'Social workers ran my life as a kid in care - now I am one'
by Judith BurnsVery early on, social workers decided the family home was not a safe place for Kerry.
"There was lots of drug and alcohol abuse, as well as domestic violence towards my mum. I got taken into care at the age of four."
The rest of her childhood and teenage years were spent in foster-care placements and children's homes in Kent.
'Scary and bewildering'
It was hard to build strong relationships. "I was constantly waiting for the next move," she says.
Kerry remembers each change being "scary and bewildering".
"You don't know if it's going to be a good placement or a bad placement."
But even with the good ones, relationships were lost. And Kerry could never be sure of staying at the same school.
She says she was always in trouble at primary school - "That classic, you sort of freak out if there's a room change or timetable change or we got a supply teacher".
She says she often thought: "I can't really remember anything awful happening so I haven't got the right to feel like this," almost wishing there had been "a car accident or something that you can hang your hat on".
In secondary school she was told she had autism and became a weekly boarder at a "really, really good" specialist school.
But by Year 10 she says she was struggling with her mental health and her behaviour.
Sofa-surfing
She was with the foster family at weekends and in the holidays until "they just made the decision they had come to the end of the work they felt they could do with me", and she was moved to a children's home.
She says the diagnosis of autism helped her, unlocking funding for specialist support and helping her understand why she struggled to cope with the noise and bustle of a typical school.
Her grades were OK but not great. "At the time I was really annoyed with myself but looking back I think it's a miracle I got any GCSEs," she says.
Kerry quit school for a string of low-paid jobs and soon afterwards left the children's home, staying in supported accommodation for care leavers until being "thrown out... after a really bad incident with one of the other residents", and ending up sofa-surfing.
"I hit a point where I thought, 'I just can't go on like this'."
As a care leaver, she was entitled to council help getting a flat, and she began working with people with learning disabilities.
It was the only job she could get but Kerry loved it.
Being able to help other people struck a chord - even as a young child, she had been the one everyone in class would come to with their problems.
"I would be the one that would sit and talk it through with people."
Back then, she'd had no idea her ability to empathise could lead to a career.
"Growing up, I didn't know anyone who was in care who had gone to university," she says.
"I didn't think it would ever be a possibility. I had kind of written it off and didn't really have a clue what I would end up doing with my life."
'It's beyond me'
But some of the staff at the learning disabilities unit were really encouraging, telling her: "This is what you should be doing. You should go to university."
So Kerry went back to college, took an A-level-equivalent Access course, and began a degree in psychology at the University of Kent.
It was tough, she says, but she feared she would be kicked out if she admitted needing extra help.
Statistics showing that only six per cent of care leavers aged 19-21 go to university - and are almost twice as likely to drop out as their peers - particularly worried her.
"I remember thinking about that in my second year and being like, maybe it's just kids in care can't do uni and it's beyond me.
"There was a lot of imposter syndrome, I was waiting for someone to come and tap me on the shoulder and be like, 'We made a mistake. You're not meant to be here'."
And then, about halfway through her second year, her local authority gave her access to her case file.
"It brought a lot up," she remembers.
"There wasn't much that I didn't know, necessarily, but seeing it written in black and white is very different from fleeting memories from when you were a kid."
The file sparked depression. She stopped going to lectures and fell so far behind that she didn't think she would be able to catch up, and she wanted to quit.
"It was an evil circle," she says.
But the university wouldn't let her.
She says staff were "fantastic", helping her make a plan to get the work done.
"I didn't have parents so I was really lucky the staff stepped in and helped and carried on pushing me."
Just being around people who believed in her really helped.
"For the first time I had friends who were really positive role models."

She knew she desperately wanted to do a masters degree and work in mental health but it wasn't until her third year that she heard about mental-health social work. She decided: "That's what I want to be doing."
She graduated in July 2019 and two weeks later started on the Think Ahead fast-track training scheme for mental-health social workers, which includes a masters degree - and a caseload from the outset.
In echoes of her school days, she's moved - this time to Preston, 300 miles from her home town of Ramsgate. But "everyone is really friendly".
People on her caseload are already making progress, and she has started to build a support network, so she's planning to stay.
"I like helping people find their way through things and I like that moment where you can see something's really clicked with someone," she says.
Kerry believes the distress and bewilderment of so much upheaval as a child will help her be a better social worker and is hoping ultimately to work with adults who have suffered trauma as children.
"I want to use the experiences I've had in the past to help people."


