When I was younger, people would give me nicknames such as goldfish because I had braces. People said I was retarded because I was shy and not so fluent with English. As a result, I will never forget those memories. Sometimes, I’m still afraid to see the people who gave me those names.
Until I met a friend who told me...
“Be yourself. Don't worry about what other people are thinking of you, because they're probably feeling the same kind of scared, horrible feelings that everyone does.”

It's ok to be weird because that that's what makes you stnad out STRONGLY.


---
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---
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Andrew Munteanu is a 10th grade student in Romania, he loves web design and advocating for mental health, which is why he works as a Mentor and web development leader at Magnify
Rachel Xie is a high school junior who likes programming, baking, and drawing in her free time. She joined Magnify because she believes that mental health is a crucial component in everybody’s lives and that mental health resources should be available to all those that may need or want it.
Stephanie is a junior in high school who enjoys programming and playing the flute. She joined the Magnify team in hopes to educate the youth about the importance of mental well-being.
Marisa is 18 and a college freshman studying Computer Engineering from Texas. She joined Magnify Wellness because of her passion to help others and to advocate for everyone to have access to the mental health resources they need.
Coco is a grade 8 student at St. Johns School. She lives in Vancouver west, BC Canada. Her hobbies are playing the piano, cello, and singing. Coco also enjoys physical activities like volleyball and basketball. Coco also likes to explore the things she is interested in.
Alexandra Telescu is a sophomore in high school , and she likes drawing and listening to music in her free time. She joined Magnify because she loves helping people and because she believes that mental health is really important.
Hi. My name is Hannah Lexer and I am a Junior at Springside Chestnut Hill Academy and I am 17 years old. I joined magnify to help promote free access to mental health resources.


| DOSE: |
100 mg |
rectal |
MDMA |
(powder / crystals) |
Understandably, many people may feel uncomfortable with the idea of sticking a drug up one's ass. I, however, enjoy this route of administration for certain substances. I usually feel less of a toxic load, less stomach upset, and a smoother ramp up. Why people find it ok to shoot up and taboo to talk about rectal administration is completely mysterious to me.
Anyway, for the experience in question. First time trying MDMA rectally. 100mg was dissolved in sterile water, put in a dropper syringe, and inserted rectally. Within 20 minutes, I found that my headspace and significantly altered: I was suddenly, yet smoothly pulled to the peak experience that usually happens to me about 1 hour after oral administration. The ramp up, unlike with the oral ingestion, was completely smooth: a clean transition, with none of the usual bumpiness I often associate with MDMA coming on. I also seemed to come down much faster, but again, quite smoothly, and I found the the ecstatic rush was over within an hour and a half or so, even though I had an afterglow that lasted for a few hours.
In concluding, I could differentiate the rectal route from the oral one by comparing it with the difference between travelling on a prop engine plane, as opposed to a private jet: the former choice offers a bumpy, yet longer ride, while the latter, a smoother, yet shorter one.
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.


Score %wins_count% : %loses_count%
%player1_name_race% - %player2_name_race%
MMR: %player1_mmr_with_progress% vs MMR: %player2_mmr%


| Sep 19, 2019 at 5:00 PM (CET) | Aprox. 40 min |
The Catalyst Network is a fast, light, secure and decentralised network that enables all developers to quickly develop and deploy dapps and smart contracts — without needing to invest time and money learning new programming languages. Use your existing skills to tap into a rapidly expanding market and join a growing community of developers working to bring blockchain technology into the mainstream.
The first in our series of monthly virtual meetups will cover everything you need to know to get involved in the community, including:
So, are you ready to join the decentralised computing revolution? Sign up here for the first Catalyst Virtual Meetup!
Are you ready to dive right in and explore? Click below to register with us on GitHub and access the Catalyst codebase.
Enter your address below to get the latest updates
& announcements on our progress.


See: Description
| Interface | Description |
|---|---|
| Allocatable |
Objects that implement allocatable can be allocated by reservations.
|
| Appointment |
The basic building blocks of reservations.
|
| AppointmentFormater |
Formats the different appointment outputs.
|
| Period |
Most universities and schools are planning for fixed periods/terms
rather than arbitrary dates.
|
| Permission |
New feature to restrict the access to allocatables on a per user/group basis.
|
| Repeating |
Encapsulates the repeating rule for an appointment.
|
| Reservation |
The
Reservation interface is the central interface of
Rapla. |
| Class | Description |
|---|---|
| AppointmentBlock |
This class represents a time block of an appointment and is currently only used for performance
reasons
|
| AppointmentBlockEndComparator | |
| AppointmentBlockStartComparator | |
| AppointmentStartComparator | |
| RepeatingEnding |
Currently Rapla supports the following types:
weekly
daily
|
| RepeatingType |
Currently Rapla supports the following types:
weekly
daily
|
| ReservationHelper | |
| ReservationStartComparator |
Copyright © 2000-2012 Rapla Team. All Rights Reseserved.


We work closely with Employers across all industry sectors to ensure that their internal sed Human Resource systems processes align to their business requirements idea of denouncing pleasure and praising pain was born and I will give you a complete account of the system, and expound the great explorer of the truth.
Take a 360-degree sed view of yours situations using our seds deep experience, industries specialization and global reach ensure that their internal sed Human Resource systems processes align to their business requirements idea of denouncing pleasure and praising pain was born and I will give you a complete account of the system, and expound the actual teachings ensure that their internal sed Human Resource systems processes align to their business requirements ut idea of denouncing pleasure and praising pain was born and I will give you a complete account seds expound the actual teachings
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Beguiled and demoralized by the charms of pleasure of the moment, so blinded by desire, that they cannot foresee the pain and trouble that are bound to ensue beguiled and demoralized by the charms of pleasure of the moment, so blinded by desire trouble.


title: ECMAScript 'class'-property access expressions
stage: 1
contributors: Ron Buckton, Ecma International
Introduction
Executable Code and Execution Contexts
Environment Records
Environment Record Operations
Execution Contexts
Ordinary and Exotic Object Behaviors
ECMAScript Language: Expressions
Primary Expression
Object Initializer
ECMAScript Language: Functions and Classes
Function Definitions
Arrow Function Definitions
Method Definitions
Generator Function Definitions
Async Generator Function Definitions
Class Definitions
Async Function Definitions
Async Arrow Function Definitions
Tail Position Calls
Static Semantics: HasCallInTailPosition
ECMAScript Language: Scripts and Modules
Scripts
modules
Module Semantics
The Global Object
Function Properties of the Global Object
eval ( _x_ )
Fundamental Objects
Function Objects
The Function Constructor
Function ( _p1_, _p2_, ..., _pn_, _body_ )


Follow these steps to import the mlfinlab
library:
- Open a project.
- In the Project panel, click the LEAN Environment field and then click Hudson & Thames from the drop-down menu.
- Open one of the code files in your project.
- At the top of the code file, add the following snippet:
import ht_auth
ht_auth.SetToken("<mlfinlab_api_key>")
import mlfinlab as ml
MlFinLab costs £100 (+VAT) per month, per user. Follow these steps to get your API key:
- Create a profile on the Hudson & Thames website and log in.
- On the dashboard page under MlFinLab, click Buy.
- On the MlFinLab page, click Buy.
- In the Terms of Use window, accept the terms of use and then click Purchase.
- In the Purchase MlFinLab window, enter your payment card details and then click Subscribe Now.
- Wait for the success window to display.
- On the dashboard page, under MlFinLab, click View.
- On the MlFinLab page, click Copy API Key.


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viewCareProviderComments(View)
- Method in class com.example.healthtracker.Activities.
EditProblem
viewCareProviderComments(View)
- Method in class com.example.healthtracker.View.
CareProviderProblemView
ViewCareProviderComments
- Class in
com.example.healthtracker.View
ViewCareProviderComments()
- Constructor for class com.example.healthtracker.View.
ViewCareProviderComments
viewMap(View)
- Method in class com.example.healthtracker.View.
PatientHomeView
viewMyProblems(View)
- Method in class com.example.healthtracker.View.
PatientHomeView
ViewMyProblems
- Class in
com.example.healthtracker.View
ViewMyProblems()
- Constructor for class com.example.healthtracker.View.
ViewMyProblems
ViewPageAdapterActivity
- Class in
com.example.healthtracker.Contollers
ViewPageAdapterActivity handles loading the arrayList containing the images to be displayed when a problem's slideshow is desired to be seen.
ViewPageAdapterActivity(Activity, ArrayList<String>)
- Constructor for class com.example.healthtracker.Contollers.
ViewPageAdapterActivity
Constructor for the ViewPageAdapterActivity that handles slideshow functionality
ViewPatients(View)
- Method in class com.example.healthtracker.View.
CareProviderHomeView
ViewPatients
- Class in
com.example.healthtracker.View
ViewPatients()
- Constructor for class com.example.healthtracker.View.
ViewPatients
ViewPatientsProblems
- Class in
com.example.healthtracker.View
ViewPatientsProblems()
- Constructor for class com.example.healthtracker.View.
ViewPatientsProblems
ViewPatientsTest
- Class in
com.example.healthtracker
ViewPatientsTest()
- Constructor for class com.example.healthtracker.
ViewPatientsTest
viewPhotos(View)
- Method in class com.example.healthtracker.View.
AddorEditRecordView
viewProblemsPhotos(View)
- Method in class com.example.healthtracker.Activities.
EditProblem
viewProblemsPhotos(View)
- Method in class com.example.healthtracker.View.
CareProviderProblemView
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Show pokemons:
Forbidden numbers:
Counter of killed wild pokemons:
EXP control, %:
Auto heal:
Way to healing:
HP control, %:
Alarm:
Alarm volume (0-100):
Alarm source:
Show IV HP:
Autocatch:
Save


