Convert some function to be a proper pdqr-function of specific class, i.e. a function describing distribution with finite support and finite values of probability/density.
as_p(f, ...)
# S3 method for default
as_p(f, support = NULL, ..., n_grid = 10001)
# S3 method for pdqr
as_p(f, ...)
as_d(f, ...)
# S3 method for default
as_d(f, support = NULL, ..., n_grid = 10001)
# S3 method for pdqr
as_d(f, ...)
as_q(f, ...)
# S3 method for default
as_q(f, support = NULL, ..., n_grid = 10001)
# S3 method for pdqr
as_q(f, ...)
as_r(f, ...)
# S3 method for default
as_r(f, support = NULL, ..., n_grid = 10001,
n_sample = 10000, args_new = list())
# S3 method for pdqr
as_r(f, ...)
| f | Appropriate function to be converted (see Details). |
|---|---|
| ... | Extra arguments to |
| support | Numeric vector with two increasing elements describing desired
support of output. If |
| n_grid | Number of grid points at which |
| n_sample | Number of points to sample from |
| args_new | List of extra arguments for |
A pdqr-function of corresponding class.
General purpose of as_*()
functions is to create a proper
pdqr-function of desired class from input which doesn't satisfy these
conditions. Here is described sequence of steps which are taken to achieve
that goal.
If f
is already a pdqr-function, as_*()
functions properly update it
to have specific class. They take input's "x_tbl" metadata
and type to use with corresponding new_*()
function. For example, as_p(f)
in case of pdqr-function f
is essentially
the same as new_p(x = meta_x_tbl(f), type = meta_type(f))
.
If f
is a function describing "honored" distribution, it is detected
and output is created in predefined way taking into account extra arguments
in ...
. For more details see "Honored distributions" section.
If f
is some other unknown function, as_*()
functions use heuristics
for approximating input distribution with a "proper" pdqr-function. Outputs
of as_*()
can be only pdqr-functions of type "continuous" (because of
issues with support detection). It is assumed that f
returns values
appropriate for desired output class of as_*()
function and output type
"continuous". For example, input for as_p()
should return values of some
continuous cumulative distribution function (monotonically non-increasing
values from 0 to 1). To manually create function of type "discrete", supply
data frame input describing it to appropriate new_*()
function.
General algorithm of how as_*()
functions work for unknown function is as
follows:
Detect support. See "Support detection" section for more details.
Create data frame input for new_*()
. The exact process differs:
In as_p()
equidistant grid of n_grid
points is created inside
detected support. After that, input's values at the grid is taken as
reference points of cumulative distribution function used to
approximate density at that same grid. This method showed to work more
reliably in case density goes to infinity. That grid and density values
are used as "x" and "y" columns of data frame input for new_p()
.
In as_d()
"x" column of data frame is the same equidistant grid is
taken as in as_p()
. "y" column is taken as input's values at this grid
after possibly imputing infinity values. This imputation is done by
taking maximum from left and right linear extrapolations on mentioned
grid.
In as_q()
, at first inverse of input f
function is computed on [0;
1] interval. It is done by approximating it with piecewise-linear
function on [0; 1] equidistant grid with n_grid
points, imputing
infinity values (which ensures finite support), and computing inverse of
approximation. This inverse of f
is used to create data frame input
with as_p()
.
In as_r()
at first d-function with new_d()
is created based on the
same sample used for support detection and extra arguments supplied as
list in args_new
argument. In other words, density estimation is done
based on sample, generated from input f
. After that, its values are
used to create data frame with as_d()
.
Use appropriate new_*()
function with data frame from previous step
and type = "continuous"
. This step implies that all tails outside detected
support are trimmed and data frame is normalized to represent proper
piecewise-linear density.
For efficient workflow, some commonly used distributions are recognized as
special ("honored"). Those receive different treatment in as_*()
functions.
Basically, there is a manually selected list of "honored" distributions with all their information enough to detect them. Currently that list has all common univariate distributions from 'stats' package, i.e. all except multinomial and "less common distributions of test statistics".
"Honored" distribution is recognized only if f
is one of p*()
, d*()
,
q*()
, or r*()
function describing honored distribution and is supplied as
variable with original name. For example, as_d(dunif)
will be treated as
"honored" distribution but as_d(function(x) {dunif(x)})
will not.
After it is recognized that input f
represents "honored" distribution,
its support is computed based on predefined rules. Those take into
account special features of distribution (like infinite support or infinite
density values) and supplied extra arguments in ...
. Usually output support
"loses" only around 1e-6
probability on each infinite tail.
After that, for "discrete" type output new_d()
is used for appropriate data
frame input and for "continuous" - as_d()
with appropriate d*()
function
and support. D-function is then converted to desired class with as_*()
.
In case input is a function without any extra information, as_*()
functions
must know which finite support its output should have. User can supply
desired support directly with support
argument, which can also be NULL
(mean automatic detection of both edges) or have NA
to detect only those
edges.
Support is detected in order to preserve as much information as practically reasonable. Exact methods differ:
In as_p()
support is detected as values at which input function is equal
to 1e-6
(left edge detection) and 1 - 1e-6
(right edge), which means
"losing" 1e-6
probability on each tail. Note that those values are
searched inside [-10^100; 10^100] interval.
In as_d()
, at first an attempt at finding one point of non-zero density
is made by probing 10000 points spread across wide range of real line
(approximately from -1e7
to 1e7
). If input's value at all of them is
zero, error is thrown. After finding such point, cumulative distribution
function is made by integrating input with integrate()
using found point as reference (without this there will be poor accuracy of
integrate()
). Created CDF function is used to find 1e-6
and 1 - 1e-6
quantiles as in as_p()
, which serve as detected support.
In as_q()
quantiles for 0 and 1 are probed for being infinite. If they
are, 1e-6
and 1 - 1e-6
quantiles are used respectively instead of
infinite values to form detected support.
In as_r()
sample of size n_sample
is generated and detected support is
its range stretched by mean difference of sorted points (to account for
possible tails at which points were not generated). Note that this means
that original input f
"demonstrates its randomness" only once inside
as_r()
, with output then used for approximation of "original randomness".
pdqr_approx_error()
for computing approximation errors compared to
some reference function (usually input to as_*()
family).
# Convert existing "proper" pdqr-function
set.seed(101)
x <- rnorm(10)
my_d <- new_d(x, "continuous")
my_p <- as_p(my_d)
# Convert "honored" function to be a proper pdqr-function. To use this
# option, supply originally named function.
p_unif <- as_p(punif)
r_beta <- as_r(rbeta, shape1 = 2, shape2 = 2)
d_pois <- as_d(dpois, lambda = 5)
## `pdqr_approx_error()` computes pdqr approximation error
summary(pdqr_approx_error(as_d(dnorm), dnorm))
#> grid error abserror
#> Min. :-4.753 Min. :-7.979e-07 Min. :9.900e-12
#> 1st Qu.:-2.377 1st Qu.:-4.000e-07 1st Qu.:1.975e-09
#> Median : 0.000 Median :-5.552e-08 Median :5.552e-08
#> Mean : 0.000 Mean :-2.104e-07 Mean :2.104e-07
#> 3rd Qu.: 2.377 3rd Qu.:-1.975e-09 3rd Qu.:4.000e-07
#> Max. : 4.753 Max. :-9.900e-12 Max. :7.979e-07
## This will work as if input is unkonw function because of unsupported
## variable name
my_runif <- function(n) {
runif(n)
}
r_unif_2 <- as_r(my_runif)
plot(as_d(r_unif_2))
# Convert some other function to be a "proper" pdqr-function
my_d_quadr <- as_d(function(x) {
0.75 * (1 - x^2)
}, support = c(-1, 1))
# Support detection
unknown <- function(x) {
dnorm(x, mean = 1)
}
## Completely automatic support detection
as_d(unknown)
#> Density function of continuous type
#> Support: ~[-37.36926, 39.36951] (10000 intervals)#> Density function of continuous type
#> Support: ~[-4, 39.36951] (10000 intervals)#> Density function of continuous type
#> Support: ~[-37.36926, 5] (10000 intervals)
## If support is very small and very distant from zero, it probably won't
## get detected in `as_d()` (throwing a relevant error)
if (FALSE) {
as_d(function(x) {
dnorm(x, mean = 10000, sd = 0.1)
})
}
# Using different level of granularity
as_d(unknown, n_grid = 1001)
#> Density function of continuous type
#> Support: ~[-37.36926, 39.36951] (1000 intervals)


This is our main Div element.
This is child Div.
Move the
mouse over
it to see some effects.
This is the special div that can change color.
Set color 1
Set color 2
Hide
This is the second div. You can show and hide it with the button above this one.