Before Change
utterances_index = []
utt_start = len(self.utterances)
for utt_index, (utterance, speaker_id) in enumerate(zip_longest(utterances, utterances_speaker)):
if utterance is None:
break
// Pipe currently broken in spacy 2 alpha
// Also, spacy 2 currently throws an exception on empty strings
try:
doc = self.nlp(utterance)
except IndexError:
doc = self.nlp(u" ")
if self.debug: print("Empty string")
if speaker_id not in self.speakers:
speaker_name = speakers_names.get(speaker_id, None) if speakers_names else None
self.speakers[speaker_id] = Speaker(speaker_id, speaker_name)
After Change
utterances_speaker = ((i + a + 1) % 2 for i in range(len(utterances)))
utterances_index = []
utt_start = len(self.utterances)
docs = list(self.nlp.pipe(utterances))
m_spans = list(extract_mentions_spans(doc, use_no_coref_list=self.use_no_coref_list) for doc in docs)
for utt_index, (doc, m_spans, speaker_id) in enumerate(zip_longest(docs, m_spans, utterances_speaker)):
if speaker_id not in self.speakers:


EQUIVS-BUILD(1) Debian/GNU EQUIVS-BUILD(1) NAME equivs-build - make a Debian package to register local software SYNOPSIS equivs-build [--full|-f] [--arch=foo|-a=foo] controlfile DESCRIPTION equivs-build is a program that creates Debian packages which can be used to inform dpkg about locally installed packages and their dependencies. Also empty packages that just require other packages can be created with equivs. These can be used as "profile" packages which just mark other ones for installation. Please note that this is a crude hack and if thoughtlessly used, it might possibly do damage to your packaging system. And please note as well that using it is not the recommended way of dealing with broken dependencies. Better file a bug report instead. The control file has a structure like the control files in "real" Debian packages, but it doesn't have to define all keys, as default values will be used if you omit one. There are several additional fields that can be used: Changelog: File to be used as the changelog Version: If you don't use a local changelog, equivs will create a dummy one. As the version of the package is defined in the changelog, equivs will assume the version 1.0. With this field, you can set an explicit version. Readme: A file to be used as the README.Debian file. You can use the strings @packagename@ and @depends@ to insert the package name and the dependency information used. Copyright: The copyright file to by used. If omitted, the GPL 2 will be used. Preinst: Postinst: Prerm: Postrm: Maintainerscripts executed before/after installation/removal of the package. See the Debian developers reference for a description (package developers-reference). Extra-Files: A comma-separated list of files to copy to the /usr/share/doc/packagename directory. Files: Files to be copied into some directory of the created package. Each line contains a source filename and a destination directory, space- separated. As with other multi-line headers, all lines but the first must be indented. Example: Files: foo-cron /etc/cron.d/ foo-cron-helper /usr/local/bin/ File: Files to be copied into some directory of the created package, specified inline in the source control file. The field syntax is the name of the files to be created, followed by lines indented by 1 space denoting file content; leading space will be trimmed. An octal file mode may be specified after the filename; it defaults to 644, and will be normalised to 644 or 755. Example: File: /usr/local/bin/true 755 #!/bin/sh exit 0 See /usr/share/doc/equivs/README.Debian for more information. OPTIONS --full | -f Do a complete build. debuild will be called, that is, a full package will be built and signed, suitable for upload to the Debian servers. The ID used to signed is taken from, in that order, the user from the last entry of a supplied changelog, the Maintainer: field in the equivs control file, or the local username. --arch | -a Build package for the specified architecture. Used e.g. for building packages for hurd under linux. equivs-build uses dpkg-architecture to provide this facility. DISTRIBUTION Distribution is subject to the GNU public licence version 2. AUTHOR Martin Bialasinski<EMAIL_ADDRESS>SEE ALSO equivs-control(1), /usr/share/doc/equivs/README.Debian 30 Sep 2011 EQUIVS-BUILD(1)


