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The Herbaria are on the campus of the University of California in Berkeley. Hours are 8:00 to 5:00, Monday through Friday.
The doors are locked during the
lunch hour (12:00 to 1:00.).
Digital Access to Herbaria Resources
You may be able to get the information you need through the
UC/JEPS Specimen Portal, powered by CollectionSpace,
or our type specimen images and catalogues.
DIRECTIONS
The University and Jepson Herbaria are located on the ground floor of the central atrium of the Valley Life Sciences Building. The entrance is prominently marked, flanked by planters, and directly in front of a Tyrannosaurus rex skeleton. The Valley Life Sciences Building is a large building easily identified by its "neo-Babylonian" bas relief, located east of the prominent eucalyptus grove that is near the West Gate entry kiosk at the east end of University Avenue.
By car: From Interstate 80/580 in Berkeley, take the University Avenue exit and drive east (away from the Bay). University Avenue ends at the University of California campus. Parking is at a premium in Berkeley during business hours; the following options are available to visitors (see also
campus website):
- Metered street parking adjacent to campus (20 minutes per quarter; 2 hour limit).
- City and commercial parking lots and structures (see
map and look up "parking" in the map index).
- Parking in residential area north of campus (2 hour limit, Monday - Friday).
- Central campus parking
- Student Union Garage at Bancroft & Telegraph
By BART: The University and Jepson Herbaria are within a few blocks of the downtown Berkeley BART station. Follow Central Avenue to the east (the main flow of students) to campus, and then continue east on the walkway that crosses the bridge and goes through a wooded area. This will take you to the south side of the Valley Life Sciences Building.
A
campus map is available online, as well as directions to campus by transit and directions to campus by car.
Public and
Visitor Parking Information
If you want to use the collection, and you have not received
a formal orientation from us previously, we strongly suggest that you
make an appointment ahead of time to ensure that an appropriate staff member will be available to
orient you. You will not be allowed to use the collection without
an orientation.
If you have specimens that you want to use in the Herbaria,
consider mailing them ahead of time so that they may be frozen to
kill pests. Please call<PHONE_NUMBER> or email the
Administrative Curator for an appointment.
VISITOR RESPONSIBILITIES AND HERBARIUM POLICIES
- When arriving at the
Herbaria please check in at the front desk.
- Please sign the Visitor
Logbook. (Note that there are two pages that need to be filled out.)
- In order to prevent
accidental introduction of pests all specimens brought into the herbaria
must go through the appropriate pest control treatment.
- Each visitor must undergo
authorized orientation for use of the herbaria.
- There is no food or drink
allowed inside the collections area.
- Do not remove anything
(ie. specimens, books, equipment) from the herbaria without consent of
the collections manager.
- Information for
photocopying, phone use, etc. can be provided at the front desk.
- The building must be
evacuated if the fire alarm sounds.
LIBRARY POLICY
- The non-circulating
herbarium library is open only to in-house staff, students working under
the sponsorship of the herbaria, and visiting researchers.
- New visiting researchers
need to make an appointment with Amy Kasameyer at<PHONE_NUMBER> or
email her at
<EMAIL_ADDRESS>HANDLING SPECIMENS
- Keep folders horizontal
when removing from the cases.
- Keep herbarium sheets
specimen-side-up. Do not turn them over like pages in a book.
- Please refile specimens
correctly; misfiled specimens are effectively lost.
LOANS
Please pull specimens you
would like to request on loan, and let the collections management staff
know that you have done so.
When you return home,
please have a formal letter of request for the loan sent. The loan will
then be packaged and shipped to you.
PUBLIC EXHIBITS AND TOURS
A representation of herbarium materials can be seen in the Hall of Life, Valley Life Sciences Building. The herbaria are open for use by legitimate researchers and will respond to public service queries from 8 am to noon and 1 to 5 pm Monday through Friday. The herbaria also provide a limited number of tours to professional or academic groups on request. Please contact Andrew Doran at<PHONE_NUMBER> or by email for an appointment.
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Unlike other cats, Dexter is very social. He is very good friends with cats and humans alike. But his closest friends are:
His foster sister. She is known as "Boss Ruby" and likes to supervise Dexter's work.
His cousin from South America. He arrived to Canada and Dexter has helped him to adjust to his new life.


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{% extends "base.html" %} {% block title %}Claim {{ TEXT_BRANDING.your_account }}{% endblock %} {% block header %}
To get started, enter the activation code you were provided. This will often come by email, but you might have received it in a letter. If you can't find it, you can request a new one here.


Get Sh*t Done
Let's get it done:
Name of your sh*t
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Due Date:
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Hello my name is Alex Dominguez and I'm a huge anime and Star Wars fan!
They're many animes I like including: Naruto, Attack on Titans and Sakamoto Desu Ka.
The following projects is what I made so far in order:
First Program saying "Hello" in HTML
Link of my project click Here
Second program saying goodbye in HTML
Link of my project click Here
My first webpage including my interests in HTML and CSS
Link of my project click Here
Website of my lovely cat Gizmo and sharing her Diva moments. HTML and CSS inspired on a template from CSS Zen Garden
Link of my project click Here
A delicious chocolate cookie recipe in HTML
Link of my project click Here
An HTML and CSS website dedicated to a delicious cupcake recipes and making people hungry
Link of my project click Here
An HTML website about very dull and boring college lecture
Link of my project click Here
A personal blog about myself and my hobbies. Written in HTML/CSS
Link of my project click Here
A website dedicated to one of the best rock bands in the world. Made mostly in CSS and HTML
Link of my project click Here
Website dedicated to Marvel superheroes becoming pets. Written in CSS and HTML
Link of my project click Here
Website about my favorite destinations across the African continent. Written in CSS , HTML and a bit in JavaScript
Link of my project click Here
Sample of an edited wikepedia. Written in HTML
Link of my project click Here
I'm originally from El Salvador and came to Oregon when I was 10 years old.
I got my Associates of Science Trasfer from PCC and worked three years as lab tech for Beaverton School District.
My favorite hobbies include:


If you are experiencing problems, here are some things you can try:
This is usually caused by filling up object space so much that the OS cannot allocate more memory for objects. This may be because you needed to run a garbage collection before object space grew to this point. This can happen right now because the full garbage collector does not run automatically in the implementation right now, unfortunately. It can be invoked from the UI from the "System/Object Memory/Collect Garbage" menu item on the launcher, or programmatically by calling "VM collectGarbage".
This happens when the system runs out of space for compiled code, which is held in a memory area whose size is fixed at system startup. You can change the size of this area by changing the CodeSize parameter in the Strongtalk startup options file, .strongtalkrc. The code size is specified in kilobytes. 6000kb is usually plenty big enough. The startup options do not take effect until the next session.
This error has been seen in the past due to some unknown bug, probably a deadlock of some kind in the UI. We think that this bug has been eliminated, but if you encounter it, it often clears up on its own if you wait a bit or move the mouse over the window and other windows, and then try clicking a few times on various windows.
Other kinds of system crashes (other than stack overflows) usually are caused by some other bug in the native code compiler, which have not all been worked out. These are often hard to reproduce, so just trying the operation again after restarting may work. If it happens repeatedly and you need to make the system more stable, you can try the following steps:


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{% extends 'base.html' %} {% block title_block %} Page Expired {% endblock %} {% block body_block %}
This page expired right before that milk you bought three weeks ago.


Minesweeper simulator. Click to uncover tile. Shift-click to flag/unflag suspected bombs. Game won when all bombs are correctly flagged. Game lost when it blows up in your face.
If you need more help, feed the Google "how to play minesweeper" or some such.
GitHub


A multi-vowel morphing format filter. As the interpolation value changes, the filter shifts through a sequence of vowels.
- Input
- The partials input source.
- Interpolation
- The current position of the filter in the sequence of vowels.
- Num Vowels
- The number of vowels in the sequence.
- Gain
- A post-filtering increase in amplitude for the partials.
- Num Formants
- The number of formants used in producing the vowel.
- Resonance
- The strength of each formant. A higher resonance makes formants peakier and narrower.
- Vowel n
- The vowels available are A, E, I, and U for bass, tenor, contra tenor, alto, and soprano voices.
- Four Pole
- Sets the bandpass filters in the formant filter to be four pole rather than two-pole


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Issue 3363
: groupCollapsed gets confused
Create log groups
Steps to reproduce
Open Firebug
Enable and switch to the
Console
panel
Click the button
Create log groups
above
Expected result
The
Console
panel should list five collapsed groups named
Group 1
to
Group 5
.


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Between four people, and conflicting schedules, our glorious group has developed this webpage!
We're not breaking any records here, but we didn't slack off and have to carry any of our team members. That, my friends, is a true and undisputed vicory for everyone involved.