@() @main("Payment page", "false", "pay-page") {
Thank you for joining EFSManager.com.
It is my firm belief that you are taking steps now to increase your profits and productivity by signing up for our site.
Please use the paypal button below to pay for your membership. You account will be activated shortly after payment is received.


UX Designer working alongside a talented team of designers at California State University, Fullerton. Contributed to interview planning and analysis. Helped develop three user personas
Develop a service to facilitate new music discovery for "affinity listeners" - defined as those who actively seek out music that is similar to what they already listen to.
To observe how people interact with their preferred apps to discover new music online.
Twelve participants will be selected from the researchers’ networks who care about discovering new music. These participants will be interviewed for 20-30 minutes and asked a series of questions. They will be asked to perform a brief music discovery task as well. Each interviewer will provide a half page summary of each interview. If the participant agrees, each interview will be audio recorded by the interviewer.
The team will debrief from the interviews and create an affinity map of findings. From this diagram and the research team’s inferences, three personas will be assembled based on common themes among the responses.
We used contextual research to interview and gather insights from 12 users for this project. We wanted to make sure that these personas accurately depict the users' interests and the context in which they use music apps.
Interviewee responses were quite similar, so we opted to utilize an affinity diagram to accurately represent common themes.
Streaming Sally is an Event Planner in her early 20s. She likes to listen to music about 2-3 hours per day, most of which occurs while commuting between home and work.
She primarily uses a paid subscription to Spotify because she despises ads and the subscription allows her to listen to the music that she wants to at any time. The paid subscription also allows her to save Spotify music to her phone for offline playback, which works great since she does not want to deplete her mobile data during her long commutes.
She primarily discovers new music through playlists or genre radio stations on Spotify, and enjoys the suggestions given to her based off her eclectic music tastes. She enjoys matching the music she’s listening to with her mood at any given moment.
Good Spotify suggestions makes her listening experience more enjoyable and she likes that her music service “knows” her tastes.
Likewise, she much prefers when the suggestions have little or no repetition. She wishes that she could use voice command to easily start playing music.
Steady Eddie is a young professional in his late twenties. He is generally an easy going, laid back person that likes consistency and predictability.
Eddie primarily uses Pandora to listen to and discover new music, although he much prefers to listen to familiar music he knows and likes. He likes decades channels and very specific genre channels.
If he’s listening to music, you would most likely find him in his car, commuting to or from work. He prefers to have music on in the background while doing something rather than actively listening to it.
Eddie wants an easy way to listen to music he already likes; he has to be gently introduced to new music and doesn’t like getting his boat rocked too much.
Jeff is 33 years old and lives in Los Angeles, CA. He listens to music 70 hours or more per week - mostly electronic dance music (EDM). Jeff considers himself a “power” music listener and estimates that 50% of the music he listens to is new. Jeff prefers listening to his own playlists over curated streams, and builds playlists for every part of his day.
He primarily uses his computer at home to build his playlists, but will also create playlists on his phone while on the go. His playlists are always evolving based on the latest releases, which he finds using Spotify’s various music discovery features, particularly those that update weekly.
Because he predominantly listens to EDM, he needs to stay on the cutting edge of all new music in this fast-paced genre. Jeff is not concerned about subscription fees. If it gives him more control and and access to the latest music releases, he’ll pay for it.
His ideal music app would include stats on what he listened to that week, give him the ability to customize the user interface, and access all to all the metadata a physical CD would have (he is nostalgic for the days of album artwork and CD cover booklets).
For Jeff, user experience, sound quality, and minimal buffering are important considerations when it comes to selecting a music app.


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{% extends './layouts/default.html' %} {% block title %}{{title}}{% endblock %} {% block content %}
Expected value is calculated using formula $$ \frac jt(1-e^{-tp}) $$ where $j$ is jackpot size, $t$ is number of tickets sold, and $p$ is probability of winning jackpot.


new PolyK() Based on the Polyk library http://polyk.ivank.net released under MIT licence. This is an amazing lib! Slightly modified by Mat Groves (matgroves.com); Source - pixi/utils/Polyk.js, line 34 Methods Triangulate() Triangulates shapes for webGL graphic fills. Source - pixi/utils/Polyk.js, line 42


| Stock | Ticker | Sector | Shares Held | Market Value | % of Portfolio | Change in Shares | % Change in shares |
|---|---|---|---|---|---|---|---|
| {{stock.Filer_Name}} | {{stock.ticker}} | {{stock.sector}} | {{stock.principalAmount}} | {{stock.marketValue }} | {{stock.perPortfolio}} % | {{stock.changeInShares}} | {{stock.percentChange}} % |


Great solution for e-commerce and jobportal companies. Automize your data management workflows, use built-in NLP solutions. Dedicated servers, high data privacy, sophisticated Rest API, related GUI.
Data storing and processing features. Use built-in features and automize your data management.
Effective integration, improved working experience. Use our official SDKs improving your integration, or implement your own business processes.
PHP SDK. Download from Github. Available in Compose.
.Net C# SDK. Download from Nuget Store.
Node.JS SDK. Download from Github. Available in NPM as slamby-sdk.
Python SDK. Download from Github.


often abbreviated as JS, is a high-level, dynamic, weakly typed, prototype-based, multi-paradigm, and interpreted programming language. Alongside HTML and CSS, JavaScript is one of the three core technologies of World Wide Web content production. It is used to make webpages interactive and provide online programs, including video games.
is a multi-paradigm programming language encompassing strong typing, imperative, declarative, functional, generic, object-oriented (class-based), and component-oriented programming disciplines. It was developed by Microsoft within its .NET initiative and later approved as a standard by Ecma (ECMA-334) and ISO (ISO/IEC 23270:2006). C# is one of the programming languages designed for the Common Language Infrastructure.
is a general-purpose computer programming language that is concurrent, class-based, object-oriented,[15] and specifically designed to have as few implementation dependencies as possible. It is intended to let application developers "write once, run anywhere" (WORA),[16] meaning that compiled Java code can run on all platforms that support Java without the need for recompilation.
is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossum and first released in 1991, Python has a design philosophy that emphasizes code readability, and a syntax that allows programmers to express concepts in fewer lines of code,[25][26] notably using significant whitespace. It provides constructs that enable clear programming on both small and large scales.[27]
A lot of languages are object-oriented, but they still have the primitive data type. They’ll have mostly everything as an object, but still with exceptions for integers or maybe characters or something. In Ruby, absolutely everything is an object, meaning everything operates in the same paradigm of sending and receiving messages.
often abbreviated as JS, is a high-level, dynamic, weakly typed, prototype-based, multi-paradigm, and interpreted programming language. Alongside HTML and CSS, JavaScript is one of the three core technologies of World Wide Web content production. It is used to make webpages interactive and provide online programs, including video games.


Reports say Barcelona target Martinez, Kimmich and Milenkovic
by FSA EditorInter forward Lautaro Martinez is reportedly one of Barcelona’s main transfer targets ahead of next season.
Barcelona are preparing for next season and they have several players on their wish list.
The LaLiga leaders are enjoying a decent campaign in 2019-20, but they already have one eye on 2020-2021.
Inter’s Lautaro Martinez, Bayern Munich’s Joshua Kimmich and Fiorentina’s Nikola Milenkovic are reportedly their main targets.
TOP STORY – BARCELONA TARGET MARTINEZ, KIMMICH AND MILENKOVIC
Martinez, Kimmich and Milenkovic are Barcelona‘s main targets ahead of 2020-21, according to AS.
If they miss out on Kimmich, they will look to RB Leipzig’s Lukas Klostermann, the report says.
Meanwhile, Chelsea defender Andreas Christensen is another option, while Arsenal’s Pierre-Emerick Aubameyang is a target if they cannot land Martinez.
ROUND-UP
– Mauricio Pochettino could be set for a quick return to management. Bayern Munich want to hold talks with the former Tottenham manager, according to The Telegraph.
– Could the impact of Jose Mourinho be felt at Tottenham already? Toby Alderweireld, who is out of contract at the end of the season, could stay at Spurs after Mourinho’s arrival, The Telegraph also reports.
– Roma could extend Lorenzo Pellegrini‘s contract, but several clubs are interested in the midfielder. Manchester United, Tottenham, Juventus, AC Milan and Inter have shown some interest in Pellegrini, according to CalcioMercato.
– Marko Pjaca has failed to have an impact since his arrival at Juventus in 2016. CalcioMercato reports the Croatian attacker, who has already been on loan at Schalke and Fiorentina since his arrival in Turin, could be set for another spell away from Juve in January.
– Chelsea have a target in January if their transfer ban is overturned. The Premier League giants have been monitoring CSKA Moscow striker Fedor Chalov, according to Sky Sports. The 21-year-old scored 15 Russian Premier League goals last season and has five in 16 games in 2019-20.


