Hello there. Sadly the picture links in this blogpost are broken: /post/23048895912/tacoconf-anarchism It would be great, if you could find the time to update them. All the best, Per
Ack, looks like they got lost in the switch to a new hosting provided. Fixed now, thanks.


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@JvmStatic fun <T> constantValueProvider(value: T): DefaultValueProvider<T>
A DefaultValueProvider that always returns the value it is initialised with.
T
- The type of value that this provider gives.
value
- The value the provider should give.
Returns
A provider that always gives value.


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| 1 | /* | |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more | |
| 3 | * contributor license agreements. See the NOTICE file distributed with | |
| 4 | * this work for additional information regarding copyright ownership. | |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 | |
| 6 | * (the "License"); you may not use this file except in compliance with | |
| 7 | * the License. You may obtain a copy of the License at | |
| 8 | * | |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 | |
| 10 | * | |
| 11 | * Unless required by applicable law or agreed to in writing, software | |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, | |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| 14 | * See the License for the specific language governing permissions and | |
| 15 | * limitations under the License. | |
| 16 | */ | |
| 17 | ||
| 18 | package org.apache.commons.math3.analysis.solvers; | |
| 19 | ||
| 20 | import org.apache.commons.math3.analysis.DifferentiableUnivariateFunction; | |
| 21 | import org.apache.commons.math3.util.FastMath; | |
| 22 | import org.apache.commons.math3.exception.TooManyEvaluationsException; | |
| 23 | ||
| 24 | /** | |
| 25 | * Implements  | |
| 26 | * Newton's Method for finding zeros of real univariate functions. | |
| 27 | *
 | |
| 28 | * The function should be continuous but not necessarily smooth.
 | |
| 29 | * | |
| 30 | * @deprecated as of 3.1, replaced by {@link NewtonRaphsonSolver} | |
| 31 | * @version $Id$ | |
| 32 | */ | |
| 33 | @Deprecated | |
| 34 | public class NewtonSolver extends AbstractDifferentiableUnivariateSolver { | |
| 35 | /** Default absolute accuracy. */ | |
| 36 | private static final double DEFAULT_ABSOLUTE_ACCURACY = 1e-6; | |
| 37 | ||
| 38 | /** | |
| 39 | * Construct a solver. | |
| 40 | */ | |
| 41 | public NewtonSolver() { | |
| 42 | this(DEFAULT_ABSOLUTE_ACCURACY); | |
| 43 | } | |
| 44 | /** | |
| 45 | * Construct a solver. | |
| 46 | * | |
| 47 | * @param absoluteAccuracy Absolute accuracy. | |
| 48 | */ | |
| 49 | public NewtonSolver(double absoluteAccuracy) { | |
| 50 | super(absoluteAccuracy); | |
| 51 | } | |
| 52 | ||
| 53 | /** | |
| 54 | * Find a zero near the midpoint of {@code min} and {@code max}. | |
| 55 | * | |
| 56 | * @param f Function to solve. | |
| 57 | * @param min Lower bound for the interval. | |
| 58 | * @param max Upper bound for the interval. | |
| 59 | * @param maxEval Maximum number of evaluations. | |
| 60 | * @return the value where the function is zero. | |
| 61 | * @throws org.apache.commons.math3.exception.TooManyEvaluationsException | |
| 62 | * if the maximum evaluation count is exceeded. | |
| 63 | * @throws org.apache.commons.math3.exception.NumberIsTooLargeException | |
| 64 | * if {@code min >= max}. | |
| 65 | */ | |
| 66 | @Override | |
| 67 | public double solve(int maxEval, final DifferentiableUnivariateFunction f, | |
| 68 | final double min, final double max) | |
| 69 | throws TooManyEvaluationsException { | |
| 70 |
1
1. solve : replaced return of double value with -(x + 1) for org/apache/commons/math3/analysis/solvers/NewtonSolver::solve → KILLED |
return super.solve(maxEval, f, UnivariateSolverUtils.midpoint(min, max)); |
| 71 | } | |
| 72 | ||
| 73 | /** | |
| 74 | * {@inheritDoc} | |
| 75 | */ | |
| 76 | @Override | |
| 77 | protected double doSolve() | |
| 78 | throws TooManyEvaluationsException { | |
| 79 | final double startValue = getStartValue(); | |
| 80 | final double absoluteAccuracy = getAbsoluteAccuracy(); | |
| 81 | ||
| 82 | double x0 = startValue; | |
| 83 | double x1; | |
| 84 | while (true) { | |
| 85 |
2
1. doSolve : Replaced double division with multiplication → KILLED 2. doSolve : Replaced double subtraction with addition → KILLED |
x1 = x0 - (computeObjectiveValue(x0) / computeDerivativeObjectiveValue(x0)); |
| 86 |
3
1. doSolve : changed conditional boundary → SURVIVED 2. doSolve : Replaced double subtraction with addition → KILLED 3. doSolve : negated conditional → KILLED |
if (FastMath.abs(x1 - x0) <= absoluteAccuracy) { |
| 87 |
1
1. doSolve : replaced return of double value with -(x + 1) for org/apache/commons/math3/analysis/solvers/NewtonSolver::doSolve → KILLED |
return x1; |
| 88 | } | |
| 89 | ||
| 90 | x0 = x1; | |
| 91 | } | |
| 92 | } | |
| 93 | } | |
Mutations | ||
| 70 |
1.1 |
|
| 85 |
1.1 2.2 |
|
| 86 |
1.1 2.2 3.3 |
|
| 87 |
1.1 |


--- layout: post title: giant robots smashing into other giant robots date: 2012-05-18 10:27:11.000000000 -07:00 type: post published: true status: publish categories: tags: [] meta: _edit_last: '1' author: login: admin email<EMAIL_ADDRESS>display_name: admin first_name: Carina C. last_name: Zon ---
http://robots.thoughtbot.com/post/23288959017/designing-for-touch


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Study for JavaScript Buffer/ArrayBuffer/Blob/File APIs and HTML5 multipart CSRF exercise at Jan 2018.
ArrayBuffer/Blob/File APIs Mocha test on browser
XHR2 + Blob demo
webpack + babel build demo


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Hi eveyone,
Thought i'd share what I've been working on and get some help too. I wanted to use the ESP8266 with the Arduino IDE to run the transmitter modules for the open energy monitor. The ESP-12 doesn't seem to have the power (or RAM??) to process the power calculations in real time. Trying this just crashed the ESP. So the ESP and MCP3008 just reads the data and sends a stream over MQTT to the Raspberry Pi. At the end of a stream of data points, the ESP sends a trigger message to start the RPi calculating.
The RPi can then fit a sine curve to the raw data with a least squares optimisation. The results give offset, magnitude, phase, amplitude. Everything we need for power calcs on a device with plenty of processing power.
My transmitter:
- ESP-12 NodeMCU dev board
- MCP3008 analog to digital converter (8 channel) on HSPI
- The usual current and voltage Tx's for sensing.
- Works just fine
My Raspberry Pi hub:
- mosquitto
- mqttwarn (https://github.com/jpmens/mqttwarn)
- emonCMS
I have mqttwarn configured to dump the messages to a text file (comma separated, working well), and I think I have a script to process the raw data (needs testing).
The problem I have is mqttwarn related, mainly how to pass the file name to the processing script to do the sine curve fit. Anyone know how to do this in the mqttwarn code?
I have included the sketch and python code as attachments.
Thanks,
Mark
Re: Native ESP8266 code, published to MQTT and RPi calc engine
I'm curious as to why you fit a sine wave to the data. Do you realise the signal is rarely anything like a sinewave (ok, voltage is usually pretty close to one, but current is rarely anything like one)? To accurately measure power you really want the samples to be as unmolested (both in time and value) as possible.
Any specifics on why you couldn't do the power calculations at the front end? I would have thought storing all those samples and shipping them across the wifi would be way more resource intensive than calculating power up front and just shipping that.
Re: Native ESP8266 code, published to MQTT and RPi calc engine
i hadn't really considered the effect of the sine curve on the calculation, just thought it was an efficient way of doing the calc. This could be changed in the python code to do any form of calculation really.
I had tried the calculation in the ESP, it crashed every boot once the calculations started. Might have been some other fault crashing the ESP though.
Re: Native ESP8266 code, published to MQTT and RPi calc engine
Mark, without seeing your original code, I'd guess the reason its crashing is that maybe you are trying to do the calculations in the interrupt routine. I've found that you need to just grab the data and set a flag so that the calculations are done in the main loop.
You'd certainly need to be doing this to do any PV diversion.
Is there any chance you can post your original code?
Simon
Re: Native ESP8266 code, published to MQTT and RPi calc engine
So I had another go at modifying emontx firmware to run on the ESP, using mqtt to transmit the data
The 1.6.7 build of Arduino seems to support sqrt with the ESP which is a bonus. The pull request for emonLib doesn't seem like it is needed anymore. And the squareRoot function itself seemed to cause soft WDT resets.
Sketch seems to be running much better now. My own noob mistakes? probably.
Sketch is attached, with the modified emonLib.cpp (to use the external ADC).
I have tested the external ADC with the calc_rms function (AC-AC detection) which works well, so the sampling frequency is high enough. I have a different voltage Tx to the ones on the wiki pages though, so my calibration constant is very different. I have also been able to take raw data to excel to view the Voltage waveform in a graph.