I am a product and technical lead, program manager, and engineer with over 12 years of experience in product and people management, software engineering, computer vision, robotics.
I worked in all kinds of different environments; early-stage startup, mid-size company, goverment and multinational corporate job. My current interest are autonomy, spacecrafts, home automation and the software that drives these. I'm also interested in deep tech startups and disruption in general. The past couple years I've been focusing on growing experience in product and people management. I have experience in growing teams from 1 to 40+ and delivering products from scratch to $100M+ in sales.
I love clean code, solid architecture, and comprehensive documentation (maybe a bit too much). Though not actively coding at the moment, I still consider myself as an engineer but I am strategically trying myself in different types of roles to expand my skills and experience. I am always trying to bring cheer and fun to my teams, I am quite a bit of joker and very laid back while also not afraid of hard work.
I am working on Woven Alpha's Arene, Toyota's next generation software vehicle platform and development tooling. This is my first time in a product role where my responsibilities include interfacing with the customer and adjacent teams, roadmap planning, requirements management and supporting our scrum process. I am currently owning a product portfolio that has over a thousand users and used not only internally at Woven and Toyota but at 3rd party suppliers as well. The 2 main areas I focused on while at Woven:
- Simulation for ADAS testing and validation- Software automation (CI) for building, testing and deploying both in-vehicle and out-of-vehicle code
I was leading 3 client-facing projects on the order of few hundred thousand to 1.5 million dollars. I'm was charge of planning, project management, hiring and also contribute to development and serve as a scrum master for my team. My area of focus was automation in construction and warehouses (automated forklifts). My team was involved with multiple sub-projects ranging from perception/ML to customizing our SaaS/PaaS cloud backend to our customers' needs. I was also responsible for some of our product management work and hiring.
I was leading the self-driving vehicle department, being involved both in the technical and the operations/management side of the organization. I was acting both as an Engineering Manager for a multidisciplinary team and as a Technical Program Manager of our self-driving vehicle and simulation development. My roles at Ascent also covered a range of software development and software management tasks, including: SW architecture design, budgeting, helping out ML research, robotics software development, systems engineering, self-driving operation management, field testing, code reviewing, roadmap planning and technical hiring. Throughout all my positions at Ascent, I've been directly reporting to the CTO and CEO.
I was leading the growing automotive (self-driving) teams of Ascent Robotics while the organization was expanding after our Series A investment. I was managing the vehicle robotics team, infrastructure team and software tools team. I was leading 30+ people in a constantly evolving environment and served as an interim CTO for a while while also still actively participating in development and code reviews.
I was leading a team of 5-7 engineers, developing the core framework of our deep learning and robotics platform and designed our self-driving R&D car platform (Lexus RX450h). I led HW and SW architecture design, software engineering, technical hiring and project management. Additional to this, I was also working on financial intelligence projects as a software engineer.
I joined Ascent Robotics as the company's 2nd engineer. I was actively participating in shaping the company from the beginning, both on the code/architecture and on the organizational level. My initial tasks involved computer vision and software prototype development, architecture design and tech pitch to investors and potential partners.
I worked on the then next-generation RoboTaxi, ZMP's self driving platfrom. I mostly worked on computer vision and robotic system software development. I also participated in systems setup, sensor calibation and testing the self-driving car both on the test track and in traffic. Briefly I also worked on the company's delivery robot platform.
I worked on industrial vision software and SDKs (Cognex VisionView, Cognex Designer) the company ships with their camera systems. I mostly worked in C# and TypeScript with some JavaScript and C++. In most of my time at the company I was responsible for HMI framework design and implementation, script parsing, object serialization-deserialization, automatic code generation and software licensing. The products I worked on have a combined sales volume over $100M.
I joined as an intern and stayed at the Academy as a contractor Research Engineer. I was part of the Machine Perception Research Laboratory research group and worked on 2D and 3D computer vision, image processing and 3D object recognition and registration in Point Clouds. Most of my work was related to object detection, segmentation and scene understanding in lidar point clouds.
Remote studies. I dropped out because of my demanding startup work.
I took a vide array of classes including Electrical and Mechanical Engineering classes to explore my interests. I also won two scholarships (see below).
I won an EU scholarship (STARS - Sensing Technology and Robotics Scholarship) to spend a year in the US. During the year I was able to complete all Florida Tech courses that were disjunct from my program and gradudated both at Florida Tech and my home institute.
I was awarded an opportunity to take courses at Gabor Bojar's private college. I took courses in computer vision, computational biology, enterpreneurship and UX
When not working, I enjoy traveling, taking photos of random things, exploring the city, trying new food places or riding my motorbike.
My technical interests outside of work are home automation, space exploration, airplanes, cars and motorbikes.


Your Expert Real Estate Consultants For Life!
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cdemi's
07 May 2019 |
Software Development |
Typeclasses in Haskell (eventbrite.com) |
| This Wednesday we are organising the second TechIgnite Functional Programming Series event. This series will focus on introducing functional concepts from first principals. No prior knowledge of FP is required. Book now! Let's do this!... Read more |
.NET |
.NET Core is the Future of .NET (devblogs.microsoft.com) |
| We introduced .NET Core 1.0 on November 2014. The goal with .NET Core was to take the learning from our experience building, shipping and servicing .NET Framework over the previous 12 years and build a better product. Some examples of these improvements are side-by-side installations (you can install a new version and not worry about breaking existing apps),... Read more |
Introducing .NET 5 (devblogs.microsoft.com) |
| Today, we’re announcing that the next release after .NET Core 3.0 will be .NET 5. This will be the next big release in the .NET family. There will be just one .NET going forward, and you will be able to use it to target Windows,... Read more |
ASP.NET Core updates in .NET Core 3.0 Preview 5 (devblogs.microsoft.com) |
| .NET Core 3.0 Preview 5 is now available. This iteration was brief for the team and primarily includes bug fixes and improvements to the more significant updates in Preview 4. This post summarizes the important points in this release.... Read more |
Rider 2019.1 Has Arrived! (blog.jetbrains.com) |
| Today we are thrilled to introduce this year’s first major update for Rider – please welcome Rider 2019.1! With this release, Rider 2019.1 gets a new pack of features and improvements: WinForms designer. The long-awaited WinForms designer helps you create, … Continue reading →... Read more |
Using .NET and Docker Together – DockerCon 2019 Update (devblogs.microsoft.com) |
| DockerCon 2019 is being held this week, in San Francisco. We posted a DockerCon 2018 update last year, and it is time to share how we’ve improved the experience of using .NET and Docker together over the last year. We have a group of .NET Core team members attending the conference again this year.... Read more |
User Experience |
Fluent Design can now be built into Android, iOS, and web apps (neowin.net) |
| Microsoft is making it easier for developers to bring Fluent Design to Android and iOS apps as well as the web, with new tools to implement the company's styles and controls across platforms.... Read more |
Cloud |
What a pain in the Azzz-ure: Microsoft Azure, SharePoint, etc knocked offline by DNS blunder (theregister.co.uk) |
| Technical term for today's outage is TITSUP: Total Inability To Support Users' Packets... Read more |
Microsoft looks ready to add some new Azure regions, while it takes one away (zdnet.com) |
| Microsoft is closing its US Government Iowa Azure region next year, while it prepares to add other new cloud regions worldwide.... Read more |
Data |
ClickHouse In the Storm. Part 2: Maximum QPS for key-value lookups (altinity.com) |
| The previous post surveyed connectivity benchmarks for ClickHouse to estimate general performance of server concurrency. In this next post we will take on real-life examples and explore concurrency performance when actual data are involved.... Read more |
ClickHouse In the Storm. Part 1 (altinity.com) |
| ClickHouse is an OLAP database for analytics, so the typical use scenario is processing a relatively small number of requests -- from several per hour to many dozens or even low hundreds per second --affecting huge ranges of data (gigabytes/millions of rows). But how it will behave in ... Read more |
Improving PostgreSQL queries (dev.to) |
| Improving the performance of BI queries in PostgreSQL.... Read more |
Infrastructure |
Virtual Blocks: Quantifying vSAN performance (blogs.vmware.com) |
| The objective of this blog post is to show how to measure the performance of a vSAN cluster in terms of IOPS, throughput and latency. We do this by using a benchmarking utility: HCIBench. Benchmarking is generally performed using synthetic I/O testing.... Read more |
IoT |
Did I leave the garage door open? A no-code project with Azure IoT Central and the MXChip DevKit (hanselman.com) |
| I wanted to use this little Arduino friendly device and have it talk to Azure. My goal was to see how quickly and simply I could make a solution that would: Detect if my garage door is open, If it's open for more than 4 minutes, text me, Later, perhaps I'll figure out how to reply to the Text or take an action to close the door remotely.... Read more |
Security |
EnableSecurity/wafw00f (github.com) |
| WAFW00F allows one to identify and fingerprint Web Application Firewall (WAF) products protecting a website. - EnableSecurity/wafw00f... Read more |
SecLists - The Pentester's Companion (github.com) |
| SecLists is the security tester's companion. It's a collection of multiple types of lists used during security assessments, collected in one place. List types include usernames, passwords, URLs, sensitive data patterns, fuzzing payloads, web shells, and many more. The goal is to enable a security tester to pull this repository onto a new testing box and have access to every type of list that may be needed.... Read more |
General Tech |
Microsoft unveils Windows Terminal, a new command line app for Windows (theverge.com) |
| For developers, developers, developers... Read more |
iGaming |
GiG fined $25k for New Jersey geolocation failings (igamingbusiness.com) |
| Gaming Innovation Group (GiG) has been fined $25,000 (£19,140/€22,300) by the New Jersey Division of Gaming Enforcement (NJDGE) for breaching geolocation rules in the US state.... Read more |
UKGC LCCP changes - new age verification rules require new approach from the industry (linkedin.com) |
| The UK Gambling Commission’s new rules requiring operators to verify the age of players before any game play can occur is further proof that the industry must move with the times and adopt the tech that will enable safe and secure online interactions and the long-term viability of the sector.... Read more |
Politics |
European elections 2019: what’s next? (infographic) (europarl.europa.eu) |
| What happens after the new Parliament is elected? Learn more about the next steps in our infographic. ...  But in an Op-Ed last week it peddled the tired and disingenuous notion that electric cars, by the very virtue of their production, pollute more than fossil fuel cars. This is wrong, and it cannot stand. ... Read more |
Humor |
Songs about Git (open.spotify.com) |
| A Spotify playlist with Songs about Git...... Read more |
|
This newsletter is made with 💖 by cdemi and sponsored by moo Research. You can find this MIT Licensed open-source project on GitHub cdemi/NewsletterCurator. You can also find an archive of all sent newsletters on GitHub at https://newsletters.cdemi.io/. If you want to unsubscribe from this newsletter, please click here. If you're new and you want to subscribe to this newsletter, please click here. |