Taking the Coursera Machine Learning course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng.
Assumes you have knowledge of Week 5.
Table of Contents
Advice for Applying Machine Learning
Evaluating a Learning Algorithm
Evaluating a Hypothesis
Once we have done some trouble shooting for errors in our predictions by:
- Getting more training examples
- Trying out smaller sets of features
- Trying additional features
- Trying polynomial features
- Increasing or decreasing $\lambda$
We can move on to evaluating our new hypothesis.
A hypothesis may have a low error for the training data but still be inaccurate, due to overfitting. One way to evaluate a hypothesis, given a dataset of training examples, is to split the data up into two sets: a training set and a test set. Typically, the training set consists of 70% of the data and the test set is the remaining 30%. The data should be randomized so the 70% and 30% do not reflect any sort of ordering.
The new procedure using the two sets is:
- Learn $\Theta$ and minimize $J_\text{train}(\Theta)$ using the training set
- Compute the test set error $J_\text{test}(\Theta)$.
The test set error for linear regression is:
$$ J_\text{test}(\Theta) = \dfrac{1}{2m_\text{test}} \sum\limits_{i=1}^{m_\text{test}} (h_\Theta(x_\text{test}^{(i)}) - y_\text{test}^{(i)})^2 $$
For classification, we use the following to determine misclassification error:
$$ \text{err}(h_\Theta(x),y) = \begin{matrix} 1 & \mbox{if } h_\Theta(x) \geq 0.5\ \text{and}\ y = 0\ \text{or}\ h_\Theta(x) < 0.5\ \text{and}\ y = 1 \newline 0 & \mbox{otherwise} \end{matrix} $$
This gives us a binary 0 or 1 error result based on a misclassification. The average test error for the test set is:
$$ \text{Test Error} = \dfrac{1}{m_\text{test}} \sum\limits_{i=1}^{m_\text{test}} \text{err}(h_\Theta(x_\text{test}^{(i)}), y_\text{test}^{(i)}) $$
That tells us the proportion of the test data that was misclassified.
Model Selection and Train/Validation/Test Sets
Simply because a learning algorithm fits the training set well, does not mean that it is a good hypothesis. It could over fit and as a result your predictions on the test set would be poor. The error of your hypothesis as measured on the data set with which you trained the parameters will be lower than the error on any other data set/
Given many models with different polynomial degrees, we can use a systematic approach to identify the ‘best’ function. In order to choose the model of your hypothesis, you can test each degree of polynomial and look at the error result.
One way to partition our dataset into three sets is:
- Training Set : 60%
- Cross Validation Set: 20%
- Test Set: 20%
We can now calculate three separate error values for the three sets using the following method:
- Optimize the parameters in $\Theta$ using the training set for each polynomial degree.
- Find the polynomial degree d with the least error using the cross validation set.
- Estimate the generalization error using the test set with $ J_\text{test}(\Theta^{(d)}) $, where d is the theta from the polynomial with the lowest error.
This way, the degree of the polynomial d has not been trained using the test set.
Diagnosing Bias versus Variance
In this section, we examine the relationship between the degree of the polynomial d and the underfitting or the overfitting of our hypothesis.
- We need to distinguish whether bias or variance is the problem contributing to bad predictions.
- High bias is underfitting and high variance is overfitting. We should find a value that minimizes both.
The training error tends to decrease as we increase the degree d of the polynomial.
At the same time, the cross validation error will tend to decrease as we increase d up to a certain point, then it will increase as d increased, forming a convex curve.
High bias (underfitting) Both $J_\text{train}(\Theta)$ and $J_\text{CV}(\Theta)$ will be high. Also, $J_\text{CV}(\Theta) \approx J_\text{train}(\Theta)$.
High variance (overfitting): $J_\text{train}(\Theta)$ will be low and $J_\text{CV}(\Theta)$ will be much greater than $J_\text{train}(\Theta)$.
This is the summarized figure:
Regularization and Bias/Variance
Given the following equations, we get the following plots:
$$ h_\theta(x) = \theta_0 + \theta_1x + \theta_2x^2 + \theta_3x^3 + \theta_4x^4 $$ $$ J(\theta) = \dfrac{1}{2m} \sum\limits_{i=1}^{m} (h_\theta(x^{(i)}) - y^{(i)})^2 + \dfrac{\lambda}{2m} \sum\limits_{j=1}^{n} \theta_j^2 $$
In the figure above, as $\lambda$ increases, our fit becomes more rigid. On the otherhand, as $\lambda$ approaches 0, we tend to overfit the data. How do we choose our parameter $\lambda$ to get it ‘just right’? In order to choose the model and the regularization term $\lambda$, we need to:
- Create a list of lambdas, for example increment by powers of 2 from 0.01: $$ \lambda \in \{0, 0.01, 0.02, 0.04, 0.08, \dots, 10.24 \} $$
- Create a set of models with different degrees or any other variants.
- Iterate through the $\lambda$s and for each $\lambda$ go through all the models to learn some $\Theta$.
- Compute the cross validation error using the learned $\Theta$ (computed with $\lambda$) on the $J_\text{CV}(\Theta)$ without regularization.
- Select the best combo that produces the lowest error on the cross validation set.
- Using the best combo $\Theta$ and $\lambda$, apply it on $J_\text{test}(\Theta)$ to see if it has a good generalization of the problem.
Learning Curves
Training an algorithm on a very few number of data points will easily have 0 errors because we can always find a quadratic curve that touches exactly those number of points. Therefore:
- As the training set gets larger, the error for a quadratic function increases.
- The error value will plateau out after a certain m, or training set size.
Experiencing High Bias:
- Small training set size causes $J_\text{train}(\Theta)$ to be low and $J_\text{CV}(\Theta)$ to be high.
- Large training set size causes $J_\text{train}(\Theta)$ and $J_\text{CV}(\Theta)$ to be high with $J_\text{train}(\Theta) \approx J_\text{CV}(\Theta)$.
If a learning algorithm is suffering from high bias, getting more training data will not (by itself) help much.
Experiencing High Variance:
- Small training set size will have $J_\text{train}(\Theta)$ be low and $J_\text{CV}(\Theta)$ be high.
- Large training set size will have $J_\text{train}(\Theta)$ increase with training set size and $J_\text{CV}(\Theta)$ continue to decrease without leveling off. $J_\text{train}(\Theta) \lt J_\text{CV}(\Theta)$ but the difference between the two remains significant.
If a learning algorithm is suffering from high variance, getting more training data is likely to help.
Deciding What to Do Next
Our decision process can be broken down as follows:
| Action | Fix |
|---|---|
| Getting more training examples | Fixes high variance |
| Trying smaller sets of features | Fixes high variance |
| Adding features | Fixes high bias |
| Adding polynomial features | Fixes high bias |
| Decreasing $\lambda$ | Fixes high bias |
| Increasing $\lambda$ | Fixes high variance |
Diagnosing Neural Networks
- A neural network with fewer parameters is prone to underfitting. It is also computationally cheaper.
- A large neural network with more parameters is prone to overfitting. It is also computationally expensive. In this case, you can use regularization (increase $\lambda$) to address the overfitting.
Using a single hidden layer is a good starting default. You can train the neural network on a number of hidden layers using your cross validation set. You can then select the one that performs best.
Model Complexity Effects:
- Lower-order polynomials (lower model complexity) have high bias and low variance. In this case, the model fits poorly consistently.
- Higher-order polynomials (high model complexity) fit the training data extremely well and the test data extremely poorly. These have low bias but high variance.
- In reality, we want to choose a model in between that generalizes well and also fits the data reasonably well.
Machine Learning System Design
Building a Spam Classifier
Prioritizing What to Work On
System Design Example:
Given a data set of emails, one could construct a vector for each email. An entry in this vector represents a word. The vector contains 10,000-50,000 entries gathered by finding the most frequently used words in the dataset. If a word is found in the email, we assign its entry as a 1, otherwise the entry would be 0. Once we have our X vectors ready, we train the algorithm and use it to classify if an email is spam or not.
There are many ways to improve the accuracy of the this classifier.
- Collect lots of data (ex: honeypot project, but this doesn’t always work)
- Develop more sophisticated features (ex: using email header data in spam emails)
- Develop algorithms to process input in different ways (recognize mispellings in spam)
It is difficult to tell which of the options will be most helpful.
Error Analysis
The recommended approach to solving a machine learning problem is to:
- Start with a simple algorithm that you can implement quickly and test it early on your cross validation data.
- Plot learning curves to decide if more data, more features, etc. are likely to help.
- Manually examine the examples (in cross validation set) that your algorithm made errors on. See if you can spot systematic trends in what type of examples it is making errors on.
For example, assume that we have 500 emails and the algorithm misclassifies 100 of them. Manually analyze the 100 emails and categorize them based on what type of emails they are. Then one could try to come up with new cues and features that woudl help classify these 100 emails correctly. For example, if most of the misclassified emails are those which try to steal passwords, we could find some features that are particular to those emails and add them to our model. We can also see how classifying each word according to its root changes our error rate.
It is important to get error results as a single, numerical value. Otherwise, it is difficult to assess the algorithm’s performance.
Machine Learning Practical Tips
How to Handle Skewed Data
Precision/Recall:
The following metric is useful for datasets with very skewed data:
Trade off precision and recall depending on the use case of your classifier. You can compare various precision/recall numbers using a value called an $\text{F}_1$ score.
When to Utilize Large Data Sets
Large data rationale; assume features $x \in \mathbb{R}^{n+1}$ has sufficient information to predict $y$ accurately.
- Useful test: Given the input $x$, can a human expert confidently predict $y$?
Use a learning algorithm with many parameters (eg logistic/linear regression with many features; neural network with many hidden units). Low bias algorithms
$$ J_{\text{train}}(\Theta) \text{will be small.} $$
Use a very large training set (unlikely to overfit).
$$ J_{\text{train}}(\Theta) \approx J_{\text{test}}(\Theta) $$
Move on to Week 7.