The Future of Education is here
In this world of 7 billion people, we are here to make a impact on all of them by transforming education online. We are very proud of it.
One stop solution for online education
Education in India is primarily provided by public schools (controlled and funded by the government at three levels: central, state and local) and private schools. Under various articles of the Indian Constitution, free and compulsory education is provided as a fundamental right to children aged 6 to 14. The approximate ratio of public schools to private schools in India is 7:5. Major policy initiatives in Indian education are numerous. Up until 1976, education policies and implementation were determined legally by each of India’s constitutional states. The 42nd amendment to the constitution in 1976 made education a ‘concurrent subject’. From this point on the central and state governments shared formal responsibility for funding and administration of education. In a country as large as India, now with 28 states and seven union territories 1, this means that the potential for variations between states in the policies, plans, programs and initiatives for elementary education is vast. Periodically, national policy frameworks are created to guide states in their creation of state-level programs and policies.
Leader in Online Education
Although there are private schools in India, they are highly regulated in terms of what they can teach, in what form they can operate (must be a non-profit to run any accredited educational institution) and all the other aspects of operation. Hence, the differentiation of government schools and private schools can be misleading.[22] However, in a report by Geeta Gandhi Kingdon entitled: The emptying of public Schools and growth of private schools in India, it is said that For sensible education policy making, it is vital to take account of the changing trends in the size of the private and public schooling sectors in India. Ignoring these trends involves the risk of poor policies/legislation, with attendant adverse consequences for children’s life chances.
Transforming Education in India
Takshasila (in modern-day Pakistan) was the earliest recorded centre of higher learning in India from possibly 8th century BCE, and it is debatable whether it could be regarded a university or not in modern sense, since teachers living there may not have had official membership of particular colleges, and there did not seem to have existed purpose-built lecture halls and residential quarters in Taxila, in contrast to the later Nalanda university in eastern India. Nalanda was the oldest university-system of education in the world in the modern sense of university. There all subjects were taught in Ariano -Páli language. Secular institutions cropped up along Buddhist monasteries. These institutions imparted practical education, e.g. medicine. A number of urban learning centres became increasingly visible from the period between 500 BCE to 400 CE. The important urban centers of learning were Nalanda (in modern-day Bihar) and Manassa in Nagpur, among others. These institutions systematically imparted knowledge and attracted a number of foreign students to study topics such as Buddhist Páli literature, logic, páli grammar, etc. Chanakya, a Brahmin teacher, was among the most famous teachers, associated with founding of Mauryan Empire.


| D3Chord | D3 Chord |
| D3Dendro | D3 Dendrogram |
| D3Force | D3 Force |
| D3Sankey | D3 Sankey |
| D3Sunburst | D3 Sunburst |
| D3Tree | D3 Tree |
| D3Venn | D3 Venn |
| D3WordCloud | D3 Word Cloud |
| D3XtabHeat | D3 XtabHeat |
| dfToJSON | Converts data to json form |
| jsonCompare | Multiple results for comparison to json |
| jsonHC | Hierachical Cluster output to json |
| jsonMatrix | Matrix json |
| jsonNestedData | Data frame to nested json |
| jsonNodesLinks | Nodes and Links json |
| jsonOverlaps | Data frame to overlaps json |
| jsonSeqFreq | Sequence frequency json |
| jsonXtabs | Data frame to cross tabs json |


kai.reduce.util
Module Contents
Functions
|
Remove list of files without confirmation. |
|
Make directory if it doesn't already exist. |
|
IRAF doesn't like long file names. This reduces them. |
|
IRAF doesn't like long file names. This reduces them. |
|
Takes files beginning with arg1 and replaces them with arg2 |
|
Takes files ending with arg1 and replaces them with arg2 |
|
Updates coordinates in the header for XREF, YREF |
Attributes
- kai.reduce.util.cp_change_prefix(arg1, arg2)[source]
Takes files beginning with arg1 and replaces them with arg2 Must be in the directory where files live


You've told us that the worker's business has arranged and paid for a substitute to work on this engagement, and that the end client accepted it. This suggests that the end client is engaging the worker on a business to business basis, rather than on a personal service basis.
If HMRC investigate this engagement, the worker and the end client will need to demonstrate that paid substitution has happened.


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All exceptions raised by a JDO implementation are runtime exceptions which means that you can choose to catch the exceptions or not. The reason for JDO using unchecked exceptions is that JDO tries to provide transparent persistence, and so needs to present the same interface and exceptions whatever the state an object has.
This is the base class for all JDO exceptions. It is a subclass of RuntimeException, and need not be declared or caught.
| Exception | Description |
|---|---|
JDOCanRetryException |
This is the base class for errors that can be retried. |
JDOUserException |
This is the base class for user errors that can be retried. |
JDODataStoreException |
This is the base class for datastore errors that can be retried. Any root SQLException that caused this will typically be nested within this exception. |
JDOObjectNotFoundException |
This exception is to notify the application that an object does not exist in the datastore. When this exception is thrown during a transaction, there has been no change in the status of the transaction in progress. If this exception is a nested exception thrown during commit, then the transaction is rolled back. |
JDOUnsupportedOptionException |
This exception is thrown by an implementation to indicate that it does not implement a JDO optional feature. |
| Exception | Description |
|---|---|
JDOFatalException |
This is the base class for errors that cannot be retried. This exception generally means that the transaction associated with the PersistenceManager has been rolled back, and the transaction should be abandoned. |
JDOFatalUserException |
This is the base class for user errors that cannot be retried. |
JDOFatalInternalException |
This is the base class for JDO implementation failures. |
JDOFatalDataStoreException |
This is the base class for fatal datastore errors. When this exception is thrown, the transaction has been rolled back. Any root SQLException that caused this will typically be nested within this exception. |
JDOOptimisticVerificationException |
This exception is the result of a user commit operation in an optimistic transaction where the verification of new, modified, or deleted instances fails the verification. The user will never see this exception except as a result of commit. |
JDODetachedFieldAccessException |
This exception is the result of a user accessing a field of a detached instance, where the field was not copied to the detached instance. |


{% load static %}
Dashboard - SpartanData
SpartanData
Home
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Dashboard
Model Selection
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Attributes selected must be classifyable
{{attributes}}
Select Feature
{{features}}
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Merge keyframe and counter style identifiers.
With npm do:
npm install postcss-merge-idents --save
This module will merge identifiers such as @keyframes
and @counter-style
,
if their properties are identical. Then, it will update those declarations that
depend on the duplicated property.
```css @keyframes rotate { from { transform: rotate(0) } to { transform: rotate(360deg) } }
@keyframes flip { from { transform: rotate(0) } to { transform: rotate(360deg) } }
.rotate { animation-name: rotate }
.flip { animation-name: flip } ```
```css @keyframes flip { from { transform: rotate(0) } to { transform: rotate(360deg) } }
.rotate { animation-name: flip }
.flip { animation-name: flip } ```
See the PostCSS documentation for examples for your environment.
Pull requests are welcome. If you add functionality, then please add unit tests to cover it.
MIT © Ben Briggs


A leader is a person or thing that leads. Leaders guide people in the direction that they believe. TO be a leader, someone must be able to stick up for what they believe in, and cannot be fazed by what is said about them. Malala wrote for education, and when she was threatened, she continued to write, and even when she was shot at, she still continued to write, because she believes in having an education


--- layout: home title: The Pizza Stand - Stories ---
You came to a fair when you see a pizza stand! Your family looks at the menu:
Mom says, "We shall eat pepperoni pizza. Pepperoni pizza tastes better than cheese pizza."
Dad agrees. Dad says, "May I have pepperoni pizza?"
The cashier accepts the order. Oh no. You don't like pepperoni pizza. What should you do?
Never mind. You can always take off the pepperoni. | Say to the cashier, "May I also have a cheese pizza?"