Templating
Dynamic Templates for your content
Let's first clarify potential confusion: There are two types of templates. The ones used for generation with the cli-tool, and the view templates.
Here we want to focus on creating dynamic view templates.
learn more about neoan3-apps/template
What you will find here is based on the assumption you are using the default neoan3-apps/template package (installs with neoan3).
You can easily hook up any template engine as well. As a matter of fact, you can inject your complete own render logic.
Passing variables to the template
component/MyComponent/MyComponent.ctrl.php
<?php
namespace Neoan3\Components;
use Neoan3\Core\Unicore;
class MyComponentController extends Unicore{
function init()
{
$this->uni('myFrame')
->addRenderParameter('headline', fn() => 'My variable')
->addRenderParameter('content', fn() => 'This is some fine text')
// the hook-method accepts 2-3 arguments
// the first argument describes the targeted part of the page
// (possible values are 'header', 'main' & 'footer')
// the second argument provides the wanted template
// (here the file /component/MyComponent/myComponent.view.html)
// the third argument accepts variables as array
// (usually the data will directly come from a model / database)
->hook('main', 'myComponent', [
'renderParam' => 'We might also declare render parameters here.'
])
->output();
}
}
component/MyComponent/myComponent.view.html

{{headline}}

{{content}}

{{renderParam}}

Result:

My variable

This is some fine text

We might also declare render parameters here.

A step further
While the full documentation for templating can be found at GitHub,
let's look into an examples of advanced usage:

Numbers:

{{number.name}}

Result:

Numbers:

two


In this post, I will explain how to perform a simple regression with Keras - Tensorflow backend. I will use the data from Kaggle. There are plenty of very good kernels existing for this dataset, and I will use the preprocessing steps from: here. There are also very good introductions to Keras, but I found this one very straightforward : Machine Learning Mastery.
Installing Tensorflow and Keras
Being on a Linux OS, I chose to install Tensorflow from sources. These are the command that I used for Ubuntu 16.04, with GPU support and SSEx support.
sudo apt-get install python-numpy python-dev python-pip python-wheel
sudo apt-get install libcupti-dev
git clone https://github.com/tensorflow/tensorflow
cd tensorflow
./configure
bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-mfpmath=both --copt=-msse4.2 --config=cuda -k //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
sudo pip install /tmp/tensorflow_pkg/tensorflow-1.1.0-py2-none-any.whl # use completion to get the right .whl file
Now is a good time to test if the installation worked, by typing python in a new command window, and typing import tensorflow to check if everything worked!
Now let's move on to the Keras installation. Simple as that:
sudo pip install keras
The Tensorflow backend should be the one by default.
Getting and formatting the data
You can download the data from Kaggle.
Now, we have the Python packages, we have the data. let's get started:
# Data preprocessing; from https://www.kaggle.com/apapiu/regularized-linear-models/notebook/notebook
# loading data
train = pd.read_csv("/path/to/your/datasets/train.csv")
test = pd.read_csv("/path/to/your/datasets/test.csv")
dataset = train.values
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'],
test.loc[:, 'MSSubClass':'SaleCondition']))
# log transform the target:
train["SalePrice"] = np.log1p(train["SalePrice"])
#log transform skewed numeric features:
numeric_feats = all_data.dtypes[all_data.dtypes != "object"].index
skewed_feats = train[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness
skewed_feats = skewed_feats[skewed_feats > 0.75]
skewed_feats = skewed_feats.index
all_data[skewed_feats] = np.log1p(all_data[skewed_feats])
all_data = pd.get_dummies(all_data)
#filling NA's with the mean of the column:
all_data = all_data.fillna(all_data.mean())
#creating matrices for sklearn:
X_train = all_data[:train.shape[0]]
X_test = all_data[train.shape[0]:]
y = train.SalePrice
# Scale the data:
X_train_sc = StandardScaler().fit_transform(X_train)
X_tr, X_val, y_tr, y_val = train_test_split(X_train_sc, y, random_state=10)
We know have data in a numpy array, usable by Keras!
Defining Simple Neural Nets
We are now going to define three simple neural nets in order to process the data
def simple_model():
"""
Simple one Layer Network to estimate the sale price of the kaggle regression dataset.
This model was tested in the very good Kaggle Kernel: https://www.kaggle.com/apapiu/regularized-linear-models/notebook/notebook
:return: keras model
"""
# Create model
model = Sequential()
model.add(Dense(1, input_dim=X_train.shape[1], W_regularizer=l1(0.001)))
# Compile model
model.compile(loss="mse", optimizer="adam")
return model
def larger_model():
"""
Creates a larger model with 5 layers. The width of the layers are: k -> k -> k -> 6 -> 1.
:return: Keras model.
"""
# create model
model = Sequential()
model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(6, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
def wider_model():
"""
Creates a Keras model that is wider than the dimension of the feature space.
The layers width are like this:
k -> 2*k -> k -> k -> 1.
:return: Keras model.
"""
# create model
model = Sequential()
model.add(Dense(2*X_train.shape[1], input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(X_train.shape[1], input_dim=2*X_train.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
Training, Visualizing and Evaluating the Networks
We are now going to create the networks, train them and visualize the loss functions in Tensorboard.
# First model
model = simple_model()
hist = model.fit(X_tr, y_tr, validation_data=(X_val, y_val))
scores = model.evaluate(X_val, y_val, verbose=0)
print "Score = ", scores
# Second model
model_2 = larger_model()
# Create tensorflow call back to view the tensorboard associated with this model.
tbCallBack = keras.callbacks.TensorBoard(log_dir='Graph', histogram_freq=0,
write_graph=True, write_images=True)
tbCallBack.set_model(model_2)
# Training the model
hist2 = model_2.fit(X_tr, y_tr, validation_data=(X_val, y_val), callbacks=[tbCallBack])
scores = model_2.evaluate(X_val, y_val, verbose=0)
print scores
# Third model
model_3 = wider_model()
hist3 = model_3.fit(X_tr, y_tr, validation_data=(X_val, y_val))
scores = model_3.evaluate(X_val, y_val, verbose=0)
print scores
# Custom model
model_4 = custom_model()
hist4 = model_4.fit(X_tr, y_tr, validation_data=(X_val, y_val))
scores = model_4.evaluate(X_val, y_val, verbose=0)
print scores
The first network is really just one layer, so there is very little chance that is can perform well on this regression dataset. The second one has five layers, and performs quite well on this dataset. given the mean squared error, and the final values of the training and validation losses. The third model is wider than the second one, with the same number of layers, so the training loss is decreasing faster at first, but at the end of the epochs, it is slightly higher than the previous model loss. We are encountering some overfitting.
I used one more model, with four layers, but I added some regularizarion on the loss in the dense layers to prevent overfitting from happening. The model looks like this:
def regularized_model():
"""
Creates a Keras model with L1 regularization to prevent overfitting.
:return: Keras model.
"""
# create model
model = Sequential()
model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu',
W_regularizer=l1(0.1)))
model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu',
W_regularizer=l1(0.1)))
model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], kernel_initializer='normal', activation='relu',
W_regularizer=l1(0.1)))
model.add(Dense(1, kernel_initializer='normal'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
The Mean Squared Errors (MSE) and \( R^2 \) score on the validation set for each network give us a good idea on how well each network performs :
Model 1 MSE:<PHONE_NUMBER> Model 2 MSE: 0.447272525725 Model 3 MSE: 0.602414087793 Model 4 MSE: 0.108447734133 Model 1 R^2: -18.5689555865 Model 2 R^2: 0.0916395170566 Model 3 R^2: 0.215758728629 Model 4 R^2: 0.336460848879
The MSE and \( R^2 \) score tell us that the Model 4 performs the best for the validation dataset. This also tells us, even though we don't have some test data to check, that the Models 2 and 3 most likely did some overfitting, and that the Regularization used in the Model 4 helped in dealing with this phenomenon.
To visualize your loss in Tensorboard, all you have to do now is:
tensorboard --logdir /absolute/path/to/your/log/directory/Graph
On the "scalars" tab, you should be able to visualize the losses like this:
This tool may come in very handy when using a more complex network on a large data set; it allows to check if the network has actually converged or if some overfitting has occurred (if the test loss curve had increased a lot before the validation loss curve increased for instance).
You can get the code from this post at this Github page.


