measurements to identify changes in alertness, attention i.e. mental effort. To understand the consumer’s tendency toward a certain subject by reenacting sensory and emotional experiences (such as taste, video and more) BrainVu uses a set of questions and responses measured by the system.
Model-Based Algorythm for Mental Analysis
By correlating the participants emotional response to a specific event in time or the subject’s gaze BrainVu can identify which element creates the impact.
Real Time Emotional Representation
By combining the indices developed together with additional bio markers such as HRV, Gaze, facial coding, response time and others BrainVu has created the BrainVu Sentiment Index. It gauges both tendency towards a specific products/touch points as well as the emotional engagement (stress excitement, mental load) of the consumer during the research experience.


About
I like DIY Hobby! When creating a new design or program, I often forget about time. I get real pleasure and relaxation in the process of assembling various electronics.
I am very interested in 3D printers, CNC machines, quadcopters, electric scooters, PCB manufacturing, recycling, product design, Arduino / STM / RPi / Linux / OpenWRT / BuildRoot.
But three products are of particular importance to me, they are MacBook, Arduino, Python. (Everything is possible with this.)
IT specialist
The IT market has a large coverage and requires a wide range of specialists.
- Birthday: 21 November 1991
- Web Site: almazkairat.github.io
- Phone: +7(707)505-38-18
- Address: Nur-Sultan (Astana) city
- Age: 28
- Education: Bachelor of Computer Science and Software
- eMail<EMAIL_ADDRESS>- Language: English (A2 Elementary)
Perhaps a multidisciplinary specialist cannot be compared with the competence of a narrowly specialized one. But it's like picking one expensive tool, or picking up a set of tools. It all depends on the tasks ahead.
Achievements
For 10 years of experience, there were many achievements and no one counted them. But there is something that I am especially proud of.
Joint Venture with SinoVoip
Ideas brought to life
Pieces produced own products
Years of experience in IT
Languages
Listed are the languages that I have ever worked with. Most of all experience in Arduino, Python, C#, Bash.
Resume
Кратко
Almaz Kairat
Multidisciplinary IT specialist aimed at solving a wide range of tasks
- Administration
- Programming
- Circuit design
- Prototyping
- Design
Education
Bachelor of Computer Science and Software
2010 - 2014
Turan-Astana University
Upon graduation, he was already a Software Engineer with experience
Courses completed
WebForMySelf: PYTHON
2019
Video course from WebForMySelf
The video course consists of 2 large sections and includes 72 lessons with a total duration of more than 23 hours
WebForMySelf: DJANGO
2020
Video course from WebForMySelf
The video course consists of two parts and includes 73 lessons with a total duration of over 21 hours of video
Read literature
Inspired
2015
How To Create Products Customers Love.
Author: Marty Cagan
Comments: This is a must read if you have any hope of building a company based on great products.
Chuck Geiger - Former CTO, PayPal, Travelocity and Ask.com
Value Proposition Design
2015
How to Create Products and Services Customers Want. Your first step...
Authors:
Alexander Osterwalder
Yves Pigneur
Gregory Bernarda
Alan Smith
Comments: Excellent book on business patterns with illustrations.
60 Minute Brand Strategist
2015
This book includes the very latest thinking on branding and brand strategy.
Author: Idris Mootee
Comments: Allows you to understand and create value.
Talk Like TED
2015
The 9 Public-Speaking Secrets of the World's Top Minds.
Author: Carmine Gallo
Comments: Talk Like TED is a smart, practical book that will teach you how to give a kick-butt presentation.
The Startup Owner's Manual
2015
The Step-By-Step Guide for Building a Great Company.
Authors:
Steve Blank
Bob Dorf
Comments: It will allow you to realize and create true value.
The Effortless Experience
2015
Conquering the New Battleground for Customer Loyalty.
Authors:
Matthew Dixon
Nick Toman
Rick DeLisi
REWORK
2014
Change the Way You Work Forever.
Authors:
Jason Fried
David Heinemeier Hansson
Professional experience
IT specialist
2018 - 2020
Talshik Astyk Agro LLP
Republic of Kazakhstan, Nur-Sultan (Astana) city
- Supervised the implementation of navigation systems for agricultural machinery;
- Managed technical staff, search, hiring and firing employees;
- Repair, assembly, setup of computers and office equipment;
- Installation, configuration of operating systems, Windows / Linux / Mac;
- Installation, configuration of programs: office applications, electronic portals salyk, egov, sono, internet banking, accounting software;
- Carried out installation, configuration, monitoring of network equipment and Internet access (VPN, WIFI access points, VLAN switches, routers);
- Carried out administration, installation, configuration, monitoring of server software and hardware based on Windows / Linux / Mac OS (VMWare virtual machines, Hyper-V, XenServer, KVM, web servers, etc.);
# Ensured the smooth operation of the office network infrastructure. The accidents were eliminated in 15 minutes. Accidents at telecom operators were resolved no longer than 1 hour. - Organization of new job spaces, technical support for employees;
# Organized remote access for employees to work computers, so that they could work on the road, from home and even from the phone. Therefore, the period of the pandemic did not affect employee productivity in any way. For which he was awarded after the first wave of the pandemic. - User support locally and remotely (RDP, Teamviewer, Anydesk, by phone);
- Provided smooth operation and maintenance of office equipment (printers, scanners, MFPs);
- Budgeting for office equipment and planned purchases;
- Selection of suppliers, collection of commercial offers, control of equipment delivery;
- Programmed in PYTHON / PHP / HTML / CSS / JS / BASH / C #;
# Automation of tasks and support for multiple sites; - Implementation and testing of pilot projects and products;
- IT project management;
- Backup of servers and workstations.
Technical Director
2014 - 2018
MAKIRA LLP
Republic of Kazakhstan, Nur-Sultan (Astana) city
- Developed and produced a new product, its brand and design, called MultiQb;
- Produced a batch of products that were purchased by Kazakhtelecom JSC;
- Supervised and coordinated the activities of the company;
- Conducted business meetings and negotiations, concluded deals and contracts;
- Traveled on a business trip to China;
- Managed company finances, bills, payments, bank transfers;
- Opened a joint venture with Shenzhen SINOVOIP Co., Ltd in China;
- Staff management, search, recruitment and dismissal of employees;
- Monitoring the work of contractors. Assessment of the quality of work.
Software engineer
2012 - 2014
Korkem Telecom LLP
Republic of Kazakhstan, Nur-Sultan (Astana) city
- Programming languages С#;
- Designed and developed firmware for x86 Motion tablets based on Windows 7 through the Windows AIK toolkit;
- Designed and developed a user interface for a tablet with a differentiated access level;
- Designed and developed a set of custom user applications for the tablet (VoIP SoftPhone based on PortSIP, GPS navigator based on Gobi2000, help system, e-learning and testing system for ambulance paramedics, application for remote component updates, web browser with remote control of access to the web resources);
- Administration of web servers based on Linux (LAMP / VestaCP);
- Worked in a team and supervised technical staff;
- Implemented two major projects for the capital of Kazakhstan.
Electronics technician
2010 - 2011
Compass LLP
Republic of Kazakhstan, Taraz city
- Assembly and repair of computer components;
- Maintenance of computer electronics;
- Installation of software and operation system.
Tools
Photoshop
Usually all my work begins with him
TinkerCAD
Best Imagination App
Arduino
On it I can do everything and a little more
Mobirise
When you need it quickly and beautifully
Meld
The world of code would be hell without it
VMWare
Simply the best on Linux and Windows
Parallels
Simply the best on Mac OS X
Visual Studio
When comments are unnecessary
PyCharm
Python wouldn't be so wonderful without it
Final Cut Pro
Without it, editing seems almost impossible on Mac OS X
Premiere Pro
Without it, editing seems almost impossible on Windows
After Effects
Color correction and special effects, I recommend
Motion
Best animation tool, I recommend
Sketch
When everything is serious, you can't do without it
Flask
When Django is too big
Django
When Flask Is Not Enough
One-time services
If I have free time and interest, I can perform the services described below for a fee. The terms and price of payment are discussed personally.
Setting up a 3D Printer
The difference between an expensive and a cheap printer is only in the hands of the master who will set it up
Make a website
Website development is a creative work, I make websites slowly and for an expensive price
Arduino help
If you are experiencing some difficulties in mastering Arduino, then you need to urgently contact me
Restoration of plastic parts
If you have a thing made of plastic dear to you, then I can help you restore it
Shooting photos and aerial video
If you need a personal photographer or aerial videographer, please contact
Contacts
If you are interested in my candidacy or you have a business proposal for me, please contact me via the feedback form now.
Address:
Republic of Kazakhstan, Nur-Sultan (Astana) city
eMail:
<EMAIL_ADDRESS>Phone:
+7 (707) 505 38 18


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{% extends 'layouts/layout-manage-access-v3c.html' %} {% block pageTitle %} Manage your NHS login {% endblock %} {% block content %}
You can connect another person to your NHS login. You can then choose which services they can access on your behalf.
{% if data.version === 'v1' %}Add a person to your NHS login
{% elif data.version === 'v2' %}Add a person to your NHS login
{% else %}Add a person to your NHS login
{% endif %}You can remove a person from your NHS login, or you can remove their access to services. If you need to remove access to a person or remove a single service, then you will need to contact the helpdesk.
{% endblock %}