Dan Smith
January 15th, 2021
Figma / Balsamiq / Principle / Miro / Google Forms / Keynote
Myco Ally integrates machine learning architecture to connect individuals and mental health providers. Through an interactive questionnaire, Myco Ally adapts based on individuals answers to best suit their therapeutic needs.
The results include recommended match and highest rated matches based on individual needs. The platform would include scheduling integration using Calendly and provider verification.
An all-in-one mental health provider matching and scheduling tool targeting the access to care problem in the United States.
Using the double diamond design process gives flexibility and the opportunity to learn without fully commiting to a design. Spending time to simply ask, “What will make finding a mental health professional easier for people?”, “How will this platform help people commit to scheduling?”, and “How can I keep this simple?” are common guiding questions to learn from the user.
After I learn more about the problem by using research methods, I begin building out personas, scenario maps, and user flows. This helps keep the design path specific to user needs. Once I have an idea mapped out, I build wireframes for testing usability. Using wireframes for usability testing helps iterate quickly for changes. Once I have usability tested, I move into mid and high fidelity screens for prototype and testing. After testing the mid fidelity for iterations, I finalize the high fidelity for development.
People across the U.S. continue to struggle with access to mental health care. 1 in 5 adults in the U.S. struggle with mental illness each year and majority of States are under 50% of meeting people's mental health needs .
With multiple factors creating hurdles for people to find and schedule new appointments with therapists, it is common for people to abandon their search for help. From insurance to availability, the mental health system in the U.S. is increasing difficulty for people to access care. In addtion, mental health providers have different credentials, different specialties, and different types of treatments they offer leading the new potential client to feel overwhelmed by the information.
By utilizing machine learning and psychological intake questions, Myco Ally attempts to sift through the jargon, find available therapists, and match based on client needs. By offering Myco Ally service free for new and potential clients without requiring registration, people may feel more inclined to follow through with finding and scheduling with a new mental health professional.
For mental health professionals, building a profile will be free but offering a verified account service for a nominal annual fee offers a verified profile, video integration, and increased visibility in results. Professionals of all plans will be able to integrate Calendly booking links to easily manage their schedule cross platform.
Conducted a small sample research to direct preliminary designs. Conducting competitive analysis, survey and questionnaires, card sorting, and NUF ratings. Findings include pain points when searching for therapists, prioritizing needs, helpful features, and competitive platform critique.
Before jumping in to build personas, I attempt to empathize with my users by using empathy maps. I narrowed my users down to two main groups; the quality over quantity user and the financially struggling user. I chose these two primary users based on user research findings looking for specialization (quality) insurance coverage (financial).
Using empathy maps, I’m able to brainstorm quickly and narrow down the characteristics of each persona. Using empathy maps also helps me understand the actions, thoughts, emotions, and social perspectives of each user.
Developing a journey based on persona characteristics helps put myself in the user’s shoes. Identifying possible pain points and solutions, emotional connections, opportunities, and planning for where certain features will reside.
User flow for the main feature (matching) helps build the possible outline and sequence of questions. From basic demographics to clinical questions, the line of questioning should keep the user engaged and reduce bounce rate. By using interactive steps, such as the type of service screen, keeps the user engaged in the content opposed to just clicking buttons and filling text boxes.
Using this as a maleable outline helps the iterative process once testing comes into play. Being open to changes without feeling fully commited to a design helps produce a quality product based on the user needs opposed to the designer’s perception of needs.
After research and understanding users, I begin with quick sketches of possible features and build screens around the features. Using simple pencil and paper, I sketch several ideas quickly. I tend to iterate sketch ideas until I settle on an idea.
Once I have a general idea of screens, I’ll use design platforms such as Balsamiq to build out lo-fi wireframes. This is also an iterative process as I am able to see layouts better using platforms and import them to test on devices and web apps.
Once I have a set of wireframes I’m comfortable with, I build out a prototype using the wireframes for usability testing. This helps me confirm or identify necessary changes to improve usability before building out mid- to high-fidelity screens.
Results confirmed majority of the usability needs except the types of service screen. Changing the copy around ‘hybrid’ to ‘modern’ and using plan language to describe the therapies helped users determine the therapy they are most interested in.
During testing, I am also beginning to discover the style guide informing the color palette and typography. Considering Myco Ally is a mental health app, I am wanting to invoke the feeling of warmth, support, and welcoming change. In the PNW of the U.S., the fall is a time to get out the warm coats and watch the leaves change. Using colors from changing leaves helped pick the color palette
As a licensed therapist myself, communicating clearly and directly with empathy is a trait important to mental health. Using typography that is modern and exhibits clarity is why I chose Helvetica Now.
Building out components and varients before and during high fidelity mockup design helps quickly make changes for iteration. I try to identify all of the components prior to building out high-fidelity mockups for consistency, however new components and varients can be added as the high-fidelity mockups come into fruition.
Judging a book by it’s cover does have some truth. Making the landing page appeal to users on first glance helps convert users to using the web app function. Building out two landing page mockups and having users decide which page they liked most decided the aesthetic and feel.
After I’ve finished research, tested usability, created the style guide, and identified the emotion of the product, I move to build out several high fidelity mockups. This can be the lengthiest part of my process so I put emphasis on my research before spending the time to build out the high fidelity mockups.


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Port Tomis is the name of the yacht harbour located in the ancient sea port of Constanta. It lies 4 NM North of the commercial port.
Constanta has been a port since the 1st century BC and is the oldest city in Romania. The area around the marina is particularly attractive.
Position 44°10.5' N, 28°39.9'E.
Port of Entry. See Romania Formalities for details.
Call on VHF Channel 67 on approach, but do not be surprised if no one responds. The harbour is serviced by the Border Police, housed in a white metal container about halfway along the East quay. It is best to tie up alongside the East quay, close to the sentry box, to complete formalities.
The friendly Border Police officer fills out the customs declaration and advise you that the Port Authorities may or may not visit the boat. If you are already cleared into Romania this is all that is required, if not, more formalities might be necessary.
On departure, inform the Border Police of your intended destination.
Last updated January 2014.
Take care to keep at least 1nm off the coast as the water is shallow close in.
This is an all-weather harbour and safe in winds from any direction. The entrance can be difficult to see until almost upon it, especially when arriving with the sun setting in the west. Use extra care and attention when arriving in strong S or SE winds.
photo of Tomis Marina from Ioan Cobzarenco (Oct 2015)
The marina is in the old harbour north of the commercial port of Constanta and consists of 4 floating pontoon.  'B' is deep both sides. Call Contantin Sailing on VHF 16 for help and advice on mooring.
There is no security; the entire area is open to the public.
Last updated October 2015.


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BTS treats fans with slew of preview cuts for '2020 Season's Greetings' packages
by sl278Christmas must have come early because BTS is treating fans to a bunch of preview cuts from their 'Seasons Greetings 2020' package!
'Season's Greetings' is an annual package containing planners and a behind the scenes DVD, a gift to prepare fans for the new year. The preview cuts show off the boys' handsome looks and charm.
Check out all the images below!


Idris is an experimental functional programming language with full dependent types. This tutorial is intended as a brief introduction to the language, and is aimed at readers already familiar with a functional language such as Haskell or OCaml. In particular, a certain amount of familiarity with Haskell syntax is assumed, although most concepts will at least be explained briefly.
The tutorial is, at present, more of a "crash course", intended to cover as many features as quickly as possible. Hopefully over time it will grow and include more detailed illustrative examples. Suggestions for improvements (e.g. more examples, more details, exercises) are welcome, and since the tutorial is in the darcs repository, suggestions for improvements with patches are even more welcome.
Idris is very much a work in progress, and new features are being added all the time. As a result, there are always likely to be features which are not documented here, although I will do my best to keep it up to date.
Note:This version of the tutorial requires at least Idris version
0.1.5. The only syntactic difference between this and earlier
versions is that Set
, the type of types,
was rendered as #
up to version 0.1.4.
Dependent types allow types to be predicated
on values. For example, rather than giving a type List
Int
for lists of integers, we could be more precise and give a
type List Int n
for lists of integers of
length n
. As we will see throughout the tutorial, this
allows us to give much more precise types to programs, and even to
prove certain properties of programs.
The tutorial consists of a number of example programs with explanations. Each of the following chapters is generated from an Idris source file, so that you can download each one and experiment.
Further chapters will be added as time permits.
Edwin Brady --<EMAIL_ADDRESS>29th November 2009


nips nips2013 nips2013-260 nips2013-260-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Benigno Uria, Iain Murray, Hugo Larochelle
Abstract: We introduce RNADE, a new model for joint density estimation of real-valued vectors. Our model calculates the density of a datapoint as the product of onedimensional conditionals modeled using mixture density networks with shared parameters. RNADE learns a distributed representation of the data, while having a tractable expression for the calculation of densities. A tractable likelihood allows direct comparison with other methods and training by standard gradientbased optimizers. We compare the performance of RNADE on several datasets of heterogeneous and perceptual data, ﬁnding it outperforms mixture models in all but one case. 1