FROM :DR.  AS A MATTER OF FACT,I AM CIVIL SERVANT WORKING WITH THE NIGERIA NATIONAL PETROLEUM CORPORATION (NNPC)ALSO,I AM THE CHAIRMAN (CONTRACT PAYMENT RELEASE REVIEW COMMITTE)DUE TO THE MANIPULATIONS OF CONTRACT AWARD IN THE CORPORATIONS TEN YEARS AGO TILL DATE BY THE PAST AND PRESENT MILITARY AND CIVILIAN GOVERNEMNT OFFICIALS.   AS AT THIS MOMENT,WE ARE IN POCESSION OF SOME UNCLAIMED CONTRACT PAYMENT VALUED AT $650M (SIX HUNDRED AND FIFTY MILLION UNITED STATES ONLY) AS UN CLAIMED CONTRACT PAYMENT.AND AS SUCH,THE TOTAL PAYMENT ARE MEANT TO BE RETURNED BACK TO THE GOOVERNMENT TREASURY.AFTER A CLOSE DOOR MEETING WITH THE REST OF THE BOARD,WE HAVE DECEIDED TO TO DECLARE PART OF THIS PAYMENT TO THE GOVERNMENT.AND WHILE WE WILL SEEK YOUR URGENT ASSISTANT FOR THE CLAIM OF PART OF IT.  SO THAT WE WILL KNOW EXACTLY HOW MUCH TO BE DECLARE TO THE GIVERNMENT.  BECUASE,IN AFRICA,POLITICS WITHOUT MONEY IS BASELESS.  A:YOUR FULL COMPLETE NAME,ADDRESS AND DIRECT PHONE NUMBER. B:COMAPNY NAME,ADDRESS AND YOUR POSITION C:BANK NAME,ADDRESS AND ACCOUNT DETAILS D:MARRIED OR NOT.AND YOUR AGE.   BEST REGARDS DR, KOLA DEVIS


| Top |
GtkTreeSortable is implemented by GtkListStore, GtkTreeModelSort and GtkTreeStore.
GtkTreeSortable is an interface to be implemented by tree models which support sorting. The GtkTreeView uses the methods provided by this interface to sort the model.
gint (*GtkTreeIterCompareFunc) (GtkTreeModel *model
,GtkTreeIter *a
,GtkTreeIter *b
,gpointer user_data
);
A GtkTreeIterCompareFunc should return a negative integer, zero, or a positive
integer if a
sorts before b
, a
sorts with b
, or a
sorts after b
respectively. If two iters compare as equal, their order in the sorted model
is undefined. In order to ensure that the GtkTreeSortable behaves as
expected, the GtkTreeIterCompareFunc must define a partial order on
the model, i.e. it must be reflexive, antisymmetric and transitive.
For example, if model
is a product catalogue, then a compare function
for the “price” column could be one which returns
price_of(@a) - price_of(@b)
.
model |
The GtkTreeModel the comparison is within |
|
a |
A GtkTreeIter in |
|
b |
Another GtkTreeIter in |
|
user_data |
Data passed when the compare func is assigned e.g. by
|
void
gtk_tree_sortable_sort_column_changed (GtkTreeSortable *sortable
);
Emits a “sort-column-changed” signal on sortable
.
gboolean gtk_tree_sortable_get_sort_column_id (GtkTreeSortable *sortable
,gint *sort_column_id
,GtkSortType *order
);
Fills in sort_column_id
and order
with the current sort column and the
order. It returns TRUE
unless the sort_column_id
is
GTK_TREE_SORTABLE_DEFAULT_SORT_COLUMN_ID
or
GTK_TREE_SORTABLE_UNSORTED_SORT_COLUMN_ID
.
sortable |
||
sort_column_id |
The sort column id to be filled in. |
[out] |
order |
The GtkSortType to be filled in. |
[out] |
void gtk_tree_sortable_set_sort_column_id (GtkTreeSortable *sortable
,gint sort_column_id
,GtkSortType order
);
Sets the current sort column to be sort_column_id
. The sortable
will
resort itself to reflect this change, after emitting a
“sort-column-changed” signal. sort_column_id
may either be
a regular column id, or one of the following special values:
GTK_TREE_SORTABLE_DEFAULT_SORT_COLUMN_ID
: the default sort function
will be used, if it is set
GTK_TREE_SORTABLE_UNSORTED_SORT_COLUMN_ID
: no sorting will occur
void gtk_tree_sortable_set_sort_func (GtkTreeSortable *sortable
,gint sort_column_id
,GtkTreeIterCompareFunc sort_func
,gpointer user_data
,GDestroyNotify destroy
);
Sets the comparison function used when sorting to be sort_func
. If the
current sort column id of sortable
is the same as sort_column_id
, then
the model will sort using this function.
void gtk_tree_sortable_set_default_sort_func (GtkTreeSortable *sortable
,GtkTreeIterCompareFunc sort_func
,gpointer user_data
,GDestroyNotify destroy
);
Sets the default comparison function used when sorting to be sort_func
.
If the current sort column id of sortable
is
GTK_TREE_SORTABLE_DEFAULT_SORT_COLUMN_ID
, then the model will sort using
this function.
If sort_func
is NULL
, then there will be no default comparison function.
This means that once the model has been sorted, it can’t go back to the
default state. In this case, when the current sort column id of sortable
is GTK_TREE_SORTABLE_DEFAULT_SORT_COLUMN_ID
, the model will be unsorted.
gboolean
gtk_tree_sortable_has_default_sort_func
(GtkTreeSortable *sortable
);
Returns TRUE
if the model has a default sort function. This is used
primarily by GtkTreeViewColumns in order to determine if a model can
go back to the default state, or not.
struct GtkTreeSortableIface { /* signals */ void (* sort_column_changed) (GtkTreeSortable *sortable); /* virtual table */ gboolean (* get_sort_column_id) (GtkTreeSortable *sortable, gint *sort_column_id, GtkSortType *order); void (* set_sort_column_id) (GtkTreeSortable *sortable, gint sort_column_id, GtkSortType order); void (* set_sort_func) (GtkTreeSortable *sortable, gint sort_column_id, GtkTreeIterCompareFunc sort_func, gpointer user_data, GDestroyNotify destroy); void (* set_default_sort_func) (GtkTreeSortable *sortable, GtkTreeIterCompareFunc sort_func, gpointer user_data, GDestroyNotify destroy); gboolean (* has_default_sort_func) (GtkTreeSortable *sortable); };
Signal emitted when the sort column or sort order of sortable is changed. |
||
Fills in sort_column_id and order with the current sort column and the order. |
||
Sets the current sort column to be sort_column_id. |
||
Sets the comparison function used when sorting to be sort_func. |
||
Sets the default comparison function used when sorting to be sort_func. |
||
|
#define GTK_TREE_SORTABLE_DEFAULT_SORT_COLUMN_ID (-1)
The GTK_TREE_SORTABLE_DEFAULT_SORT_COLUMN_ID can be used to make a GtkTreeSortable use the default sort function.
#define GTK_TREE_SORTABLE_UNSORTED_SORT_COLUMN_ID (-2)
The GTK_TREE_SORTABLE_DEFAULT_SORT_COLUMN_ID can be used to make a GtkTreeSortable use no sorting.
“sort-column-changed”
signalvoid user_function (GtkTreeSortable *sortable, gpointer user_data)
The ::sort-column-changed signal is emitted when the sort column
or sort order of sortable
is changed. The signal is emitted before
the contents of sortable
are resorted.
sortable |
the object on which the signal is emitted |
|
user_data |
user data set when the signal handler was connected. |
Flags: Run Last


--- layout: post title: 'ignore the code: Opinions vs. Data' date: 2012-08-13 22:04:28.000000000 -07:00 categories: - Bits and Pieces tags: - Design - empirical design - usability status: publish type: post published: true meta: _edit_last: '5759' _publicize_pending: '1' author: ---
Lukas Mathis on the mysterious Gmail checkbox-with-dropdown, and why empirical design is important:
“What Leggett described is exactly how I felt about the widget when I first saw it. I immediately figured out how to use it, but my gut reaction was «most people are not going to get how this works.» It seems I was wrong. This is one of the reasons why I don’t put too much trust into opinion-based usability reviews: There’s a lot of guesswork involved, and guessing how humans behave is an endeavor fraught with peril.”


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At the World's
Parliament of
Religions, Chicago
11th September, 1893
Sisters and Brothers of America,
It fills my heart with joy unspeakable to rise in response to the warm and cordial welcome which you have given us. I thank you in the name of the most ancient order of monks in the world; I thank you in the name of the mother of religions; and I thank you in the name of millions and millions of Hindu people of all classes and sects.
My thanks, also, to some of the speakers on this platform who, referring to the delegates from the Orient, have told you that these men from far-off nations may well claim the honour of bearing to different lands the idea of toleration. I am proud to belong to a religion which has taught the world both tolerance and universal acceptance. We believe not only in universal toleration, but we accept all religions as true. I am proud to belong to a nation which has sheltered the persecuted and the refugees of all religions and all nations of the earth. I am proud to tell you that we have gathered in our bosom the purest remnant of the Israelites, who came to Southern India and took refuge with us in the very year in which their holy temple was shattered to pieces by Roman tyranny. I am proud to belong to the religion which has sheltered and is still fostering the remnant of the grand Zoroastrian nation. I will quote to you, brethren, a few lines from a hymn which I remember to have repeated from my earliest boyhood, which is every day repeated by millions of human beings: “As the different streams having their sources in different places all mingle their water in the sea, so, O Lord, the different paths which men take through different tendencies, various though they appear, crooked or straight, all lead to Thee.”
The present convention, which is one of the most august assemblies ever held, is in itself a vindication, a declaration to the world of the wonderful doctrine preached in the Gita: “Whosoever comes to Me, through whatsoever form, I reach him; all men are struggling through paths which in the end lead to me.” Sectarianism, bigotry, and its horrible descendant, fanaticism, have long possessed this beautiful earth. They have filled the earth with violence, drenched it often and often with human blood, destroyed civilisation and sent whole nations to despair. Had it not been for these horrible demons, human society would be far more advanced than it is now. But their time is come; and I fervently hope that the bell that tolled this morning in honour of this convention may be the death-knell of all fanaticism, of all persecutions with the sword or with the pen, and of all uncharitable feelings between persons wending their way to the same goal.