In this report, I will explore the red wine dataset. First, I will look at each individual attribute of this dataset and plot its distribution. Second, I will look at two attributes at a time to explore 1) how each input attribute is related to the output attribute–quality; 2) interesting pairs of input attributes. Third, I will examine three attributes by adding one additional attribute to some of the interesting two-attribute pairs. Finally, I will summarize the successes and problems I encounter during the data exploration process.
# Load all of the packages in this code chunk.
library(ggplot2)
library(corrplot)
library(RColorBrewer)
library(memisc)
# Load the Data
wine_reds = read.csv('wineQualityReds.csv', row.names = 1)
In this section, I will first look at the structure of the data set. Then I will examine the distribution of each attribute individually by plotting its distribution.
dim(wine_reds)
## [1] 1599 12
# Convert quality into ordered categorical variable
wine_reds$quality <- ordered(wine_reds$quality)
str(wine_reds)
## 'data.frame': 1599 obs. of 12 variables:
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : Ord.factor w/ 6 levels "3"<"4"<"5"<"6"<..: 3 3 3 4 3 3 3 5 5 3 ...
summary(wine_reds)
## fixed.acidity volatile.acidity citric.acid residual.sugar
## Min. : 4.60 Min. :0.1200 Min. :0.000 Min. : 0.900
## 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090 1st Qu.: 1.900
## Median : 7.90 Median :0.5200 Median :0.260 Median : 2.200
## Mean : 8.32 Mean :0.5278 Mean :0.271 Mean : 2.539
## 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420 3rd Qu.: 2.600
## Max. :15.90 Max. :1.5800 Max. :1.000 Max. :15.500
## chlorides free.sulfur.dioxide total.sulfur.dioxide
## Min. :0.01200 Min. : 1.00 Min. : 6.00
## 1st Qu.:0.07000 1st Qu.: 7.00 1st Qu.: 22.00
## Median :0.07900 Median :14.00 Median : 38.00
## Mean :0.08747 Mean :15.87 Mean : 46.47
## 3rd Qu.:0.09000 3rd Qu.:21.00 3rd Qu.: 62.00
## Max. :0.61100 Max. :72.00 Max. :289.00
## density pH sulphates alcohol quality
## Min. :0.9901 Min. :2.740 Min. :0.3300 Min. : 8.40 3: 10
## 1st Qu.:0.9956 1st Qu.:3.210 1st Qu.:0.5500 1st Qu.: 9.50 4: 53
## Median :0.9968 Median :3.310 Median :0.6200 Median :10.20 5:681
## Mean :0.9967 Mean :3.311 Mean :0.6581 Mean :10.42 6:638
## 3rd Qu.:0.9978 3rd Qu.:3.400 3rd Qu.:0.7300 3rd Qu.:11.10 7:199
## Max. :1.0037 Max. :4.010 Max. :2.0000 Max. :14.90 8: 18
The red wine dataset contains 12 variables–11 input numerical variables based on physicochemical tests and 1 categorical output variable (quality) based on sensory data, with 1599 observations.
# Function to plot histogram of a single variable
plot_uni_var <- function(variable, bins = 50) {
return (ggplot(aes_string(x = variable), data = wine_reds) +
geom_histogram(bins = bins))
}
ggplot(aes(x = quality), data = wine_reds) +
geom_bar()
Out of the possible quality scores between 0 and 10, all of our red wines’ quality scores fall between 3 and 8. The dataset is not well balanced. Majority of red wines have a quality score of either 5 or 6. I am curious about what attributes make a wine earn a low quality score (quality = 3) or a high quality score (quality = 8).
Most acids involved with wine are fixed or nonvolatile (do not evaporate readily).
plot_uni_var('fixed.acidity')
plot_uni_var('fixed.acidity') + scale_x_log10()
Fixed acidity values range between 4 and 16, with most values range between 7 and 9. The distribution is slightly positively skewed. Transforming the x-axis into log scale can make it more normally distributed.
The amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste.
plot_uni_var('volatile.acidity')
plot_uni_var('volatile.acidity') + scale_x_continuous(limits = c(0, 1))
## Warning: Removed 21 rows containing non-finite values (stat_bin).
Fixed acidity values range between 0.1 and 1.6, with most values range between 0.3 and 0.7. The distribution is slightly positively skewed. When zoom in to values below 1, the distribution seems normal.
Found in small quantities, citric acid can add ‘freshness’ and flavor to wines.
plot_uni_var('citric.acid')
# number of red wines with citric acid = 0
dim(wine_reds[wine_reds$citric.acid ==0, ])[1]
## [1] 132
Citric acid values range between 0 and 1 with most values below 0.6. There are 132 red wines in our data set with no detectable citric acid concentration.
The amount of sugar remaining after fermentation stops, it is rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet.
plot_uni_var('residual.sugar')
plot_uni_var('residual.sugar') + scale_x_continuous(limits = c(0, 5))
## Warning: Removed 84 rows containing non-finite values (stat_bin).
Most residual sugar values range between 1.5 and 2.5. There are a few outliers with large values. When zoom in and look at values below 5, the distribution appears normal.
The amount of salt in the wine.
plot_uni_var('chlorides')
plot_uni_var('chlorides') + scale_x_continuous(limits = c(0, 0.2))
## Warning: Removed 41 rows containing non-finite values (stat_bin).
Most chlorides values range between 0.05 to 0.1. There are a few outliers with large values. When zoom in and look at values below 0.2, the distribution appears normal.
The free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine.
plot_uni_var('free.sulfur.dioxide')
The distribution of free sulfur dioxide is highly positively skewed.
The amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine.
plot_uni_var('total.sulfur.dioxide')
plot_uni_var('total.sulfur.dioxide') + scale_x_log10()
The distribution of total sulfur dioxide is higly positively skewed. And there are a few outliers with very large values. Transforming the x-axis into log scale can make it more normally distributed.
The density of wine is close to that of water depending on the percent alcohol and sugar content.
plot_uni_var('density')
Density values range between 0.990 and 1.004 with most values range from 0.995 and 0.998. The distribution of density values are symmetrical centered around 0.9965.
Describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale.
plot_uni_var('pH')
Most pH values range between 3.15 and 3.45. The distribution of pH is symmetrical centered around 3.3.
A wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant.
plot_uni_var('sulphates')
plot_uni_var('sulphates') + scale_x_log10()
Most sulphates values range between 0.5 and 0.75. The distribution is positively skewed.  Transforming the x-axis into log scale can make it more normally distributed.
The percent alcohol content of the wine
plot_uni_var('alcohol')
The alcohol values range between 8.5 and 15. The distribution of alcohol value is positively skewed.
What is the structure of your dataset?
There are 12 attributes in the dataset. 11 of them (fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates, alcohol) are input attributes based on physicochemical tests. The other attribute (quality) is the output attribute based on sensory data. Each row corresponds to one particular wine with total 1599 different red wines in the data set.
What is/are the main feature(s) of interest in your dataset?
The main feature of interest is the output attribute quality. I am trying to figure out which of the 11 input attribute contribute to a high quality value.
What other features in the dataset do you think will help support your investigation into your feature(s) of interest?
The 11 input attributes are equally likely to contribute to the quality value at this point. I will look more closely at how each of the attributes is distributed with a given quality value in the bivariate exploration section.
Did you create any new variables from existing variables in the dataset?
No.
Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?
The possible quality values are from 0 to 10, but our data set only has quality values from 3 to 8, which means there are no extremely bad red wines or extrememly good wines in out data set. The vast majority of red wines in the data set has a quality value either 5 or 6, with very fewer wines with quality values 3, 4, 7 or 8, which makes the data set unbalanced.
In this section, I will look at how each input attribute is related to the output quanlity variable.
# Function to plot boxplot of variable grouped by quality
plot_quality_vs_var <- function(variable) {
# Uncomment the next line to print a summary of variable grouped by quality values
# print (by(wine_reds[[variable]], wine_reds$quality, summary))
return (ggplot(aes_string(x = 'quality', y = variable), data = wine_reds) +
geom_boxplot())
}
plot_quality_vs_var('fixed.acidity')
There isn’t a clear trend between fixed acidity and quality.
plot_quality_vs_var('volatile.acidity')
The higher the quality, the lower the volatile acidity.
plot_quality_vs_var('citric.acid')
The higher the quality, the higher the citric acid.
plot_quality_vs_var('residual.sugar')
There isn’t a clear trend between residual sugar and quality.
plot_quality_vs_var('chlorides')
plot_quality_vs_var('chlorides') + scale_y_continuous(limits = c(0.05, 0.15))
## Warning: Removed 117 rows containing non-finite values (stat_boxplot).
After zoom in, one can see the higher the quality, the lower the chlorides.
plot_quality_vs_var('free.sulfur.dioxide')
There isn’t a clear trend between free sulfur dioxide and quality.
plot_quality_vs_var('total.sulfur.dioxide')
There isn’t a clear trend between total sulfur dioxide and quality.
plot_quality_vs_var('density')
The higher the quality, the lower the density.
plot_quality_vs_var('pH')
The higher the quality, the lower the pH.
plot_quality_vs_var('sulphates')
The higher the quality, the higher the sulphates.
plot_quality_vs_var('alcohol')
The higher the quality, the higher the alcohol.
In this section, I will first plot the correlation matrix to find out if there are any interesting pairs of input attributes that look interesting. Then I will examine these pairs.
cor_matrix <- cor(wine_reds[, 1:11])
corrplot(cor_matrix, type = 'lower')
There are a few pairs of input variables deserve further examing from the correlation matrix plot: fixed acidity vs citric acid, fixed acidity vs density, fixed acidity vs pH, volatile acidity vs citric acid, citric acid vs pH, total sulfur dioxide vs free sulfur dioxide, density vs alcohol.
# Function to plot scatter plot of variable2 vs variable1
plot_bi_var <- function(variable1, variable2) {
return (ggplot(aes_string(x = variable1, y = variable2), data = wine_reds) +
geom_point(alpha = 1/4, position = position_jitter(width = 0)))
}
plot_bi_var('citric.acid', 'fixed.acidity')
cor(wine_reds$citric.acid, wine_reds$fixed.acidity)
## [1] 0.6717034
Fixed acidity and citric acid have a correlation coefficient of 0.6717034. This is probably because citric acid is also a kind of fixed acidity.
plot_bi_var('citric.acid', 'volatile.acidity')
cor(wine_reds$citric.acid, wine_reds$volatile.acidity)
## [1] -0.5524957
Volatile acidity and citric acid have a correlation coefficient of -0.5524957. I cannot think of an explaination for this correlation.
plot_bi_var('log10(fixed.acidity)', 'pH')
cor(log10(wine_reds$fixed.acidity), wine_reds$pH)
## [1] -0.7063602
The pH scale is logarithmic, so I first transform fixed acidity into log10(fixed acidity). log10(fixed acidity) and pH have a correlation coefficient of -0.7063602. This is because hihger fixed acidity concentration leads to lower pH values.
plot_bi_var('citric.acid', 'pH')
cor(wine_reds$citric.acid, wine_reds$pH)
## [1] -0.5419041
Citric acid and pH have a correlation coefficient of -0.5419041. Because we have 132 wines with 0 citric acid values, we cannot perform a logarithmic transfrom here. The correaltion is due to citric acid is non volatile, therefore higher citiric acid concentration leads to lower pH value.
plot_bi_var('free.sulfur.dioxide', 'total.sulfur.dioxide') + geom_abline(slope=1)
cor(wine_reds$free.sulfur.dioxide, wine_reds$total.sulfur.dioxide)
## [1] 0.6676665
Total sulfur dioxide and free sulfur dioxide have a correlation coefficient of 0.6676665. This is because free sulfur dioxide is part of total sulfur dioxide, and this can also be seen from all the points are above the y=x line.