hello  world
cars
dogs
countries
buggati
laborador
india
chiron
german
RSA
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Humans had lost Humanity?
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If yes, then against what?   Dogs
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Robin sidhu
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Document information
-
Product: EM
-
Document: EM datagram on *.kmall format
-
Document number: 410224
-
Revision: F
-
Date of issue: November 2018
Revision history
Rev F. 16 NOV 2018
The following content and pages have been added to Rev F.
Code changes:
- Common
Line 279: Define added
Line 359, 444, 548, 781, 1454: Definition of time changed. Leap seconds are not ignored.
Old: From Time in second. Epoch 1970-01-01. Ignoring leap seconds.
New: UTC time in seconds. Epoch 1970-01-01.
- #SVP datagram, updated to version 1.
Line 686: float absCoeff_dBPerkm voided. Replaced by uint32_t padding;
- #MRZ datagram
Line 861: Description of attitudeSensorStatus changed.
Line 968: Table describing depthMode updated. New table including all EM system.
Line 1013: Description of detectionMode updated. Added 100 to indicate data from system simulator.
Line 1141: Description of pulseShading changed.
Rev E. 25 MAY 2018
- #CPO datagram added, version 0.
- #CHE datagram added, version 0.
- #MWC datagram
Line 1365: description changed.
Line 1395: table describing of how to decode beamData updated.
Rev D. 08 DES 2017
- Common
Line 273: Defines describing unavailable data added.
- Common #Sxx (sensor) datagrams
Line 336: Table descring time synchronization source added.
- #SPO datagrams
Line 406-410 417: Descriptions changed.
- #SVP datagram
Line 641, 674, 677: Descriptions changed.
- #SVT datagram added, version 0.
Line 686: Multiple new structs and data fields.
- #SDE datagram
Line 813, 815: Decriptions changed.
- #MRZ datagram
Line 957, 1005, 1076, 1077: Descriptions changed.
Line 1195: Content swapped.
float reflectivity1_dB and float reflectivity2_dB swapped data.
- #IB datagram
Line 1462: Variable definition change.
int8_t BISTNumber to uint8_t BISTNumber
Line 1463: Variable definition change.
uint8_t BISTStatus to int8_t BISTStatus
Rev C. 05 JUL 2017
- #SHA datagram removed.
- #SPO datagram:
Line 325: Table describing sensorStatus added.
- #SKM datagram, version updated to 1.
Line 418: Table describing sensorStatus added. Information of active sensors.
Line 449: New data field sensorDataContents added replacing padding.
- #MRZ datagram, no version change.
Line 845: Table describing depthMode changed:
Line 954: Variable name change
float x_txTransducerArm_SCS_m renamed to float x_kmallToall_m.
float y_txTransducerArm_SCS_m renamed to float float y_kmallToall_m.
Rev B. 01 MAR 2017
- Common
Line 259: defines added.
#define MAX_NUM_MST_DGMS 256
#define MAX_NUM_MWC_DGMS 256
#define MAX_NUM_MRZ_DGMS 32
- #SPO datagram
Line 365: Description only.
float speedOverGround_mPerSec;
float courseOverGround_deg;
- #SKM datagram
Line 397: Description only.
uint8_t sensorSystem;
Copyright
The information contained in this document remains the sole property of Kongsberg Maritime AS. No part of this document may be copied or reproduced in any form or by any means, and the information contained within it is not to be communicated to a third party, without the prior written consent of Kongsberg Maritime AS.
Warning
The equipment to which this manual applies must only be used for the purpose for which it was designed. Improper use or maintenance may cause damage to the equipment and/or injury to personnel. The user must be familiar with the contents of the appropriate manuals before attempting to operate or work on the equipment.
Kongsberg Maritime disclaims any responsibility for damage or injury caused by improper installation, use or maintenance of the equipment.
Disclaimer
Kongsberg Maritime AS endeavours to ensure that all information in this document is correct and fairly stated, but does not accept liability for any errors or omissions.


--- layout: default title: Frederik Rudeck page_category: home ---
Hi! I‘m a JavaScript developer at K.lab Berlin in Germany, currently working on meinUnterricht.de. I recently graduated from the Hasso-Plattner-Institute with a M.Sc. in IT-Systems Engineering.
I use this site as a repository for my university and spare time projects. Feel free to drop me a line if you have any questions or comments on some of the projects.


WITNESS LIST
Natural Resources
September 19, 2014 9:00 AM
Studying Impacts of Mexico's Energy Reforms on Texas
ON:
Canas, Jesus Economist (also providing written testimony) (U.S. Federal Reserve Bank),
Dallas, TX
Craddick, Christi Chairman (Railroad Commission), Austin, TX
Karp, Nathaniel Economists (also providing written testimony) (Self), Houston, TX
Lujambio, Jose Maria Attorney (also providing written testimony) (Cacheaux, Cavazos &
Newton, L.L.P., Law Firm), Austin, TX
Martinez, Cesar Vianovo V.P. (also providing written testimony) (Vianovo), Austin, TX
Martinez, Leodoro Chairman (also providing written testimony) (Eagle Ford Consortium),
TX
Pinon, Jorge Director (also providing written testimony) (Self), Austin, TX
Prozzi, Jolanda (also providing written testimony) (Self), Austin, TX
Ramos, Alfredo (also providing written testimony) (Self; Gardere, Wynne, Sewell LLP),
Houston, TX
Rubinstein, Carlos Texas Water Development Board Chairman (also providing written
testimony) (Texas Water Development Board), Austin, TX
Ruckel, Grant (also providing written testimony) (Self; Energy Transfer), Austin, TX
Tunstall, Thomas UTSA Institute of Economics Research Director (also providing written
testimony) (UTSA Institute of Economics), San Antonio, TX


This page has been viewed times.
The counter above is fetched using an Ajax call then added to all empty elements with the class pagecounter
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View the page source for an example script fetches data from http://localhost:3000/counter.json
.


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import views.html.helper.CSPNonce @import config.ApplicationConfig @import uk.gov.hmrc.hmrcfrontend.views.html.components.{HmrcPageHeading, PageHeading} @import views.html.helper.form @this( govuk_wrapper: views.html.govuk_wrapper, govukBackLink: GovukBackLink, govukErrorSummary: GovukErrorSummary, govukFileUpload: GovukFileUpload, govukButton: GovukButton, hmrcPageHeading: HmrcPageHeading ) @(fileUploadUrl: String, error: String)(implicit request: Request[_], messages: Messages, appConfig: ApplicationConfig) @scriptElem = { } @govuk_wrapper(pageTitle = messages("file.upload.page.title"), scriptElem = Some(scriptElem)) { @govukBackLink(BackLink( href = routes.FileUploadController.back.url, content = Text(messages("back")), attributes = Map("data-journey-click" -> "navigation - link:Upload a file:Back") )) @hmrcPageHeading(PageHeading( text = messages("file.upload.page.header") )) @if(error.nonEmpty) { @govukErrorSummary(ErrorSummary( title = Text(messages("generic.errors_heading")), errorList = Seq( ErrorLink( content = Text(messages(error)), href = Some("#choose-file"), attributes = Map("id" -> "upload-error") ) ) )) }
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EbolaResponse
What is the name of the clinic?
What is the phone number of the clinic?
How many patients are being treated at the clinic?
What supplies are you in need of?
How many nurses work at the clinic?
1 / 5


Reports a missing mandatory attribute in an XML/HTML tag. Suggests configuring attributes that should not be reported.


Instagator helps keep track of group participants and helps make sure that everyone is up to date on trip details.
Instagator makes it easy for planners and participants to communicate over activity preferences so that the optimal itinerary can be created.
Instagator helps trip participants stay on top of deadlines and other to-dos, helping take some stress off of the trip planner.
Founder and CEO
Founder and CFO
Founder and CTO
Founder and CMO


This page offers a basic example on how to use the methods offered by the dom module within protractor tests.
Every time you press the Start button, a $timeout
will be set to a random time value. After that time period yeoman will hide. Then you are able to press the Seek button, to search for him. After another time period of random length, yeoman will reappear.


I am a goal-driven and determined person, who is passionate about deriving data driven solutions to complex problems. I am very passionate about my work and consistent skill development. My experience extends to working as a data analyst during my tenure at Pick n Pay Ltd. I further augmented my skills with a Master of Science specializing in Data Science. I am experienced in machine learning, natural language processing for sentiment analysis, and image data analysis with deep learning models. Proficient in various programming languages, namely Python, Pyspark, R and SQL.
Developed dashboards using Excel for tracking participation and outcomes for university events; provided technology support to the Director of Operations and Event Managers; designed and published daily event boards using applications such as Trello, Zapier, Formstack and Social Tables; led trainings on Microsoft Excel for Conference Services staff, including pivot tables, power pivots and VBA.
Maintained database from external sources and monitored pricing against competitors in the market to ensure competitive price position; Performed statistical analysis on promotional activities to optimize stock picking and timing of promotions; Forecasted sales of upcoming promotion to ensure promotional activities are within budget; Worked with the Pricing Director and Merchandising Teams to understand pricing strategies and complete financial scenario analysis; Collaborated with aligned category teams to serve as main point of contact for pricing needs.
GPA: 3.97
Using SQL and Power Query in Excel, I developed a planning model to assist store managers to better predict product demand for promotional activity, resulting in 12% reduction in wastage in that category across 240 stores.
Using TensorFlow and Keras in Python, I designed and created deep layered neural networks to classify MRI images with the purpose of developing a tool to assist in the classification of patients with brain tumors.
Built pipelines using Apache Spark to ingest and analyze +1 million emails stored in a Hadoop database with the purpose of assisting a legal firm to statistically summarize text and algorithmically derive topics over time frames.
Sentiment analysis on tweets mined on various political campaigns, to understand public emotion of certain campaigns and area-based clustering of individuals and the main topics involved in conversations using Python.
Built deep layered neural networks to classify hand written digits utilizing Tensorflow-GPU.
Apart from being a data analyst, I enjoy playing soccer, volleyball and ultimate frisby during summer months. During winter, I am engaged in strategy games, board games and bowling.
I do enjoy staying up to date on the advancements in the machine learning and AI world and dabbling in new Python libraries (who doesn't right?).


Submitting information of religious organization
Submit the brief of your religious organization. Please fill in each section below
- Name of organization
- Religion
- Denomination
- Vision
- (optional) Logo image
- (optional) Web site address
- (optional) Affiliate
We will reply ASAP.


Two ways to upload a file to AWS. The first form will reload the page, the second will upload the file asynchronously in the background. Uplaods in this example are limted to 4KB, I've included some images smaller than 4KB below:


TouchMusic @CalHacks
- Team Members: Alexa F. Siu, Michael Lin, Kenneth Lin, Kevin Chen
- Institutions: Georgia Institute of Technology, University of California Berkeley
- Challenge Post
- Github repository
The TouchMusic glove is a wearable gadget that enables a music lover to physically experience their favorite songs. The glove does this by decoding a song’s rhythm and melody to construct patterns to vibrate into the hand. These palpable patterns will match the audible music for a synergistic, pleasurable sensation.
The software component (written in Python) first decodes the song into different bands of frequencies. After analyzing the bands with Fast Fourier Transform (FFT), it creates a sequence of vibrations that matches the song’s rhythm and melody. The sequence of vibrations is then serialized and sent to the micro-controller for the glove. Sewn with conductive thread, the glove has vibration motors and LEDs that are driven by the micro-controller.
I traveled a long way for this hackathon but the experience was very worth it! Approaching my undergraduate graduation, this hackathon made me realize there was a thing or two I had learned through my undergraduate courses. This project brought to light my experience/knowledge in different domains from CAD, electrical and mechanical design, to more theoretical concepts such as signal processing and programming.