Class: Aws::MediaConvert::Types::AudioDescription
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb |
Overview
Settings related to one audio tab on the ::Aws::MediaConvert
console. In your
job JSON, an instance of AudioDescription
is equivalent to one audio
tab in the console. Usually, one audio tab corresponds to one output
audio track. Depending on how you set up your input audio selectors
and whether you use audio selector groups, one audio tab can
correspond to a group of output audio tracks.
Constant Summary
Instance Attribute Summary
-
#audio_channel_tagging_settings ⇒ Types::AudioChannelTaggingSettings
rw
When you mimic a multi-channel audio layout with multiple mono-channel tracks, you can tag each channel layout manually.
-
#audio_normalization_settings ⇒ Types::AudioNormalizationSettings
rw
Advanced audio normalization settings.
-
#audio_source_name ⇒ String
rw
Specifies which audio data to use from each input.
-
#audio_type ⇒ Integer
rw
Applies only if Follow Input Audio Type is unchecked (false).
-
#audio_type_control ⇒ String
rw
When set to FOLLOW_INPUT, if the input contains an ISO 639 audio_type, then that value is passed through to the output.
-
#codec_settings ⇒ Types::AudioCodecSettings
rw
Settings related to audio encoding.
-
#custom_language_code ⇒ String
rw
Specify the language for this audio output track.
-
#language_code ⇒ String
rw
Indicates the language of the audio output track.
-
#language_code_control ⇒ String
rw
Specify which source for language code takes precedence for this audio track.
-
#remix_settings ⇒ Types::RemixSettings
rw
Advanced audio remixing settings.
-
#stream_name ⇒ String
rw
Specify a label for this output audio stream.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#audio_channel_tagging_settings ⇒ Types::AudioChannelTaggingSettings (rw)
When you mimic a multi-channel audio layout with multiple
mono-channel tracks, you can tag each channel layout manually. For
example, you would tag the tracks that contain your left, right, and
center audio with Left (L), Right (R), and Center (C), respectively.
When you don't specify a value, ::Aws::MediaConvert
labels your track as
Center (C) by default. To use audio layout tagging, your output must
be in a QuickTime (.mov) container; your audio codec must be AAC,
WAV, or AIFF; and you must set up your audio track to have only one
channel.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end
#audio_normalization_settings ⇒ Types::AudioNormalizationSettings (rw)
Advanced audio normalization settings. Ignore these settings unless you need to comply with a loudness standard.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end
#audio_source_name ⇒ String
(rw)
Specifies which audio data to use from each input. In the simplest case, specify an "Audio Selector":#inputs-audio_selector by name based on its order within each input. For example if you specify "Audio Selector 3", then the third audio selector will be used from each input. If an input does not have an "Audio Selector 3", then the audio selector marked as "default" in that input will be used. If there is no audio selector marked as "default", silence will be inserted for the duration of that input. Alternatively, an "Audio Selector Group":#inputs-audio_selector_group name may be specified, with similar default/silence behavior. If no audio_source_name is specified, then "Audio Selector 1" will be chosen automatically.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end
#audio_type ⇒ Integer
(rw)
Applies only if Follow Input Audio Type is unchecked (false). A number between 0 and 255. The following are defined in ISO-IEC 13818-1: 0 = Undefined, 1 = Clean Effects, 2 = Hearing Impaired, 3 = Visually Impaired Commentary, 4-255 = Reserved.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end
#audio_type_control ⇒ String
(rw)
When set to FOLLOW_INPUT, if the input contains an ISO 639 audio_type, then that value is passed through to the output. If the input contains no ISO 639 audio_type, the value in Audio Type is included in the output. Otherwise the value in Audio Type is included in the output. Note that this field and audioType are both ignored if audioDescriptionBroadcasterMix is set to BROADCASTER_MIXED_AD.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end
#codec_settings ⇒ Types::AudioCodecSettings (rw)
Settings related to audio encoding. The settings in this group vary depending on the value that you choose for your audio codec.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end
#custom_language_code ⇒ String
(rw)
Specify the language for this audio output track. The service puts this language code into your output audio track when you set Language code control to Use configured. The service also uses your specified custom language code when you set Language code control to Follow input, but your input file doesn't specify a language code. For all outputs, you can use an ISO 639-2 or ISO 639-3 code. For streaming outputs, you can also use any other code in the full RFC-5646 specification. Streaming outputs are those that are in one of the following output groups: CMAF, DASH ISO, Apple HLS, or Microsoft Smooth Streaming.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end
#language_code ⇒ String
(rw)
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end
#language_code_control ⇒ String
(rw)
Specify which source for language code takes precedence for this audio track. When you choose Follow input, the service uses the language code from the input track if it's present. If there's no languge code on the input track, the service uses the code that you specify in the setting Language code. When you choose Use configured, the service uses the language code that you specify.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end
#remix_settings ⇒ Types::RemixSettings (rw)
Advanced audio remixing settings.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end
#stream_name ⇒ String
(rw)
Specify a label for this output audio stream. For example,
"English", "Director commentary", or "track_2". For streaming
outputs, ::Aws::MediaConvert
passes this information into destination
manifests for display on the end-viewer's player device. For
outputs in other output groups, the service ignores this setting.
# File 'gems/aws-sdk-mediaconvert/lib/aws-sdk-mediaconvert/types.rb', line 578
class AudioDescription < Struct.new( :audio_channel_tagging_settings, :audio_normalization_settings, :audio_source_name, :audio_type, :audio_type_control, :codec_settings, :custom_language_code, :language_code, :language_code_control, :remix_settings, :stream_name) SENSITIVE = [] include Aws::Structure end


These leading institutions and individuals have stepped up to advance open research and community ownership of scholarship by financially supporting our work.
We’re incredibly grateful for their generous, essential support.
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Founder
CJ de Jong
Access Services Coordinator, Librarian
You’ve seen our tools, now here’s the strategy. Redirect money and power in scholarly communications by reducing reliance on subscriptions so libraries have leverage to negotiate for better prices and terms with publishers in order to make a more equitable and open world. To reduce reliance on subscriptions, community owned tools have to Deliver Articles (differently) and Fill your Repository. We won’t let paying us to get in the way, it’s hard enough already, or stop libraries who can’t afford subscriptions already using our tools.
To continue our work, we need Leadership from libraries who want to join us on the cutting edge of open, or just benefit from our tools, to financially support our work.
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{% extends "base.html" %} {% block title %}About{% endblock %} {% block content %}
Odyssey SpeedTech is a website for finding speedrunning clips and strategies for Super Mario Odyssey. The goal is to get all of the clips scattered around Twitter, YouTube, and Twitch and compile them all into one location where people can easily find them.
This website is created by Charlie Schuy. Contact him on Twitter (@Krankdud) or on Discord (Krankdud#1273) if you have questions or suggestions!
The code for the website is open source! You can check it out on GitHub.
{% endblock %}