plot_bi_var('alcohol', 'density')
cor(wine_reds$alcohol, wine_reds$density)
## [1] -0.4961798
Density and alcohol have a correlation coefficient of -0.4961798. This is probably because alcohol has a lower density comparing to water. Therefore, the higher concentration of alcohol, the lower the wine density is.
plot_bi_var('fixed.acidity', 'density')
cor(wine_reds$fixed.acidity, wine_reds$density)
## [1] 0.6680473
Densidy and fixed acidity have a correlation coefficient of 0.6680473. This is probably because fixed acidity in wine is mainly tartaric acid, and tartaric acid has a density of 1.79 g/mL, which is greater than the wine’s main component–water. Therefore, higher concentration of fixed acidity leads to higher density value.
Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?
There are a few attributes exhibit some trends that look promising to be used to predict quality.
We can summarize these relations in the following table:
| Attribute Name | Relation with Quality |
|---|---|
| fixed acidity | ~ |
| volatile acidity | - |
| citric acid | + |
| residual sugar | ~ |
| chlorides | - |
| free sulfur dioxide | ~ |
| total sulfur dioxide | ~ |
| density | - |
| pH | - |
| sulphates | + |
| alcohol | + |
Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?
There are a few attributes that are correlated based on physical and chemical principles:
What was the strongest relationship you found?
Observing from the plot, volatile acidity has the strongest relationship with quality.
In this section, I will first look at how quality, volatile acidity and one other input variable are related.
# Function to plot scatter plot of variable2 vs variable1 colored by quality
plot_quality_vs_two_var <- function(variable1, variable2) {
ggplot(aes_string(x = variable1, y = variable2, color = 'quality'), data = wine_reds) +
geom_jitter(alpha = 1/2) +
scale_color_brewer(type = 'div')
}
plot_quality_vs_two_var('volatile.acidity', 'citric.acid')
High quality wines tend to have low volatile acidity and high citric acid (upper left corner); low quality wines tend to have high volatile acidity and low citric acid ( lower right corner).
plot_quality_vs_two_var('sulphates', 'volatile.acidity')
High quality wines tend to have low volatile acidity and high sulphates (lower right corner); low quality wines tend to have high volatile acidity and low sulphates (upper left corner).
plot_quality_vs_two_var('alcohol', 'volatile.acidity')
High quality wines tend to have low volatile acidity and high alcohol (lower right corner); low quality wines tend to have high volatile acidity and low alcohol (upper left corner).
plot_quality_vs_two_var('chlorides', 'volatile.acidity') +
scale_x_continuous(limits = c(0, 0.2))
## Warning: Removed 41 rows containing missing values (geom_point).
High quality wines tend to have low volatile acidity and low chlorides (lower left corner); low quality wines tend to have high volatile acidity and high chlorides (upper right corner).
plot_quality_vs_two_var('density', 'volatile.acidity')
High quality wines tend to have low volatile acidity and low density (lower left corner); low quality wines tend to have high volatile acidity and high density (upper right corner).
plot_quality_vs_two_var('pH', 'volatile.acidity')
High quality wines tend to have low volatile acidity and low pH (lower left corner); low quality wines tend to have high volatile acidity and high pH (upper right corner).
In this section, I will examine the relation among some of input variable triplets.
# Function to cut given column from wine_reds data set into quartiles
cut_quartiles <- function(var_to_cut) {
return (cut(wine_reds[[var_to_cut]],
breaks = quantile(wine_reds[[var_to_cut]],
probs = seq(0, 1, 0.25),
na.rm = TRUE),
include.lowest = TRUE))
}
# Function to plot scatter plot of variable2 vs variable1 colored by variable3
plot_tri_var <- function(variable1, variable2, variable3){
ggplot(aes_string(x = variable1, y = variable2, color = variable3),
data = wine_reds) +
geom_jitter() +
scale_color_brewer(type = 'seq') +
theme_dark()
}
wine_reds$pH_quartiles <- cut_quartiles('pH')
plot_tri_var('citric.acid', 'fixed.acidity', 'pH_quartiles')
Fixed acidity and citric acid both contribute to wines’ pH values. The lower both acids concentrations, the higher the pH (lower left corner), and the higher both acides concentrations, the lower the pH (upper right corner).
wine_reds$density_quartiles <- cut_quartiles('density')
plot_tri_var('alcohol', 'fixed.acidity', 'density_quartiles')
Fixed acidity is positively correlated with density, and alcohol is negatively correlated with density. We can see from the plot that fixed acidity has a larger impact on the density of wine than alcohol. Because for a given alcohol value, the density increases as fixed acidity increases, whereas for a given fixed acidity value, the density does not change as the alcohol increases.
In order to fit a linear model, I will convert quality from ordered factors back into real numbers.
m1 <- lm(as.numeric(levels(quality))[quality] ~ volatile.acidity,
data = wine_reds)
m2 <- update(m1, ~ . + volatile.acidity + citric.acid + sulphates + alcohol +
chlorides + density + pH)
m3 <- update(m2, ~ . + fixed.acidity + residual.sugar + free.sulfur.dioxide +
total.sulfur.dioxide)
mtable(m1, m2, m3, sdigits = 3)
##
## Calls:
## m1: lm(formula = as.numeric(levels(quality))[quality] ~ volatile.acidity,
## data = wine_reds)
## m2: lm(formula = as.numeric(levels(quality))[quality] ~ volatile.acidity +
## citric.acid + sulphates + alcohol + chlorides + density +
## pH, data = wine_reds)
## m3: lm(formula = as.numeric(levels(quality))[quality] ~ volatile.acidity +
## citric.acid + sulphates + alcohol + chlorides + density +
## pH + fixed.acidity + residual.sugar + free.sulfur.dioxide +
## total.sulfur.dioxide, data = wine_reds)
##
## ===========================================================
## m1 m2 m3
## -----------------------------------------------------------
## (Intercept) 6.566*** -5.885 21.965
## (0.058) (11.930) (21.195)
## volatile.acidity -1.761*** -1.193*** -1.084***
## (0.104) (0.119) (0.121)
## citric.acid -0.269* -0.183
## (0.136) (0.147)
## sulphates 0.848*** 0.916***
## (0.112) (0.114)
## alcohol 0.321*** 0.276***
## (0.020) (0.026)
## chlorides -1.782*** -1.874***
## (0.407) (0.419)
## density 10.281 -17.881
## (11.886) (21.633)
## pH -0.534*** -0.414*
## (0.134) (0.192)
## fixed.acidity 0.025
## (0.026)
## residual.sugar 0.016
## (0.015)
## free.sulfur.dioxide 0.004*
## (0.002)
## total.sulfur.dioxide -0.003***
## (0.001)
## -----------------------------------------------------------
## R-squared 0.153 0.350 0.361
## adj. R-squared 0.152 0.347 0.356
## sigma 0.744 0.653 0.648
## F 287.444 122.332 81.348
## p 0.000 0.000 0.000
## Log-likelihood -1794.312 -1582.343 -1569.138
## Deviance 883.198 677.509 666.411
## AIC 3594.624 3182.686 3164.277
## BIC 3610.756 3231.080 3234.179
## N 1599 1599 1599
## ===========================================================
As one can see, the most promising attribute volatile acidicy alone has R-squared value of 0.153. By adding the other 6 promsing attributes, R-squared value is a little more than doubled becoming 0.350. But adding the rest 4 attributes only increases the R-squared value a little to 0.361.
Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?
By combining the most promising attribute from bivariate section, volatile acidity, with one of the other attributes (citric acid, sulphates, alcohol, chlorides, density and pH), one can further separate high quality wines and low quality wines.
Were there any interesting or surprising interactions between features?
By looking at density vs fixed acidity and alcohol, one can see that fixed acidity has a larger impact on the density of the wine than alcohol.
OPTIONAL: Did you create any models with your dataset? Discuss the strengths and limitations of your model.
I created three linear models to predict the output attribute quality. The strength of the model is that it is a simple linear model and it is easy to interpret. However, because the quality values are discrete integer values, the model is less accurate comparing to a more nuanced continuous quality value model. Also due to the limitation of the dataset, only physical and chemical attributes are available, and other import attributes, such as price, color, smell, etc are missing. The other attributes may influence the quality values to a large extent.
ggplot(aes(x = quality), data = wine_reds) +
geom_bar(aes(y = (..count..)/sum(..count..))) +
geom_text(aes(y = ((..count..)/sum(..count..)),
label = scales::percent((..count..)/sum(..count..))),
stat = "count",
vjust = -0.25) +
scale_y_continuous(labels = scales::percent) +
xlab('Quality') +
ylab('Percent') +
ggtitle('Quality Relative Frequency Histogram') +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
The possible quality values are ranging from 0 to 10, however, all red wines in the dataset have quality values between 3 and 8. There is no any really bad wine with quality below 3 or any really good wine with quality above 8. Also, most of the red wines have quality 5 or 6, which make the dataset not well balanced.
plot_quality_vs_two_var('sulphates', 'volatile.acidity') +
scale_x_continuous(limits = c(min(wine_reds$sulphates),
quantile(wine_reds$sulphates, 0.99))) +
scale_y_continuous(limits = c(min(wine_reds$volatile.acidity),
quantile(wine_reds$volatile.acidity, 0.99))) +
scale_color_brewer(type = 'div', name = 'Quality') +
xlab(expression(Sulphates~(g~dm^{-3}))) +
ylab(expression(Volatile~Acidity~(g~dm^{-3}))) +
ggtitle('Quality vs Volatile Acidity and Sulphates') +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
## Warning: Removed 35 rows containing missing values (geom_point).
Excluding the upper 1% outliers, one can see from the plot that higher quality wines (dark green) tend to have low volatile acidity and high sulphates (lower right corner), while lower quality wines (dark brown) tend to have high volatile acidity and low sulphates (upper left corner).
wine_reds$pH_quartiles <- cut_quartiles('density')
plot_tri_var('alcohol', 'fixed.acidity', 'density_quartiles') +
xlab(expression(Alcohol~('%'))) +
ylab(expression(Fixed~Acidity~(g~dm^{-3}))) +
ggtitle('Density vs Fixed Acidity and Alcohol') +
scale_color_brewer(name = expression(Density~(g~cm^{-3})), type = 'seq') +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank())
Fixed acidity is positively correlated with density, and alcohol is negatively correlated with density. The fixed acidity has a larger impact on the density of wine than alcohol. Because for a given alcohol value, the density increases (from light blue to dark blue) as fixed acidity increases, whereas for a given fixed acidity value, the density does not change much as the alcohol increases.
The red wine data set has 11 input physicochemical attributes and 1 quality output attribute. Throughout the analysis, I focused on answering two quations:
I ran into difficulties when answering question 1. There were many combinations of the 11 input attributes I could pick to plot against the quality attribute. But I did not have good criteria to determine the order of importance of these attributes. I relied on visualization and chose the volatile acidicy as the most promising attribute and used it as the fixed attribute during multivariate plot section.
I did observe some good results when answering question 2. The plots showing between and among different input physicochemical attributes were in accordance with actual physical and chemical properties and laws, such high acid concentration correlates with low pH, and high alcohol percent correlates with low density.
I believe by incorporating other types of attributes, such as price, color and smell, I can build a better model to predict the quality of wine than using only physicochemical attributes. Also, one more interesting project can be combining the red wine data set with the white wine data set, and to find out if attributes correlate to high quality red wines also correlate to high quality white wines.