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Dakota Den
Asking the Right Questions
October 23, 2014
There a main points to keep in mind when asking a question. The first one, and obviously the easiest one, is whether or not you are asking a question in person or posting it online. In person you can be a lot more vague when starting, because you can go into more detail later. Questions asked in person would be useless online (example: "I have a Ruby Class Issue"). So for this post I'm going to go in depth with asking questions over the internet.
Be Specific
The biggest key is to be specific. After writing your question, read it over and try to put yourself in an outsider's shoes, who is coming into your situation cold. Is there anything that would be confusing? Better to add too much than not enough. That being said, be careful not to go on tangents either.
Be Precise
It's a good idea to put the general concept of what you're trying to achieve with the entire program, but you don't need to put meticulous details from every aspect of your code, just what you're having issues with. For example, if you copy and paste your code into your question (which is recommended) and you're having an issue on line 32, don't paste lines 1-130!
Have English Skills
This one isn't the most important, but I would argue is the easiest way for someone to skip your question. Have good punctuation and grammar, so people can actually read what you're trying to ask.
Be Open
Lastly, be active in looking at the question you posted. If people are commenting with questions to clear up what you're asking, make sure to give more details. If someone correctly answered your questions, make sure the online community knows so people don't waste any more effort and can help other people. If the question hasn't been answered in a way you find acceptable, respond stating why. Help those you help you!


The paper explores HyperNetworks. The idea is to use one network (HyperNetwork) to generate the weights for another network.
Consider a $D$ layer CNN where the parameters for the $j^{th}$ layer are stored in a matrix $K^j$ of the shape $N_{in}f_{size} \times N_{out}f_{size}$.
The HyperNetwork is implemented as a two-layer linear network where the input is a layer embedding $z^j$, and the output is $K^j$.
The first layer (of the HyperNetwork) maps the input to $N_{in}$ different outputs using $N_{in}$ weight matrices.
The second layer maps the different $N_{in}$ inputs to $K_{i}$ using a shared matrix. The resulting $N_{in}$ (number of) $K_{i}$ matrices are concatenated to obtain $K^j$.
As a side note, HyperNetworks have much fewer params than the network for which it produces weights.
In a general case, the kernel dimensions (across layers) are not of the same size but integer multiples of some basic sizes. In that case, the HyperNetwork can generate kernels for the basic size, which can be concatenated to form larger kernels. This would require additional input embeddings but not require a change in the architecture of HyperNetwork.
HyperRNNs/HyperLSTMs denote HyperNetworks that generates weights for RNNs/LSTMs.
HyperRNNs implement a form of relaxed weight sharing - an alternative to the full weight sharing of the traditional RNNs.
At any timestamp $t$, the input to the HyperRNN is the concatenated vector $x_{t}$ (input to the RNN at time $t$) and the hidden state $h_{t-1}$ of the RNN. The output is the weight for the main RNN at timestep $t$.
In practice, a weight scaling vector $d$ is used to reduce the memory footprint, which would otherwise be $dim$ times the memory of a standard RNN. $dim$ is the dimensionality of the embedding vector $z_j$.
HyperNetworks are used to train standard CNNs for MNIST and ResNets for CIFAR 10. In these experiments, HyperNetworks slightly underperform the best performing models but uses much fewer parameters.
HyperLSTMs trained on the Penn Treebank dataset and Hutter Prize Wikipedia dataset outperform the stacked LSTMs and perform similar to layer-norm LSTMs. Interestingly, using HyperLSTMs with layer-norm improves performance over HyperLSTMs.
Given the similar performance of HyperLSTMs and layer-norm LSTMs, the paper conducted an ablation study to understand if HyperLSTMs learned a weight adjustment policy similar to the statistics-based approach used by layer-norm LSTMs.
HyperLSTMs are also evaluated for handwriting sequence generation by training in the IAM online handwriting dataset.
On the WMT’14 En-to-Fr machine translation task, HyperLSTMs outperform LSTM based approaches.