Mattapoisett, Massachusetts
Salty the Seahorse
Once a kitschy symbol for a gift shop, this 38-foot seahorse is now a staple roadside attraction.
by IJVinThis entry is a stub
Help improve Atlas Obscura by expanding Salty the Seahorse with additional information or photos.
During the 1950s, Henry Dunseith and his family owned a patch of land in Mattapoisett, Massachusetts on which they lived and operated the Dunseith Sea Horse Gift Shop. Dunseith decided that the best way to attract customers to his shop was to construct an enormous, pastel-colored seahorse right out front.
The seahorse quickly became a marker for vacationers traveling to Cape Cod. However, after the completion of Interstate 195 most of the gift shops in the area, including Dunseith’s, were forced to close due to a lack of business.
When Dunseith died in 1988, he didn’t want the land to fall into the hands of a commercial enterprise. His family decided to give the property over to the town, which immediately sparked debate. The buildings were razed in the late 1990s, but the fate of the giant seahorse was still up in the air. Eventually, due to popular demand, the seahorse was briefly taken down, restored, then reerected and dedicated in 2000.
Today, the seahorse stands in a small park, complete with a playground and several flower gardens maintained by the Mattapoisett Land Trust.
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13 GEE, Binary Outcome: Respiratory Illness
13.1 Packages
13.1.1 CRAN
library(tidyverse) # all things tidy
library(pander) # nice looking genderal tabulations
library(furniture) # nice table1() descriptives
library(texreg) # Convert Regression Output to LaTeX or HTML Tables
library(psych) # contains some useful functions, like headTail
library(lme4) # Linear, generalized linear, & nonlinear mixed models
library(corrplot) # Vizualize correlation matrix
library(gee) # Genderalized Estimation Equation Solver
library(geepack) # Genderalized Estimation Equation Package
library(MuMIn) # Multi-Model Inference (caluclate QIC)
library(HSAUR) # package with the dataset
13.1.2 GitHub
Helper extract
functions for exponentiating parameters form generalized regression models within a texreg
table of model parameters.
13.2 Prepare and get to know the dataset
13.2.1 Read in the data
This data set is in the HSAUR package.
In each of two centres, eligible patients were randomly assigned to active treatment or placebo. During the treatment, the respiratory status (categorised poor or good) was determined at each of four, monthly visits. The trial recruited 111 participants (54 in the active group, 57 in the placebo group) and there were no missing data for either the responses or the covariates. The question of interest is to assess whether the treatment is effective and to estimate its effect.
Note that the data (555 observations on the following 7 variables) are in long form, i.e, repeated measurments are stored as additional rows in the data frame.
Indicators
subject
the patient ID, a factor with levels 1 to 111centre
the study center, a factor with levels 1 and 2
month
the month, each patient was examined at months 0, 1, 2, 3 and 4
Outcome or dependent variable
status
the respiratory status (response variable), a factor with levels poor and good
Main predictor or independent variable of interest
treatment
the treatment arm, a factor with levels placebo and treatment
Time-invariant Covariates
sex
a factor with levels female and male
age
the age of the patient
'data.frame': 555 obs. of 7 variables:
$ centre : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
$ treatment: Factor w/ 2 levels "placebo","treatment": 1 1 1 1 1 1 1 1 1 1 ...
$ sex : Factor w/ 2 levels "female","male": 1 1 1 1 1 1 1 1 1 1 ...
$ age : num 46 46 46 46 46 28 28 28 28 28 ...
$ status : Factor w/ 2 levels "poor","good": 1 1 1 1 1 1 1 1 1 1 ...
$ month : Ord.factor w/ 5 levels "0"<"1"<"2"<"3"<..: 1 2 3 4 5 1 2 3 4 5 ...
$ subject : Factor w/ 111 levels "1","2","3","4",..: 1 1 1 1 1 2 2 2 2 2 ...
centre treatment sex age status month subject
1 1 placebo female 46 poor 0 1
112 1 placebo female 46 poor 1 1
223 1 placebo female 46 poor 2 1
334 1 placebo female 46 poor 3 1
... <NA> <NA> <NA> ... <NA> <NA> <NA>
222 2 treatment female 31 good 1 111
333 2 treatment female 31 good 2 111
444 2 treatment female 31 good 3 111
555 2 treatment female 31 good 4 111
13.2.2 Wide Format
data_wide <- respiratory %>%
tidyr::spread(key = month,
value = status,
sep = "_") %>%
dplyr::rename("BL_status" = "month_0") %>%
dplyr::arrange(subject) %>%
dplyr::select(subject, centre, sex, age, treatment, BL_status, starts_with("month"))
str(data_wide)
'data.frame': 111 obs. of 10 variables:
$ subject : Factor w/ 111 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
$ centre : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
$ sex : Factor w/ 2 levels "female","male": 1 1 1 1 2 1 1 1 1 1 ...
$ age : num 46 28 23 44 13 34 43 28 31 37 ...
$ treatment: Factor w/ 2 levels "placebo","treatment": 1 1 2 1 1 2 1 2 2 1 ...
$ BL_status: Factor w/ 2 levels "poor","good": 1 1 2 2 2 1 1 1 2 2 ...
$ month_1 : Factor w/ 2 levels "poor","good": 1 1 2 2 2 1 2 1 2 1 ...
$ month_2 : Factor w/ 2 levels "poor","good": 1 1 2 2 2 1 1 1 2 2 ...
$ month_3 : Factor w/ 2 levels "poor","good": 1 1 2 2 2 1 2 1 2 2 ...
$ month_4 : Factor w/ 2 levels "poor","good": 1 1 2 1 2 1 2 1 2 1 ...
subject centre sex age treatment BL_status month_1 month_2 month_3
1 1 1 female 46 placebo poor poor poor poor
2 2 1 female 28 placebo poor poor poor poor
3 3 1 female 23 treatment good good good good
4 4 1 female 44 placebo good good good good
... <NA> <NA> <NA> ... <NA> <NA> <NA> <NA> <NA>
108 108 2 male 39 treatment poor good good good
109 109 2 female 68 treatment poor good good good
110 110 2 male 63 treatment good good good good
111 111 2 female 31 treatment good good good good
month_4
1 poor
2 poor
3 good
4 poor
... <NA>
108 good
109 good
110 good
111 good
13.2.3 Long Format
data_long <- data_wide%>%
tidyr::gather(key = month,
value = status,
starts_with("month")) %>%
dplyr::mutate(month = str_sub(month, start = -1) %>% as.numeric) %>%
dplyr::mutate(status = case_when(status == "poor" ~ 0,
status == "good" ~ 1)) %>%
dplyr::arrange(subject, month) %>%
dplyr::select(subject, centre, sex, age, treatment, BL_status, month, status)
str(data_long)
'data.frame': 444 obs. of 8 variables:
$ subject : Factor w/ 111 levels "1","2","3","4",..: 1 1 1 1 2 2 2 2 3 3 ...
$ centre : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
$ sex : Factor w/ 2 levels "female","male": 1 1 1 1 1 1 1 1 1 1 ...
$ age : num 46 46 46 46 28 28 28 28 23 23 ...
$ treatment: Factor w/ 2 levels "placebo","treatment": 1 1 1 1 1 1 1 1 2 2 ...
$ BL_status: Factor w/ 2 levels "poor","good": 1 1 1 1 1 1 1 1 2 2 ...
$ month : num 1 2 3 4 1 2 3 4 1 2 ...
$ status : num 0 0 0 0 0 0 0 0 1 1 ...
subject centre sex age treatment BL_status month status
1 1 1 female 46 placebo poor 1 0
2 1 1 female 46 placebo poor 2 0
3 1 1 female 46 placebo poor 3 0
4 1 1 female 46 placebo poor 4 0
... <NA> <NA> <NA> ... <NA> <NA> ... ...
441 111 2 female 31 treatment good 1 1
442 111 2 female 31 treatment good 2 1
443 111 2 female 31 treatment good 3 1
444 111 2 female 31 treatment good 4 1
13.3 Exploratory Data Analysis
13.3.1 Summary Statistics
<IP_ADDRESS> Demographics and Baseline Measure
data_wide %>%
furniture::table1(centre, sex, age, BL_status,
splitby = ~ treatment,
output = "html")
| placebo | treatment | |
|---|---|---|
| n = 57 | n = 54 | |
| centre | ||
| 1 | 29 (50.9%) | 27 (50%) |
| 2 | 28 (49.1%) | 27 (50%) |
| sex | ||
| female | 40 (70.2%) | 48 (88.9%) |
| male | 17 (29.8%) | 6 (11.1%) |
| age | ||
| 33.6 (13.4) | 32.9 (14.0) | |
| BL_status | ||
| poor | 31 (54.4%) | 30 (55.6%) |
| good | 26 (45.6%) | 24 (44.4%) |
<IP_ADDRESS> Status Over Time
data_wide %>%
furniture::table1(month_1, month_2, month_3, month_4,
splitby = ~ treatment,
output = "markdown")
| placebo | treatment | |
|---|---|---|
| n = 57 | n = 54 | |
| month_1 | ||
| poor | 29 (50.9%) | 17 (31.5%) |
| good | 28 (49.1%) | 37 (68.5%) |
| month_2 | ||
| poor | 35 (61.4%) | 16 (29.6%) |
| good | 22 (38.6%) | 38 (70.4%) |
| month_3 | ||
| poor | 31 (54.4%) | 15 (27.8%) |
| good | 26 (45.6%) | 39 (72.2%) |
| month_4 | ||
| poor | 32 (56.1%) | 20 (37%) |
| good | 25 (43.9%) | 34 (63%) |
data_month_trt_prop <- data_long %>%
dplyr::group_by(treatment, month) %>%
dplyr::summarise(n = n(),
prop_good = mean(status),
prop_sd = sd(status),
prop_se = prop_sd/n)
psych::headTail(data_month_trt_prop)
treatment month n prop_good prop_sd prop_se
1 placebo 1 57 0.49 0.5 0.01
2 placebo 2 57 0.39 0.49 0.01
3 placebo 3 57 0.46 0.5 0.01
4 placebo 4 57 0.44 0.5 0.01
5 <NA> ... ... ... ... ...
6 treatment 1 54 0.69 0.47 0.01
7 treatment 2 54 0.7 0.46 0.01
8 treatment 3 54 0.72 0.45 0.01
9 treatment 4 54 0.63 0.49 0.01
13.3.2 Visualization
13.4 Logisitc Regression (GLM)
resp_glm <- glm(status ~ centre + treatment + sex + BL_status +
I(age-33) + I((age-33)^2),
data = data_long,
family = binomial(link = "logit"))
summary(resp_glm)
Call:
glm(formula = status ~ centre + treatment + sex + BL_status +
I(age - 33) + I((age - 33)^2), family = binomial(link = "logit"),
data = data_long)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5965 -0.9178 0.3985 0.8388 2.0988
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.9685725 0.2733549 -7.202 5.95e-13 ***
centre2 0.5347938 0.2464412 2.170 0.030002 *
treatmenttreatment 1.3561814 0.2447533 5.541 3.01e-08 ***
sexmale 0.4263433 0.3175081 1.343 0.179343
BL_statusgood 1.9193401 0.2500033 7.677 1.63e-14 ***
I(age - 33) -0.0368535 0.0106382 -3.464 0.000532 ***
I((age - 33)^2) 0.0025169 0.0006352 3.963 7.41e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 608.93 on 443 degrees of freedom
Residual deviance: 465.25 on 437 degrees of freedom
AIC: 479.25
Number of Fisher Scoring iterations: 4
| status | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.14 | 0.08 – 0.24 | <0.001 |
| centre 2 | 1.71 | 1.05 – 2.77 | 0.030 |
| treatment | 3.88 | 2.40 – 6.27 | <0.001 |
| male | 1.53 | 0.82 – 2.85 | 0.179 |