Table of Contents
In order to be able to make requests to the API, you should first obtain an ApiKey for your account. For this, log into Tutum, click on the menu on the upper right corner of the screen, and select Get Api Key
The Tutum HTTP API is reachable through the following hostname:
https://app.tutum.co/
All requests should be sent to this endpoint with the appropriate Authorization header:
curl -H "Authorization: ApiKey username:apikey" https://app.tutum.co/api/v1/application/
This operation returns a list of all active and recently terminated (less than 5 minutes ago) applications.
Example request:
GET /api/v1/application/ HTTP/1.1
Host: app.tutum.co
Accept: application/json
Authorization: ApiKey username:apikey
Example response:
HTTP/1.1 200 OK
Cache-Control: must-revalidate, max-age=0
Content-Type: application/json
Vary: Accept, Authorization, Cookie
{
"meta": {
"limit": 25,
"next": null,
"offset": 0,
"previous": null,
"total_count": 1
},
"objects": [
{
"autodestroy": "OFF",
"autoreplace": "OFF",
"autorestart": "OFF",
"container_ports": [
{
"container_cluster": "/api/v1/application/c2b6448c-6a77-465f-b2e1-7b8c2af5125f/",
"inner_port": 3306,
"outer_port": null,
"protocol": "tcp"
}
],
"container_size": "XS",
"current_num_containers": 1,
"deployed_datetime": "Sun, 23 Mar 2014 18:12:11 +0000",
"destroyed_datetime": null,
"entrypoint": "",
"image_tag": "/api/v1/image/tutum/mysql/tag/latest/",
"name": "mysql",
"resource_uri": "/api/v1/application/b2b6498c-6a77-465f-b6e1-7b8c2af5125f/",
"run_command": "/run.sh",
"running_num_containers": 1,
"started_datetime": "Sun, 23 Mar 2014 18:12:11 +0000",
"state": "Running",
"stopped_datetime": null,
"stopped_num_containers": 0,
"target_num_containers": 1,
"unique_name": "mysql",
"uuid": "b2b6498c-6a77-465f-b6e1-7b8c2af5125f"
}
]
}
| Request Headers: | |
|---|---|
|
|
| Status Codes: |
|
Get all the details of an specific application
Example request:
GET /api/v1/application/b2b6498c-6a77-465f-b6e1-7b8c2af5125f/ HTTP/1.1
Host: app.tutum.co
Accept: application/json
Authorization: ApiKey username:apikey
Example response:
HTTP/1.1 200 OK
Cache-Control: must-revalidate, max-age=0
Content-Type: application/json
Vary: Accept, Authorization, Cookie
{
"autodestroy": "OFF",
"autoreplace": "OFF",
"autorestart": "OFF",
"container_envvars": [],
"container_ports": [
{
"container_cluster": "/api/v1/application/b2b6498c-6a77-465f-b6e1-7b8c2af5125f/",
"inner_port": 3306,
"outer_port": null,
"protocol": "tcp"
}
],
"container_size": "XS",
"containers": [
"/api/v1/container/3f421179-cc61-4bb7-900a-2454e659ec78/"
],
"current_num_containers": 1,
"deployed_datetime": "Sun, 23 Mar 2014 18:12:11 +0000",
"destroyed_datetime": null,
"entrypoint": "",
"image_tag": "/api/v1/image/tutum/mysql/tag/latest/",
"link_variables": {
"MYSQL-1_PORT": "tcp://mysql-1-admin.beta.tutum.io:49230",
"MYSQL-1_PORT_3306_TCP": "tcp://mysql-1-admin.beta.tutum.io:49230",
"MYSQL-1_PORT_3306_TCP_ADDR": "mysql-1-admin.beta.tutum.io",
"MYSQL-1_PORT_3306_TCP_PORT": "49230",
"MYSQL-1_PORT_3306_TCP_PROTO": "tcp",
"MYSQL_TUTUM_API_URL": "https://app.tutum.co/api/v1/application/b2b6498c-6a77-465f-b6e1-7b8c2af5125f/"
},
"linked_from_application": [],
"linked_to_application": [],
"name": "mysql",
"resource_uri": "/api/v1/application/b2b6498c-6a77-465f-b6e1-7b8c2af5125f/",
"roles": [],
"run_command": "/run.sh",
"running_num_containers": 1,
"started_datetime": "Sun, 23 Mar 2014 18:12:11 +0000",
"state": "Running",
"stopped_datetime": null,
"stopped_num_containers": 0,
"target_num_containers": 1,
"unique_name": "mysql",
"uuid": "b2b6498c-6a77-465f-b6e1-7b8c2af5125f"
}
| Query Parameters: | |
|---|---|
|
|
| Request Headers: | |
|
|
| Status Codes: |
|
Creates and deploys a new application
| Json Parameters: | |
|---|---|
|
|
| Request Headers: | |
|
|
| Status Codes: |
|
Updates the application details and scales the application up or down accordingly
| Query Parameters: | |
|---|---|
|
|
| Json Parameters: | |
|
|
| Request Headers: | |
|
|
| Status Codes: |
|
Starts all the containers in a stopped application
| Query Parameters: | |
|---|---|
|
|
| Request Headers: | |
|
|
| Status Codes: |
|
Stops all the containers in a running application
| Query Parameters: | |
|---|---|
|
|
| Request Headers: | |
|
|
| Status Codes: |
|
Destroy all the containers in an application. This is not reversible. All the data stored in all the application containers will be permanently deleted.
| Query Parameters: | |
|---|---|
|
|
| Request Headers: | |
|
|
| Status Codes: |
|
Returns a paginated list of all containers for all applications for the authenticated user
| Request Headers: | |
|---|---|
|
|
| Status Codes: |
|
Get all the details of an specific container
| Request Headers: | |
|---|---|
|
|
| Status Codes: |
|
Starts a container that was previously stopped
| Query Parameters: | |
|---|---|
|
|
| Request Headers: | |
|
|
| Status Codes: |
|
Stops a running container
| Query Parameters: | |
|---|---|
|
|
| Request Headers: | |
|
|
| Status Codes: |
|
Returns the logs of the specified container
| Query Parameters: | |
|---|---|
|
|
| Request Headers: | |
|
|
| Status Codes: |
|
Destroy the specified container and update the target number of containers of the related application. This is not reversible. All the data stored in the container will be permanently deleted.
| Query Parameters: | |
|---|---|
|
|
| Request Headers: | |
|
|
| Status Codes: |
|