Keyed Alike Definitions
What is Keyed Alike? What does Keyed Alike mean?
Keyed Alike simply means that all of the locks can be unlocked using the same key. In a keyed alike system, all of the locks in that system can be opened using the same key. Keyed Alike is often abbreviated as KA, especially when referring to padlocks.
Before we go any further, here are a few definition that will be necessary to your full understanding:
What are the benefits of Keyed Alike?
Because Keyed Alike offers the option to use the same key to open multiple locks, the greatest benefit to be gained with a keyed alike system is the convenience of one key. No longer will you have to deal with key ring bulk like a High School Custodian and search for the correct key at every lock, nor will you have to use cleverly colored key head covers to identify which key goes to which lock; with a keyed alike system, all you will need is the one key to open all of your locks.
Same Key Convenience also eliminates the time wasted searching for keys and allows you to rest easy with the knowledge that all of your physical security devices are doing their job and only allowing your key access.
Another benefit to a keyed alike system is that more locks can be added to your system. You can purchase locks Keyed Alike to your Key Number knowing that the key you already have will work with all of the new locks you are procuring. In certain cases, rekeyable locks and rekeyable padlocks can also be rekeyed to match your existing key that you are using with your keyed alike system.
It's also very easy and inexpensive to or order duplicate and/or replacement keys because you only have the one key number, based on the one key blank to get the one cut key to work with all of the locks in your keyed alike system. Otherwise, you might have to order any number of keys from any number of different key blanks. Honestly, that would be a hassle that no one should have to deal with, especially you.
What is required for Locks and Padlock to be Keyed Alike?
There are a few specific, and simple, requirements that will need to be met in order for your locks and padlocks to be keyed alike and match your key. The Cylinder and the Keyway must be identical. If the Keyway doesn't match, there is no way your key is even going to go into the lock. The Cylinder must not only be the same type of cylinder, but it must also have the same number of Discs, Pins, or Wafers each aligned to match the existing bitting specified by your key number so that your key will be able to function properly in the lock. A Cut Key for a 4-Pin Tumbler Cylinder will not work on a 5-Pin Tumbler Cylinder. Oftentimes key numbers are associated with a specific Cylinder and Keyway, it is always a good idea to ask a Padlock Professional any questions you may have to ensure that you are ordering the proper locks to match your key.
What are the weaknesses of a Keyed Alike Systems?
Before we get to that, let us be clear here that physical security devices are not always foolproof. Their main purpose is to deterring theft, but sometimes, even the strongest lock in the world cannot stop crime. Padlock vulnerability is one of the few times in life where the phrase, "you get what you pay for", is applicable. If you are securing equipment with significant value, do not be a bargain hunter and go cheap with your locks and padlocks. The price of a lock or padlock generally reflects the level of security that it shall provide.
With a Keyed Alike System, Key Holder Responsibility will be the greatest concern. If a key goes missing, that means that there is the real possibility that someone else will have access to all of the locks in your Keyed Alike System. Ultimately, all of the locks in your system will need to be rekeyed or replaced.
What if I have trouble remember my Key Number?
What if I have trouble remember my Key Number? The Master Lock Company LLC offers a free app that allows you to safety save your passwords, or key numbers. This is a very handy tool for those who are responsible for managing many Keyed Alike Padlock Systems because it will allow you to save the Key Numbers for each of your Keyed Alike Systems. https://www.masterlockvault.com


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Hello, my name is Kevin McNeil and I am a musician
I play several instruments including...
- Guitar
- Bass
- Piano
- Bagpipes
- Chanter
- Penny Whistle
- Cello
- Violin
- and many others
the instrument highlighted in green is my favorite to play
Some quick facts about me
- I went to SPSCC (South Puget Sound Community College) for Computer Network Administration
- I quit that field because i didnt like it and decided to pursue Music
- I have been in the music industry for a long time, having been a music teacher and played in several bands
- I currently play in a Hardcore/Metal band called
Deathbreaker - Deathbreaker is signed to Facedown Records
- We just released our sophmore album with Facedown called Isolate
- I have spent the last 5 years on the road with Deathbreaker, touring all over the USA
- We even had a European tour lined up for this year but due to Covid19 we had to cancel it, and all tours for the forseeable future
On a more personal level i
Here is a list of my top 10 Video Games
- The Legend of Zelda: Ocorina of Time (1998 N64)
- Chrono Trigger(1995 SNES)
- Super Smash Bros (1999 N64)
- The Legend of Zelda: Link's Awakening (1993 GB)
- The Legend of Dragoon (1999 PS)
- Final Fantasy 7 (1997 PS)
- Lunar: Silver Star Story Complete (1996 PS)
- Lunar 2: Eternal Blue complete (2000 PS)
- The Legend of Zelda Breath of the Wild (2017 NS)
- The Legend of Zelda (1986 NES)
To take a QUIZ on me, CLICK HERE
TA Please note, the original quiz is the only quiz where everything is wrapped in Functions as per the instructions of Lab 4. the yes or no version of the quiz is NOT wrapped in functions
To take a "YES or NO" version of the quiz, CLICK HERE


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{% extends 'base.html' %} {% block head %}
Students in India preparing for NEET or AIIMS couldn't continue their dream of becoming a MBBS doctor because of lesser seats in government medical universities and very expensive college fees in private colleges. As a results students either keeps appearing for NEET or switches their stream from MBBS to other medical courses like nursing, dentistry, etc. We at Admissions in Ukraine provide an opportunity to students of pursuing their dreams of becoming a doctor. Doing MBBS in Ukraine is very cheaper as compared to the private medical colleges in India. So, we guide the students with one of our possible solution to make a carrier in MBBS. Our journey started in the year 2019 where we sent some students from India to Ivano-Frankvisk. Learning from that experience this year we are trying to make more students dream come true. One shouldn't drop the idea of becoming a doctor because of his/her incapability of paying high college fees. If you have qualified NEET and got admitted in a government college then its the best, else give us an opportunity to open new paths to you.
To get the MBBS license, now all students have to appear for the NEXT Exam whether they have done MBBS from India or abroad.
There is no other entrance test for your admission to the university. NEET is the only exam which needs to be qualified.
All of our selected universities are very reputed one. They are old and well established. All of them are ranked among top universities of Ukraine.
You will get a chance to spend 6 years of your life in a foreign country. Learning a new language and experience a different culture would be very wonderful.
MBBS from Ukraine is much cheaper as compared from the private medical colleges in India. Your MBBS would be completed under a budget of 30 lakh rupees including everything.
Ukraine is becoming a hotspot for MBBS aspirants. There are currently 1000+ Indian students in each our mentioned university.
We will provide you with the invitation letter from the university which is the first step for your admission in a foreign university.
Your Air Ticket to Ukraine will be provided by us. You need not to pay or worry about your travelling.
Our team will do all the documentation work and will help to get your student VISA at earliest.
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Nameless Spirit:
You've brought all four goblets! Restoring the sepulcher to its original state is going to take some time. Please continue to destroy as many of the evil creatures there as you can.
"I want to stop."
"I want to continue."
"Tell me more about the sepulcher."