| good | 6.82 | 4.18 – 11.13 | <0.001 |
| I(age - 33) | 0.96 | 0.94 – 0.98 | 0.001 |
| I((age - 33)^2) | 1.00 | 1.00 – 1.00 | <0.001 |
| Observations | 444 | ||
| R2 Tjur | 0.293 |
13.5 Generalized Estimating Equations (GEE)
13.5.1 Indepdendence
resp_gee_in <- gee::gee(status ~ centre + treatment + sex + BL_status +
I(age-33) + I((age-33)^2),
data = data_long,
family = binomial(link = "logit"),
id = subject,
corstr = "independence",
scale.fix = TRUE,
scale.value = 1)
(Intercept) centre2 treatmenttreatment
-1.968572485 0.534793799 1.356181372
sexmale BL_statusgood I(age - 33)
0.426343291 1.919340141 -0.036853528
I((age - 33)^2)
0.002516859
GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
gee S-function, version 4.13 modified 98/01/27 (1998)
Model:
Link: Logit
Variance to Mean Relation: Binomial
Correlation Structure: Independent
Call:
gee::gee(formula = status ~ centre + treatment + sex + BL_status +
I(age - 33) + I((age - 33)^2), id = subject, data = data_long,
family = binomial(link = "logit"), corstr = "independence",
scale.fix = TRUE, scale.value = 1)
Summary of Residuals:
Min 1Q Median 3Q Max
-0.96563962 -0.34372730 0.07631922 0.29658264 0.88947816
Coefficients:
Estimate Naive S.E. Naive z Robust S.E.
(Intercept) -1.968572493 0.2733635751 -7.201298 0.4457014141
centre2 0.534793799 0.2464443046 2.170039 0.3795759846
treatmenttreatment 1.356181377 0.2447584751 5.540896 0.3777998909
sexmale 0.426343293 0.3175134753 1.342757 0.4832336627
BL_statusgood 1.919340146 0.2500092510 7.677077 0.3772812271
I(age - 33) -0.036853528 0.0106384086 -3.464196 0.0150120266
I((age - 33)^2) 0.002516859 0.0006351834 3.962414 0.0007592432
Robust z
(Intercept) -4.4167966
centre2 1.4089242
treatmenttreatment 3.5896818
sexmale 0.8822715
BL_statusgood 5.0872930
I(age - 33) -2.4549336
I((age - 33)^2) 3.3149582
Estimated Scale Parameter: 1
Number of Iterations: 1
Working Correlation
[,1] [,2] [,3] [,4]
[1,] 1 0 0 0
[2,] 0 1 0 0
[3,] 0 0 1 0
[4,] 0 0 0 1
The results for GEE fit with the independence correlation structure produces results that are nearly identical to the GLM model.
The robust (sandwhich) standard errors are however much larger than the naive stadard errors
13.5.2 Exchangeable
resp_gee_ex <- gee::gee(status ~ centre + treatment + sex + BL_status +
I(age-33) + I((age-33)^2),
data = data_long,
family = binomial(link = "logit"),
id = subject,
corstr = "exchangeable",
scale.fix = TRUE,
scale.value = 1)
(Intercept) centre2 treatmenttreatment
-1.968572485 0.534793799 1.356181372
sexmale BL_statusgood I(age - 33)
0.426343291 1.919340141 -0.036853528
I((age - 33)^2)
0.002516859
GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
gee S-function, version 4.13 modified 98/01/27 (1998)
Model:
Link: Logit
Variance to Mean Relation: Binomial
Correlation Structure: Exchangeable
Call:
gee::gee(formula = status ~ centre + treatment + sex + BL_status +
I(age - 33) + I((age - 33)^2), id = subject, data = data_long,
family = binomial(link = "logit"), corstr = "exchangeable",
scale.fix = TRUE, scale.value = 1)
Summary of Residuals:
Min 1Q Median 3Q Max
-0.96563962 -0.34372730 0.07631922 0.29658264 0.88947816
Coefficients:
Estimate Naive S.E. Naive z Robust S.E.
(Intercept) -1.968572493 0.379830796 -5.1827617 0.4457014141
centre2 0.534793799 0.342427246 1.5617735 0.3795759846
treatmenttreatment 1.356181377 0.340084835 3.9877737 0.3777998909
sexmale 0.426343293 0.441175807 0.9663796 0.4832336627
BL_statusgood 1.919340146 0.347380636 5.5251789 0.3772812271
I(age - 33) -0.036853528 0.014781762 -2.4931757 0.0150120266
I((age - 33)^2) 0.002516859 0.000882569 2.8517424 0.0007592432
Robust z
(Intercept) -4.4167966
centre2 1.4089242
treatmenttreatment 3.5896818
sexmale 0.8822715
BL_statusgood 5.0872930
I(age - 33) -2.4549336
I((age - 33)^2) 3.3149582
Estimated Scale Parameter: 1
Number of Iterations: 1
Working Correlation
[,1] [,2] [,3] [,4]
[1,] 1.00000 0.31021 0.31021 0.31021
[2,] 0.31021 1.00000 0.31021 0.31021
[3,] 0.31021 0.31021 1.00000 0.31021
[4,] 0.31021 0.31021 0.31021 1.00000
Notice that the naive stadard errors are more similar to the robust (sandwhich) standard errors, supporting that this is a better fitting model
13.5.3 Paramgeter Estimates Table
The GEE models display the robus (sandwhich) standard errors.
<IP_ADDRESS> Raw Estimates (Logit Scale)
# Knit to Website: texreg::htmlreg()
# Knit to PDF: texreg::texreg()
# View on Screen: texreg::screenreg()
texreg::htmlreg(list(resp_glm,
resp_gee_in,
resp_gee_ex),
custom.model.names = c("GLM",
"GEE-INDEP",
"GEE-EXCH"),
caption = "Estimates on Logit Scale",
digits = 4)
| GLM | GEE-INDEP | GEE-EXCH | ||
|---|---|---|---|---|
| (Intercept) | -1.9686*** | -1.9686*** | -1.9686*** | |
| (0.2734) | (0.4457) | (0.4457) | ||
| centre2 | 0.5348* | 0.5348 | 0.5348 | |
| (0.2464) | (0.3796) | (0.3796) | ||
| treatmenttreatment | 1.3562*** | 1.3562*** | 1.3562*** | |
| (0.2448) | (0.3778) | (0.3778) | ||
| sexmale | 0.4263 | 0.4263 | 0.4263 | |
| (0.3175) | (0.4832) | (0.4832) | ||
| BL_statusgood | 1.9193*** | 1.9193*** | 1.9193*** | |
| (0.2500) | (0.3773) | (0.3773) | ||
| I(age - 33) | -0.0369*** | -0.0369* | -0.0369* | |
| (0.0106) | (0.0150) | (0.0150) | ||
| I((age - 33)^2) | 0.0025*** | 0.0025*** | 0.0025*** | |
| (0.0006) | (0.0008) | (0.0008) | ||
| AIC | 479.2530 | |||
| BIC | 507.9238 | |||
| Log Likelihood | -232.6265 | |||
| Deviance | 465.2530 | |||
| Num. obs. | 444 | 444 | 444 | |
| Dispersion | 1.0000 | 1.0000 | ||
| p < 0.001, p < 0.01, p < 0.05 |
Comparing the two GEE models: parameters are identical and so are the robust (sandwhich) standard errors.
<IP_ADDRESS> Exponentiate the Estimates (odds ratio scale)
# Knit to Website: texreg::htmlreg()
# Knit to PDF: texreg::texreg()
# View on Screen: texreg::screenreg()
texreg::htmlreg(list(extract_glm_exp(resp_glm),
extract_gee_exp(resp_gee_in),
extract_gee_exp(resp_gee_ex)),
custom.model.names = c("GLM",
"GEE-INDEP",
"GEE-EXCH"),
caption = "Estimates on Odds-Ratio Scale",
ci.test = 1,
digits = 4)
| GLM | GEE-INDEP | GEE-EXCH | ||
|---|---|---|---|---|
| (Intercept) | 0.1397* | 0.1397* | 0.1397* | |
| [0.0801; 0.2343] | [0.0583; 0.3345] | [0.0583; 0.3345] | ||
| centre2 | 1.7071* | 1.7071 | 1.7071 | |
| [1.0540; 2.7748] | [0.8113; 3.5922] | [0.8113; 3.5922] | ||
| treatmenttreatment | 3.8813* | 3.8813* | 3.8813* | |
| [2.4221; 6.3344] | [1.8509; 8.1390] | [1.8509; 8.1390] | ||
| sexmale | 1.5316 | 1.5316 | 1.5316 | |
| [0.8257; 2.8745] | [0.5940; 3.9491] | [0.5940; 3.9491] | ||
| BL_statusgood | 6.8165* | 6.8165* | 6.8165* | |
| [4.2214; 11.2710] | [3.2539; 14.2793] | [3.2539; 14.2793] | ||
| I(age - 33) | 0.9638* | 0.9638* | 0.9638* | |
| [0.9435; 0.9837] | [0.9359; 0.9926] | [0.9359; 0.9926] | ||
| I((age - 33)^2) | 1.0025* | 1.0025* | 1.0025* | |
| [1.0013; 1.0038] | [1.0010; 1.0040] | [1.0010; 1.0040] | ||
| AIC | 479.2530 | |||
| BIC | 507.9238 | |||
| Log Likelihood | -232.6265 | |||
| Deviance | 465.2530 | |||
| Num. obs. | 444 | 444 | 444 | |
| Dispersion | 1.0000 | 1.0000 | ||
| * 1 outside the confidence interval |
<IP_ADDRESS> Manual Extraction
(Intercept) centre2 treatmenttreatment
0.1396561 1.7070962 3.8813436
sexmale BL_statusgood I(age - 33)
1.5316465 6.8164591 0.9638173
I((age - 33)^2)
1.0025200
[1] 1.356181
[1] 3.881344
[1] 0.3777999
[1] 0.6156936 2.0966692
[1] 1.850940 8.139015
13.5.4 Refit with the geepack
package
resp_geeglm_ex <- geepack::geeglm(status ~ centre + treatment + sex + BL_status + I(age-33) + I((age-33)^2),
data = data_long,
family = binomial(link = "logit"),
id = subject,
waves = month,
corstr = "exchangeable")
Call:
geepack::geeglm(formula = status ~ centre + treatment + sex +
BL_status + I(age - 33) + I((age - 33)^2), family = binomial(link = "logit"),
data = data_long, id = subject, waves = month, corstr = "exchangeable")
Coefficients:
Estimate Std.err Wald Pr(>|W|)
(Intercept) -1.9685725 0.4457014 19.508 1.00e-05 ***
centre2 0.5347938 0.3795760 1.985 0.158858
treatmenttreatment 1.3561814 0.3777999 12.886 0.000331 ***
sexmale 0.4263433 0.4832337 0.778 0.377630
BL_statusgood 1.9193401 0.3772812 25.881 3.63e-07 ***
I(age - 33) -0.0368535 0.0150120 6.027 0.014091 *
I((age - 33)^2) 0.0025169 0.0007592 10.989 0.000917 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Estimated Scale Parameters:
Estimate Std.err
(Intercept) 1.033 0.4694
Correlation: Structure = exchangeable Link = identity
Estimated Correlation Parameters:
Estimate Std.err
alpha 0.3119 0.1515
Number of clusters: 111 Maximum cluster size: 4
(Intercept) centre2 treatmenttreatment
0.1397 1.7071 3.8813
sexmale BL_statusgood I(age - 33)
1.5316 6.8165 0.9638
I((age - 33)^2)
1.0025
13.5.5 Visualize the Model
<IP_ADDRESS> Range of Data Values
subject centre sex age treatment
1 : 4 1:224 female:352 Min. :11.0 placebo :228
2 : 4 2:220 male : 92 1st Qu.:23.0 treatment:216
3 : 4 Median :31.0
4 : 4 Mean :33.3
5 : 4 3rd Qu.:43.0
6 : 4 Max. :68.0
(Other):420
BL_status month status
poor:244 Min. :1.00 Min. :0.000
good:200 1st Qu.:1.75 1st Qu.:0.000
Median :2.50 Median :1.000
Mean :2.50 Mean :0.561
3rd Qu.:3.25 3rd Qu.:1.000
Max. :4.00 Max. :1.000
<IP_ADDRESS> Females in Center 1
expand.grid(centre = "1",
treatment = levels(data_long$treatment),
sex = "female",
age = seq(from = 11, to = 68, by = 1),
BL_status = levels(data_long$BL_status)) %>%
dplyr::mutate(fit = predict(resp_geeglm_ex,
newdata = .,
type = "response")) %>%
ggplot(aes(x = age,
y = fit,
linetype = treatment)) +
geom_line() +
theme_bw() +
facet_grid(.~ BL_status) +
labs(x = "Age, years",
y = "Predicted Probability of GOOD Respiratory Status",
title = "For Females at Center 1, by Baseline Status")
expand.grid(centre = "2",
treatment = levels(data_long$treatment),
sex = "male",
age = seq(from = 11, to = 68, by = 1),
BL_status = levels(data_long$BL_status)) %>%
dplyr::mutate(fit = predict(resp_geeglm_ex,
newdata = .,
type = "response")) %>%
ggplot(aes(x = age,
y = fit,
linetype = treatment)) +
geom_line() +
theme_bw() +
facet_grid(.~ BL_status) +
labs(x = "Age, years",
y = "Predicted Probability of GOOD Respiratory Status",
title = "For Males at Center 2, by Baseline Status")