Tutorial
Install
Zouw needs below requirements to run.
- Python 3.6 or 3.7
- PySpark 2.4.3
- Ray 0.7.2
- Tensorflow 1.15.0
- aiohttp
- setproctitle
- scikit-learn >=0.20.0
- psutil
- requests
- featuretools
- pandas
- Note that Keras is not needed to use Zouwu. But if you have Keras installed, make sure it is Keras 1.2.2. Other verisons might cause unexpected problems.
You can install above python dependencies manually, or install using below command.
pip install analytics-zoo[automl]
Using built-in forecast models
The built-in forecast models are all derived from tfpark.KerasModels.
1.To start, you need to create a forecast model first. Specify horizon and feature_dim in constructor.
horizon
: no. of steps to forecastfeature_dim
: dimension of input feature
Refer to API doc for detailed explaination of all arguments for each forecast model.
Below are some example code to create forecast models.
#import forecast models
from zoo.zouwu.model.forecast import LSTMForecaster
from zoo.zouwu.model.forecast import MTNetForecaster
#build a lstm forecast model
lstm_forecaster = LSTMForecaster(horizon=1,
feature_dim=4)
#build a mtnet forecast model
mtnet_forecaster = MTNetForecaster(horizon=1,
feature_dim=4,
lb_long_steps=1,
lb_long_stepsize=3
2.Use forecaster.fit/evalute/predict
in the same way as tfpark.KerasModel
3.For univariant forecasting (i.e. to predict one series at a time), you can use either LSTMForecaster or MTNetForecaster. The input data shape for fit/evaluation/predict
should match the arguments you used to create the forecaster. Specifically:
- X shape should be
(num of samples, lookback, feature_dim)
- Y shape should be
(num of samples, horizon)
- Where,
feature_dim
is the number of features as specified in Forecaster constructors.horizon
is the number of steps to look forward as specified in Forecaster constructors.lookback
is the number of time steps you want to look back in history.
4.For multivariant forecasting (i.e. to predict several series at the same time), you have to use MTNetForecaster. The input data shape should meet below criteria. Note for multivariant forecasting, horizon must be 1.
- X shape should be
(num of samples, lookback, feature_dim)
- Y shape should be
(num of samples, num_of_targets)
- Where
lookback
should equal(lb_long_steps+1) * lb_long_stepsize
, wherelb_long_steps
andlb_long_stepsize
are as specified inMTNetForecaster
constructor.num_targets
is the number of series to forecast at the same time
Using AutoTS
The automated training in zouwu is built upon Analytics Zoo AutoML module (refer to AutoML ProgrammingGuide and AutoML APIGuide for details), which uses Ray Tune for hyper parameter tuning and runs on Analytics Zoo RayOnSpark.
The general workflow using automated training contains below two steps.
- create a
AutoTSTrainer
to train aTSPipeline
, save it to file to use later or elsewhere if you wish. - use
TSPipeline
to do prediction, evaluation, and incremental fitting as well.
You'll need RayOnSpark
for training with AutoTSTrainer
, so you have to init it before auto training, and stop it after training is completed. Note RayOnSpark is not needed if you just use TSPipeline for inference, evaluation or incremental training.
- init RayOnSpark in local mode
from zoo import init_spark_on_local
from zoo.ray import RayContext
sc = init_spark_on_local(cores=4)
ray_ctx = RayContext(sc=sc)
ray_ctx.init()
- init RayOnSpark on yarn
from zoo import init_spark_on_yarn
from zoo.ray import RayContext
slave_num = 2
sc = init_spark_on_yarn(
hadoop_conf=args.hadoop_conf,
conda_name="ray36",
num_executor=slave_num,
executor_cores=4,
executor_memory="8g ",
driver_memory="2g",
driver_cores=4,
extra_executor_memory_for_ray="10g")
ray_ctx = RayContext(sc=sc, object_store_memory="5g")
ray_ctx.init()
- After training, stop RayOnSpark.
ray_ctx.stop()
Both AutoTSTrainer and TSPipeline accepts data frames as input. An exmaple data frame looks like below.
| datetime | value | extra_feature_1 | extra_feature_2 |
|---|---|---|---|
| 2019-06-06 | 1.2 | 1 | 2 |
| 2019-06-07 | 2.3 | 0 | 2 |
1.To create an AutoTSTrainer. Specify below arguments in constructor. See below example.
dt_col
: the column specifying datetimetarget_col
: target column to predicthorizon
: num of steps to look forwardextra_feature_col
: a list of columns which are also included in input as features except target column
from zoo.zouwu.autots.forecast import AutoTSTrainer
trainer = AutoTSTrainer(dt_col="datetime",
target_col="value"
horizon=1,
extra_features_col=None)
2.Use AutoTSTrainer.fit
on train data and validation data. A TSPipeline will be returned.
ts_pipeline = trainer.fit(train_df, validation_df)
3.Use TSPipeline.fit/evaluate/predict
to train pipeline (incremental fitting), evaluate or predict.
#incremental fitting
ts_pipeline.fit(new_train_df, new_val_df, epochs=10)
#evaluate
ts_pipeline.evalute(val_df)
ts_pipeline.predict(test_df)
4.Use TSPipeline.save/load
to load from file or save to file.
from zoo.zouwu.autots.forecast import TSPipeline
loaded_ppl = TSPipeline.load(file)
# ... do sth. e.g. incremental fitting
loaded_ppl.save(another_file)


SushiLab
SushiLab is JavaScript REPL environment for matrix computing.
Almost all operations are performed on your computer;
not on the server.
The user interface is alpha version. Future improvements
are planned.
Name: Import:


supdate
is a small Clojure/ClojureScript library
for transforming nested data structures in a straightforward and efficient way.
supdate
lets you express nested transformations as data structures which match the schema of the input,
and then executes these transformations efficiently.
You could say supdate
is to data transformation what plumatic/schema
is to data validation. It plays in the same arena as Specter
, complementing
it nicely for many basic cases where Specter would be too much.
(require '[vvvvalvalval.supdate.api :as supd])
Here's a basic example (it's interactive, feel free to modify the code!):
(require '[clojure.string :as str])
;; Our example uses some string functions
(require '[clojure.string :as str])
;; Let's define some example data
(def input
{:bands [{:band/id 3141
:band/name "Led Zeppelin"
:band/members [{:person/name "robert plant"}
{:person/name "jimmy page"}
{:person/name "john bonham"}
{:person/name "john paul jones"}]}
{:band/id 8242
:band/name "The White stripes"
:band/members [{:person/name "jack white"}
{:person/name "meg white"}]}]})
;; ... then transform it:
(supd/supdate
input
{:bands [{:band/name str/upper-case
:band/members [{:person/name str/capitalize}]}]})
supdate
works by defining transforms as data structures.
The most elementary transform you can encounter is just a function:
(supd/supdate 0 inc)
(supd/supdate
"Jimmy Page"
(fn [s] (str/split s " ")))
(supd/supdate
[2 3 5 7 11 13 17 19]
[inc])
(supd/supdate
(range 12)
[#(* % %)])
(supd/supdate
{:name "Alice" :age 7}
{:age inc})
Note that the transformation only occurs if the key is present!
(supd/supdate
{:name "Ilúvatar"}
{:age inc})
Finally, if you use false
as a value instead of a transform, it means you want the key to be dissoc'ed:
(supd/supdate
{:name "Alice" :age 7}
{:age false})
(supd/supdate
" jimmy page "
[str/trim str/capitalize])
What if you want to do both, chaining tranforms and applying them in sequences? Well, you can use 2 vectors:
(supd/supdate
(range 10)
[[inc inc inc]])
supdate
really becomes interesting when you start nesting transforms together:
(def input1
{:band/members [{:id 1 :name ["jimmy" "page"] :plays "guitar"}
{:id 2 :name ["robert" "plant"] :plays "voice"}
{:id 3 :name ["john" "paul" "jones"] :plays "bass"}
{:id 4 :name ["john" "bonham"] :plays "drums"}]})
(supd/supdate
input1
{:band/members [{:id false
:name [[str/capitalize]
#(str/join " " %)]
:plays keyword}]})
supdate
fast?Yes! Supdate will tend to outperform code hand-written using update-in
or assoc-in
, using several optimizations.
It will avoid doing several 'deep dives' in the data, doing all it has to do once at a given path.
What's more, supd/supdate
is actually a macro, leveraging static information on a best-effort basis to compile to efficient code.
Finally, you can also use supd/compile
to pre-compile transforms, and achieve a similar effect more dynamically:
(def input
{:bands [{:band/id 3141
:band/name "Led Zeppelin"
:band/members [{:person/name "robert plant"}
{:person/name "jimmy page"}
{:person/name "john bonham"}
{:person/name "john paul jones"}]}
{:band/id 8242
:band/name "The White stripes"
:band/members [{:person/name "jack white"}
{:person/name "meg white"}]}]})
;; Pre-compiling our transform
(def prettify-bands
(supd/compile
{:bands [{:band/name str/upper-case
:band/members [{:person/name str/capitalize}]}]}))
;; Running it later:
(prettify-bands input)
Github issue
!
Ths page is live and interactive powered by the klipse plugin:


Wineries and Restaurants
There are more than 55 wineries to choose from - offering cellar door sales, restaurants, cafes and tours. Along the way discover a foodie's paradise. Meet the growers, producers, providers and chefs. Experience the fun of picking your own produce or buying dinner ingredients at the farm gate. Dine with views of the sea, lakes and vineyards.
Golf
The Mornington Peninsula is blessed with 19 courses that both challenge and excite.
Many golf courses provide the most exhilarating views over Port Phillip Bay,
Western Port and Bass Strait.
The sand hills and coastal hinterland provide wonderful examples of links,
resort and traditional courses. Visitors are always welcome.
Peninsula Hot Springs
Another world awaits. Enjoy the thermal bathing area or book a private indoor or outdoor hot springs
bath experience.
Activities include reflexology treatments, foot and hand baths, sauna, steam baths, cold plunges and
yoga. The cafe at the bath house provides light meals and gourmet pizzas. Feel revitalised and
relaxed at the same time.
Queenscliff-Sorrento Ferry
There's no better way to cross the Bay either with your car or as a foot passenger. It only takes 40 minutes. Spot the playful dolphins whilst enjoying a coffee and light refreshments. The ferries run every day of the year on the hour. Wonderful restaurants at historic Queenscliff on the Bellarine peninsula offer perfect lunchtime adventures.
Walking Tracks
Fabulous walking tracks in any direction for all ages. Beautiful beach walks, amazing cliff top strolls and for the more serious walkers, a 100km walk combines the Bay trial, National Parks and the two Bays walk through the hinterland.
Arts and Culture
The Mornington Peninsula hosts an array of exhibitions and galleries, featuring fine art, sculpture, ceramics, jewelry, hand blown glass and clothing. Small local galleries and craft markets are in abundance across the region.
Shopping Escapades
Lovely, leisurely shopping - for pure pleasure - is listed as the number one activity for holidaymakers. Sorrento and Mornington will not disappoint. Bookshops, giftware, home living and of course the odd handbag or "must have that" top is yours for the exploring.
Water Sports
Endless Fun for all the family. Sailing, fishing, kayaking, paddle boarding, snorkeling, scuba diving and surfing are all on offer, not to mention the cricket match on the beach.