Imagine being tracked 24/7 by an invisible network, then classified and ranked according to a secret formula.  Imagine this (low-ball) number turned out to be a self-fulfilling prophecy, sealing your fate in a financial caste system.
These are the basic dynamics of the system of surveillance and control known as “consumer reporting.” Over 90% of the adult population is somehow entangled in the “dragnet,” yet few know precisely how—it’s hard to keep track. Consumer reporting agencies (CRAs, or simply “bureaus”) gather, organize, and standardize consumer data—how we pay bills, how much money we owe, where we work and live, whether we’ve been sued, arrested, or filed for bankruptcy—then sell the information to creditors, as well as employers, insurers, and increasingly, to consumers themselves. The “Big Three”—Equifax, TransUnion, and Experian—have long dominated the consumer reporting industry. Often labeled the “national credit bureaus,” they are actually global corporate conglomerates, which source labor and operate in markets around the world. (Experian isn’t even based in the United States, but in Ireland.)
CRAs play a crucial role in our ability to access even the basic requirements of life in our society. A survey by the Society for Human Resource Management found that almost 60% of its member employers used credit reports to screen applicants for at least some of their positions. Credit scores also frequently factor into insurance rates—approximately 92% of auto and home insurers rely on them, according to a survey in 2001. Alarmingly, hospitals have begun using credit scores to determine access to health care and to set costs. This chapter guides you through the consumer-reporting matrix, but also questions many of its basic assumptions and standard operating procedures. Decades of industry surveillance and control—often with government assistance or approval—have steadily transformed our culture, altering our values and behavior and generally instilling financial obedience.
Today, credit scores are treated as a measure not just of our creditworthiness but of our all-around personal integrity. Low scores virtually guarantee punishing credit terms and ruinous cycles of debt. And because someone won’t have a credit score unless they use credit, it penalizes people who do not use credit products. Hence, it gives a boost to the widespread use of credit and propagates debt relations. People no longer have a choice of whether to use credit or not. But even “good” behavior cannot guarantee high scores in our systematically flawed consumer reporting system. Financial surveillance is a corrupt and impersonal machine, not a system that genuinely determines people’s trustworthiness. Can’t make a credit card payment because of health costs this month? It’s recorded. Got laid off and couldn’t pay tuition? It’s recorded. Tried to pay a mortgage fee with an already low checking account balance? It’s recorded.
The fact that the credit reporting and scoring system is set up to maximize profits for the corporations involved instead of helping consumers is leading more and more people to consider alternatives. Thus, in addition to suggestions for individual “self-defense” and collective “harm reduction” in the context of the current credit regime, this chapter also offers ideas for moving outside or beyond it.
The CRA Industry
Operating much like a typical oligopoly, credit reporting in the United States has long been dominated by the Big Three of Equifax, TransUnion, and Experian—with combined revenue of more than $6.7 billion in 2009. More recently, two different firms—Innovis and CoreLogic—have been given the distinction of the “fourth” CRA. Collecting “supplementary credit information,” they represent the biggest “specialty” CRAs, companies which compile reports about much more than credit history—for example, medical history, employment history, rental history, checking account history, auto and property insurance claims, and utility bill accounts. These other non-credit reports are proving to be just as influential as credit reports; yet, even less is known about specialty CRAs than about the notoriously obscure Big Three, not to mention the shadowy connections between them.
CRAs started in the 1950s as regionally based companies that would
track the publicly available details of your life—when you got
married, if you got a speeding ticket, or if you committed a crime.
CRAs persistently drew criticism for mishandling data and violating
privacy; protest grew more intense and organized over the course of
the 1960s. There was mounting evidence that credit files were
riddled with errors and that they were being sold indiscriminately,
to practically anyone who requested them. Columbia University
professor Alan Westin became a vocal critic of industry practices
after reviewing a sample of files from Equifax, which at the time
went by the name Retail Credit Company. According to an article he
wrote in 1970, Westin discovered “‘facts, statistics, inaccuracies
and rumors’…about virtually
every phase of a person’s life; his [sic] marital troubles, jobs, school history,
childhood, sex life, and political activities.” The looming
computerization of credit files, which were increasingly
concentrated in the databases of a few newly “national” CRAs (or
NCRAs), led to the 1970 passage of the Fair Credit Reporting Act
(FCRA). Setting regulations on the collection, dissemination, and
use of consumer data, the FCRA was the first federal law to
implement privacy protections in the private sector in the United
States (Jentzsch 2007, 122).
The expansion of retail credit and major innovations in informational technology led to a consolidation of the industry in the early 1970s, which meant data became shared across industries. (Up to that point, companies could only compile information about a particular type of credit, like your regional banking history or your mortgage.) CRAs saw rapid growth in the 1970s and 1980s with the proliferation of bank credit cards, and in the 1990s with the automation of mortgage underwriting.
Today, each of the Big Three has credit files on over 200 million adults and receives information from approximately ten thousand furnishers of data, including banks and finance companies. Every month, furnishers report on over one billion consumer credit accounts and other types of accounts.
The informational structure generated by consumer reporting is the basis of today’s credit-oriented consumer economy and a primary means of statistically defining and differentiating the population. Its networks span financial service providers, retailers, insurers, telecom and utility providers, and transportation companies. This data is increasingly used to determine our access to the basic necessities of life. Landlords use credit reports and scores for screening rental applicants, as do employers for job applicants.
A tremendous amount of power over the daily lives of people is given to profit-driven entities that, until recently, operated almost entirely outside of public oversight. In July 2012, the Consumer Financial Protection Bureau announced that it would become the sole federal regulator for thirty credit reporting agencies that account for 94% of the credit reporting market. (Previously, the industry was only subject to occasional congressional oversight and the restrictions of the FCRA.) But CRAs have already amassed huge amounts of sensitive data and their business is selling it as widely as possible.
Credit Reports and Credit Files
Credit reports are reports provided by CRAs to lenders and other users. They factor critically in decisions to grant credit—for example, mortgage loans, auto loans, credit cards, and private student loans—and in other financial spheres, including eligibility for rental housing, setting premiums for auto and homeowners insurance in some states, and job hiring. Consumer reports, which include other information in addition to credit activity, are reports by a CRA “bearing on a consumer’s credit worthiness, credit standing, credit capacity, character, general reputation, personal characteristics, or mode of living.” They are used to determine a consumer’s eligibility for credit or insurance, for employment purposes, or for other purposes specified in the FCRA.
Credit reports are based on “credit files,” information about a consumer contained in the databases of the NCRAs. Credit files have some or all of the following components:
- identifying information such as name (and other names previously used), current and former addresses, Social Security number, date of birth, and phone numbers;
- trade lines (accounts) in a consumer’s name reported by creditors, including type of credit, credit limit or loan amount, account balance, payment history, delinquencies, collections, and dates of activity;
- public record information of a financial nature, including bankruptcies, judgments, and state and federal tax liens;
- third-party collections by debt buyers or collections agencies; and
- inquiries in the last two years for employment-related uses and for at least the last year for credit uses and most non-employment uses (e.g., tenant screening, insurance, government benefits).
Examples of important information not contained in credit files include: income or asset data, credit terms such as interest rates, records of arrests and convictions (but specialty CRAs such as employment screening agencies include them), marriage records, adoptions, and records of civil suits that have not resulted in judgments.
Credit Scores
Credit scores are mathematical models that input a consumer’s bureau credit report and output a number that is called a “credit score.” The most common scores range from 300 to 850. The higher the number, the less likely a consumer is to default, hence making a better credit risk for the lender.
A credit score is used by a bank or financing company to decide whether to lend to someone, and at what interest rate. Because banks say that interest is charged partly to make up for the chance of default, people with lower credit scores end up paying higher interest rates. Usually, scores below 660 are considered “subprime,” and consumers with such scores will be denied credit or given credit on bad terms. Consumers with scores above 720 will get the best interest rates.
Banks use credit scores to make their lending decisions because the CRAs have a consumer’s entire credit report, including accounts from other banks. This gives more information than just the bank’s internal data. Banks also use credit scores because the ease of using the algorithm, or mathematical formula, allows banks to implement strategies quickly. The fact that a consumer is reduced to a three-digit number allows banks to pull large batches of credit scores and treat people based on that factor alone. However, for major lending decisions with high potential losses, such as a mortgage, banks will use additional factors, such as income verification, down payment amount, and an assessment of other debt obligations.
Types of credit scores
There are many variants of credit scores, making it difficult for a consumer to navigate them all. Credit scores have two main components: the data that goes into the score, and the scoring algorithm itself. Equifax, TransUnion, and Experian are the three main providers of data. There are many different providers for the scoring algorithm, the most famous being FICO, and the credit bureaus themselves. When you encounter a “credit score,” it is important to denote which bureau the data in the score came from and what algorithm is used. Different algorithms use different score ranges, which can further confuse consumers. Below, some common examples of scores are noted and explained.
FICO score
The FICO score’s range is 300–850, and the median score is about
720. Experts agree that if your score is above the median, you will
have
access to the best rates.
The FICO score is created by a publicly traded company called FICO (formerly known as Fair Isaac Corporation) founded in 1956. Because they have been around for so long and have patented their algorithms, FICO enjoys a near monopoly in credit scoring, boasting that an overwhelming majority of lending decisions in the United States are made using the FICO score.
FICO, as a company, does not own any data from which it creates the credit score. FICO is not a CRA; they only create statistical algorithms. FICO “borrows” data (through complicated contracts) from the bureaus for the sole purpose of creating the algorithm. This algorithm is then encoded into a black box and placed at each of the Big Three bureaus, none of which know the contents. When lending entities, such as banks or cell phone companies, want a FICO score, they ping an inquiry to the bureau with which they have a relationship. The bureau runs the person’s Social Security number to retrieve information from their database. That information goes through the black box algorithm, and out comes a score which the entity receives. The money made from that transaction is split between FICO and the bureau that provided the information.
Because there are three different bureaus that can provide information, there are three FICO scores. Because different banks use different bureaus, and some banks use all three, all three scores can affect lending decisions. What compounds the confusion is that FICO uses different branding with different bureaus, so the score names can vary. If you see that you received a BEACON score, it means that you received a score with a FICO algorithm and Equifax underlying data. “Classic” refers to TransUnion, and “Experian/FICO Risk Model” to Experian.
For each bureau, FICO creates other scores specially calibrated for different purposes, such as the Auto Industry Option FICO score. The algorithm in that score is customized to predict only default in autos, instead of default in any line of credit. When someone gets a car loan, they think they received a FICO score, but it most likely isn’t the one that would be used to give them a credit card or mortgage. Also, there is no way for the public to get these custom industry score options; only auto dealers with special contractual relationships can get these scores. Other specialty industry option scores include mortgage and bank card.
Finally, because FICO has been making scores for decades, there are many different versions of the same type of algorithm. The newest algorithm is called FICO8, but many banks still use an older version. Consequently, two different banks may pull the same FICO score type from the same bureau, and still get different scores.
There are approximately fifty different FICO branded scores a consumer can have. FICO allows you to check your general FICO8 score for all three bureaus on myFICO.com for a hefty fee of approximately $60. The only other option is to get the score from your lender.
VantageScore
Experian, Equifax, and TransUnion got together and developed the VantageScore algorithm as a response to the FICO score. Its current range is 300–850, although it used to be 501–990. They own their own data and don’t have to share revenue with FICO for the score. FICO still dominates the space of scores that are used for lending decisions, but the VantageScore is often used by companies—such as Fitch and Standard & Poor’s—to assess portfolio risk, since this score is cheaper. Due to differences in algorithms, VantageScore and the FICO score can differ quite dramatically for certain people.
TransUnion and Experian offer their VantageScore to consumers through their websites for a fee. Credit Karma also offers the TransUnion VantageScore for a fee, but it doesn’t have to be paid if the subscription is canceled within seven days. There is no way for a consumer to get the Equifax VantageScore.
Credit scores marketed to consumers
Although savvy consumers can find their FICO scores and VantageScores, they are not marketed to consumers. Because FICO’s primary focus has been on servicing banks and maintaining their monopoly position in the lending space, they have not spent the time or money necessary to market their scores to consumers. FICO’s consumer-facing site, myFICO.com, is rarely advertised.
That hole has been filled by dubious consumer-facing offerings from the credit bureaus themselves and other entrepreneurs. These offerings profit from confusing people about credit scores and propagating the myth of a “single credit score,” so that the public will buy the score. Almost all of the scores marketed to consumers are not used in lending decisions. Lending decisions are almost exclusively made using the FICO algorithm, and some may be made using the VantageScore. Rent decisions, however, are often made using the scores outlined below.
If you read the fine print in these consumer offerings, you will find excerpts such as, “The PLUS score is derived from information based on a credit report, using a similar formula to those used by lenders.” These made-for-consumer scores are called “educational” credit scores, meaning that they aren’t actually the algorithms used in credit decisions, but instead are meant to just “educate” people. These “educational” scores are often created and distributed by the bureaus themselves—like Experian’s freecreditreport.com—or sold to other companies like creditkarma.com or creditsesame.com. It makes financial sense for these companies to buy data from the bureaus and give it away for free to consumers because they make money from credit card companies who pay for credit card leads obtained from their site. There are also many affiliate sites that give a “free” score only to monetize it in credit card offers.
Some common “educational” credit scores include:
-
PLUS score
- Range: 330–830
- Created by Experian using Experian data
- Found on freecreditreport.com
-
TransRisk score
- Range: 300–850
- Created by TransUnion using TransUnion data
- Found on creditkarma.com
-
National Equivalency Score
- Range: 360–840
- Created by Experian using Experian data
- Found on creditsesame.com
-
CE score
- Range: 350–850
- Created by CE Analytics using Experian data
- Found on quizzle.com
Factors in scoring algorithm
Only information that can be found in a bureau credit report or in public records can go into the scoring algorithm. Even though we can’t control how the algorithms are constructed, we can take steps to make sure that the underlying information is correct by reviewing all three bureaus’ reports.
Characteristics that are not allowed to be used in the scoring algorithm include age, geographical location, race, and income or any other employment information. Interest rates that you are currently charged also can’t be used in the algorithm. Even though these factors don’t come in directly, they can be correlated with other factors that do. For example, age doesn’t go into the score, but credit history length does. Since many people get their first credit card around eighteen years of age, this statistic is highly correlated with age.
Below are the factors that make up most scoring algorithms. The percentages indicate the weight to that attribute given by the FICO scoring algorithm. VantageScore uses similar attributes and weights. The scoring algorithms reward behavior that uses a lot of credit “responsibly” (e.g., making all scheduled minimum payments), and penalize people for not using credit or using credit but missing payments.
Length of credit history (15%)
Your credit history is usually calculated as beginning with your oldest loan or credit card. Hence, many websites say that it is better if you refrain from closing the oldest cards you have. How long it’s been since you last used your accounts is also factored into this portion of the score. Also, the average age of your accounts is considered, so opening new accounts can lower the score.
Payment history (35%)
Payment history information includes details on how payments were made on existing accounts. Late or missed payments (delinquencies) and collection items will severely hurt your score. The more “good” accounts with no late or delinquent payments there are, the better the score. To have a good payment history, you only need to have made the minimum payments on time. There isn’t a negative impact on this portion of the score from just paying the minimum, although there may be adverse effects if you retain a high utilization (see below). Also, if you only pay the minimum, there are negative effects from paying a lot to the credit card companies in interest and fees.
Utilization (30%)
Utilization is how much of your credit limit you are using. For example, if your credit limit is $10,000 and your balance is $3,000, your utilization is $3,000 / $10,000 = 30%. Utilization can be calculated for each card and over all cards. As a rule of thumb, it’s best to use less than 25% of your overall limit, and less than 25% each card’s limit. Many people have a few cards, but use only one; it’s better to use all your cards a little bit. Asking for an increase in limit while spending the same amount on the card would help your score as well.