Conway's Game of Life
Play (P)
Random (R)
Clear (C)
Reset (O)
Generations:
0
Population:
0
Speed:
1
generation(s)/sec


Tinder is an app that allows user to like or dislike profiles based on a small bio and common interests. The app was one of the first dating apps to exist and served to connect people through their contacts and Facebook. In 2014, the app reached it's peak of one billion "swipes" per day. To increase the chance of a right swipe, users can upload several pictures and include a bio that reflects one's personality.


Couples Resort Project - The situation
THE SITUATION
The challenge for this project was to take an already-existing website for the Couples Resort company, analyze the main features, values, ideas, colours, target, and so on to re-design the landing page for a more aesthetic and fresher look.
Couples Resort Project - The solution
THE SOLUTION
The result is a stylish website that receives the user with a full-width image of the beautiful hotel installations. There is a button at the very top of the website that invites the user to book a room, which is one of the main reasons why the users visit their website.
The second reason? To see images and activities, which brings us the next section of the website that describes some of the activities that they offer at the resort and so on. The imagery used for this website can be described as professional, fancy, romantic, warm and of high-definition.
The website contains a carrousel with the different types of rooms that the resort has to offer, tree key points to compare with the rest and a button that takes the user to another page with all the details of the room, pictures and reservation forms. In the end, the site has a section where some real testimonials are displayed. Since most of these are posted on Facebook, the idea is to connect them so that they’re also displayed on the website.


Converts an if-statement containing a conjunction operation in its condition into two nested if-statements with simplified conditions.


Mark Lawrenson states his prediction for Sheffield United v Man United
Former Liverpool FC defender Mark Lawrenson is backing Man United to beat Sheff United on Sunday
by The Sport Review staffMark Lawrenson is backing Manchester United to ease to a 2-0 win against Sheffield United on Sunday afternoon.
The Red Devils will return to Premier League action with a tricky game against the promoted side at Bramall Lane.
Manchester United signed off for the international break with a 3-1 victory over Brighton and Hove Albion.
Sheffield United are unbeaten in their last five Premier League games to hoist themselves up to fifth spot in the table.
The Blades are a point ahead of Manchester United in the race to finish in the Premier League’s top four this season.
Manchester United have only managed to win one of their six Premier League games on the road in the current campaign.
Former Liverpool FC defender Lawrenson believes Manchester United will secure a vital win at Sheffield United on Sunday afternoon.
“Sheffield United have been brilliant so far this season but Manchester United are starting to look like a better team, just because Anthony Martial is fit again,” Lawrenson told BBC Sport.
“I know Martial can be very up and down as an individual, but just having him on the pitch seems to make a difference and United’s other attacking players – Marcus Rashford and Daniel James – are also looking very sharp.
“I’ve already mentioned a couple of managers facing their old teams this weekend, but this game sees a return to a former club for a player – Manchester United’s Harry Maguire, who started his career with the Blades.
“I think Maguire will leave with the points.”
Manchester United finished in sixth spot in the Premier League table last season after Ole Gunnar Solskjaer replaced Jose Mourinho back in December.
The Red Devils signed Aaron Wan-Bissaka, Harry Maguire and Daniel James in the summer transfer window to bolster their squad ahead of the new season.
However, Solskjaer’s side have struggled to find consistency throughout the current campaign.
Sheffield United have only lost three of their 12 games since being promoted to the top flight.


Welcome to The Texan and The Canadian. We are currently in the process of reworking our blog. For now, you can find us on FaceBook:
Sean
/
Melissa
. Check back soon!


Ronalds Obituary was in the SMH 24/8/2007. He was educated at Randwick, Woollahra & Sydney University. He was one of the universities three honorees when alumm awards were institutured in 1992 with a citation for brain scanning. After graduation, he joined A.W.A., then CSIRO, where he worked with radar & meet Helen Elliott, whom he married. He earned his PhD in physics at Cambridge University in 1950, then returned to CSIRO for four years before moving to the United States in 1954. After a year at the University of California at Berkeley, he moved to Stanford University, where he spent the rest of his career, & he died on campus.
With colleagues at Stanford, he designed a radio telescope called a spectroheliograph, to receive & evaluate microwaves emitted by the sun. In 1961 he built an array of 32 radio telescope dishes with which he monitored the sun's surface for 11 years, producing daily maps of surface temperatures. NASA relied on the system for warnings of sunspot eruptions during the first moon landing. The 32 small dishes were demolished in the 1970's & replaced with five larger dishes, naming it the Bracewell Observatory. In the 1970's other scientists applies these techniques in developing X-Ray imiging of tumours, called tomography, & other forms of medical imiging that scan eloctro-magnetic & radio waves. His contributions to medical imiging led him to be the first Australian to become an Associate of the Institute of Medicine.
In the 1980's he combined his expertise in astronomy with geology to examine layers of red sandstones that he & others theorised must be connected with the sun's activity. The researchers proposed that sunspots had effected temperatures on Earth.
He stopped teaching in 1991, when he became a professor emeritus of electrical engineering, but still went on working. Although he became a US citizen, he was named Officer of the Order of Australia in 1998.
He is survived by his wife Helen, a son Mark in San Jose, & a daughter Wendy, who lives in England, & a brother Mark, in Melbourne.


Paste an Emacs URL, that can contain whitespace and backslashes where Emacs has split it across lines in your terminal, then press Enter:


May represent a face that has previously been enrolled, in which case name will hold the name that it was enrolled with.
Each Face instance has a FaceId integer - This may change if Vector later gets an improved view and makes a different prediction about which face he is looking at.
Implements
Inherited Members
Namespace: Anki.Vector.Objects
Assembly: Anki.Vector.dll
Syntax
public class Face : ObservableObject, INotifyPropertyChanged
Properties
| Improve this Doc View SourceExpression
Gets the facial expression Vector has recognized on the face.
public FacialExpression Expression { get; }
Property Type
| FacialExpression |
ExpressionValues
Gets individual expression values histogram, sums to 100
public IReadOnlyList<uint> ExpressionValues { get; }
Property Type
| IReadOnlyList<UInt32> |
FaceId
Gets the internal ID assigned to the face.
public int FaceId { get; }
Property Type
| Int32 |
LeftEye
Gets points representing the outline of the left eye.
public IReadOnlyList<Point> LeftEye { get; }
Property Type
| IReadOnlyList<Point> |
Mouth
Gets points representing the outline of the mouth
public IReadOnlyList<Point> Mouth { get; }
Property Type
| IReadOnlyList<Point> |
Name
Gets the name Vector has associated with the face. Empty if face is not recognized or enrolled
public string Name { get; }
Property Type
| String |
Nose
Gets points representing the outline of the nose.
public IReadOnlyList<Point> Nose { get; }
Property Type
| IReadOnlyList<Point> |
ObjectTypeName
Gets the name of the object type.
public override string ObjectTypeName { get; }
Property Type
| String |
Overrides
| Improve this Doc View SourceRightEye
Gets points representing the outline of the right eye.
public IReadOnlyList<Point> RightEye { get; }
Property Type
| IReadOnlyList<Point> |
Methods
| Improve this Doc View SourceToString()
Converts to string.
public override string ToString()
Returns
| Type | Description |
|---|---|
| String | A String that represents this instance. |


MOOS is one of the most light-weight middlewares available.
Works on Mac, Windows, and Linux (and embedded).
All you need to know is a little C++
(other language bindings exist).
Back in the days, Paul Newman and Mike Benjamin were two talented PhD students that wanted their code to be reused easily. They created the MOOS (Multi-Objective Operating Suite) middleware and IvP (Interval Programming) autonomy suite.
Ten years after the original version, MOOS core gets a refresh with faster performances and added stability. In the benchmark, MOOS beats the most known robotic middlewares.
With an ever increasing number of sensors on today’s robots, a one-stop place to get the necessary sensors interface for your next project makes a lot of sense. MOOS-drivers community is created to tackle this particular problem.
Roboticist, Enthusiast, Hacker
MOOS-Drivers is an open-source community where everybody can create his own interface and contribute to already existing interfaces and drivers.