This project is based on SketchRNN. The SketchRNN is a recurrent neural network model trained on millions of doodles collected from the Quick, Draw! game. The SketchRNN model can create new drawings (from a list of categories) based on an initial path.This original paper and implementation of SketchRNN was made in TensorFlow and ported to Magenta.js by David Ha. The ml5.js implementation was ported by Reiichiro Nakano.
Reference : Draw Together with a Neural Network (Link)
Project by Ayan Bag


com.jme3.control
package provides
{@link com.jme3.scene.control.Control controls}.
Controls represent the "logical" programming of scene graph elements, containing
callbacks for when a {@link com.jme3.scene.Spatial} is rendered or updated
by the engine.


Michael Mayor
Contact
About
(current)
Contact
Portfolio
Contact Information
Github:
Github.com/MichaelMayor
Linkedin:
Michael Mayor
Email:
<EMAIL_ADDRESS>Phone Number:
(203) 200-9817
Contact Me
Name
Email address
Message
Submit


Welcome...
It is my pleasure to extend a very warm welcome to you! Thank you for visiting Xyrine Academy's website. We are a mixed, full-boarding, co-educational Christian secondary school for boys and girls from around the world. The school is situated on a 57-acre piece of land on the outskirts of Apo, Abuja State, Nigeria. The school benefits from a wide range of superb facilities which include sporting and recreational facilities which make life at school home...
Read More »