The scoring algorithms usually don’t take into account whether the balance is “transacted” (paid off in full) or “revolved” (interest paid on a balance that remains). The credit scoring algorithms only take into account the balance and the credit limit, but it is better to transact balances because fewer fees are paid to the predatory credit card issuers. If you have a $5,000 credit limit and you spend $5,000 on your card and pay it off every month, it will be just as detrimental to your score as having a $5,000 balance the entire time on which you pay off only the interest every month.
Although lower utilization is generally better, there is one exception: 0% utilization. One way to boost your score is to take any credit cards that aren’t being used and put a monthly bill and an automatic payment on them. This way, it’ll look like the card is being used, and used “responsibly.” If you’ve already set up a bill to be paid via automatic withdrawal from your bank account, instead set it to automatically withdraw from your card, and then have your card automatically paid from your bank account. Utilization of installment loans, which is how much of the loan has been paid off, may also be considered in this portion of the score.
Mix of credit (10%)
From the viewpoint of credit scoring algorithms, having both installment loans (a fixed loan such as a car, student, or mortgage loan) and revolving loans (a credit card or retail card) is best. People with the best credit scores have at least a couple of credit cards and at least one installment loan that is being regularly paid off.
This is yet another part of the credit score that benefits the use of credit and thus promotes debt. Someone who doesn’t have student loans or a mortgage and pays for their car with cash will have a lower score than if they had financed the car and paid it off every month. Also, if someone doesn’t use credit cards, they will have a lower score.
Inquiries (10%)
Inquiries are recorded as “credit pulls” when a bank or other institution looks up the borrower’s score or credit report. There are “soft” and “hard” credit pulls. When you are seeking a loan for a car, mortgage, student loan, home equity line of credit, credit card, or other credit products, it counts as a “hard” credit pull and will affect your score. Personal pulls on credit websites or for rental purposes count as a “soft” inquiry and will not affect your score at all. Another example of a “soft” inquiry is one done by lenders in order to pre-approve credit card offers or evaluate portfolio risk. However, once you fill out a credit application from the offer you received in the mail, you will get a “hard” inquiry on your credit report.
Inquiries remain on the record for twenty-four months, but the FICO score algorithm only considers the last twelve months of inquiries. Large amounts of inquiries will hurt your score a lot more than just one. However, FICO counts several inquiries for the same type of loan as one inquiry, as long as they were all made in a relatively brief period of time. This is not the case for credit cards, where every inquiry counts.
Unscoreable populations
People who don’t have a score are referred to in the industry as “unscoreable.” Estimates put the unscoreable U.S. population at twenty-seven to thirty million. There are two primary reasons why someone would be unscoreable. The first is that they have never used a credit product and hence do not have a credit report. The second is that they have a credit report, but there is not enough information in the report to make a score, such as someone who has only one credit card and no other credit. Unscoreable people are often young, recent immigrants, people who have declared bankruptcy, or “unbanked” people (see Chapters Seven and Eight). There are many businesses springing up to create scores for these populations based on information other than credit history—like rental information or cell phone payments—but there is no standard yet used by lenders. Being “unscoreable” puts many people in a catch-22 situation, where they are denied credit and hence can’t build credit.
Problems with Credit Scores
Credit scores are created by for-profit institutions whose mission is to maximize profits. The data and the algorithm that go into the score are managed by opaque, unwieldy institutions. A 2004 study revealed that 79% of credit reports contained errors; 25% of these mistakes were serious enough to result in a credit denial. More than half of all credit reports contained outdated information or information belonging to someone else. You might think that because the rich use credit so much more, they’d be the ones mainly affected by these errors. In actuality, the poorer you are, the more likely your credit agency is to make a mistake that influences your rating. There is no accountability or transparency for the CRAs and no incentive for them to provide accurate information.
There are also problems with FICO, a for-profit institution, creating algorithms. Because an algorithm is just a mathematical formula, FICO has to use restrictive patents so it can make money from the algorithm. No one can know the formula, lest they replicate it, causing FICO to lose profits. Hence, it is in FICO’s best interest to keep it secret. When individuals are told “their scores,” these usually aren’t the FICO scores that are used in most lending decisions. It’s in the score-providers’s interest to obfuscate this fact so that consumers continue to buy their scores.
Besides the fact that the algorithm created for a profit, there are methodological issues. First, the model omits variables that are critical to repaying debts on time, such as income, assets, and credit terms. The model also assumes that borrowers are “statistically independent” of one another, meaning that one’s default depends only on one’s individual characteristics and is unaffected by the defaults of others. However, defaults are often strongly correlated (e.g., a foreclosure depresses housing values across a neighborhood, making property owners de facto poorer, which increases the probability of further foreclosures). Finally, the model assumes that consumers’ riskiness or creditworthiness determines their credit scores. But in practice, low scores result in worse loan conditions that in turn increase their riskiness. Thus, the credit score functions more like a self-fulfilling prophecy than an “objective” prediction.
As with other components of the debt system, credit scoring
negatively impacts people of color at a disproportionate rate. About
42% of Latino/as and nearly half of Black people in the United
States have credit scores under 660, compared to just under 20% for
Whites. While the median credit score for Whites rose from 727 to
738 during the 1990s, it decreased for Blacks from 693 to 676, and
for Latino/as from 695 to 670. Just like the racial wealth gap (see
Introduction), the
racial gap in credit scores
has worsened, not improved, over recent decades.
Finally, misuse of credit scores is now rampant. Using them to determine access to housing, employment, or health care is absolutely deplorable, even more misguided and harmful than using them to determine simple access to credit. These decisions have grave consequences for people’s lives; housing, health care, and the means of subsistence are fundamental human needs that should be available to all, not granted or withheld based on a person’s credit history.
Some things we can do
-
Check our reports. We can change this system, but we have
to know it first. To receive a copy of your credit report, go to
Annualcreditreport.com
or call<PHONE_NUMBER>. Complete the Annual Credit Report Request
Form and mail it to:
Annual Credit Report Service
P.O. Box 105281
Atlanta, GA 30348-5281
In order to receive your free report, you’ll need to provide your name, address, Social Security number, and date of birth. You may need to give your previous address if you’ve moved in the past two years. For security reasons, you may also have to give additional information like an account or a monthly payment you make. Beware of anyone charging you to get your report or signing you up for “free” services in order to access it. And beware of anyone offering to help improve your credit score; there is nothing they can do that you can’t do more effectively for free. - Demand accuracy. There are laws that protect debtors from unfair and inaccurate credit score practices: the Truth in Lending Act, Fair Credit Reporting Act, Fair Credit Billing Act, and Equal Credit Opportunity Act. All guarantee protection and the possibility for citizen-directed credit scoring and reporting.
- Demand accountability. The Consumer Financial Protection Bureau is now an operating governmental body. We should ask if it’s doing its job when it comes to regulating credit scoring, and if it can do more.
- Demand regulation. Seven EU countries and seventeen Latin American countries have public credit scoring agencies. Why don’t we? Ultimately, we would prefer to get rid of credit scores altogether, but in the meantime we must at the very least remove the profit motive from a system that plays such a vital role in determining our access to the means to live.
-
Reject the system. It is possible to live without a good
credit score. If you can muster the time and energy to make some
life changes, you can go totally “off-grid” (see
Chapter Eleven). Below are some recommendations on how to live without the
benefits of a good credit score:
- Prepaid cell phones are always an option.
- For housing utilities, if you have roommates, you can ask them to put the accounts in their names. If you live alone, ask a relative or friend.
- Create your own credit report.
- Check listings for housing, cars, and other necessities that are informal and don’t go through brokers or other formal agencies.
- Offer to put down larger deposits in lieu of a credit check.
- Build networks of mutual support in your community so you can rely less on outside services.
DIY credit repair
To repair an error on your credit report, it is best to do it yourself. This helps you avoid scams and fly under the radar of the CRAs who are looking to block credit repair companies from gaming their system. There are many books, websites, articles, and other resources dealing with this issue. Below are the steps we recommend:
- Get a copy of all three of your credit reports.
- Review your credit reports and note every single error. Note incorrect spellings of your name, inaccurate data, and any “derogatory” information.
- Write letters to the corresponding agencies disputing negative information and errors.
- Describe in your letter how you found out your credit was bad and how shocked you were at all of the errors the agencies have been reporting. Then ask them, “per the Fair Credit Reporting Act (FCRA) enacted by Congress in 1970,” to either provide documented proof of their claims or delete the mistakes immediately. Include your name, current address, and Social Security number on your letter. You should not include any additional information. Simply list the entry that you are challenging and briefly explain why you are challenging it. You only need to write a couple of words to do this—less is more. Say things like, “This is not mine” or “This record is inaccurate.” Be sure to make the letter sound unique to you. If you do not, you may find that the CRA responds by saying that your claim is “frivolous.” This is how they typically respond to credit repair companies, and why you should not use them. There are competing theories on whether or not you should challenge everything on your report all at once. You are legally entitled to have each item you challenge verified at any point in time. When sending your letter, request a “return receipt” or “delivery confirmation.” The CRA has thirty days to respond to your dispute. If they do not respond within that time frame, you will have evidence that they are in violation of the Fair Credit Reporting Act. Don’t give up after the first round. Within a month, you will likely receive a response from the CRAs. Typically, they will only state whether or not they were able to verify an item. For any items they claim to have verified, you should contact the creditor directly and demand that they provide proof that the debt is yours. You can also continue to challenge the entry with the CRA. (See Appendix A for sample letters.)
If you play this game, there’s a good chance you could eventually win. Keep hammering them with letters demanding they correct their mistakes. They may eventually get sick of your letters and start deleting negative trade lines from your report. You will likely be writing letters for six months to a year, but you should see a substantial improvement in your credit report and score within three months. As usual, the person who yells the loudest for the longest wins. And don’t forget, repairing your credit score isn’t about regaining validity in the eyes of the system: it’s about challenging an exclusionary and unjust surveillance machine.
Consumer Reporting Agencies for Checking Accounts
The credit score is an essential piece of economic surveillance, but it’s not the only one. There are other ways of watching us and keeping us in line. Many people, for example, have a checking account. Just as a series of private corporations monitor your borrowing activity in the economy, a different group of private corporations monitors your checking account. And just as credit score companies make a profit from calculating your score, consumer reporting agencies that monitor checking accounts make a killing when you overdraft or miss a payment.
ChexSystems and TeleCheck are two examples. Usually banks are very private about their own information, especially checking accounts, but in the interest of “preventing fraudulent activity,” financial institutions report instances of “account mishandling” to these agencies. TeleCheck primarily deals with bad check-writing while ChexSystems, used by over 80% of banks in the United States, deals with that and more: “non-sufficient funds” (NSF), overdrafts, fraud, suspected fraud, and account abuse. Retailers report bad checks to Shared Check Authorization Networks (SCAN), which in turn report to ChexSystems. When someone tries to open an account elsewhere, the agency notifies the institution about that applicant’s history.
ChexSystems, unlike credit bureaus, only provides negative information in their reports. Therefore, a single banking error can result in losing an account and could mean immense difficulty trying to open one elsewhere. Once in their systems, it is extremely difficult to get out, even if you ended up there in error. Something as inconsequential as failing to rectify a deliberately confusing overdraft fee may be enough to negate decades worth of “responsible” banking.
There are some steps that can be taken to avoid triggering a
ChexSystems or TeleCheck report in the first place. Know your
balance before writing checks to make sure they won’t bounce. And if
your checkbook ever gets stolen, report it to your bank or credit
union immediately. When you are closing an account, be sure to
discontinue all automatic payments, wait until you’re certain that
all checks you’ve written have cleared, and formally close
the account instead of simply taking all of your money out.
Regardless of your checking account history, you are entitled to a free copy of your ChexSystems or TeleCheck report every twelve months. If you are denied a checking account because of your report, you are entitled to a free copy within sixty days from the CRA that is responsible. To request a copy of your report, go to consumerdebit.com for ChexSystems or firstdata.com for TeleCheck.
If the agency refuses to provide you with a copy of your report or you fail to receive it within sixty days of being denied an account, you can submit a complaint to the Federal Trade Commission (FTC) at ftccomplaintassistant.gov. Then send a letter via certified mail to ChexSystems or TeleCheck notifying them that they are in violation of the Fair Credit Reporting Act and that they have fifteen days to send you a copy of your report. Let them know that you are willing to pursue legal action and that you have already contacted the FTC. (See Appendix B for sample letters.)
If you are unable to open a checking account because of a negative report from one of these agencies, there are several possible courses of action. You can dispute your checking account report. For information on doing this, visit the website chexsystemsvictims.com. Another approach is to open an account at a financial institution that does not use ChexSystems or TeleCheck. A state-by-state directory is available at nochexbanks.org. In addition, in some states you can take a six-hour “Get Checking” course, upon completion of which you can open an account at a participating financial institution. But there is a $50 course fee, and guess who sponsors the program—a parent corporation of ChexSystems by the name of eFunds. The final option is to try to live without an account. As Chapter Eleven will illustrate, this can be difficult, but many people have no option but to survive “unbanked.”
Resources
Websites
- ChexSystems Victims
- National Consumer Law Center: Credit Reports
- Privacy Rights Clearinghouse: Credit and Credit Reports
Articles
- Kelly Gates, “The Securitization of Financial Identity and the Expansion of the Consumer Credit Industry,” Journal of Communication Inquiry 34, no. 4 (2010), 417–31.
- “Information on Free Credit Reports,” NEDAP.
- Mark Kantrowitz, “Credit Scores,” FinAid!, 2012.
- Chi Chi Wu, “Automated Injustice: How a Mechanized Dispute System Frustrates Consumers Seeking to Fix Errors in Their Credit Reports,” National Consumer Law Center, January 2009.
References
- Berger, Rob. “ChexSystems: The Banks’ Secret Watch Dog Is Watching You.” Dough Roller, June 18, 2011.
- Berner, Robert, and Chad Terhune. “Hospitals X-Ray Patient Credit Scores.” Business Week, November 19, 2008.
- Board of Governors of the Federal Reserve System. Report to Congress on Credit Scoring and Its Effect on the Availability and Affordability of Credit. Washington, DC: GPO, 2007.
- Calem, Paul S., and Michael LaCour-Little. “Risk-Based Capital Requirements for Mortgage Loans.” Federal Reserve Board, November 2001.
- Consumer Financial Protection Bureau. “Key Dimensions and Processes in the U.S. Credit Reporting System: A Review of How the Nation’s Largest Credit Bureaus Manage Consumer Data.” December 2012.
- Credit Karma. “Credit Card Utilization and Average Credit Scores.” January 2, 2009.
- “Credit Reporting Agencies.” Title 15 U.S. Code, Sec. 1681. 2010.
- Dratch, Dana. “Taking a Look at ‘Good’ Credit.” Bankrate, June 9, 2008.
- Elmer, Vickie. “U.S. Consumer Watchdog to Oversee Credit Bureaus.” New York Times, July 16, 2012.
- Fair Isaac Corporation. “Understanding Your FICO Score.” myFICO, 2011.
- Fremstad, Shawn, and Amy Traub. “Discrediting America: The Urgent Need to Reform the Nation’s Credit Reporting Industry.” Dēmos, 2011.
- Gale Group. “Credit Reporting Services Market Report.” Highbeam Business. Accessed July 12, 2013.
- Garfinkel, Simson. “Separating Equifax from Fiction.” Wired, September 1995.
- Grow, Brian, and Pallavi Gogoi. “Insurance: A New Way to Squeeze the Weak?” Business Week, January 27, 2002.
- Jentzsch, Nicola. Financial Privacy: An International Comparison of Credit Reporting Systems. New York: Springer, 2007.
- Löffler, Gunter. “The Effects of Estimation Error on Measures of Portfolio Credit Risk.” Journal of Banking and Finance 27, no. 8, (2003): 1427–1453.
- Privacy Rights Clearinghouse. “‘Other’ Consumer Reports: What You Should Know about ‘Specialty’ Reports.” June 2013.
- Rona-Tas, Akos, and Stefanie Hiß. “Consumer and Corporate Credit Ratings and the Subprime Crisis in the U.S. with Some Lessons for Germany.” SCHUFA, September 2008.
- Society for Human Resource Management. “SHRM Research Spotlight: Credit Background Checks.” September 2010.
- Taylor, Don. “Negative ChexSystems Report Nixes Account.” Bankrate, March 8, 2006.
- Ulzheimer, John. “Scores, Scores, and More Scores: How Many FICO Scores Do You Have?” Credit Sesame, August 27, 2012.
- Wilson, Cheryl. “VantageScore 3.0 Promises a Better Picture of Previously ‘Unscoreable’ Individuals.” PR Web, April 11, 2013.
- Wozniacka, Malgorzata, and Snigdha Sen. “Credit Scores: What You Should Know about Your Own.” PBS Frontline, November 23, 2004.
- Wu, Chi Chi, and Birny Birnbaum. “Credit Scoring and Insurance: Costing Consumers Billions and Perpetuating the Economic Racial Divide.” National Consumer Law Center and Center for Economic Justice, June 2007.