Hienghene is on the NE coast of Grande Terre, New Caledonia. This is a rugged coastline with incredible rock formations, many resembling well known icons (such as the "Nesting Chicken", la poule couveuse and the sphinx that form the headlands of the Hienghene harbour).
Yachts should report to the Port Captain (Capitaine de Port) on arrival. No clearance procedures are possible here at all anymore. All clearance must be completed in Noumea.
Last updated September 2012.
Anchoring in the river is no longer possible here, so boats must use the marina which is only possible for shallow-draft vessels. There is however a deep water anchorage outside the entrance with easy access to "the Hen" - good except in N winds. As you are close to the entrance of the Hienghene River it is prudent to show anchor nights at light.
Last updated June 2016.


Before Change
cost = torch.FloatTensor([0.0])
else:
cost = torch.FloatTensor([10])
return Variable(state.flattened_linking_scores.data.new(cost)).float()
@classmethod
def from_params(cls, vocab, params: Params) -> "WikiTablesErmSemanticParser":
After Change
// This is the number of items on the agenda that we want to see in the decoded sequence.
// We use this as the denotation cost if the path is incorrect.
denotation_cost = torch.sum(state.checklist_state[0].checklist_target.float())
checklist_cost = self._checklist_cost_weight * checklist_cost
action_history = state.action_history[0]
batch_index = state.batch_indices[0]
action_strings = [state.possible_actions[batch_index][i][0] for i in action_history]


Vadim Markovtsev, source{d}.
Vadim Markovtsev, source{d}
"AI on code"
We started as a hiring middleware.
You may have read "how developers change programming languages".
Our contacts:


Russia says 'no chance' of winning ban appeal
by AFPIn Summary
- Ganus said that some Russian athletes were contemplating leaving Russia so that they could train elsewhere
- Russian athletes have already faced a series of bans over the last few years and were forced to participate in the 2018 Pyeongchang Winter Games as neutral competitors
- The head of Russia's swimming federation, Vladimir Salnikov, said the country's athletes "must go to the Olympics whatever the situation."
MOSCOW
The head of Russia's anti-doping agency said Monday that his country had "no chance" of winning an appeal against a four-year international sporting ban he said was a tragedy for clean athletes.
"There is no chance of winning this case in court," Rusada chief Yury Ganus told AFP after the World Anti-Doping Agency imposed the ban that will rule Russia out of the 2020 Tokyo Olympics, the 2022 Beijing Winter Olympics and the 2022 World Cup in Qatar.
Rusada's supervisory board is set to meet on December 19 to take a decision on whether to appeal the ban, he said.
"This is a tragedy," he said. "Clean athletes are seeing their rights limited."
Ganus said that some Russian athletes were contemplating leaving Russia so that they could train elsewhere.
He described the sentiments among athletes as "awful," stressing that four years for a sportsman is a long time in what can be a short career.
Earlier Monday Wada's executive committee, meeting in Lausanne, decided that Russia be handed a four-year suspension after accusing Moscow of falsifying laboratory data.
Russian athletes have already faced a series of bans over the last few years and were forced to participate in the 2018 Pyeongchang Winter Games as neutral competitors.
The heads of several Russian sports federations said they were preparing to send athletes to the 2020 Tokyo Olympics under a neutral flag.
The head of Russia's swimming federation, Vladimir Salnikov, said the country's athletes "must go to the Olympics whatever the situation."
"Of course we'd prefer that our athletes participate under the Russian flag and hear their national anthem. But the circumstances may be different... (and) no-one has the right to deprive innocent athletes of their dreams," he told state news agency RIA Novosti.
"If (participating under a neutral flag) is the only possibility, we must go and win. Our clean athletes, I am sure, will show that they are strong, even in these circumstances," said the head of the waterpolo, diving and synchronised swimming federation, Alexei Vlasenko.


{% extends "base.html" %} {% block content %}
Generate playlists from over 150 top songs played on Spotify as a launchpad for your next favorite playlist.
Enter the number (1-19) of songs you would like to see per playlist.
{{ info_text }}


Default Title
No school options available.
School Directory
Name
Address
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Website
Gender
Type
Minimum GPA
Minimum MAP Score
Specialty
Sports


WITNESS LIST Education Committee May 10, 1999-10:00A HB 217 Registering, but not testifying: For: Kelly, Jack (Texas State Teachers Association), Dripping Spring HB 964 ON: Green, Wayne (Tx. Commission on Law Enforcement), Cedar Park HB 1409 Registering, but not testifying: For: Ramos, Joe (Tx. Association of School Boards), Austin Willmann, James (Texas Nurses Association), Austin Woolbert, Lynda (Coalition for Nurses in Advanced Practice), West Columbia HB 1952 FOR: Zucker, Dr. Charles (Texas Faculty Association), Austin Registering, but not testifying: For: Lara, Rene (Texas Federation of Teachers), Austin HB 2359 FOR: Robertson, Lisa (Alliance Schools), Austin HB 3138 THIS LIST HAS NO WITNESSES. HB 3158 THIS LIST HAS NO WITNESSES. HB 3226 ON: Cathey, Lesli (State Comptroller's Office), unavailable Hay, Mike (Comptroller of Public Accounts), unavailable Registering, but not testifying: On: Elliott, Roger W. (Tx. Higher Ed. Coordinating Board), Austin HB 3263 Registering, but not testifying: For: Westbrook, Steve (Blinn College Board of Trustees), Brenham


On March 28, 2017, the US Congress eliminated FCC regulations that would replace FTC regulations that prevented ISP's from selling your browsing history to third parties.
I'm not sure how to convince Congress that this is a mistake, so I figured that the next best thing would be to embellish my browsing history using a script.
"A little hyperbole never hurts." --Donald J. Trump


Batch download all image files from galleries on xhamster.com.
Simply enter the URL of the gallery you want to download, let the app analyze the page, select which images you want to download and your done...
http://backroom-studio.ml
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--- title: call_to_action: Contact background_image_path: large_header: true testimonials: - message: If you think compliance is expensive, try non-compliance. image: "https://placekitten.com/g/60/60?a=.png" name: former U.S. Deputy Attorney General Paul McNulty _options: image: width: 60 height: 60 ---
Aperno is currently in stealth mode. We are on mission to democratize security and compliance
{% endfor %}


Slide 1
You just spent a good amount of time writing a nice presentation. But will people even look at it?
Slide 2
So you install bespoke-ga to get some statistics from Google Analytics. But will visitors reach the second slide? Or the tenth slide?
Slide 3
This is where bespoke-analytics comes in. First install bespoke-hash or bespoke-history. Then you add bespoke-analytics alongside bespoke-ga and to get per-slide statistics in Google Analytics.
Slide 4
When users switch slides, you should be able to see it as a separate page view in Google Analytics.
Slide 5
Note that the Do No Track (DNT) flag is respected by bespoke-analytics. But you are still more tracked than you think.
Slide 6
That's it, really. I was bored, and wanted to create a bespoke.js plugin. Enjoy!


--- layout: post status: publish published: true title: Episode 5.15 - Living in Kevin Gregg's Fever Dream author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 8509 wordpress_url: http://ivyenvy.com/?p=8509 date: '2013-06-30 17:27:45 +0000' date_gmt: '2013-06-30 22:27:45 +0000' categories: - Podcast tags: [] comments: [] ---
On this episode of the Ivy Envy Podcast, we start things off by reflecting on the the Carlos Marmol era. We saw some very good years and some very bad years. How will we remember Marmol? Ian Stewart is finally released by the Cubs. The rumors are that the Diamondbacks and possibly other teams are interested in trading for Jeff Samardzija. We talk about what it would take for us to be ok with a Jeff Samardzija trade.
Then we get into some questions from listeners.  When would the Cubs have received the most in a trade for Carlos Marmol and what is the most the current regime could have gotten for him? What could the Cubs potentially get for Kevin Gregg in a trade? Who might be in the Cubs starting rotation at the end of the season?
Then Kurt sends Ian Stewart packing with one last scary stat and we pick a winner for this week's photo caption contest.


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Provides for key-value Storage services.
Package Specification
This package contains the public interface to the Storage API. All
Storage services are to be accessed using types from this package,
starting with {@link com.google.gwt.storage.client.Storage}.
Related Documentation
For tutorials, examples, guides, and background documentation, please see:


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Innovated and motivated Data and Software engineer, with two years of experience in object-oriented design, Machine learning, Big Data technologies, deep knowledge of data structure and algorithms. Currently looking for full-time opportunities as
Data Engineer/Data Scientist/Software Engineer.
Education
New York University, TANDON SCHOOL OF ENGINEERING, BROOKLYN, NY Aug 2016 – May 2018
- Masters of Science in Computer Science(GPA 3.633)
SRM University, NCR CAMPUS, INDIA Aug 2011 - May 2015
- Bachelors of Technology in Computer Science (GPA 3.4)
Technical Skills
- Languages : Python, C, C++, Java, SQL, HTML, Prolog, R, Shell, HTML5, CSS3, SQL, Apache Spark, Pig
- Frameworks : Django
- Database : MongoDB, PostgreSQL, Cassandra, SQLite, Mysql
- DevOps : Jenkins, Docker
- Tools & Methods : Linux, Agile, Scrum, RESTful APIs, ElasticSearch, Microsoft Suite, Oracle 9i, IntelliJ, Scikit-learn, Jupyter, Visio, Git, Tensorflow, MySql workbench, AWS, Kafka, Rabbit mq, zmq
Professional Experience
MTA New York City Transit, New York, USA
SYSTEM & DATA SCIENCE RESEARCH INTERN SEP 2017 – Present
- Built an api using python for calculating performance metrics and ridership estimations by producing aggregated data
- Writing and maintaining ETL scripts
- Implementing KAFKA to build real-time data pipelines and streaming applications by collecting data from different zmq's
- Built different classifiers to predict future performance metrics using machine learning classification algorithms
- Statistical analysis done to gain insights of the performance metrics
HCL TECHNOLOGIES, NOIDA, INDIA
SOFTWARE ENGINEER INTERN OCT 2015 - APRIL 2016
- Performed bug tracking in C++
- Designed and developed software for operating systems
- Fixed ill-defined requirements of the software
- Deployment of various requirements using Scrum Methodology
Projects
iNEWS SEP 2017 - DEC 2017
- Mobile application that uses a News API to fetch the latest news from sources including CNN and the New York Times
and deliver it to the user; whether or not user is connected to the Internet
- Sends a push notification to user every day at 9 AM EST to awaken the app, so that the latest news can be downloaded
- Developed backend of the application using Amazon Web Services such as Lambda, SQS, SNS and DynamoDB to
retrieve unique news from source. Also, developed APIs to retrieve and search news from AWS ElasticSearch
- Two factor authentications are provided for security purposes using AWS Cognito
ANALYZING NYPD COMPLAINT DATA (HISTORIC) July2017 – Aug2017
- Analyzed NYPD Complaint data to uncover hidden patterns, unknown correlations, crime trends and other anomalies
- Generated hypothesis based on revelations by correlating with datasets like Weather, census and employment data.
- Used Spark Scripts, Map-reduce and Sql queries to clean data
- Python libraries such as seaborn, matlib, bokeh and folium are used for data visualization
YELP DATASET ANALYSIS June2017 – July2017
- Performed some basic statistics like summarizing reviews by city and category, ratings of businesses around University of
Wisconsin-Madison, based on number of reviews on the dataset by executing scripts written in PigLatin and ApacheSpark
- Performed port mapping to run Hue web Interface through local browser by setting cloudera quick start container in Docker
- Created visualizations in Tableau based on data obtained from the resulted scripts
CLASSIFIERS TO IDENTIFY TWITTER ACCOUNTS AS BOTS OR NOT BOTS Jan 2017 – April 2017
- Built four classifiers (Multinomial Naive Bayes, Decision Trees, Logistic Regression and Random Forest) using python
libraries pandas to train our model to predict twitter accounts as bots
- Compared model’s accuracy from different classifiers we built
ANALYZING TRAITS SHARED BETWEEN TWO TWITTER USERS April 2017 – May 2017
- Built a fully functional, Watson-powered application using Python to interact with the Twitter API and IBM's Personality Insights API in order to analyze traits shared between two Twitter users
- Displayed the top 5 personality traits shared between two Twitter users