| Name: MR.NARASIMHA | Reg No: 2-10SMMHC18 |
|---|---|
| Age / Sex: 33/M | Contact No:8095476355 |
| Marital Status:ML- 4.5 YEARS | Date:2/10/18 |
| Occupation:FINANCE HEAD OF INDIAN MONEY.COM | Dr. |
Address: NATIVE OF BGM. IN BLOE SINCE 2010 |
DIAGNOSIS- |
MOLES INC FROM FRM AN YEAR.
DNS.
PAST H/O:
CHICKEN POX 10 YRS OLD.
USE TO HAVE FLESHY WARTS EXACTLY BEHIND THE EAR RT. CAUTERISED IT WITH AGARBATTI FINALLY USE TO COME AGAIN AND AGAIN SO WENT TO HOMOE THEN GOT CURED – 15 YRS.
LAST WEEK AFTER LONG EXPOSURE IN SUN GOT SOME RASH ON THE RT ARM.
FAMILY H/O:
DAD: 70 YRS HTN BORDER
MOM 70 YRS– HTN , DM
MATERNAL UNCLE HEART PBM.
THIS GENERATION PTS MOM HAD TO WAIT FOR 16 YEARS TO GET KIDS. EARLIER USE TO BREAK THINGS AND FIGHT WITH PEOPLE. WIFE NO TILL NOW. I DON’T THINK I WILL EVER DO.
IF SLEEPS ALONE HE GETS FEAR OF SLEEPING ALONE.  THEN SUDDENLY CAME OUT IN THE HALL AND TOOK SOME TIMES TO NORMAL. AND DISTRUBED SLEEP AND THIS DREAM RECURRED. FALLING FROM HEIGHTS AND NOT REACHING THE GROUND. USE TO SWEAT OUT .BUT NOW THT DOESN’T COME.
QUARRELL REVOLVE AROUND PARENTS.
EARLIER USE TO SWEAT . STOPPED SWEATING FROM 2 YEARS. SAYS STOPED JOGGING AND NOW A DAYS FEELING LAZY.
MAJOR REGRETS IN LIFE: STARTED ONE BUSINESS DIN PURSUE IT PROPERLY. I SHODULD HAVE. THO IT DINT NEGETIVELY HAVE AN IMPACT ON ME STILL I FEEL.
SHOULD HAVE DONE SMTHG ELSE I SHOULDN’T HAVE DONE ENGINEERING. ALL HAVE TO DO IT HERD MENTALITY. (TALKS LOT) CULDNT SPEAK ENGLISH AFTER STUDIES. ..
VILLAGE – ANKILGI STAYED TILL 4TH STANDARD.
HAPPIEST MOMENTS INLIFE: WHEN GOT JOBS. AND SONS BIRTH. OFCOURSE MARRIAGE WAS A HAPPY MOMENT BUT LOT OF STRESS.
LIKES RAINY AND SUNNY. DOENST LIKE WINTER.
RX:
THUJA 1M– 4-4-4-4. ALTERNATE WEEKLY REPEAT B/FOOD. 4 DAYS.
RUB 30 – 1 DOSE NIGHT.
NAT SULPH 6X 4-4-4.


Amando Abreu is a serial entrepreneur, Fractional CTO, and engineer who has been involved in several startups and launched dozens of products. He has worked with companies such as trivago, Portugal Telecom, and Vizrt. He has experience in several industries, most notably e-commerce, SaaS, media, travel, insurance, property development, and construction.


Interactive Sleep Visualization Type 3
Blue: Normal Sleep OOO
Purple: OversleptOOO
Green: NapOOO
Yellow: Noise AnomolyOOO
Orange: Movement AnomolyOOO
Magenta: Noise & Movement AnomolyOOO
White: AwakeOOO
Grey: Early AwakeningOOO
Dashed Segment: Weekend
Filter by Keywords |
|
|


This is default style of the text (none).
This text inherits the decoration of the spanarent.
This is overlined text.
This is underlined text.
This is lined-through text.
This is underlined and overline text.
This is underlined and overline text.


Play the game »
You will be given a satalite image of a random country and you have to guess which country it is. There will be a multiple choice option between three countries and you need to select the correct one.


{% extends "base.html" %} {% load humanize %} {% load bootstrap %} {% block title %}Delete Car{% endblock %} {% block content %}
Are you sure you want to delete this Car?
{{ object.year }} {{ object.make }} {{object.model }}
VIN: {{ object.vin }}


new WebServer(inRequestListener)
Web server for node.
Implements support for POST on top of the regular incoming-request class.
Parameters:
| Name | Type | Description |
|---|---|---|
inRequestListener |
function | function to call with request when complete |
- Source:
Methods
-
listen(inPort)
-
Listen for requests.
Parameters:
Name Type Description inPort
integer port on which to listen, eg 80 for HTTP - Source:
-
on(inEventName, inEventHandler)
-
Dispatch event handler assignments. This class stands in for the regular server class, so we have to proxy the setting of various event handlers. Check for setting the request event handler, as we will call that when WE think the request is complete, ie once we've checked for POST, etc. Forward on any other event handler assignments to our server instance.
Parameters:
Name Type Description inEventName
string name of event, eg request inEventHandler
function event handler function - Source:
-
onRequest(inRequest, outResponse)
-
Handle incoming HTTP requests. This implementation extends the regular Node http package by supporting POST.
Parameters:
Name Type Description inRequest
object HTTP request outResponse
object HTTP response - Source:
-
stop()
-
Stop serving requests.
- Source:


All sessions
The sessions page allows you to view, search and delete all of your payload's persistent sessions. Use it to track online and offline sessions.Information 0s ago
{%if hasSessions}
{%/if}
{%!if hasSessions}
| Client ID | Browser | IP | Current page | Payload | Requests | Last online |
|---|---|---|---|---|---|---|
| {session->clientid} | {session->browser} | {session->ip} | {session->shorturi} | {session->payload} | {session->requests} | {session->last} |
with selected:
Execute
There are no sessions yet for this payload.
{%/!if}


GET TICKETS
Galle Gladiators vs Colombo Kings
| Team | PLD | WON | LOST | TIED | PTS | NET RR | |
|---|---|---|---|---|---|---|---|
| 1 | GALLE GLADIATORS | 10 | 7 | 3 | 0 | 14 | +1.448 |
| 2 | COLOMBO KINGS | 10 | 7 | 3 | 0 | 14 | +0.434 |
| 3 | KANDY TASKERS | 10 | 7 | 3 | 0 | 14 | +0.182 |
| 4 | DAMBULLA HAWKS | 10 | 5 | 5 | 0 | 10 | -0.476 |
| 5 | JAFFNA STALLIONS | 10 | 7 | 3 | 0 | 14 | +0.092 |


| PostgreSQL 9.0.10 Documentation | ||||
|---|---|---|---|---|
| Prev | Up | Chapter 52. GiST Indexes | Next |
Usually, replay of the WAL log is sufficient to restore the integrity of a GiST index following a database crash. However, there are some corner cases in which the index state is not fully rebuilt. The index will still be functionally correct, but there might be some performance degradation. When this occurs, the index can be repaired by VACUUMing its table, or by rebuilding the index using REINDEX. In some cases a plain VACUUM is not sufficient, and either VACUUM FULL or REINDEX is needed. The need for one of these procedures is indicated by occurrence of this log message during crash recovery:
LOG: index NNN/NNN/NNN needs VACUUM or REINDEX to finish crash recovery
or this log message during routine index insertions:
LOG: index "FOO" needs VACUUM or REINDEX to finish crash recovery
If a plain VACUUM finds itself unable to complete recovery fully, it will return a notice:
NOTICE: index "FOO" needs VACUUM FULL or REINDEX to finish crash recovery


easybook test book #3
Javier Eguiluz
Chapter 1
Publishing your first book
Chapter 2
Publishing your second book
Appendix A
Publishing your third book


{% extends "base.html" %} {% block head_title %}Home{% endblock %} {% block content_outer %}
Welcome to the web application that let's you create, manage and share playlists!