Event Location:
University of Portland
5000 North Willamette Blvd
Portland, Oregon
Abstract:
Data Architecture – a “Corner Stone” of Enterprise Architecture -As the organizations across the globe, aggressively pursues their strategic objectives; a key strategy and method is required in this pursuit, and that is ‘Data/Information Availability’. Accurate, reliable and available information enables the organization to make timely and better business decisions. We have seen many differing methodologies over the years with CRM, Portals, B2B efforts and score carding capabilities. The need for well structured and quality information is more important than ever. As a result, we are seeing a significant acceptance and synergy relative to the importance and value having a robust Data Architecture. Most organizations today have a co
Speaker(s): Atul Borkar,
Abstract:
Modern day computing power along with SQL Server makes it possible to add sophisticated data mining models to our applications that forecast and predict, perform anomaly detection, and classify data. This presentation will demonstrate how to use Analysis Services to incorporate these complex algorithms seamlessly into your databases, from transactional processing to integration, data analysis and performance management. Attendees will be shown how to create a data mining model using SSAS, how to use SSIS to query and load mining forecasts, and a sample application that seamlessly uses the mining model. Take a step forward towards making BI pervasive in your organization by integrating data mining into your company’s applications.
Speaker(s): Carlos Bossy,
Abstract:
Many DBAs are thrust into a data warehouse project with little training and no experience, or they are forced to take over the existing Data Warehouse. Designing or fixing a Data Warehouse model that supports the full range of BI functions can be a challenge for the OLTP DBA. This session will reveal the essential components that you should put in place for a successful implementation, including concepts such as the Importance of Transformation, Redundancy and why Reuse can be Detrimental, and one of his favorite topics, Ease of Query. Additionally, the significance of Integration, Metadata, and Governance towards creating a rock-solid, sometimes brilliant, foundation for your Business Intelligence Data Architecture will be presented.
Speaker(s): Carlos Bossy,
Abstract:
SQL Server Integration Services is Microsoft’s Extract Transform Load/Data Integration (ETL/DI) tool, replacing DTS. SSIS is a very powerful and complex tool that can read data in from virtually any source, transform and mash that data however you like, then send it out to virtually any type of destination. Understand how SSIS can be used to do just that – from the basics. Find out how to get started using SSIS, hear what it’s really good at, and figure out what things you need a hacker’s spirit to accomplish with it. This session is demo heavy; Tasks, expressions, precedence constraints, text files, Excel, SQL, data type conversions, performance, scripts, configurations, scheduling, and more will be covered with examples.
Speaker(s): Todd McDermid,
Abstract:
Your CIO's top priority in 2010 is probably Business Intelligence. If you don't know what BI is, or are trying to deliver it directly from your OLTP database, you need to find out what you're getting into. BI is all about helping your business users make better decisions, faster. Find out what it will take to deliver what your CIO is asking for. BI from your OLTP database has problems - it requires technical knowledge of your data model, compromises your line of business application responsiveness, and won't perform well. The solution to all of those issues is to construct a Data Warehouse based on Dimensional Modeling. I'll show you why normalized OLTP databases have problems supporting BI, and how Kimball method Data Warehouses don't.
Speaker(s): Todd McDermid,
Abstract:
"R2"? What does THAT mean? Is this a "full" release or not? Buck Woody, Microsoft's real-world DBA and all around Gadfly will regale you with the explanation of such acronyms as "DAC", "UCP" and "MDW", and he'll explain what StreamInsight, Master Data Services and PowerPivot can really do for you. You'll take a quick tour of the features you should know about in SQL Server 2008 R2, the version of SQL Server that just couldn't wait for another year.
Speaker(s): Buck Woody,
Abstract:
Learn the fundamentals of MDX query design for Analysis Services cubes. Migrate your SQL skills to this simple and elegant language that will enable you to unlock the awsome power of a cube and to gain deep insight from a single version of the truth. Learn to develop dynamic, advanced reports by parameterizing MDX queries using expressions and custom code.
Speaker(s): Paul Turley,
Abstract:
Visual Studio Database Edition (VSDB) offers many benefits for the deployment process as well as the development of database updates. You may know it by its nickname - the "Data Dude". It provides for declarative style of approaching updates in a collaborative environment. This tool has been undergone significant enhancements recently and has many compelling features. We'll view some the key functional areas of the tool, discuss best practices, and take a look at what features companies could take advantage of. VSDB is free to download for use with Visual Studio 2008 Developer and Team Suite Edition. It is also included as part of the Visual Studio 2010 Premium edition.
Speaker(s): Tim Giorgi,
Abstract:
The secret to designing compelling report solutions is in the ingredients and the technique. Learn to apply proven design patterns and best practices to create a reporting solution masterpiece. These techniques employ expressions, parameters, custom programming and years of field experience. This presentation is based in-part from our forthcoming Wrox Press Book: Microsoft SQL Server Reporting Services Recipes: for Designing Expert Reports.
Speaker(s): Paul Turley,
Abstract:
In this session, learn how to set up standard and data-driven subscriptions using Report Manager. We discuss creating file-share, email, and null subscriptions; and how to deal with potential issues with parameters and security. We also demonstrate a sophisticated Microsoft ASP.NET-based application that creates subscriptions by calling the SSRS Web Services API.
Speaker(s): Paul Litwin,
Abstract:
A look at the basic Nested Sets versus Adjacency List models. Why Nested sets model hierarchies and Adjacency List model trees. This will stress the need for constraints to prevent cycles in either model. The problems of hierarchical aggregation -- Nested Sets requires one set-oriented query and Adjacency List requires iterations or recursion.
Speaker(s): Joe Celko,
Abstract:
In this session we will be looking at the best and worse practices for indexing tables within your SQL Server 2008 databases. We will also be looking into the new indexing features that are available in SQL Server 2008 (and SQL Server 2005) and how you the .NET developer can make the best use of them to get your code running its best.
Speaker(s): Denny Cherry,
Abstract:
Interpreting SQL performance counters and OS related counters is an essential skill for a SQL DBA. In this session, we will discuss most relevant OS and SQL performance counters, vital signs, and preferred threshold values.
Speaker(s): Joshua Jin,
Abstract:
We’ll be looking into the pros and cons of moving SQL Servers into a virtual server environment. Specifically we’ll be looking into when it’s a good idea and when it’s probably not a good idea. Like all problems in the database world there are no hard set answers as to if virtualization is a good idea, but there are some times when virtualizing a SQL Server is a good idea, and can save you some money. There are some other times when you will be shooting yourself in the foot and shouldn’t. We’ll be focusing on when how to make this decision, and how to gather the metrics that you need in order to come to this decision.
Speaker(s): Denny Cherry,
Abstract:
The SCD Wizard included in Integration Services is easy to use, and has all the features you need for smaller, simple dimension processing. However, it is not the easiest component to adjust, and doesn't perform very well with larger dimensions. This session will cover three alternative techniques for processing changes to dimension tables within SSIS: "rolling your own SCD" with Lookups and Conditional Splits, using the T-SQL MERGE statement, and the Kimball Method SCD component. The strengths and weaknesses of each technique will be described and demonstrated.
Speaker(s): Todd McDermid,
Abstract:
Come hear Buck Woody, Microsoft's "Real World DBA" give a marketing-free introduction to SQL Server for the Oracle professional. No experience in SQL Server is necessary - we'll cover the basics of SQL Server Architecture using Oracle concepts as a guide. If you're an Oracle professional and you want to add SQL Server to your "knowledge arsenal", come hear this overview. You'll also get a list of resources that will enable you to research further.
Speaker(s): Buck Woody,
Abstract:
Use the right tool from your SQL toolbox to maintain those indexes, determine which indexes are being used (and more importantly which ones are NOT being used), and get recommendations on indexes to create all from Dynamic Management Views. Walk away with scripts you can use in your environment IMMEDIATELY.
Speaker(s): Tim Ford,
Abstract:
This session is targeted for those who have found themselves accidentally responsible for the SQL Server databases where they work, or are generally new to SQL Server. We'll cover the basics of Backup and Restore processes as well as a high overview of HA architectures available including Database Mirroring, Clustering, Replication, and Log Shipping. Scripts will be provided for immediate use in your own environment as a take-away from this presentation.
Speaker(s): Tim Ford,
Abstract:
Many DBAs and developers are faced with tuning poorly performing SQL statements. There is no way to learn everything you need to know about SQL tuning in an hour, but you can learn a process to employ when badly written SQLs are ruining database performance. However, many tuning projects fail because the process being used is inefficient. This presentation will walk through a process I use with great success and it will include topics such as: SQL diagramming, wait type data, column selectivity, and others that will help you succeed on future tuning projects.
Speaker(s): Dean Richards,
Abstract:
This session will cover T-SQL coding best practices. The basics on how to code T-SQL statements to optimize your queries will be covered. In this session, you will discover some of the common pitfalls that can cause T-SQL statements to run slow. A number of demos will be done to show how by making slight changes in your code will minimize the resources used to process your queries. From this session, you will be able to take home a list of T-SQL coding guidelines to help your code more efficient T-SQL statements.
Speaker(s): Greg Larsen,
Abstract:
This session will show how to achieve 99.9999 High Availability of SQL Server database on physical cluster and on Cloud.• SQL Server 2008 cluster Deployment• SQL Server 2008 Mirroring• Sql Server cloud deplyment.
Speaker(s): Munir Ghamrawi,
Abstract:
Solid-state technologies are changing the way that SQL users run their databases. DBA’s are achieving significant TCO savings through performance enhancements, reliability improvements, and reduced energy and real estate costs using solid-state technologies. Solid state is changing the way datacenters look and how SQL performs. 1) On overview of solid state technology options for SQL Server 2) Why solid state makes a difference? 3) An comparison of the different types of NAND Flash-based products and the advantages and disadvantages of each: 4) Customer proof: Wine.com case study
Speaker(s): Sumeet Bansal,
Abstract:
Microsoft SQL Server 2005 and 2008 include a fantastic feature that few people understand or use. That feature is the Microsoft SQL Server Service Broker. The SQL Service Broker is one of the least used features within the database engine, and that lack of use is simply from last of knowledge about the feature. In this session we’ll dig into how to configure the service broker for not only intra-database message queuing, but database to database queuing as well as server to server database queuing. We will also dig into a real life scenario where the SQL Server Service Broker was used to do ETL from an OLTP database to an OLAP database in near real time for near real time reporting.
Speaker(s): Denny Cherry,
Abstract:
High volume contiguous ETL is always problematic, and even more so when near-real-time is desired. This presentation goes through a solution collects and aggregates security audit data for nearly 400000 machines, a contiguous 24x7 stream of nearly 200 events per second, using a budget tight solution that involves SQL Express, Service Broker and Database Mirroring.
Speaker(s): Remus Rusanu,
Abstract:
This session will explore different DMV and how they can be used to obtain information about your SQL Server environment. There will be a number of demo's. In these demos different DMV's will be used provide different infomation about how your SQL Server instance is running. Peeking under the covers is now only a DMV away.
Speaker(s): Greg Larsen,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
Joe Celko served 10 years on ANSI/ISO SQL Standards Committee and contributed to the SQL-89 and SQL-92 Standards. He has written over 1200 columns in the computer trade and academic press, mostly dealing with data and databases. He is author of eight books on SQL for Morgan-Kaufmann: SQL FOR SMARTIES (1995, 1999, 2005, 2010), SQL PUZZLES ANSWERS (1997, 2006), DATA DATABASES (1999) and TREES HIERARCHIES IN SQL (2004), SQL PROGRAMMING STYLE (2005) and ANALYTICS OLAP IN SQL (2005) and THINKING IN SETS (2008). Mr. Celko's past columns include: Columns for Simple-Talk (Redgate Software); "CELKO" in INTELLIGENT ENTERPRISE magazine (CMP); BMC's DBAzine.com e-magazine; "SQL Explorer" in DBMS (Miller-Freeman); "Celko on SQL" in DATABASE PROGRAMMING DESIGN (Miller-Freeman); "WATCOM SQL Corner" in POWERBUILDER DEVELOPERS' JOURNAL (SysCon); "SQL Puzzle" in BOXES ARROWS (Frank Sweet Publishing); "DBMS/Report" in SYSTEMS INTEGRATION (Cahner-Ziff); "Data Desk" in TECH SPECIALIST (RD); "Data Points" in PC TECHNIQUES (Coriolis Group); "Celko on Software" in COMPUTING (VNC Publications, UK), "SELECT * FROM Austin" (Array Publications, The Netherlands), and he was editor for the "Puzzles Problems" section of ABACUS (Springer-Verlag) and he ran the CASEFORUM section 18, "Celko on SQL", on CompuServe.
LinkedIn: http://www.linkedin/in/carlosbossy
Contact: http://www.carlosbossy.com
Carlos Bossy (MCTS, MCP BI, CBIP) is a data and cloud analytics architect with 25 years of experience in software and database development. As a principal of Datalere, Carlos focuses on developing BI, Data Science, and Advanced Analytics solutions, including modeling data warehouses and delivering predictive models, integration, and visualization. He has developed warehouses and BI solutions for a variety of industries and state agencies, including health insurance, solar energy, foster care, telecom, and manufacturing.
Contact: http://thesqlagentman.com
Tim Ford is a Microsoft SQL Server MVP and co-founder of SQL Cruise (sqlcruise.com). He is currently a Senior DBA and the SQL Server SME with Spectrum Health, a major healthcare system located in West Michigan. He is the leader of the West Michigan SQL Server Users Group (westmisql.org) and is a community speaker and author. His book with Louis Davidson: Performance Tuning With SQL Server Dynamic Management Views is now widely available.
Over 25 Years of Information Technology experience mainly with various relational database technologies and business capabilities. I am currently working in Intel Corporation as an Enterprise Architect, with having expertise in Business (B), Data (D), Application (A) and Technology (T) areas. I am also an active board member of Local DAMA Chapter for Portland, OR. Currently I am actively involved in using, practicing and advocating Lean Six Sigma Methodologies (Black Belt) across Intel.
LinkedIn: http://www.linkedin.com/in/buckwoody
Contact: http://buckwoody.wordpress.com/
Born helpless, naked, and unable to provide for himself, Buck Woody overcame these amazing handicaps to become a software developer, data professional, teacher, and a productive invertebrate member of society.
LinkedIn: https://www.linkedin.com/in/grlarsen/
Greg Larsen has been working with SQL Server since 1999. He has authored 100's of articles related to SQL Server. He holds a MCSE Data Management and Analytics. Greg also a SQL Server Consultant, adjunct professor and technical writer.
LinkedIn: https://www.linkedin.com/in/pturley
Contact: http://www.sqlserverbiblog.com
Paul is a Principal Consultant for Pragmatic Works, a Mentor and Microsoft Data Platform MVP. He consults, writes, speaks, teaches blogs about business intelligence and reporting solutions. He works with companies around the world to model data, visualize and deliver critical information to make informed business decisions; using the Microsoft data platform and business analytics tools. He is a Director of the Oregon Data Community PASS chapter user group, the author and lead author of Professional SQL Server 2016 Reporting Services and 14 other titles from Wrox Microsoft Press. He holds several certifications including MCSE for the Data Platform and BI.
LinkedIn: https://www.linkedin.com/in/grlarsen/
Greg Larsen has been working with SQL Server since 1999. He has authored 100's of articles related to SQL Server. He holds a MCSE Data Management and Analytics. Greg also a SQL Server Consultant, adjunct professor and technical writer.
Contact: http://www.optsolutions.com
Tim Giorgi is in his 15th year as an independent consultant, specializing in custom software development. He has worked in many different roles on software teams ranging from architecture, design, development and project management. Tim gets jazzed out of reviewing technology and finding ways to integrate it efficiently for organizations. He is active in the tech community with the Portland SQL Server User’s group, the Scoutmaster of a Boy Scout Troop, and a Damascus City Planning Commissioner.
Contact: http://www.deeptraining.com
Paul Litwin is a developer specializing in ASP.NET, C#, SQL Server, SQL Server Reporting Services, and related technologies. Paul is a Programmer Manager with Fred Hutchinson Cancer Research Center in Seattle. He is also the owner of Deep Training. Paul has authored a number of books and training courses on ASP.NET, SQL Server Reporting Services, and Micrsoft Access. Paul is the conference chair of Microsoft ASP.NET Connections, a Microsoft MVP, and a member of the INETA Speakers Bureau.
LinkedIn: http://www.linkedin/in/carlosbossy
Contact: http://www.carlosbossy.com
Carlos Bossy (MCTS, MCP BI, CBIP) is a data and cloud analytics architect with 25 years of experience in software and database development. As a principal of Datalere, Carlos focuses on developing BI, Data Science, and Advanced Analytics solutions, including modeling data warehouses and delivering predictive models, integration, and visualization. He has developed warehouses and BI solutions for a variety of industries and state agencies, including health insurance, solar energy, foster care, telecom, and manufacturing.
LinkedIn: https://www.linkedin.com/in/mrdenny
Contact: https://www.dcac.com
Denny Cherry is the owner and principal consultant for Denny Cherry Associates Consulting and has over a decade of experience working with platforms such as Microsoft Azure, Amazon AWS, Microsoft SQL Server, Hyper-V, vSphere, and Enterprise Storage solutions. Denny’s areas of technical expertise include system architecture, performance tuning, security, replication, and troubleshooting. Denny currently holds several Microsoft Certifications related to SQL Server 2000 through 2019, including being a Microsoft Certified Master, Microsoft MVP and VMware vExpert Award.
LinkedIn: https://ca.linkedin.com/in/toddmcdermid
Contact: https://toddmcdermid.blogspot.com
Todd McDermid is a local BC developer who’s turned into a data warehouse and business intelligence evangelist. He started out programming DBase, VB6, and C# - but is now passionate about the SQL Server BI stack. He’s concentrated on using Integration Services to build the backbone of BI delivery – a dimensional warehouse. Todd has been known to blog, forum troll, code open-source extensions, and present on BI topics across the continent.
Sumeet Bansal is Principal Solutions Architect for Fusion-io. He designs architectural solutions with Fusion-io products that deliver unprecedented levels of scalability, performance and simplicity. Sumeet has more than 11 years IT and database-infrastructure experience, including work as vice-president of information technology at MS SQL-based Internet retailer Wine.com, senior database architect for MS SQL-based eVineyard inc., and systems analyst for Akiba Bank Limited.
LinkedIn: https://www.linkedin.com/in/mrdenny
Contact: https://www.dcac.com
Denny Cherry is the owner and principal consultant for Denny Cherry Associates Consulting and has over a decade of experience working with platforms such as Microsoft Azure, Amazon AWS, Microsoft SQL Server, Hyper-V, vSphere, and Enterprise Storage solutions. Denny’s areas of technical expertise include system architecture, performance tuning, security, replication, and troubleshooting. Denny currently holds several Microsoft Certifications related to SQL Server 2000 through 2019, including being a Microsoft Certified Master, Microsoft MVP and VMware vExpert Award.
Contact: http://rusanu.com
Remus Rusanu is a developer working with Microsoft SQL Server team in Redmond, WA. He blogs about his interest in SQL Server at http://rusanu.com and answers many sql-server tagged question on stackoverflow.com.
LinkedIn: https://www.linkedin.com/in/mrdenny
Contact: https://www.dcac.com
Denny Cherry is the owner and principal consultant for Denny Cherry Associates Consulting and has over a decade of experience working with platforms such as Microsoft Azure, Amazon AWS, Microsoft SQL Server, Hyper-V, vSphere, and Enterprise Storage solutions. Denny’s areas of technical expertise include system architecture, performance tuning, security, replication, and troubleshooting. Denny currently holds several Microsoft Certifications related to SQL Server 2000 through 2019, including being a Microsoft Certified Master, Microsoft MVP and VMware vExpert Award.
LinkedIn: https://ca.linkedin.com/in/toddmcdermid
Contact: https://toddmcdermid.blogspot.com
Todd McDermid is a local BC developer who’s turned into a data warehouse and business intelligence evangelist. He started out programming DBase, VB6, and C# - but is now passionate about the SQL Server BI stack. He’s concentrated on using Integration Services to build the backbone of BI delivery – a dimensional warehouse. Todd has been known to blog, forum troll, code open-source extensions, and present on BI topics across the continent.
LinkedIn: http://www.linkedin.com/pub/dean-richards/6/167/a90
Dean Richards is a Senior DBA for DBMS Insights and has helped hundreds of companies solve database and virtualization performance issues. Dean has over 20 years of experience in both development and database administration and has focused his entire career on performance tuning of Oracle and SQL Server environments. Most recently Dean has focused on the trend to virtualize and move databases to the cloud.
Munir Ghamrawi is a Sr Database Architect . He is one of contributing engineers for a private Cloud computing architecture with respect to Database technologies. Currently he is working on achieving 99.99999 high availability of SqlServer database applications include patching, maintenance.
Joshua Jin is a frequent speaker at SQL PASS local chapter in Portland, Oregon. He holds MCITP Business Intelligence Developer, Database Administrator, and Database Developer certifications for SQL Server 2005. Joshua is one of contributing authors of “Professional SQL Server 2005 Performance Tuning” book by Wiley/Wrox. At Intel IT, currently he is the development lead of a large SQL BI project for a private Cloud.
LinkedIn: http://www.linkedin.com/in/buckwoody
Contact: http://buckwoody.wordpress.com/
Born helpless, naked, and unable to provide for himself, Buck Woody overcame these amazing handicaps to become a software developer, data professional, teacher, and a productive invertebrate member of society.
Contact: http://thesqlagentman.com
Tim Ford is a Microsoft SQL Server MVP and co-founder of SQL Cruise (sqlcruise.com). He is currently a Senior DBA and the SQL Server SME with Spectrum Health, a major healthcare system located in West Michigan. He is the leader of the West Michigan SQL Server Users Group (westmisql.org) and is a community speaker and author. His book with Louis Davidson: Performance Tuning With SQL Server Dynamic Management Views is now widely available.
LinkedIn: https://www.linkedin.com/in/pturley
Contact: http://www.sqlserverbiblog.com
Paul is a Principal Consultant for Pragmatic Works, a Mentor and Microsoft Data Platform MVP. He consults, writes, speaks, teaches blogs about business intelligence and reporting solutions. He works with companies around the world to model data, visualize and deliver critical information to make informed business decisions; using the Microsoft data platform and business analytics tools. He is a Director of the Oregon Data Community PASS chapter user group, the author and lead author of Professional SQL Server 2016 Reporting Services and 14 other titles from Wrox Microsoft Press. He holds several certifications including MCSE for the Data Platform and BI.
LinkedIn: https://ca.linkedin.com/in/toddmcdermid
Contact: https://toddmcdermid.blogspot.com
Todd McDermid is a local BC developer who’s turned into a data warehouse and business intelligence evangelist. He started out programming DBase, VB6, and C# - but is now passionate about the SQL Server BI stack. He’s concentrated on using Integration Services to build the backbone of BI delivery – a dimensional warehouse. Todd has been known to blog, forum troll, code open-source extensions, and present on BI topics across the continent.
The following is a list of sponsors that helped fund the event.