Suicide of Mitsubishi Electric unit employee caused by overwork
KOBE — A labor standard office has concluded that the 2017 suicide of an employee at a Mitsubishi Electric Corp unit was work-related, recognizing he did more than 100 hours of overtime per month while in a supervisory position, company officials said.
The Oct 4 certification for the death of the engineer in his 40s at Melco Semiconductor Engineering Corp came as labor authorities have stepped up monitoring after finding that a slew of firms have promoted workers to supervisory positions to make them work longer hours.
The Japanese labor law limits working hours of regular employees but the restrictions do not apply to workers in supervisory positions.
The worker was on loan to another Mitsubishi Electric unit from April 2013 and became mentally ill after working extensively at a plant in Toyooka in Hyogo Prefecture.
He killed himself in December 2017 after being transferred to work in Fukuoka, southwestern Japan, and his family filed for the certification of his death as work-related in July last year.
In Fukuoka, the discretionary labor system was applied to him. It rewards workers based on fixed overtime work hours instead of actual hours worked.
"We take seriously the fact that a worker at a related company has died. We will work on thoroughly implementing appropriate labor control," Mitsubishi Electric said in a statement Friday.
The labor authorities awarded workers' compensation to five employees at Mitsubishi Electric between 2014 and 2017, of whom two had committed suicide.
© KYODO


These functions are the R wrappers of
effect(),
hide(),
show() in jQuery UI library. They
should be used in server
of a shiny document.
jqui_effect(selector, effect, options = NULL, duration = 400, complete = NULL) jqui_show(selector, effect, options = NULL, duration = 400, complete = NULL) jqui_hide(selector, effect, options = NULL, duration = 400, complete = NULL) jqui_toggle(selector, effect, options = NULL, duration = 400, complete = NULL)
| selector | A jQuery's selector that determines the shiny tag element(s) whose interaction is enable or disable interactions. |
|---|---|
| effect | A string indicating which jQuery UI effect to use for the transition. |
| options | A list of effect-specific properties and easing. |
| duration | A string or number determining how long the animation will run. |
| complete | A function to call once the animation is complete, called once per matched element. |
jqui_effect
Apply an animation effect to matched element(s).
jqui_hide
Hide the matched element(s) with animation effect.
jqui_show
Display the matched element(s) with animation effect.
jqui_toggle
Display or hide the matched element(s) with animation effect.
# NOT RUN { # in shiny ui create a plot plotOutput('foo') # in shiny server apply a 'bounce' effect to the plot jqui_effect('#foo', 'bounce') # in shiny server hide the plot with a 'fold' effect jqui_hide('#foo', 'fold') # in shiny server show the plot with a 'blind' effect jqui_show('#foo', 'blind') # }


A query. Queries are created from strings with Query#new. If parsing the string fails, a ParseError is thrown.
At the lowest level, queries are composed of space-separated terms. Matches against that term are restricted to the default field specified at parse time.
hello # search for "hello" in the default field
Term matches can be restricted to another field by by prefixing them with the field name and “:”, e.g. “subject:hello”.
subject:hello # search for "hello" in the "subject" field
Multiple terms are considered conjunctive (i.e. all must match) unless the special token “OR” appears between them. The “OR” must be capitalized in this case.
word1 word2 # search for word1 and word2 word1 OR word2 # search for word1 or word2 subject:hello bob # "hello" in the subject field and "bob" in the # default field
Parentheses can be used to group disjunctions, conjunctions or fields.
(word1 OR word2) word3 # "word3" and either "word1" or "word2" field:(word1 OR word2) # "word1" or "word2" in field "field"
Phrases are specified by surrounding the terms with double quotes.
"bob jones" # documents with the phrase "bob jones"
Negations can be specified with a - prefix.
-word # docs without "word" -subject:(bob OR joe) # docs with neither "bob" nor "joe" in subject
Labels are specified with a ~ prefix. Labels do not have fields.
~inbox # docs with the "inbox" label -~inbox # docs without the "inbox" label -~inbox subject:hello # docs with subject "hello" and without the # inbox label
All of the above can be mixed and matched, of course.
-subject:"spam email" ~inbox (money OR cash) ("love you" OR "hate you") -(~deleted OR ~spam) etc...
Existing query objects can also be altered programmatically, at least to a limited extent, by calling Query#and and Query#or.
Creates a new query by parsing the string query_string, which must be a String. Any non-fielded terms will used the field default_field, which must also be a String. Raises a ParseError if the query cannot be parsed.
static VALUE query_new(VALUE class, VALUE default_field, VALUE string)
Returns a deep copy of the query, with any in-progress search state discarded. Useful for rerunning a query without interrupting any pagination state.
static VALUE query_clone(VALUE self)
Disabled; run with --debug to generate this.
Generated with the Darkfish Rdoc Generator 1.1.6.


This flashcard study game is geared towards art history students and enthusiasts who are looking for resources to study more effectively. Art(sy) History Study aims to make studying art history more effective, approachable, and fun.
This was made possible through Artsy, another great resource for all your art needs. This tool is a work in progress and more features will be added in the near future.
Object of the game: Guess the period or movement from which each artwork orginates. If you answer correctly, guess the artist as a bonus question. There's a total of six rounds.
Correct period/movement is worth 10pts.
Bonus question is worth an extra 10pts.
{{displayArtworkTitle}}, {{displayArtworkDate}}


Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang (2017) <arXiv:1707.08215>.
| Version: | 0.5.1 |
| Depends: | methods |
| Imports: | Rcpp, RobustGaSP |
| LinkingTo: | Rcpp, RcppEigen |
| Published: | 2018-05-14 |
| Author: | Mengyang Gu [aut, cre] |
| Maintainer: | Mengyang Gu <mgu6 at jhu.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| CRAN checks: | RobustCalibration results |
| Reference manual: | RobustCalibration.pdf |
| Package source: | RobustCalibration_0.5.1.tar.gz |
| Windows binaries: | r-devel: RobustCalibration_0.5.1.zip, r-release: RobustCalibration_0.5.1.zip, r-oldrel: RobustCalibration_0.5.1.zip |
| OS X binaries: | r-release: RobustCalibration_0.5.1.tgz, r-oldrel: RobustCalibration_0.5.1.tgz |
| Old sources: | RobustCalibration archive |
Please use the canonical form https://CRAN.R-project.org/package=RobustCalibration to link to this page.