Question:
What is Generic Product
Answer:
Stand-alone systems that are marketed and sold to any customer who wishes to buy them.
Question:
What is Systems software
Answer:
Software involving heavy interaction with hardware written to service other program.
Question:
What is Embedded Software
Answer:
It resides within a product or system and is used to implement and control features and functions for the end user and for the system itself
Question:
What is software?
Answer:
Computer programs, data structures and associated documentation. Software products may be develeoped for a particular customer or may be developed for a general market.
Question:
What are the attributes of good software?
Answer:
Good software should deliver the required functionality and performance to the user and should be maintainable, and dependable and usable.
Question:
What is Software Engineering?
Answer:
Software engineering is an engineering discipline that is concerned with all aspects of software production.
Question:
What is the difference between software engineering and compueter science?
Answer:
Compuetr science focuses on theory and fundamentals; software engineering is concerned with the practicalities of developing and delivering useful software.
Question:
What is the difference between software engineering and system engineering?
Answer:
System engineering is concerned with all aspects of computer-based systems development inculding hardware, software and process engineering. Software engineering is part of this more general process.
Question:
What is Maintainability?
Answer:
Software should be written in such way so that it can evolve to meet the changing needs of customers. This is a critical attribute because software change is an inevitable requirement of a changing business enviroment.
Question:
What is Dependability and Security?
Answer:
Software Dependability includes a range of characteristics including reliability, security and safety. Dependable software should not cause physical or economic damage in the event of system failure. Malicious users should not be able to access or damage the system.
Question:
Efficiency
Answer:
Software should not make wasteful use of system resources such as memory and processor cycles. Efficency therefore includes
Question:
Answer:
Question:
Answer:
Question:
Answer:
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Answer:
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Answer:
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Answer:


In order to access Issues from GitHub, you need a personal access token. You can get it here. You only need to grant permissions for the Repo scope
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{% extends "base.html" %} {% block content %} Preview Preview the data in your ReadAlong Set some configuration options Closed Captioning Save Temporary Files Save Text Grids Show Log Go to the next step! Please wait while the ReadAlong-Studio aligns your audio and text! {% endblock %}


public interface MapGesture
Default gestures made available through this interface include:
Various setter methods of this interface allow an application developer to set specific gestures
as either enabled or disabled. There is also a setAllGesturesEnabled(boolean)
method
which simultaneously enables or disables all gestures.
Note: the default functionality of one or more gestures can be customized by implementing the
MapGesture.OnGestureListener
class and overriding appropriate methods to define desired gesture
functionality (this must be done to make use of the long press gesture).
| Modifier and Type | Interface and Description |
|---|---|
static interface |
MapGesture.OnGestureListener
Event Listener interface for gesture events.
|
| Modifier and Type | Method and Description |
|---|---|
void |
addOnGestureListener(MapGesture.OnGestureListener listener)
Adds a
MapGesture.OnGestureListener to listen for map gesture events. |
void |
cancelKineticPanning()
Cancels all currently active motions caused by kinetic panning.
|
boolean |
isAutoSnapToNorthEnabled()
Returns a boolean value indicating whether map automatically rotates to the north
if previous rotation did not exceed certain small amount of degrees starting from north.
|
boolean |
isDoubleTapEnabled()
Returns a boolean value indicating whether double-tap is enabled for the
MapGesture . |
boolean |
isFixedMapCenterOnMapRotateZoom()
Returns a boolean value indicating whether the transform center is applied on rotate and zoom
interactions.
|
boolean |
isKineticFlickEnabled()
Returns a boolean value indicating whether flick-to-scroll (with kinetic momentum) is enabled
for the
MapGesture . |
boolean |
isLongPressEnabled()
Returns a boolean value indicating whether long-press is enabled for this
MapGesture . |
boolean |
isPanningEnabled()
Returns a boolean value indicating whether panning is enabled for this
MapGesture . |
boolean |
isPinchEnabled()
Returns a boolean value indicating whether pinch is enabled for this
MapGesture . |
boolean |
isRotateEnabled()
Returns a boolean value indicating whether two-finger rotation is enabled for this
MapGesture . |
boolean |
isSingleTapEnabled()
Returns a boolean value indicating whether single-tap is enabled for this
MapGesture . |
boolean |
isTiltEnabled()
Returns a boolean value indicating whether tilt is enabled for this
MapGesture . |
boolean |
isTwoFingerPanningEnabled()
Returns a boolean value indicating whether two-finger panning interactions are enabled for
this
MapGesture . |
boolean |
isTwoFingerTapEnabled()
Returns a boolean value indicating whether two-finger tap is enabled for this
MapGesture . |
void |
removeOnGestureListener(MapGesture.OnGestureListener listener)
Removes an existing
MapGesture.OnGestureListener . |
MapGesture |
setAllGesturesEnabled(boolean enabled)
Sets all possible kinds of gesture interaction to be either enabled or disabled for the
MapGesture . |
MapGesture |
setAutoSnapToNorthEnabled(boolean enabled)
Sets whether map automatically rotates to the north if previous rotation did not exceed
certain small amount of degrees starting from north.
|
MapGesture |
setDoubleTapEnabled(boolean enabled)
Sets whether double-tap interactions are enabled or disabled for this
MapGesture . |
MapGesture |
setFixedMapCenterOnMapRotateZoom(boolean lock)
Sets whether the transform center is applied during multitouch gestures.
|
MapGesture |
setKineticFlickEnabled(boolean enabled)
Sets whether flick-to-scroll (with kinetic momentum) interactions are enabled for this
MapGesture . |
MapGesture |
setLongPressEnabled(boolean enabled)
Sets whether long-press interactions are enabled or disabled for this
MapGesture . |
MapGesture |
setPanningEnabled(boolean enabled)
Sets panning interactions to be either enabled or disabled for this
MapGesture . |
MapGesture |
setPinchEnabled(boolean enabled)
Sets whether pinch interactions are enabled or disabled for this
MapGesture . |
MapGesture |
setRotateEnabled(boolean enabled)
Sets whether two-finger rotate interactions are enabled for this
MapGesture . |
MapGesture |
setSingleTapEnabled(boolean enabled)
Sets whether single-tap interactions are enabled or disabled for this
MapGesture . |
MapGesture |
setTiltEnabled(boolean enabled)
Sets whether tilt interactions are enabled or disabled for this
MapGesture . |
MapGesture |
setTwoFingerPanningEnabled(boolean enable)
Sets whether the two-finger panning interaction is enabled for this
MapGesture . |
MapGesture |
setTwoFingerTapEnabled(boolean enabled)
Sets whether two-finger tap gestures are enabled or disabled for this
MapGesture . |
void addOnGestureListener(MapGesture.OnGestureListener listener)
MapGesture.OnGestureListener
to listen for map gesture events. When there are multiple
listeners subscribed to the MapGesture
events, the order when a subscriber receives a
callback is determined by the order when they are added to MapGesture
.
It is important that after adding an MapGesture.OnGestureListener
in an user application,
remember to call removeOnGestureListener(OnGestureListener)
when there's no longer a
need to listen for map gesture events to free up application resources.
listener
- A MapGesture.OnGestureListener
to add to this MapGesture
removeOnGestureListener(OnGestureListener)
void removeOnGestureListener(MapGesture.OnGestureListener listener)
MapGesture.OnGestureListener
. Call this method to free up application
resources when there's no longer any need to listen for map gesture events.listener
- A MapGesture.OnGestureListener
to remove from this MapGesture
MapGesture setPanningEnabled(boolean enabled)
MapGesture
.
Note that when panning is disabled, kinetic panning will also be blocked, even if it is
enabled.enabled
- A boolean specifying whether panning is enabledMapGesture
itself.boolean isPanningEnabled()
MapGesture
. By
default, this value is set to true.MapGesture setKineticFlickEnabled(boolean enabled)
MapGesture
.enabled
- A boolean specifying whether kinetic flick is enabledMapGesture
itself.boolean isKineticFlickEnabled()
MapGesture
. By default, this value is set to true.MapGesture setPinchEnabled(boolean enabled)
MapGesture
.enabled
- A boolean
specifying whether pinch is enabledMapGesture
itself.boolean isPinchEnabled()
MapGesture
. By
default, this value is set to true.MapGesture setRotateEnabled(boolean enabled)
MapGesture
.enabled
- A boolean specifying whether rotate is enabledMapGesture
itself.boolean isRotateEnabled()
MapGesture
. By default, this value is set to true.MapGesture setAutoSnapToNorthEnabled(boolean enabled)
enabled
- A boolean specifying whether auto snap to north is enabled.MapGesture
itself.boolean isAutoSnapToNorthEnabled()
MapGesture setTiltEnabled(boolean enabled)
MapGesture
.enabled
- A boolean specifying whether tilt is enabledMapGesture
itself.boolean isTiltEnabled()
MapGesture
. By
default, this value is set to true.MapGesture setSingleTapEnabled(boolean enabled)
MapGesture
.enabled
- A boolean specifying whether single-tap is enabledMapGesture
itself.boolean isSingleTapEnabled()
MapGesture
.
By default, this value is set to true.MapGesture setDoubleTapEnabled(boolean enabled)
MapGesture
.enabled
- A boolean specifying whether double-tap is enabledMapGesture
itself.boolean isDoubleTapEnabled()
MapGesture
.
By default, this value is set to true.MapGesture setLongPressEnabled(boolean enabled)
MapGesture
.enabled
- A boolean specifying whether long-press is enabledMapGesture
itself.boolean isLongPressEnabled()
MapGesture
.
By default, this value is set to true.MapGesture setTwoFingerTapEnabled(boolean enabled)
MapGesture
.enabled
- A boolean specifying whether two-finger tap is enabledMapGesture
itself.boolean isTwoFingerTapEnabled()
MapGesture
. By default, this value is set to true.MapGesture setAllGesturesEnabled(boolean enabled)
MapGesture
.enabled
- A boolean specifying whether all gestures are enabledMapGesture
itself.MapGesture setTwoFingerPanningEnabled(boolean enable)
MapGesture
.enable
- True if two finger panning will be enabled (default). False if two-finger panning
will not be enabled.MapGesture
itself.boolean isTwoFingerPanningEnabled()
MapGesture
. By default, this value is set to true.MapGesture setFixedMapCenterOnMapRotateZoom(boolean lock)
lock
- A boolean specifying whether transform center is fixed . Default is falseMapGesture
itself.boolean isFixedMapCenterOnMapRotateZoom()
void cancelKineticPanning()