Various tools for handling fuzzy measures, calculating Shapley value and interaction index, Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using 'lpsolve' package, whose source in C adapted to the R environment is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package.
| Version: | 3.0.0 |
| Depends: | R (≥ 2.9.2) |
| Published: | 2018-06-05 |
| Author: | Gleb Beliakov [aut, cre], Quan Vu [ctb], Andrei Kelarev [ctb], Michel Berkelaar [ctb], Kjell Eikland [ctb], Samuel E. Buttrey [ctb], Stefan I. Larimore [ctb], Timothy A. Davis [ctb], John Gilbert [ctb], Esmond Ng [ctb], Peter Notebaert [ctb], Richard Stallman [ctb], Jeroen Dirks [ctb] |
| Maintainer: | Gleb Beliakov <gleb at deakin.edu.au> |
| License: | LGPL-3 |
| Copyright: | Gleb Beliakov. The 'lpsolve' library and its parts are copyright to various holders, including Kjell Eikland, Michel Berkelaar, University of Florida, National Institute of Standards and Technology, Free Software Foundation, Inc. |
| NeedsCompilation: | yes |
| CRAN checks: | Rfmtool results |
| Reference manual: | Rfmtool.pdf |
| Package source: | Rfmtool_3.0.0.tar.gz |
| Windows binaries: | r-devel: Rfmtool_3.0.0.zip, r-release: Rfmtool_3.0.0.zip, r-oldrel: Rfmtool_3.0.0.zip |
| OS X binaries: | r-release: Rfmtool_3.0.0.tgz, r-oldrel: Rfmtool_3.0.0.tgz |
| Old sources: | Rfmtool archive |
Please use the canonical form https://CRAN.R-project.org/package=Rfmtool to link to this page.


By the end of this module, you should be able to...
- draw object and reference diagrams corresponding to program executions involving list structures,
- describe how list structures are allocated and represented in memory, and
- write recursive and iterative methods that traverse, create, and manipulate linked list structures, and
- use a debugger to examine lists structures in memory.


HEATHERS A film review by Jeff Meyer Copyright 1989 Jeff Meyer
[Seen at the Seattle International Film Festival]
HEATHERS (USA, 1989)
Director: Michael Lehmann Screenwriter: Daniel Waters Cast: Winona Ryder, Christian Slater, Shannen Doherty, Lisanne Falk, Kim Walder, Penelope Milford
While not running on all eight cylinders, HEATHERS crosses the finish line with 6.8 of them going full blast, and is one of the funniest films of the year -- which should be a good enough recommendation for most folks, methinks. It has generally been labeled a "black comedy" (though not nearly as dark as I had been led to believe from local reviewers, who were apparently afraid that high school murders treated comically were more likely to offend than Freddy's latest outing. Go figure...), and seems to be mentioned in the same breath with RIVER'S EDGE, probably because both deal with high school kids and murder. That's about where the similarities end, though; HEATHERS is also a witty, acid send-up of high school conventions in the upper-middle-classes, with more on-target barbs than I can remember seeing in every Brat Pack film John Hughes has done in the last eight years. It's satire -- mean-spirited, nasty, cunning satire, which is of course the best and most honest kind.
Winona Ryder plays a high school student who, when the film opens, is a member of Westerburg High School's most exclusive clique of girls. She's getting uncomfortable, though, with the mental and social cruelty that her peers are dishing out every day, and when a handsome, sardonic outsider (Christian Slater) starts up with her, she thinks this might be her way out of the clique. It is, but *not* in the way she thinks...
The film's strong points rest between the outrageousness of the plot (murders assumed to be suicides in the school population, and the effects on the students, parents and the community) and the wonderful dialogue and mannerisms of the characters. Yeah, I've been out of high school for over a decade (thankGodthankGodthankGod), but the cadence and attitudes are right on the money. ("Fuck me gently with a chain saw -- do I look like Mother Theresa?") It's just one good line after another... The school's reaction to the "mass suicides" is hysterical; this makes FAST TIMES AT RIDGEMONT HIGH look like THE WONDER YEARS. Lehmann and Waters treat Westerburg High like an anthropology case study, detailing dominance, relationships, sex and group migration. The report based on the study, however, has more of O'Donoghue (Michael) than Darwin in it.
There are some problems, though; most of them center around the half hour before the conclusion, where the film almost becomes a pedestrian thriller. The humor and satire are temporarily muted, and we have the usual stalking/stalked chase scene, albeit taking place underneath a school assembly. It's as if the creators weren't sure what to do before the climax (where they return to the satiric tone of the rest of the film, thankfully), and filled it in with a standard suspense sequence with some humorous touches.
The whole cast is good, adding to the characterizations of an excellent script with expansive performances. Christian Slater seems to have decided to imitate Jack Nicholson at 18 (he's pretty good, at that), and it gets distracting at times; but it limits his performance without hurting it. (Daniel Waters, the writer, was at the performance, and mentioned that the psuedo-Nicholson tone was Slater's idea; Water's disliked it, but it appealed to Lehmann, so it stayed.) Ryder is the standout, in a performance that is three-dimensional, yet slips right in with the film's satiric tone. Other satiric comedies at the festival either tried mixing satire with drama and failed (YEN FAMILY, CHORUS OF DISAPPROVAL) or stuck to just satire (SCENES FROM THE CLASS STRUGGLE IN BEVERLY HILLS); HEATHERS attempted and, for the most part, succeeded with both, making it one of the strongest comic films of the year.
Recommended highly.
[Side note: I wished I'd taped Water's comments, as he is about as amusing as you would expect from the writer of HEATHERS. His description of almost selling the project to Disney was worth the price of admission.]
Moriarty, aka Jeff Meyer INTERNET<EMAIL_ADDRESS>Manual UUCP: {uw-beaver, sun, microsoft, hplsla, uiucuxc}!fluke!moriarty .
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Cartoon about russian warriors
A series of cartoon films for family viewing about Russian warriors. Appears since 2004.
The main characters are Alyosha Popovich, Dobrynya Nikitich and Ilya Muromets. Cartoons are based on folk stories and stories, but made in a modern way.