|
Smack | ||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
See:
Description
| Interface Summary | |
|---|---|
| PrivateData | Interface to represent private data. |
| Class Summary | |
|---|---|
| AdHocCommandData | Represents the state and the request of the execution of an adhoc command. |
| AdHocCommandData.SpecificError | |
| AttentionExtension | A PacketExtension that implements XEP-0224: Attention This extension is expected to be added to message stanzas of type 'headline.' Please refer to the XEP for more implementation guidelines. |
| AttentionExtension.Provider | A PacketExtensionProvider for the AttentionExtension . |
| ChatStateExtension | Represents a chat state which is an extension to message packets which is used to indicate the current status of a chat participant. |
| ChatStateExtension.Provider | |
| DataForm | Represents a form that could be use for gathering data as well as for reporting data returned from a search. |
| DataForm.Item | Represents items of reported data. |
| DataForm.ReportedData | Represents the fields that will be returned from a search. |
| DefaultPrivateData | Default implementation of the PrivateData interface. |
| DelayInfo | A decorator for the DelayInformation class to transparently support
both the new Delay Delivery specification XEP-0203 and
the old one XEP-0091. |
| DelayInformation | Represents timestamp information about data stored for later delivery. |
| DiscoverInfo | A DiscoverInfo IQ packet, which is used by XMPP clients to request and receive information to/from other XMPP entities. |
| DiscoverInfo.Feature | Represents the features offered by the item. |
| DiscoverInfo.Identity | Represents the identity of a given XMPP entity. |
| DiscoverItems | A DiscoverItems IQ packet, which is used by XMPP clients to request and receive items associated with XMPP entities. |
| DiscoverItems.Item | An item is associated with an XMPP Entity, usually thought of a children of the parent entity and normally are addressable as a JID. |
| Header | Represents a Header entry as specified by the Stanza Headers and Internet Metadata (SHIM) |
| HeadersExtension | Extension representing a list of headers as specified in Stanza Headers and Internet Metadata (SHIM) |
| LastActivity | A last activity IQ for retrieving information about the last activity associated with a Jabber ID. |
| LastActivity.Provider | The IQ Provider for LastActivity. |
| MessageEvent | Represents message events relating to the delivery, display, composition and cancellation of messages. |
| MUCAdmin | IQ packet that serves for kicking users, granting and revoking voice, banning users, modifying the ban list, granting and revoking membership and granting and revoking moderator privileges. |
| MUCAdmin.Item | Item child that holds information about roles, affiliation, jids and nicks. |
| MUCInitialPresence | Represents extended presence information whose sole purpose is to signal the ability of the occupant to speak the MUC protocol when joining a room. |
| MUCInitialPresence.History | The History class controls the number of characters or messages to receive when entering a room. |
| MUCOwner | IQ packet that serves for granting and revoking ownership privileges, granting and revoking administrative privileges and destroying a room. |
| MUCOwner.Destroy | Represents a request to the server to destroy a room. |
| MUCOwner.Item | Item child that holds information about affiliation, jids and nicks. |
| MUCUser | Represents extended presence information about roles, affiliations, full JIDs, or status codes scoped by the 'http://jabber.org/protocol/muc#user' namespace. |
| MUCUser.Decline | Represents a rejection to an invitation from another user to a room. |
| MUCUser.Destroy | Represents a notification that the room has been destroyed. |
| MUCUser.Invite | Represents an invitation for another user to a room. |
| MUCUser.Item | Item child that holds information about roles, affiliation, jids and nicks. |
| MUCUser.Status | Status code assists in presenting notification messages. |
| MultipleAddresses | Packet extension that contains the list of addresses that a packet should be sent or was sent. |
| MultipleAddresses.Address | |
| Nick | A minimalistic implementation of a PacketExtension for nicknames. |
| Nick.Provider | |
| OfflineMessageInfo | OfflineMessageInfo is an extension included in the retrieved offline messages requested by
the OfflineMessageManager . |
| OfflineMessageInfo.Provider | |
| OfflineMessageRequest | Represents a request to get some or all the offline messages of a user. |
| OfflineMessageRequest.Item | Item child that holds information about offline messages to view or delete. |
| OfflineMessageRequest.Provider | |
| PEPEvent | Represents XMPP Personal Event Protocol packets. |
| PEPItem | Represents XMPP Personal Event Protocol packets. |
| PEPPubSub | Represents XMPP PEP/XEP-163 pubsub packets. |
| RosterExchange | Represents XMPP Roster Item Exchange packets. |
| SharedGroupsInfo | IQ packet used for discovering the user's shared groups and for getting the answer back from the server. |
| SharedGroupsInfo.Provider | Internal Search service Provider. |
| StreamInitiation | The process by which two entities initiate a stream. |
| StreamInitiation.File | size: The size, in bytes, of the data to be sent. name: The name of the file that the Sender wishes to send. date: The last modification time of the file. |
| Time | A Time IQ packet, which is used by XMPP clients to exchange their respective local times. |
| VCard | A VCard class for use with the SMACK jabber library. |
| Version | A Version IQ packet, which is used by XMPP clients to discover version information about the software running at another entity's JID. |
| XHTMLExtension | An XHTML sub-packet, which is used by XMPP clients to exchange formatted text. |
XML packets that are part of the XMPP extension protocols.
|
Smack | ||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |


I have experience in building web systems or websites with the best in technologies in the market.
In addition to being a software developer, I am father, husband and I love staying with my family in my free time.
I enjoy watching action movies, adventure, comedy, biographies, I also risk playing my old guitar when possible, and sometimes I look back on my childhood time drawing anything with my kids.
And during free time I try to keep up to date by doing some web courses (Alura, Youtube, etc), tutorials and books.
Working as a software developer in sector financial, using technologies like JavaEE, JSF, SpringBoot and Angular2.
Acting as Software Developer, performing analysis and development of systems aimed at the financial sector (Credit Platforms, Commercial, HR and etc).
Acting as Software Developer, performing analysis and development of systems aimed at the financial sector (Credit Platforms, Commercial, HR and etc).
Acting as Systems Analyst, performing analysis and development of systems aimed at the motor insurance company (Claim, Reimbursement, Legal).
Analysis, development and maintenance of the former Royal Bank systems.


Red Team C2 Log Visualization
RedEye is an open-source analytic tool developed by CISA and DOE’s Pacific Northwest National Laboratory to assist Red Teams with visualizing and reporting command and control activities. This tool allows an operator to assess and display complex data, evaluate mitigation strategies, and enable effective decision making in response to a Red Team assessment. The tool parses logs, such as those from Cobalt Strike, and presents the data in an easily digestible format. The users can then tag and add comments to activities displayed within the tool. The operators can use the RedEye’s presentation mode to present findings and workflow to stakeholders.
RedEye can assist an operator to efficiently:
Follow along with the User Guide to learn about RedEye’s feature set.
./RedEye --redTeam --password <your_password>
./RedEye
from the command line.
MacOS Issue - When running RedEye for the first time, you may get a “not verified” error. You must go to “System Preferences” > “Security & Privacy” > “General” and click “Open Anyway.” More info on the Apple support page.
RedEye has two modes that cover two stages of the Red Teaming process.
Red Team mode allows importing C2 data, editing imported data, and making comments and
presentations. After curating and annotating campaign data, Red Teams can export their campaign as a standalone
.redeye
file and hand it off to a Blue Team for
reporting and remediation. Blue Team mode runs RedEye in a simplified read-only mode
for viewing curated data exported by a Red Team.
Note: Both Red and Blue Team modes can be started from the same RedEye application binary.
The downloaded binary comes in two parts:
RedEye
application binaryparsers
folder containing parser binaries (e.g. cobalt-strike-parser
Cobalt
Strike log parser binary)
There are three options to run RedEye in Red Team mode:
--redTeam
and password options:
./RedEye --redTeam --password <your_password>
docker-compose.yml
.docker-compose -f docker-compose.yml up -d redeye-core
The Blue Team mode is a simplified, read-only UI for displaying data that has been curated, annotated, and exported by a Red Team. This mode runs by default to make startup more simple for the Blue Team.
The Blue Team version can be run by double-clicking the ‘RedEye’ application binary. RedEye runs at http://<IP_ADDRESS>:4000 (by default) and will automatically open your default browser.
If a campaigns
folder is located in the same directory as the RedEye
application,
RedEye will attempt to import any .redeye
campaign files within. Campaign files can be exported in
the Red Team mode.
To prepare a version for the Blue Team, follow these two steps:
RedEye
application binary to an empty folder.campaigns
folder in the same directory and place the .redeye
campaign files
you want to send inside.
Folder/
RedEye
campaigns/
Campaign-01.redeye
Campaign-02.redeye
.redeye
files can also be uploaded in Blue Team mode via the ”+ Add Campaign” dialog.
Type ./Redeye -h
to view the options
-d, --developmentMode [boolean] put the database and server in development mode
-r, --redTeam [boolean] run the server in red team mode
--port [number] the port the server should be exposed at
-p, --password [string] the password for user authentication
--parsers [string...] A list of parsers to use or a flag to use all parsers in the parsers folder
-t, --childProcesses [number] max # of child processes the parser can use
-h, --help display help for command
you can also configure the server parameters in a config.json
file that sits next to the
RedEye
binary
{
"password": "937038570",
"redTeam": true,
"parsers": ["cobalt-strike-parser", "brute-ratel-parser"]
}
npm install -g yarn
yarn install
// Installs all packagesyarn release:all
to build a binary for Linux, macOS, and Windowsyarn release:(mac|windows|linux)
.Install Node.js >= v16 Install yarn globally via npm
npm install -g yarn
Install package dependencies
yarn install
Runs the project in development mode
yarn start
It is recommended to run the server and client in two separate terminals
yarn start:client
…in another terminal
yarn start:server
to build a binary for Linux, macOS, and Windows
yarn release:all
to build for a specific platform, replace all
with the platform name
yarn release:(mac|windows|linux)