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& Play Settings
Judge other people's Reactions!
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press Upload to upload a Reaction from your photos
This feed has Snap-To Scrolling.
So the top of the Reaction
will auto-scroll to the top,
giving you a clear view every time.
A small flick will get you to the next pic.
You'll love it in no time!
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DaVinci PDEX Plan Net - Local Development build (v0.1.0). See the Directory of published versions
Payer Directory Implementation Guide
Home Page
Introduction
The Payer Directory Implementation Guide (Provider Directory IG) is based on FHIR R4.0. It was developed in cooperation with the Da Vinci Project and the the Office of the National Coordinator for Health Information Technology (ONC) with guidance from HL7 International, the Patient Administration Workgroup, and the HL7 US Realm Steering Committee.
It describes the architectural considerations for a payer to share their provider directory. We have strived to make this guide as broadly applicable as possible. However, every implementation may not use all of the content in this guide. It serves as a “standard” for the exchange of provider data, while describing additional data elements and capabilities that support more robust implementations.
For more information on the history of Payer Directory see the Payer Directory change notes.
Payer Directory Profiles
The list of Payer Directory Profiles is shown below. Each profile defines the minimum mandatory elements, extensions and terminology requirements that SHALL be present. For each profile, requirements and guidance are given in a simple narrative summary. A formal hierarchical table that presents a logical view of the content in both a differential and snapshot view is also provided along with references to appropriate terminologies and examples. In addition, each profile has a “Quick Start” section which is intended as an implementer friendly overview of the required search and read operations.
Payer Directory Conformance Requirements
The Capability Statements Section outlines conformance requirements for Payer Directory Servers and Client applications, identifying the specific profiles, RESTful operations and search parameters that need to be supported.
Note: The individual Payer Directory profiles identify the structural constraints, terminology bindings and invariants, however, implementers must refer to the conformance requirements for details on the RESTful operations, specific profiles and the search parameters applicable to each of the Payer Directory actors.
Primary Authors: Dan Chaput, Alex Kontur, Brian Postlethwaite, Bob Dieterle
Payer Directory - CI build (v0.0.1). See the Directory of published versions
General Guidance and Background Information
Contents
Background
Provider directories play a critical role in enabling identification of individual providers and provider organizations, as well as characteristics about them. Provider directories support a variety of use cases, including:
Relation to US Core and other IGs
This implementation guide was written for a US audience. We expect different jurisdictions considering implementing a provider directory will design their implementations in accordance with jurisdictional requirements, such as national profiles/IGs. In the United States, for example, we align with the R4 version of the US Core IG.
The Payer Directory Data Model
To determine which resources to profile, extensions to create, etc. we reviewed a number of use cases. The following are relevant to this implementation guide:
Overview of Payer Directory - Resource Relationships
Note: the following diagrams provide a high-level view of the relationships between resources used in this IG. They do not necessarily reflect all of the relationships/references between resources.
Practitioner
A practitioner is a person who is directly or indirectly involved in the provisioning of healthcare.
Practitioner Role
PractionerRole describes the relationship between a practitioner and an organization. A practitioner provides services to the organization at a location. Practitioners also participate in healthcare provider insurance networks through their role at an organization.
Figure 1: PractionerRole
Organization Affiliation
Similar to PractitionerRole, OrganizationAffiliation describes relationships between organizations. For example: 1) the relationship between an organization and an association it is a member of (e.g. hospitals in a hospital association), 2) an organization that provides services to another organization, such as an organization contracted to provide mental health care for another organization as part of a healthcare provider insurance network, and 3) distinct organizations forming a partnership to provide services (e.g. a cancer center).
Figure 2: OrganizationAffiliation
Network / Insurance Plan
A network is a group of practitioners and organizations that provide healthcare services for individuals enrolled in a health insurance product/plan (typically on behalf of a payer).
Figure 3: Network / Insurance Plan
Technical Guidance for Provider Directory Implementations
Contents
Provider Directory Concept Diagram
This diagram depicts the high-level conceptual design of a central source of Payer’s provider directory data.
Figure 1: Provider Directory Diagram
Exchange
The primary focus of this implementation guide is a RESTful API for obtaining data from a Payer Directory. The exchange API only supports a one-directional flow of information from a Payer Directory into local environments (i.e. HTTP GETs).
Any Payer Directory IG conformant implementation:
In profiles, the “Must Support” flag indicates if data exists for the specific property, then it must be represented as defined in the Provider Directory IG. If the element is not available from a system, this is not required, and may be omitted.
Conceptually, this guide was written to describe the flow of information from a Payer’s provider directory to third party application.
Provider directories may contain a large amount of data that will not be relevant to all use cases or third party application needs. Therefore, the exchange API defines a number of search parameters to enable users to express the scope of a subset of data they care to access. For example, implementations are required to support searches for Organizations based on address, name/alias, and relationship to a parent organization. In general, parameters for selecting resources based on a business identifier, status, type, or relationship (i.e. reference) are required for all implementations. Most parameters may be used in combination with other parameters and support more “advanced” capabilities like modifiers and chains.
The Provider Directory API currently supports one method for accessing directory data, a real-time RESTful Get. However, applications may need other capabilities to support different business needs. For instance, applications may need access to large amounts of Provider Directory data in a single session to either initially seed or refresh their local data repositories. Depending on the scope of data an application is trying to access, a real-time pull may not be the most effective method for acquiring large data sets. The FHIR specification provides support for asynchronous interactions, which may be necessary for implementers to facilitate processing of large amounts of data.
Bulk Data and Subscriptions
Background
Bulk data is a recent addition to the FHIR related specifications and has many uses. Primarily its for out of band data extraction for distribution.
There are several basic parts to FHIR’s bulk data extract
(The items marked with * can be considered quite mature, and unlikely to change from this point)
The bulk data had been balloted and was still being reconciled and at the time of publication of this guide, the current development version can be found here:
http://hl7.org/fhir/us/bulkdata/2019May/index.html
The community discussions and questions around this draft specification are here:
https://chat.fhir.org/#narrow/stream/179250-bulk-data
Healthcare Directory Bulk Data Specifics
The scope of the data selection
For the directory bulk data extraction, to request an entire copy of all content in the directory, the scope selection can be defined at the top level, and just specifying that we would like to retrieve all content for the specified resource types from the base of the FHIR server.
GET [base]/$export?_type=Organization,Location,Practitioner,PractitionerRole,HealthcareService, ...
A healthcare directory may curate such an extract on a nightly process, and just return this without needing to scan the live system, and the value returned in the transactionTime in the result should contain the timestamp at which this was generated (including timezone information), and that may be used in a subsequent call to retrieve changes since this point in time.
Once a system has a complete set of data, it is usually more efficient to ask for changes since a point in time, in which case the request should use the value above (transactionTime) to update the local directory.
GET [base]/$export?_type=Organization,Location,Practitioner, ... &_since=[transactionTime]
This behaves just the same as the initial request, with the exception of the content.
We would expect that this is more likely to return the current information, rather than from a pre-generated snapshot, as the transactionTime could be anything.
Format of the bulk data extract
The bulk extract format is always an nd-json file (new-line delimited json), and each file can only contain 1 resource type in it, but can be spread across multiple files, with either a size limit or count limit imposed by the extracting system, not the requestor.
The list of these files will be returned in the Complete status output, as described in the standard Bulk Data documentation.
Starting the extract
There are 2 options for starting the extract, one that is a single operation specifying all the content, and the other to be for a specific type only.
Here we will only document the use of the global export, as an initial request.
The initial request:
GET [base]/$export?_type=Organization,Location,Practitioner,PractitionerRole,HealthcareService
with headers:
Accept: application/fhir+json Authentication: Bearer [bearer token] Prefer: respond-async
This will return either:
Example Content-Location: http://example.org/status-tracking/request-123 (note that this is not necessarily a FHIR endpoint, and isn’t a true FHIR resource)
Tracking the status of the extract
After a bulk data request has been started, the client MAY poll the URI provided in the Content-Location header.
GET http://example.org/status-tracking/request-123
This will return:
Refer to the build data specification for details of the completion event:
https://github.com/smart-on-fhir/fhir-bulk-data-docs/blob/master/export.md#response—complete-status
{ "transactionTime": "[instant]", "request": "[base]/$export?_type=Organization,Location,Practitioner,PractitionerRole,HealthcareService", "requiresAccessToken": true, "output": [ { "type": "Practitioner", "url": "http://serverpath2/practitioner_file_1.ndjson", "count": 10000 }, { "type": "Practitioner", "url": "http://serverpath2/practitioner_file_2.ndjson", "count": 3017 }, { "type": "Location", "url": "http://serverpath2/location_file_1.ndjson", "count": 4182 } ], // Note that this deletions property is a proposal, not part of the bulk data spec. "deletions": [ { "type": "PractitionerRole", "url": "http://serverpath2/practitionerrole_deletions_1.ndjson", // the bundle will include the total number of deletions in the file "count": 23 // this is the number of bundles in the file, not the number of resources deleted } ], "error": [ { "type": "OperationOutcome", "url": "http://serverpath2/err_file_1.ndjson", "count": 439 } ] }
Retrieving the complete extract
Once the tracking of the extract returns a 200 OK completed status, the body of the result will include the list of prepared files that you can download.
Then each of these URLs can be downloaded by a simple get, ensuring to pass the Bearer token if the result indicates requiresAccessToken = true
While downloading, also recommend including the header Accept-Encoding: gzip to compress the content as it comes down.
GET http://serverpath2/location_file_1.ndjson
Finishing the extract
This is the simplest part of the process, and that is just calling DELETE on the status tracking URL.
DELETE http://example.org/status-tracking/request-123
This then tells the server that we are all finished with the data, and it can be deleted/cleaned up. The server may also include some time based limits where it may only keep it for a set period of time before it automatically cleans it up.
Resource Profiles and Extensions
Profiles
The following profiles set the minimum expectations to search and retrieve validated directory data associated with healthcare entities:
Extensions
The following extensions are used in the above profiles:
Click to return on the top page
Terminology defined for the Payer Directory Implementation Guide
This page lists the ValueSets, CodeSystems, and ConceptMaps defined as part of the Payer Directory implementation Guide. For more information on using codes in resources, see the the FHIR specification.
Value Sets
Code Systems
See the FHIR terminology section for a complete discussion on code systems and a list of codes system names used in FHIR. The following additional names (URIs) have been identified for this implementation guide, If a URI is listed here, it SHALL be used in the Payer Directory profiles in preference to any other code system name.
Code systems published in this IG - Includes Payer Directory defined code systems and externally defined code systems.
ConceptMaps defined as part of the Payer Directory Implementation Guide
Operations and Search Parameters
The following search parameters have been defined for the Payer Directory Implementation Guide. For more information on the FHIR RESTful search api and the standard Search Param Registry see the FHIR specification.
Search Parameter
Endpoint
HealthcareService
Location
Organization
Network
OrganizationAffiliation
Practitioner
PractitionerRole
InsurancePlan
Provider_Directory_server
This Capability Statement defines the expected capabilities of a Payer directory FHIR server conforming to the Payer Directory Implementation Guide.
|
Mode |
SERVER |
|
Description |
|
|
Transaction |
|
|
System History |
|
|
System Search |
y |
|
Resource Type |
Profile |
Read |
V-Read |
Search |
|
Practitioner |
http://hl7.org/fhir/uv/vhdir/StructureDefinition/vhdir-practitioner |
y |
y |
y |
|
PractitionerRole |
http://hl7.org/fhir/uv/vhdir/StructureDefinition/vhdir-practitionerrole |
y |
y |
y |
|
Organization |
y |
y |
y |
|
|
OrganizationAffiliation |
http://hl7.org/fhir/uv/vhdir/StructureDefinition/vhdir-organizationaffiliation |
y |
y |
y |
|
HealthcareService |
http://hl7.org/fhir/uv/vhdir/StructureDefinition/vhdir-healthcareservice |
y |
y |
y |
|
Location |
http://hl7.org/fhir/uv/vhdir/StructureDefinition/vhdir-location |
y |
y |
y |
|
Endpoint |
y |
y |
y |
|
|
InsurancePlan |
http://hl7.org/fhir/uv/vhdir/StructureDefinition/vhdir-insuranceplan |
y |
y |
y |
General Security Considerations
The following are the Provider Directory security considerations that implementers should follow:
The following are security conformance requirements for Provider Directory actors:
Examples
Available for download here
Notes:


Elefoto Space / daylab studio
- Curated by 韩爽 - HAN Shuang
- about 15 hours
- version="1.0" encoding="UTF-8"? Interiors designers: daylab studio
- AreaArea of this architecture projectArea: 265.0 m2
- version="1.0" encoding="UTF-8"?YearCompletion year of this architecture project Year: 2018
- version="1.0" encoding="UTF-8"?PhotographsPhotographs: Xiaoyun
- Architect in Charge Docee Dong
- Design Team Aimee Liu, Xiao Hua, A Xiang
- Client ELEFOTO
More Specs Less Specs
CIRCULATION
This brand new service format brings completely new design challenge. The process of taking photos in such store is much more complicated than traditional F&B or retails. Customers, visitors, makeups, photographers and other employees all squeeze and move in such small area. A good circulation that does not interfere with each other, could directly enhance everybody’s experience.
The less obtrusive while strong functional guide information placed everywhere in the space. It is very friendly to customers who come to the store for the first time.
MILD FUTURE-RETROISM
The elefoto has an IP of “astronaut”, so we proposed a new style --- mild future-retroism. Therefore, in every corner of the space, there are some retro small devices, but after the secondary reconstruction of the design, it has a sense of futuristic, some like a time machine, and some like a space capsule.
SURPRISE
During the whole Store Identity upgrading process, we try to propose more and more interesting details in every single store. Like the SPACE flagship store in Beijing, a few details brought extraordinary surprise of experience.
The existing building originally contained a hollow smoke well. Because of a certain design coincidence, both the reception counter on the first floor and photo producing space on the second floor are next to this hollow well.
We have made a nuanced table for customers’ makeup removing process. The display of the makeup removers, the place to dispose the garbage and other details were well-designed to help the customers easily maintain the whole area tidy and clean, giving the following customer a nice and fresh impression. By the way, because it is nice and also functional, many shop assistants would like to have one at home.
PROFESSIONAL
Although it is the "web influencer " photo studio under the consumption upgrading era, the elefoto is based on its outstanding professional ability since it’s open, which is why, the daylab studio was paying more attention, in the style of design, on delivering the message of its profession and the taste, rather than piling up dazzling “popular elements” to cater to female consumers.
The fans of the elefoto are the first group in the country to have good sense of aesthetics. The large amount of decorative materials in the traditional design, the eye-catching colors, and the high-quality sense of the decoration patterns cannot enhance the brand value, which is also not accepted by the Daylab team. In terms of design, we believe the principle of “introverted taste”, using low-saturation brand color and meticulous detail to impress customers, which is very popular among this group of users.


XQuery mode
MIME types defined: application/xquery
.
Development of the CodeMirror XQuery mode was sponsored by MarkLogic and developed by Mike Brevoort.
MIME types defined: application/xquery
.
Development of the CodeMirror XQuery mode was sponsored by MarkLogic and developed by Mike Brevoort.


Home
Contact
About
Signup
Place
<
About
AShutosh Kumar Saw
Class 3 C
Rollno<PHONE_NUMBER>38
Phone +91<PHONE_NUMBER>
Linkedin
Github
Address
Malkera Green Basti
P.O Malkera P.S Katras
Dhanbad Jharkhand 828304


Provides 'ggplot2' geoms for drawing treemaps.
| Version: | 2.5.2 |
| Depends: | R (≥ 3.1) |
| Imports: | ggplot2 (≥ 2.2.1), ggfittext (≥ 0.5.0), grid (≥ 3.1) |
| Suggests: | knitr, rmarkdown, testthat, vdiffr |
| Published: | 2018-10-13 |
| Author: | David Wilkins |
| Maintainer: | David Wilkins <david at wilkox.org> |
| BugReports: | https://github.com/wilkox/treemapify/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/wilkox/treemapify |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | treemapify results |
| Reference manual: | treemapify.pdf |
| Vignettes: |
Introduction to 'treemapify' |
| Package source: | treemapify_2.5.2.tar.gz |
| Windows binaries: | r-devel: treemapify_2.5.2.zip, r-release: treemapify_2.5.2.zip, r-oldrel: treemapify_2.5.2.zip |
| OS X binaries: | r-release: treemapify_2.5.2.tgz, r-oldrel: treemapify_2.5.2.tgz |
| Old sources: | treemapify archive |
| Reverse imports: | phylotaR |
| Reverse suggests: | XGR |
Please use the canonical form https://CRAN.R-project.org/package=treemapify to link to this page.


Introduction¶
The nmeta (pronounced en-meta) project is founded on the belief that innovation in networks requires a foundation layer of knowledge about both the participants and their types of conversation.
Today, networks generally have only a limited view of participants and conversation types
The goal of the nmeta project is to produce network metadata enriched with participant identities and conversation types to provide a foundation for innovation in networking.
The production of enriched network metadata requires policy-based control, and ability to adapt to new purposes through extensibility.
Enriched network metadata has a number of uses, including classifying flows for QoS, billing, traffic engineering, troubleshooting and security.
Nmeta is a research platform for traffic classification on Software Defined Networking (SDN). It runs on top of the Ryu SDN controller (see: http://osrg.github.io/ryu/).
Limitations¶
Nmeta does not scale well. Every new flow has a processing overhead, and this workload cannot be scaled horizontally on the controller. The nmeta2 system is being developed to address this limitation.
Privacy Considerations¶
Collecting network metadata brings with it ethical and legal considerations around privacy. Please ensure that you have permission to monitor traffic before deploying this software.
Disclaimer¶
This code carries no warrantee whatsoever. Use at your own risk.
How to Contribute¶
Code contributions and suggestions are welcome. Enhancement or bug fixes can be raised as issues through GitHub.
Please get in touch if you want to be added as a contributor to the project:
Email: Nmeta Maintainer


SHOWGIRLS A film review by Ed Powell Copyright 1995 Ed Powell
For the 6 dollars I shelled out for this flick, I could have stayed home and bought a night of the "Spice" channel ... and gotten as much plot and more vivid images of female bodies and sex. This film is proof that it is hype, not plot and story, that really sells a film. (Sad, isn't it?)
The film revolves around a sexy young woman named Nomi (Elizabeth Berkley) who hitchhikes to Las Vegas, to seek a career as a dancer. I cannot really describe the plot, because there really isn't much of one. The film wandered aimlessly around for two hours, trying to interweave several weak story lines into one central plot. It starts nowhere ... ends nowhere ... and fills the in-between with plenty of jiggling breasts.
While all involved played their parts adequately ... there was something that didn't let me believe their parts. This may be in part to the writing of the film: I cannot believe that Las Vegas, or anywhere in the world, for that matter, is *that* sleazy. Then again, I live in a safe little suburb on the East coast....
Since most of the film is apparently shot in Las Vegas, the design of the film was filled with plenty of neon and tasteful glitz (?). In fact, the stage shows were incredibly well done ... I wouldn't be all surprised if they were actual shows being shown in Las Vegas right now.
The director should be given another chance at directing a feature film. The film wasn't too fancy with the shot setups, but some of them were obviously ill-planned. If you go to see this film, be prepared for some badly framed shots (but you'll never have to yell "Focus!").
If you are looking for an evening of sitting in a movie house watching various parts of the female anatomy wobbling, this is for you. Otherwise, just stay home and rent the "Spice" channel. This is not a film where you will find higher forms of entertainment.
* Note: I cannot judge this film on effects or family value. There were no noticeable effects, and scoring on family value just wouldn't be fair ... it is an NC-17 film, after all.
An United Artists release.
RATINGS (out of ten) -------------------- Plot/Story: 2 Writing: 3 Acting: 4 Design: 6 Effects: -- Direction: 4.5 Family Value: -- Music: 3.5
TOTAL: 23 out of 60 (Overall: 3.854)
-- Ed Powell
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Uses of Interface
io.ebean.FetchPath
Package
Description
Provides a API for plugins.
Utility objects for CSV, JSON and XML processing.
JSON formatting and parsing objects (See JsonContext).
-
Uses of FetchPath in io.ebean
-
Uses of FetchPath in io.ebean.plugin
Modifier and TypeMethodDescriptionReturn the FetchPath for the embedded document.BeanDocType.embeddedManyRoot
(String path) For embedded 'many' properties we need a FetchPath relative to the root which is used to build and replace the embedded list. -
Uses of FetchPath in io.ebean.text
Modifier and TypeClassDescriptionclass
This is a Tree like structure of paths and properties that can be used for defining which parts of an object graph to render in JSON or XML, and can also be used to define which parts to select and fetch for an ORM query. -
Uses of FetchPath in io.ebean.text.json
Modifier and TypeMethodDescriptionJsonWriteOptions.getPathProperties()
Return the properties to include by path.Modifier and TypeMethodDescriptionstatic JsonWriteOptions
JsonWriteOptions.pathProperties
(FetchPath pathProperties) Construct JsonWriteOptions with the given pathProperties.void
JsonWriteOptions.setPathProperties
(FetchPath pathProperties) Set the Map of properties to include by path.void
JsonContext.toJson
(Object value, com.fasterxml.jackson.core.JsonGenerator generator, FetchPath fetchPath) Write the bean or collection to the JsonGenerator using the FetchPath.Return the bean or collection as JSON string using FetchPath.void
Write the bean or collection as json to the writer using the FetchPath.


AP source: Browns, C Tretter agree on 3-year extension
by TOM WITHERSBEREA, Ohio (AP) - A person familiar with the negotiations says Cleveland Browns center JC Tretter has agreed to terms on a three-year, $32.5 million contract extension with the club.
Tretter, who is in his third season with the Browns, will receive $23 million guaranteed, said the person who spoke on condition of anonymity because the deal has not been finalized. An announcement could come Friday, the person said.
NFL Network was first to report the agreement.
The 28-year-old Tretter has been a steady presence up front for the Browns, who signed him as a free agent in 2017. He started all 16 games last season and played every snap despite a severely sprained ankle.
Cleveland’s offensive line did not allow a sack in five of the team’s final seven games in 2018.
Tretter was drafted by Green Bay in the fourth round in 2013 out of Cornell. He played three seasons with the Packers, making 10 starts.
More AP NFL: https://apnews.com/NFL and https://twitter.com/AP_NFL
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In typical developer style, I just spent several hours hacking on something only to find a totally different way to accomplish the same thing in about ten minutes.
The short version of setting up ThinkUp app was pretty phenomenally easy:
- Create a free account with a free plan on AppFog
- Use Launcher.io’s ThinkUp Jumpstart one-click install (golden!) to deploy to AppFog
- Upgrade ThinkUp as prompted in your navigation bar
- Update Settings > Account > Enable beta upgrades so you can get the snazzy 2.0 beta
- Upgrade ThinkUp as prompted in your navigation bar (again)
- Add social media accounts in the plugins section
- Clear your browser’s cache
- Rock and roll
I’m intrigued by ThinkUp for a few reasons:
- It’s open source!
- It has a community that first identifies itself as friendly
- It has analytics! based on data!
- Its core codebase was written by one of my favorite developer-founders, Gina Trapani
- Anil Dash, Gina’s co-founder, is one of the best twitter users I know (seriously, did you see this tweet?)
Looking forward to seeing how this goes :)