The Guess Girl loves shimmer and sparkle. this rose gold tone case surrounded bt(...)
The Guess Girl loves shimmer and sparkle. this rose gold tone case surrounded bt(...)
The Guess Girl loves shimmer and sparkle. this rose gold tone case surrounded bt(...)
The Guess Girl loves shimmer and sparkle. this rose gold tone case surrounded bt(...)
The Guess Girl loves shimmer and sparkle. this rose gold tone case surrounded bt(...)


This search experience is powered by Typesense which is
a blazing-fast, open source typo-tolerant
search-engine. It is an open source alternative to Algolia and an easier-to-use alternative to ElasticSearch.
The books dataset is from Open Library which is
an initiative of the Internet Archive. Please contribute books metadata if you're able to.
The app was built using the
Typesense Adapter for InstantSearch.js and is hosted on S3, with Cloudfront for a CDN.
The search backend is powered by a geo-distributed 3-node Typesense cluster running on Typesense Cloud,
with nodes in Oregon, Frankfurt and Mumbai.
Here is the source-code that shows you how to build your own search experience like this one: https://github.com/typesense/showcase-books-search


SendMeJunk is much more than just a website; it is a revolutionary system that simplifies the user's life through the use of reminders and messages.
SendMeJunk is perfect for people of all ages. It is also perfect for anyone that wants improve their quality of life dramastically in a short period of time.
Created in 2017, SendMeJunk is rapidly becoming the next big thing. Some have even began to make comparisons in its applicability to Google.
SendMeJunk is a group project created by students at the University of Illinois at Urbana-Champaign. The project was created as a requirement for CS196, "the best freshman honors supplement course on the planet!"
The goal of this project was to create a website that would allow for essential, as well inessential, messages to be sent to the user based on preferences the user filled out. Tell us how we did!


What is OpenVidu?
OpenVidu is a platform to facilitate the addition of video calls in your web or mobile application. It provides a complete stack of technologies very easy to integrate in your application. Our main goal is to allow developers to add real-time communications to their apps very fast and with low impact in their code.
WebRTC videoconferences
One-to-One, One-to-Many, Many-to-Many. Any combination you can think of is possible with OpenViduOpen source
OpenVidu is an OpenSource project licensed under Apache License v2. Use it for freeMultiplatform
Chrome, Firefox, Safari, Opera, Android, iOS, desktop apps. All compatible with each otherEasy to use
OpenVidu offers ready-to-use components that you can simply paste in your code to quickly get your videocalls up and running. Or if you want complete freedom when designing your videocalls, our super simple API will make it fun and quick but at the same time very customizable and powerfulEasy to deploy
Developing your app is just the beginning. And of course we know that the go-to-production process can sometimes be pretty annoying. OpenVidu makes this a seamless process by supporting the deployment in the most popular cloud provider in a few clicks. Or if you prefer, we have a very short step-by-step guide on how to successfully deploy OpenVidu on UbuntuTutorials/Demos available
As developers we believe that the best way to engage other colleagues in our platform is providing lots of working examples and guides that can serve as a starting point or a fun way to learn how to take advantage of OpenVidu full potentialKeep your videoconferences private
WebRTC encryption, our server API and our client role-based system guarantee all your calls will be completely privateCompatible with lots of frontend technologies
JavaScript, TypeScript, Angular, React, Ionic... Just choose your preferred language and frameworkCompatible with any backend technology
OpenVidu offers out of the box Java and Node clients for controlling your videocalls from your server, but it also exposes a REST API to allow any backend technology to be used. We don't want to limit any developer!Limit client capabilities with predefined roles
Decide which user is allowed to subscribe, publish or moderate video streams with predefined roles. Securing your videocalls has never been so easy!Message broadcasting
You can implement a chat within just a few lines of code with OpenVidu. Any user to user text-based communication your app may need can be implemented very easilyRecording
Record your videocalls with complete freedom: OpenVidu provides predefined layouts, but you can use your own custom layouts to accomplish any kind of resultScreen sharing
OpenVidu allows your clients to share their screensAudio and video filters
OpenVidu is the only WebRTC technology that allows you to apply real-time audio and video filters. Detecting bar codes, settting a chroma key background or amplifying your clients voices are only just a few examples of what you can achieve with OpenVidu filters. And all of this perfectly integrated in OpenVidu simple high-level APIIP cameras
You can receive IP cameras in OpenVidu. Implementing a surveillance system has never been so easy!Supported plans and professional support
We offer OpenVidu Pro for those companies interested in a more advanced production setup, focused on improved monitoring, scalability and fault tolerance. And OpenVidu team is opened to providing professional support for any project related to the platform. Contact us now!Some use cases
Some interesting use cases you can implement with OpenVidu:
- An integrated 1-to-1 video-call center to attend your customers face to face
- A videoclassroom platform where one or more teachers publish their webcams or desktops and many students receive them
- Audio-only rooms where users can also interact through a chat
- A conference service that allows the presenter to apply audio or video filters in real-time and record the lecture in a video file
- A security platform where you can receive video from security cameras/webcams. You could even receive a notification when some movement is detected in a region of the video
- An application to receive video from drones equipped with an IP camera
The possibilities are endless. You can check our Demos to get an idea of what you can do with OpenVidu. Here some cool pictures of them:
OpenVidu Architecture
OpenVidu is divided into two parts:
- OpenVidu Browser: it is a library to use in your client side (available for JavaScript and TypeScript). It allows you to create video-calls, join users to them, send and receive video and audio, etc... All the actions available with OpenVidu are managed via OpenVidu Browser.
- OpenVidu Server: it is an application that handles the server-side stuff. It receives the operations from OpenVidu Browser and do whatever is necessary to establish and manage your video-calls. You will never have to explicitly use it: just to run it and know its IP address.
Get started
These are the recommended steps for getting started with OpenVidu:
-
The best way to get your first app working in a few minutes is following our OpenVidu Hello World Tutorial.
-
After that, you can try any of our Client-Side-Only tutorials, or maybe go straight to step 3 if you think that you already handle OpenVidu reasonably well.
-
Try now any of our Client-Side + Server-Side tutorials, where you can see how a complete web application works with OpenVidu. Choose your preferred framework, since all 4 applications are exactly the same regarding their look and functionality.
-
Finally you can deploy your first app and see it working in the real world. You can follow our tutorial, where you can deploy our Demos in your own Amazon machine in a few minutes (Deploying OpenVidu Demos on AWS). Then you can deploy your own app following these steps (Deploying OpenVidu Server and Deploying your app on AWS).
How does OpenVidu work?
WebRTC is the ultimate responsible for all media transmission at the very heart of OpenVidu. WebRTC is a modern, cross-platform framework that democratizes media transmission over the Internet. It is promoted by Google, Mozilla, Opera and others.
Kurento is the WebRTC framework on which OpenVidu is built. Openvidu was forked from KurentoRoom project.
What are the differences between Kurento and OpenVidu?
OpenVidu wraps and hides all the low-level operations. The main goal we pursue is to provide a simple, effective, easy-to-use API so you can forget about WebRTC, ICE candidates and media server tricky stuff. We internally use Kurento to generate, connect, modify and destroy media-pipelines, but OpenVidu simplifies as far as possible the process, focusing on the use-case of video-call sessions (one-to-one, one-to-many, many-to-many, users with different roles).
Ultimately, developers can create video-calls with just a few lines of code, all from their client-side.
Acknowledgments
OpenVidu has been supported under Project LERNIM (RTC-2016-4674-7), co-funded by the Ministry of Economy, Finance and Competitiveness of Spain, as well as by the European Regional Development Fund, whose main goal is to promote technological development, innovation and high-quality research.


nips nips2008 nips2008-151 nips2008-151-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Florian Steinke, Matthias Hein
Abstract: This paper discusses non-parametric regression between Riemannian manifolds. This learning problem arises frequently in many application areas ranging from signal processing, computer vision, over robotics to computer graphics. We present a new algorithmic scheme for the solution of this general learning problem based on regularized empirical risk minimization. The regularization functional takes into account the geometry of input and output manifold, and we show that it implements a prior which is particularly natural. Moreover, we demonstrate that our algorithm performs well in a difﬁcult surface registration problem. 1
[1] M. Belkin and P. Niyogi. Semi-supervised learning on manifolds. Machine Learning, 56:209–239, 2004.
[2] K.V. Mardia and P.E. Jupp. Directional statistics. Wiley, New York, 2000.
[3] A. Srivastava. A Bayesian approach to geometric subspace estimation. IEEE Trans. Sig. Proc., 48(5):1390–1400, 2000.
[4] I. U. Rahman, I. Drori, V. C. Stodden, D. L. Donoho, and P. Schr¨ der. Multiscale representations for o manifold-valued data. Multiscale Mod. and Sim., 4(4):1201–1232, 2005.
[5] F. M´ moli, G. Sapiro, and S. Osher. Solving variational problems and partial differential equations mape ping into general target manifolds. J.Comp.Phys., 195(1):263–292, 2004.
[6] F. Steinke, M. Hein, J. Peters, and B. Sch¨ lkopf. Manifold-valued Thin-Plate Splines with Applications o in Computer Graphics. Computer Graphics Forum, 27(2):437–448, 2008.
[7] M. Hofer and H. Pottmann. Energy-minimizing splines in manifolds. ACM ToG, 23:284–293, 2004.
[8] B. C. Davis, P. T. Fletcher, E. Bullitt, and S. Joshi. Population shape regression from random design data. Proc. of IEEE Int. Conf. Computer Vision (ICCV), pages 1–7, 2007.
[9] M. Kilian, N.J. Mitra, and H. Pottmann. Geometric modeling in shape space. ACM ToG, 26(3), 2007.
[10] M. Hein, F. Steinke, and B. Sch¨ lkopf. Energy functionals for manifold-valued mappings and their o properties. Technical Report 167, MPI for Biological Cybernetics, 2008.
[11] J. Eells and L. Lemaire. Selected topics in harmonic maps. AMS, Providence, RI, 1983.
[12] B. Sch¨ lkopf, F. Steinke, and V. Blanz. Object correspondence as a machine learning problem. In Proc. o of the Int. Conf. on Machine Learning (ICML), pages 777 –784, 2005.
[13] V. Blanz and T. Vetter. A morphable model for the synthesis of 3d faces. In SIGGRAPH’99 Conference Proceedings, pages 187–194, Los Angeles, 1999. ACM Press. 8