|
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| PREV LETTER NEXT LETTER | FRAMES NO FRAMES |
matches
returns true if evaluating the given XPath
expression on this node returns a non-empty node set containing this
node.
matches
returns true if the given node matches the filter
condition.
matches
returns true if the given node matches the XPath
expression.
Mode
manages a number of RuleSet instances for the mode in a
stylesheet.File
and writes it to the
specified XMLWriter
using SAX.
InputSource
and
writes it to the specified XMLWriter
using SAX.
InputStream
and writes it
to the specified XMLWriter
using SAX.
InputStream
and writes it
to the specified XMLWriter
using SAX.
Reader
and writes it to
the specified XMLWriter
using SAX.
Reader
and writes it to
the specified XMLWriter
using SAX.
URL
and writes it to the
specified XMLWriter
using SAX.
XMLWriter
using SAX.
|
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| PREV LETTER NEXT LETTER | FRAMES NO FRAMES |


Given a matrix pair (A, B), where A and B are square n x n
matrices, the generalized nonsymmetric eigenvalue problem is to find
the eigenvalues
and corresponding
eigenvectors
such that
More precisely, x and y are called right eigenvectors.
Vectors
or
satisfying
Sometimes the following, equivalent notation is used to refer to the generalized eigenproblem for the pair (A,B): The object , where is an indeterminate, is called a matrix pencil, or just pencil. So one can also refer to the generalized eigenvalues and eigenvectors of the pencil .
If the determinant of is identically zero for all values of , the eigenvalue problem is called singular; otherwise it is regular. Singularity of (A,B) is signaled by some (in the presence of roundoff, and may be very small). In this case, the eigenvalue problem is very ill-conditioned, and in fact some of the other nonzero values of and may be indeterminate (see section <IP_ADDRESS> for further discussion) [93,105,29]. The current routines in LAPACK are intended only for regular matrix pencils.
The generalized nonsymmetric eigenvalue problem can be solved via the
generalized Schur decomposition
of the matrix pair (A, B), defined in the real case as
The columns of Q and Z are called left and right generalized Schur vectors and span pairs of deflating subspaces of A and B [93]. Deflating subspaces are a generalization of invariant subspaces: For each k , the first k columns of Z span a right deflating subspace mapped by both A and B into a left deflating subspace spanned by the first k columns of Q.
More formally, let
Q = (Q1, Q2) and
Z = (Z1, Z2) be a conformal
partitioning with respect to the cluster of k eigenvalues in the
(1,1)-block of (S, T), i.e. where Q1 and Z1 both have k columns,
and S11 and T11 below are both k-by-k,
As for the standard nonsymmetric eigenproblem, two pairs of drivers are provided, one pair focusing on the generalized Schur decomposition, and the other pair on the eigenvalues and eigenvectors as shown in Table 2.6:
The subroutines xGGES and xGGEV are improved versions of the drivers, xGEGS and xGEGV, respectively. xGEGS and xGEGV have been retained for compatibility with Release 2.0 of LAPACK, but we omit references to these routines in the remainder of this users' guide.