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A Brief Research Report: Thinking Styles of Online Distance ... - sicet
THINKING STYLES. Sternberg (1997) has proposed a theory of thinking styles intended to help illuminate the differences in the way people think. Given the .
http://www.sicet.org/journals/ijttl/issue0601/Richmond,etal.Vol2.Iss1_58_64.pdf
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Mental health and thinking styles in Sternberg's theory: an ...
Psychol Rep. 2010 Dec;107(3):784-94. Mental health and thinking styles in Sternberg's theory: an exploratory study. Chen GH, Zhang LF. School of .
http://www.ncbi.nlm.nih.gov/pubmed/21323137
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Thinking Styles - Robert J. Sternberg - Google Books
Using a variety of examples that range from scientific studies to personal anecdotes, Dr. Sternberg presents a theory of thinking styles that aims to explain why .
http://books.google.com/books/about/Thinking_Styles.html?id=dgxBqGIfJjAC
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Perspectives on Thinking, Learning, and Cognitive Styles | Questia ...
This volume presents the most comprehensive, balanced, and up-to-date coverage of theory and research on cognitive, thinking, and learning styles, in a way .
http://www.questia.com/library/107259114/perspectives-on-thinking-learning-and-cognitive
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Thinking Styles and Identity Development among Chinese ... - JStor
government, and to the Erwin Identity Scale-Ill, grounded in Chickering's theory of psychosocial development. Not only were thinking styles strongly associated .
http://www.jstor.org/stable/20445460
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There are alternative theories of thinking and learning styles, all of which share a common goal--that is, to explain individual differences in performance that are .
http://egitim.aku.edu.tr/levent.doc
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Thinking Styles - Cambridge Books Online - Cambridge University ...
Using a variety of examples that range from scientific studies to personal anecdotes, Dr. Sternberg presents a theory of thinking styles that aims to explain why .
http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511584152
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The relationship between thinking styles and emotional intelligence ...
104 items . In a similar vein, theories of 'thinking styles', also called 'intellectual styles', have been developed as a link between personality trait theory and .
http://www.academia.edu/2224455/The_relationship_between_thinking_styles_and_emotional_intelligence_an_exploratory_study
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Styles of Thinking and Learning
First, it describes the nature of styles of thinking and learning. Second, it briefly reviews some of the literature on styles. Third, it presents a theory of styles of .
http://cjs.sagepub.com/content/13/2/15.refs
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Styles of thinking, abilities, and academic performance.
The theory of mental self-government is rooted in previous work on styles, and so it shares some characteristics with earlier theories. The theory of thinking styles .
http://www.freepatentsonline.com/article/Exceptional-Children/19200718.html
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Styles of Thinking as a Basis of Differentiated Instruction
Abstract: We consider how to differentiate instruction using a theory of thinking styles as a basis for differentiation. The article opens with a consideration of why .
http://www.eric.ed.gov/ERICWebPortal/recordDetail?accno=EJ692317
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mathematical thinking styles and their influence on teaching - ICME-12
So in the following, the theory of Mathematical Thinking Styles (MTS) is described theoretically and on the basis of qualitative and quantitative empirical studies.
http://www.icme12.org/upload/submission/1905_F.pdf
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Theory and Practice: thinking styles in engineering and science ...
This paper describes knowledge as an element of thinking styles, which are properties of thinking collectives. According to the theory outlined here, the choice of .
http://dl.acs.org.au/index.php/ajis/article/view/218
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Perspectives on Thinking, Learning, and Cognitive Styles - Google ...
This volume presents the most comprehensive, balanced, and up-to-date coverage of theory and research on cognitive, thinking, and learning styles, in a way .
http://books.google.com/books/about/Perspectives_on_Thinking_Learning_and_Co.html?id=72xHSz5Z9CkC
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Gelotophobia and thinking styles in Sternberg's theory'.
Psychol Rep. 2012 Feb;110(1):25-34. Gelotophobia and thinking styles in Sternberg's theory'. Chen GH, Liu Y. School of Management, Guangdong University of .
http://www.ncbi.nlm.nih.gov/pubmed/22489375
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Psychology Journal Review: Thinking Styles and Conceptions of ...
May 8, 2011 . There are 13 styles of thinking, based on the Sternberg's theory of mental self- government. These styles are further re-conceptualized into three .
http://voices.yahoo.com/psychology-journal-review-thinking-styles-conceptions-8382483.html
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Kinds of Thinking, Styles of Reasoning - Peters - Wiley Online Library
Jul 21, 2007 . Kinds of Thinking, Styles of Reasoning . Educational Philosophy and Theory . The movement of critical thinking also tends to treat thinking .
http://onlinelibrary.wiley.com/doi/10.1111/j.1469-5812.2007.00344.x/abstract
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Sternberg-Wagner Thinking Styles Inventory - Learning Disabilities ...
Oct 5, 2010 . The thinking styles inventory is based on a Theory of Mental Self-Government, developed by Robert Sternberg and his associates. The theory .
http://www.ldrc.ca/projects/projects.php?id=48
-
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THINKING STYLES BASED ON HERRMANN WHOLE BRAIN ...
The present study aims at identifying thinking styles of eight grade students in . In the Whole Brain Theory, and depending on thinking characteristics, the brain .
http://www.ijhssnet.com/journals/Vol._1_No._9_Special_Issue_July_2011/12.pdf
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Differences in Thinking Styles among Low-, Average-, and High ...
The relationship between thinking styles and academic achievement has received a great attention . In the theory, Sternberg (1988, 1997) provided categories .
http://www.ep.liu.se/ecp/021/vol1/001/ecp2107001.pdf


I read about the Spice 'incense' product in an online news article. After setting out to find it, I realized that it was already difficult to procure in the US. After some searching I found a Polish vendor selling it on eBay, and purchased a 3 gram package of Spice Silver. It took about 2 weeks to arrive.
The Saturday after receiving the package I decided to give it a shot and see if the stuff would have any effect on me. I decided to use my one-hitter that I typically use for weed. I pinched about 1/20th the content out of the shiny pouch and packed the bowl. I estimated this to be only 150 mg, which is a small amount. I took my first hit and noticed that the flavor was nothing like cannabis. It was a lot ‘fruitier’ tasting, but not harsh. I held the first hit for about 20 seconds and finished the small bowl off with another 2 hits.
After the first 5 minutes, I felt a very warm sensation in my limbs and had a little weightlessness feeling, but nothing very mental happening yet. After another 5 minutes I felt a very familiar sensation of ‘getting high’ exactly as if I had just hit some decent bud. Shortly after this feeling my eyes became very sensitive to light, which doesn’t normally happen with bud. I was definitely high, and getting my normal head-rush feeling. About 20 minutes in my eyes were no longer sensitive to light, but I was getting cotton-mouth, exactly like I would if I had some big bong hits.
I was getting all giggly, and definitely enjoying the buzz. The buzz felt exactly like that of cannabis, but I had only smoked a very small amount. About 1 hour in, I realized that I was still just as high as when it first hit me. I listened to some music, watched a little TV and before I knew it, another hour had passed and I was still just as high. After hour 3, I could feel myself starting to come down and I became very hungry. I made up a pizza and then went outside for a walk around. Upon entering the fresh air outside I couldn’t believe how high I still was. Finally around 5PM I felt pretty normal. What a long lasting high from only 3 little hits!
The next day I decided to make some tea using the Spice. I brought 1.5 cups of water to a roiling boil and made a loose-leaf tea with another ~150 mg pinch from the shiny pouch. I let the tea steep for a full 15 minutes until it was cool enough to drink. I drank the tea slowly and enjoyed the herbal flavor. When I was near the bottom of the cup I purposely drank in portions of the herbs and thoroughly chewed them before swallowing it. I started drinking the tea at ~10AM and finished up drinking, chewing the herbs about 20 minutes later.
It took about another 40 minutes until I felt the first signs of effect. I noticed that lights looked a little edgy and that I was sensitive to light again.  I felt this way until ~6PM and couldn’t believe how long the body buzz lasted. By 7PM I felt like I was back at baseline. I did not get the acute hunger like I did when I smoked it. I did however experience dry mouth a few times throughout the day.
I did not experience any ill side-effects from smoking or the tea.
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.


| PostgreSQL 9.5.19 Documentation | |||
|---|---|---|---|
| Prev | Up | Chapter 49. System Catalogs | Next |
The view pg_file_settings provides a summary of the contents of the server's configuration file(s). A row appears in this view for each "name = value" entry appearing in the files, with annotations indicating whether the value could be applied successfully. Additional row(s) may appear for problems not linked to a "name = value" entry, such as syntax errors in the files.
This view is helpful for checking whether planned changes in the configuration files will work, or for diagnosing a previous failure. Note that this view reports on the current contents of the files, not on what was last applied by the server. (The pg_settings view is usually sufficient to determine that.)
The pg_file_settings view can be read only by superusers.
Table 49-61. pg_file_settings Columns
| Name | Type | Description |
|---|---|---|
| sourcefile | text | Full path name of the configuration file |
| sourceline | integer | Line number within the configuration file where the entry appears |
| seqno | integer | Order in which the entries are processed (1..n) |
| name | text | Configuration parameter name |
| setting | text | Value to be assigned to the parameter |
| applied | boolean | True if the value can be applied successfully |
| error | text | If not null, an error message indicating why this entry could not be applied |
If the configuration file contains syntax errors or invalid parameter names, the server will not attempt to apply any settings from it, and therefore all the applied fields will read as false. In such a case there will be one or more rows with non-null error fields indicating the problem(s). Otherwise, individual settings will be applied if possible. If an individual setting cannot be applied (e.g., invalid value, or the setting cannot be changed after server start) it will have an appropriate message in the error field. Another way that an entry might have applied = false is that it is overridden by a later entry for the same parameter name; this case is not considered an error so nothing appears in the error field.
See Section 18.1 for more information about the various ways to change run-time parameters.