A B C D E F G H I L N O P R S T U V Y
All Classes All Packages
All Classes All Packages
All Classes All Packages
D
- data - Variable in class unis.stores.entities.CreateRequestResponse
- date - Variable in class unis.stores.entities.Request
-
The date of the request
- date - Variable in class unis.stores.entities.Sale
-
The date of the sale
- delete(String) - Method in class unis.stores.controller.BrandController
-
Delete a brand in the system
- delete(String) - Method in class unis.stores.controller.ClientController
-
Delete a client in the system
- delete(String) - Method in class unis.stores.controller.FabricController
-
Delete a fabric in the system
- delete(String) - Method in class unis.stores.controller.LineController
-
Delete a line in the system
- delete(String) - Method in class unis.stores.controller.OrderStateController
-
Delete a order state in the system
- delete(String) - Method in class unis.stores.controller.ProductController
-
Delete a product in the system
- delete(String) - Method in class unis.stores.controller.RequestStateController
-
Delete a request state in the system
- delete(String) - Method in class unis.stores.controller.RolController
-
Delete a rol in the system
- delete(String) - Method in class unis.stores.controller.SubscriptionController
-
Delete a subscription in the system
- delete(String) - Method in class unis.stores.controller.UserController
-
Delete a user in the system
- delete(String) - Method in class unis.stores.controller.VehicleController
-
Delete a vehicle in the system
- deleteBrand(int) - Method in class unis.stores.services.brand.BrandService
-
Deletes a brand
- deleteBrand(int) - Method in interface unis.stores.services.brand.IBrandService
-
Deletes a brand
- deleteClient(int) - Method in class unis.stores.services.client.ClientService
-
Deletes a client
- deleteClient(int) - Method in interface unis.stores.services.client.IClientService
-
Deletes a client
- deleteFabric(int) - Method in class unis.stores.services.fabric.FabricService
-
Deletes a fabric
- deleteFabric(int) - Method in interface unis.stores.services.fabric.IFabricService
-
Deletes a fabric
- deleteLine(int) - Method in interface unis.stores.services.line.ILineService
-
Deletes a line
- deleteLine(int) - Method in class unis.stores.services.line.LineService
-
Deletes a line
- deleteOrderState(int) - Method in interface unis.stores.services.orderstate.IOrderStateService
-
Deletes a order state
- deleteOrderState(int) - Method in class unis.stores.services.orderstate.OrderStateService
-
Deletes a order state
- deleteProduct(int) - Method in interface unis.stores.services.product.IProductService
-
Deletes a product
- deleteProduct(int) - Method in class unis.stores.services.product.ProductService
-
Deletes a product
- deleteRequestState(int) - Method in interface unis.stores.services.requeststate.IRequestStateService
-
Deletes a request state
- deleteRequestState(int) - Method in class unis.stores.services.requeststate.RequestStateService
-
Deletes a request state
- deleteRol(int) - Method in interface unis.stores.services.rol.IRolService
-
Deletes a rol
- deleteRol(int) - Method in class unis.stores.services.rol.RolService
-
Deletes a rol
- deleteSubscription(int) - Method in interface unis.stores.services.subscription.ISubscriptionService
-
Deletes a subscription
- deleteSubscription(int) - Method in class unis.stores.services.subscription.SubscriptionService
-
Deletes a subscription
- deleteUser(int) - Method in interface unis.stores.services.user.IUserService
-
Deletes a user
- deleteUser(int) - Method in class unis.stores.services.user.UserService
-
Deletes a user
- deleteVehicle(String) - Method in interface unis.stores.services.vehicle.IVehicleService
-
Deletes a vehicle
- deleteVehicle(String) - Method in class unis.stores.services.vehicle.VehicleService
-
Deletes a vehicle
- deliverSale(int) - Method in interface unis.stores.services.sale.ISaleService
-
Delivers a sale and updates the stock automatically
- deliverSale(int) - Method in class unis.stores.services.sale.SaleService
-
Delivers a sale and updates the stock automatically
- deliverSale(String) - Method in class unis.stores.controller.SaleController
-
Delivers a sale to the client
- deliveryDate - Variable in class unis.stores.entities.Request
-
The deliveryDate of the request
- description - Variable in class unis.stores.entities.Product
-
The description of the product
- description - Variable in class unis.stores.entities.ProductFabric
- discount - Variable in class unis.stores.entities.Subscription
-
The discount of the subscription
All Classes All Packages


Amazon CloudFront StreamingDistribution S3Origin
The
S3Origin
property type specifies information about the Amazon S3 bucket from
which you want Amazon CloudFront to get your media files for distribution. For more
information, see
S3Origin in the
Amazon CloudFront API Reference.
S3Origin
is a property of the StreamingDistributionConfig property type.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "DomainName" :
String
, "OriginAccessIdentity" :String
}
YAML
DomainName:
String
OriginAccessIdentity:String
Properties
DomainName
-
The DNS name of the Amazon S3 origin.
Required: Yes
Type: String
Update requires: No interruption
OriginAccessIdentity
-
The CloudFront origin access identity to associate with the RTMP distribution. Use an origin access identity to configure the distribution so that end users can only access objects in an Amazon S3 bucket through CloudFront. For more information, see the
OriginAccessIdentity
property for S3Origin in Amazon CloudFront API Reference.Required: Yes
Type: String
Update requires: No interruption
See Also
-
S3Origin in the Amazon CloudFront API Reference


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Women Casual Wing Tip Brogues Oxfords Dress Formal Stitched Lace up Flats Shoes
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Athletic Women Outdoor Sport Shoes Running Trainers Breathable Sneakers Casual 3
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Ladies Shoes Ballet Flats Women Flat Shoes Large Size Casual Shoe Womens Loafers
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Women's Flat Espadrille Sandals Peep Toe Summer Flat Shoes Party Club Shoes Size
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Womens Ladies Platform Cork Espadrille Sandals Wedge Ankle Strap Summer Shoes
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Ladies Womens High Chunky High Heel Cleated Sole Ankle Chelsea Boots Shoes 3-8
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Women Air Cushion Sneakers Mesh Walking Slip-On Running Shoes Breathable Fashion
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Women's Wedge Heel Ladies Summer Platform Sandals Open Toe Chunky Shoes Size 6-9
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New Soft Summer Sports Beach Shower Sandals Home Bath Slippers Women Men Shoes
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19 Women Summer Sandals Shoes Peep-toe Low Shoes Lady Roman Sandals Flip Flops
US $13.99
Womens Heels Ankle Strap High Heels Open Toe Hot Sexy Summer Party Dress Sandals
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Women Cartoon Flats Canvas Shoes Sports Outdoor Lace Up Casual Board Sneakers
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Womens Ladies Block High Heels Ankle Strapped Sandals Summer Pumps Shoes Size
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Women Sweet Soft Fluffy Home Warm Slippers Indoor Shoes Flats Winter Shoes New
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Womens Knee High Cut Out Lace Up Ladies Flat Gladiator Summer Sandals Shoes Size
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Suparna Paul
Front End Web Developer
Previous
Next
Featured Work
Festival Menu Card
https://suparnapaul393.github.io/Festival-Menu-Card/
Photography PortFolio
https://suparnapaul393.github.io/Photography.github.io/
Pixel-Art
https://codepen.io/suparnapaul393/full/wmjMLp/


Code coverage report for
All files
Statements:
93.89%
(1568 / 1670)
Branches:
81.73%
(340 / 416)
Functions:
96.62%
(257 / 266)
Lines:
93.88%
(1566 / 1668)
Ignored:
none
File
Statements
Branches
Functions
Lines
lib/
93.89%
(1568 / 1670)
81.73%
(340 / 416)
96.62%
(257 / 266)
93.88%
(1566 / 1668)


BIJAYABAR PRADHAN
Home
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Contact
Bijayabar Pradhan is an emerging photographer based out of Kathmandu, Nepal. He loves adventure, football and photography of course.


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link=Log in |
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loginPassword |
Password |
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loginForm |
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link=Site Admin |
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link=Tab management |
|
| assertTitle |
Tabs |
|
| clickAndWait |
//input[@value='Add new tab'] |
|
| assertTitle |
Tab Editing Form |
|
| type |
Title |
People |
| select |
TabtypeId |
label=primary tab |
| clickAndWait |
primaryAction |
|
| assertTitle |
Tab Control Panel |
|
| clickAndWait |
//input[@value='Add New Tab'] |
|
| assertTitle |
Tab Editing Form |
|
| type |
Title |
Faculty |
| select |
TabtypeId |
label=secondary tab |
| clickAndWait |
primaryAction |
|
| assertTitle |
Tab Control Panel |
|
| clickAndWait |
//input[@value='Add existing tab as parent tab'] |
|
| assertTitle |
Super/Subtab Editing Form |
|
| select |
ParentId |
label=People |
| clickAndWait |
primaryAction |
|
| assertTitle |
Tab Control Panel |
|
| clickAndWait |
//input[@value='Add New Tab'] |
|
| assertTitle |
Tab Editing Form |
|
| type |
Title |
Academic Staff |
| select |
TabtypeId |
label=secondary tab |
| clickAndWait |
primaryAction |
|
| assertTitle |
Tab Control Panel |
|
| clickAndWait |
//input[@value='Add existing tab as parent tab'] |
|
| assertTitle |
Super/Subtab Editing Form |
|
| select |
ParentId |
label=People |
| clickAndWait |
primaryAction |
|
| assertTitle |
Tab Control Panel |
|
| clickAndWait |
//input[@value='Add New Tab'] |
|
| assertTitle |
Tab Editing Form |
|
| type |
Title |
Librarians |
| select |
TabtypeId |
label=collection |
| clickAndWait |
primaryAction |
|
| assertTitle |
Tab Control Panel |
|
| clickAndWait |
//input[@value='Add existing tab as parent tab'] |
|
| assertTitle |
Super/Subtab Editing Form |
|
| select |
ParentId |
label=Faculty |
| clickAndWait |
primaryAction |
|
| assertTitle |
Tab Control Panel |
|
| clickAndWait |
link=Home |
|
| clickAndWait |
link=Log out |
|
| assertTitle |
VIVO |
|


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