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Manara: Gee, I wonder what that sign means?


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Ticket Id: {{item.id}} Title: {{item.title}} Created by: {{item.created_by}}
Ticket Id: {{item.id}} Title: {{item.title}} Created by: {{item.created_by}}
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ECZEMIN - pramoxine hydrochloride cream
Alva-Amco Pharmacal Companies, Inc.
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Active ingredient
Pramoxine hydrochloride, 1%
Purpose
Topical analgesic
Uses
For the temporary relief of itching skin.
Warnings
For external use only
When using this product avoid contact with the eyes.
Stop use and ask a doctor if
- Condition worsens
- symptoms persist for more than 7 days
- symptoms clear up and occur again within a few days
Keep out of reach of children. If swallowed, get medical help or contact a poison control center right away.
Directions
Read all directions and warnings before use. Use only as directed. Apply to affected area not more than 3 to 4 times daily. Intended for use by normally healthy adults only. Persons under 18 years of age or those with highly sensitive or allergic skin should use only as directed by a doctor.
Other information
You may report serious side effects to the phone number provided under Questions? below.
Inactive ingredients
Ceteareth-6, ceteareth-25, cetostearyl alcohol, diazolidinyl urea, iodopropynyl butylcarbamate, mineral oil, purified water, stearyl alcohol.
Questions?
1-800-792-2582
Alva-Amco Pharmacal Companies, Inc.


- Employee - Class in limmen.integration.entities
-
POJO that represents a Employee entity from the chinook database
- Employee(int, String, String, String, int, Date, Date, String, String, String, String, String, String, String, String) - Constructor for class limmen.integration.entities.Employee
-
Class constructor.
- Employee() - Constructor for class limmen.integration.entities.Employee
-
- EmployeeController - Class in limmen.controllers
-
RestController for the resource pointed by the url: /resources/employees*
- EmployeeController(EmployeeService) - Constructor for class limmen.controllers.EmployeeController
-
Method to be injected by the Spring container.
- EmployeeFilter - Class in limmen.business.services.filters
-
Class for implementing filtering and sorting functionality for the Employee-Resource.
- EmployeeFilter() - Constructor for class limmen.business.services.filters.EmployeeFilter
-
Default constructor
- EmployeeFilterTest - Class in limmen.business.services.filters
-
- EmployeeFilterTest() - Constructor for class limmen.business.services.filters.EmployeeFilterTest
-
- EmployeeITCase - Class in limmen.integration
-
Integration test-suite for the employee REST-resource
- EmployeeITCase() - Constructor for class limmen.integration.EmployeeITCase
-
- EmployeeRepository - Class in limmen.integration.repositories
-
CRUD-repository for the "Employee" table in the chinook database.
- EmployeeRepository() - Constructor for class limmen.integration.repositories.EmployeeRepository
-
- EmployeeRepresentation - Class in limmen.business.representations.entity_representation
-
JSON-Representation of a Employee entity.
- EmployeeRepresentation(Employee) - Constructor for class limmen.business.representations.entity_representation.EmployeeRepresentation
-
Class constructor.
- EmployeesArrayRepresentation - Class in limmen.business.representations.array_representations
-
JSON-Representation of a Array of employees.
- EmployeesArrayRepresentation(List<EmployeeRepresentation>) - Constructor for class limmen.business.representations.array_representations.EmployeesArrayRepresentation
-
Class constructor.
- EmployeeService - Interface in limmen.business.services
-
Interface for a service that provides CRUD services for employee-data.
- EmployeeServiceImpl - Class in limmen.business.services.implementations
-
Implementation of the EmployeeService interface, uses a repository for database interaction.
- EmployeeServiceImpl(EmployeeRepository) - Constructor for class limmen.business.services.implementations.EmployeeServiceImpl
-
- EmployeeServiceImplTest - Class in limmen.business.services.implementations
-
Unit test-suite for the employee service implementation
- EmployeeServiceImplTest() - Constructor for class limmen.business.services.implementations.EmployeeServiceImplTest
-
- error() - Method in class limmen.controllers.MainController
-
Method to handle HTTP GET-requests for /error


The JSwitchButton
and SwitchButtonUI
offer a toggle button that is rendered using a switch.
You can treat a JSwitchButton
like any other JToggleButton
:
JSwitchButton b = new JSwitchButton(); b.addChangeListener(changeListener);
The SwitchButtonUI
presents this button as a BasicRadioButtonUI
with an animating icon on the right hand side.
This kind of button was popularized by the rise of tablets/smartphones (and accompanying swipe gestures). As of this writing it looks to me like that switches exist occasionally on desktop computers, but they haven't really saturated desktop apps. (At least not yet.) When I look at the wifi and bluetooth menus on my Mac: the leading component is a switch button. But when I open the preferences for Pages: I see a sea of tabbed panes and checkboxes -- but no switches.
Functionally this is the same as a checkbox. It has a "selected" and "unselected" state. What's the difference? One website explains:
You should only use switches on settings that need to take effect instantaneously. If a setting requires a button press before it can take effect, you should use a checkbox instead.Another website more abstractly says checkboxes should be used for multiple characteristics, and switches better represent a binary on/off state.