This application has been developed for the TryCatch front-end coding challenge to show my software development competences, particularely in the area of front-end development.
The look and feel of the user interface has been kept as simple as possible on purpose to keep complexity at bay and to avoid using heavy UI frameworks such as Bootstrap or Foundation for this simple application.
Many best practices are employed such as the DRY and single responsibility principles by using serivces and directives provided by the AngularJS framework.
The application has been developed using a simplified version of Scrum where a branch including an issue has been created for every user story.
My name is Vincent Wochnik, I am a student of the Fontys University of Applied Sciences in Venlo currently finishing my Bachelor's degree in computer sciences.


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You can now analyze your Uber data
1. Go to https://riders.uber.com/trips and sign in
2. Click the RideshareTripStats icon in the top right of the chrome bar. You might have to click the puzzle piece to see the extension icon.


Rukmini Dental clinicConsulted by : Dr SanthoshReg No:124/90 Behind Canara bank Ramamandir, 3rd block Rajajinagar Bangalore E-mail<EMAIL_ADDRESS>Phone:+91<PHONE_NUMBER> Fax:+91 80 45671230 |
Mr. Ujjwal#120, 12th cross, Malleswaram, BangaloreE-mail<EMAIL_ADDRESS>Phone:+91<PHONE_NUMBER> |
| Sl. No. | Treatment | Quantity | Amount () |
|---|---|---|---|
| 001 | General Consultation | N/A | 350 |
| 002 | PFM Crown for 5┘ | N/A | 3000 |
| Sl. No. | Tab | Quantity | Amount () |
|---|---|---|---|
| 001 | Sensodyne | 1 | 350 |
| 002 | Nice Tab 200 MG | 10 | 50 |
| 003 | ZERODOL-SP | 5 | 100 |
| Subtotal | 850.00 | ||
| Tax (10%) | 85.00 | ||
Total |
935.00 |


Join the numbers and get to the 2048 tile!
How to play: Use your arrow keys to move the tiles. When two tiles with the same number touch, they merge into one!
Based on 2048 by Gabriele Cirulli.


Who We Are
The Inter-Ivy Space Coalition consists of a group of undergraduate students across the Ivy League Universities who are passionate about aerospace engineering and have come together to help develop the next generation of entrepreneurs and aerospace engineers. In the Spring of next year, a conference will be held at Yale University, the first of its kind to connect students interested in aerospace engineering among the Ivy League Universities.


Owned is a Bangladeshi rock band that was formed by drummer A.K. Samee and bassist/vocalist A.K. Ratul in Dhaka in 2007. Over the years, the band performed in a lot of local gigs and released 5 singles which created a loyal following. Despite the line up changes, the band is still going strong and are finally releasing their full length debut album titled - 'One'.
Influences - Radiohead, Nirvana, Queens of the Stone Age, Foo Fighters, Alice in Chains, Soundgarden, Pearl Jam, Muse, Silversun Pickups, Failure, The Devin Townsend Project, Grimes, Purity Ring, Karnivool, Artcell, Arbovirus, Cryptic Fate, Karnival
Gears:
Bass - Fender Aerodyne Jazz Bass with DiMarzio Model P and Model J
Pedals - BOSS TU3, BOSS ODB 3, Joyo Ultimate Drive, MXR Bass Fuzz Deluxe, Fuzzrocious Grey Stache, BOSS CEB-3, Tech 21 Sansamp VT Bass
Influences - Radiohead, Nirvana, Queens of the stone age, Foo Fighters, A Perfect Circle, Tool , Alice in Chains, ,Soundgarden, Pearl Jam, Muse, Silversun Pickups, Rage Against the Machine, RHCP, Arbovirus, Cryptic Fate , Nemesis , Karnival
Gears:
Roland TD4.
Influences - Radiohead, Pink Floyd, Porcupine Tree, Karnival, Nirvana
Gears:
Roland Juno Di
Influences - Radiohead, Led Zeppelin, Queens of the Stone Age, Foo Fighters, Tool, Nirvana, Alice in Chains, ,Soundgarden, Pearl Jam, Muse, Silversun Pickups, Rage Against the Machine, RHCP, Arbovirus, Karnival
Gears:
Amp - Orange Micro Terror - 1x12 Eminence Cab
Pedals - Ibanez TS9, Boss DS1, Garagetone Chainsaw, Tech 21 Blonde, Behringer Analog Delay, Hardwire Valve Distortion, Boss NS2
Guitar - Frankenstrat
Influences - Metallica, Megadeth, Pantera, Dream Theater, Iron Maiden, Queens of the Stone Age, Muse, Pink Floyd, MR.Big, Foo Fighters, Radiohead, Porcupine tree, Pearl Jam, Joe Satriani, Steve Vai, Paul Gilbert, Artcell, Arbovirus, Cryptic Fate, Black, Nemesis, Karnival , Aurthohin, DNA, Reborn, Powersurge
Gears:
Guitar: Ibanez MTM2 with DiMarzio Evo 2 and Evolution , ESP LTD EC1000 with DiMarzio Crunch Lab and Paf Pro
Pedals: Line 6 POD XT Live, Cry baby 535Q


Examples¶
Parsing many HTML-Files¶
Download and unzip the Baby Names dataset (part of the great Google Python Course). From the archive we’ll just use the /babynames-folder (you can leave the .py-files and the subfolder in place).
The dataset consists of 10 regular html-files taken from http://www.ssa.gov/. If you preview the files, you’ll find tables embedded in the html with three columns Rank, Male name, Female name and 1000 rows per year.
>>> import icy
>>> icy.preview('~/Downloads/google-python-exercises/babynames/')
This results in a flood of data on your screen. Obviously the parser detects more tables than just the names. If you scroll through the preview result you’ll find results like this:
File: baby2008.html_baby2008.html_2
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1002 entries, 0 to 1001
Data columns (total 3 columns):
0 1002 non-null object
1 1001 non-null object
2 1001 non-null object
dtypes: object(3)
memory usage: 31.3+ KB
COLUMN | first 5 VALUES
----------------------------------------
0 | ['Rank', '1', '2', '3', '4']
1 | ['Male name', 'Jacob', 'Michael', 'Ethan', 'Joshua']
2 | ['Female name', 'Emma', 'Isabella', 'Emily', 'Madison']
This looks roughly like what we are aiming for. By passing the parsing arguments header = 0 and index_col = 0 takes care of identifying the correct column names and using the Rank column as the index.
Since the html-files contain more <table> tags, we have a number of results we would want to ignore. Luckily you can identify the interesting tables by filtering for tables featuring summary=”Popularity for top 1000”. There keyword to accomplish this is attrs = {‘summary’: ‘Popularity for top 1000’}.
To get rid of the error, that the file babynames.py could not be parsed we can pass filters = ‘.html’.
>>> import icy
>>> src = '~/Downloads/google-python-exercises/babynames/'
>>> icy.preview(src,
cfg = {
'filters': '.html',
'default': {
'header': 0,
'index_col': 0,
'attrs': {'summary': 'Popularity for top 1000'}
}})
This results in a preview of the first rows of all 10 tables. To simplify generating and testing the required parsing arguments, you can provide the location of a YAML-file to the cfg-keyword. A babynames_read.yml for this example would be:
filters:
- '.html'
default:
attrs: {summary: Popularity for top 1000}
header: 0
index_col: 0
Now run the whole thing:
>>> import icy
>>> src = '~/Downloads/google-python-exercises/babynames/'
>>> cfg = '~/Downloads/google-python-exercises/babynames_read.yml'
>>> data = icy.read(src, cfg)
data is a dict of pandas.DataFrames with 10 keys and a total memory usage of 234.6 KB.
Note: We used the ‘default’-key to apply the parsing arguments to every file. For more heterogenous data, you can specify different parsing arguments by using the filename (e.g. ‘baby1990.html’) as a key. If you specify ‘default’ and specifig arguments, the ‘default’ is still applied to all files but the specific arguments override the ‘default’.
Parsing many compressed CSV-Files¶
Download the Lahman Baseball dataset (from Sean Lahman’s extensive Baseball resources) and the Catapillar Tube Pricing dataset (from Kaggles Catapillar competition). The Lahman dataset consists of 24 csv-files and a readme.txt while the Caterpillar dataset features 21 csv-files.
>>> import icy
>>> icy.preview('~/Downloads/lahman-csv_2015-01-24.zip')
# output for lahman-csv_2015-01-24.zip
>>> icy.preview('~/Downloads/data.zip')
# output for data.zip
Again a lot of data appears on your screen for each of the two datasets. Most of the results seem quite sensible but we can still do a little better with this lahman_read.yml (ignore the readme.txt and parse date-columns):
filters:
- '.csv'
Master.csv:
parse_dates: ['debut', 'finalGame']
and this cat_read.yml (parse custom nan- and boolean-values and date-columns):
default:
na_values: ['NA', 'NONE']
true_values: ['Yes', 'Y']
false_values: ['No', 'N']
test_set.csv:
parse_dates: ['quote_date']
train_set.csv:
parse_dates: ['quote_date']
Now run the whole thing:
>>> import icy
>>> src = '~/Downloads/lahman-csv_2015-01-24.zip'
>>> cfg = '~/Downloads/lahman_read.yml'
>>> data = icy.read(src, cfg)
# data for lahman-csv_2015-01-24.zip
>>> src = '~/Downloads/data.zip'
>>> cfg = '~/Downloads/cat_read.yml'
>>> data = icy.read(src, cfg)
# data for data.zip
data is a dict of pandas.DataFrames with 24 keys and a total memory usage of 82.3 MB or 21 keys and a total memory usage of 11.0 MB respectively.


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