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coulor T-shirt
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pink dress
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Boys English Jacket
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girls red dress
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|---|---|---|
| Delivery : |
Best desgners that you can count on
Visit our store
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copyright 2021 Azalea Conditions - All right reserved


Post
Create Post
Filter by Category
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John
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<EMAIL_ADDRESS>July
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july@example.com


Calculate the true curve of your blank
Measurement
measure the blank with your digital comparator
Base index
1.5
1.56
1.6
1.67
1.74
Diopter correction
0.000


|
ITKMultigridAnisotropicDiffusion
1.0.0
Implementation of an Anisotropic Diffusion filter, using Multigrid techniques
|
Class implementation of the two inter grid operators. It has to be initialized with an array prescribing which kind of coarsening has to be used between the two grids on each direction: vertex centered vs cell centered. It does not need other informations on the grids, as they are extracted from the input image when Interpolation or Restriction are called. More...
#include <itkInterGridOperators.h>
Public Types | |
| enum | PointPositionType { left, interior, right } |
| typedef double | Precision |
| typedef InterGridOperators | Self |
|
typedef Image< Precision, VDimension > | ImageType |
|
typedef Neighborhood < Precision, VDimension > | StencilType |
| typedef ImageRegion< VDimension > | ImageRegionType |
| typedef ImageType::SpacingType | SpacingType |
| typedef ImageType::SizeType | SizeType |
| typedef ImageType::IndexType | IndexType |
| typedef ImageType::OffsetType | OffsetType |
Public Member Functions | |
| InterGridOperators (const std::array< bool, VDimension > &vertexCentering) | |
| ~InterGridOperators () | |
| ImageType::Pointer | Interpolation (const ImageType *inputImage, const bool *ignoreLeftBorder=nullptr, const bool *ignoreRightBorder=nullptr) const |
| ImageType::Pointer | Restriction (const ImageType *inputImage, const bool *ignoreLeftBorder=nullptr, const bool *ignoreRightBorder=nullptr) const |
Class implementation of the two inter grid operators. It has to be initialized with an array prescribing which kind of coarsening has to be used between the two grids on each direction: vertex centered vs cell centered. It does not need other informations on the grids, as they are extracted from the input image when Interpolation or Restriction are called.
Definition at line 48 of file itkInterGridOperators.h.
| typedef double itk::mad::InterGridOperators< VDimension >::Precision |
Standard class typedefs.
Definition at line 53 of file itkInterGridOperators.h.
| enum itk::mad::InterGridOperators::PointPositionType |
Characterization of a point based on its position relative to the interior of the image region.
Definition at line 66 of file itkInterGridOperators.h.
| itk::mad::InterGridOperators< VDimension >::InterGridOperators | ( | const std::array< bool, VDimension > & | vertexCentering | ) |
Class constructor which takes as argument whether the coarse grid is obtained via vertex centered versus cell centered approach.
Definition at line 37 of file itkInterGridOperators.hxx.
|
inline |
Class destructor.
Definition at line 73 of file itkInterGridOperators.h.
| InterGridOperators< VDimension >::ImageType::Pointer itk::mad::InterGridOperators< VDimension >::Interpolation | ( | const ImageType * | inputImage, |
| const bool * | ignoreLeftBorder = nullptr , |
||
| const bool * | ignoreRightBorder = nullptr |
||
| ) | const |
Interpolation operator. It optionally accepts an array whose boolean values prescribe if the border points, on the left and right side of each direction, has to be restricted with the same stencil as that of interior points.
Definition at line 48 of file itkInterGridOperators.hxx.
| InterGridOperators< VDimension >::ImageType::Pointer itk::mad::InterGridOperators< VDimension >::Restriction | ( | const ImageType * | inputImage, |
| const bool * | ignoreLeftBorder = nullptr , |
||
| const bool * | ignoreRightBorder = nullptr |
||
| ) | const |
Restriction operator. It optionally accepts an array whose boolean values prescribe if the border points, on the left and right side of each direction, has to be restricted with the same stencil as that of interior points.
Definition at line 181 of file itkInterGridOperators.hxx.


jQuery is a popular JavaScript library which simpifies JavaScript programming
jQuery User Interface is built on the top of jQuery. It is useful to design highly interactive web applications
jQuery Mobile is a HTML5-based user interface system designed to make responsive web sites and apps that are accesible on all smartphone, tablet and desktop devices. Also in next lessons we will learn about ios programming with swift and objective c.


ul datasource
local datasource
I wanna pan&zoom the orgchart
I wanna align orgchart with different orientation
Bottom to Top
Left to Right
Right to Left
I wanna show/hide left/right sibling nodes respectively by clicking left/right arrow
I wanna load datasource through ajax
I wanna load data on-demand
I wanna customize the structure of node
I wanna export the organization chart as a picture
I wanna itegrate organization chart with geographic information
I wanna edit orgchart
I wanna drag & drop the nodes of orgchart
I want a method that can decribe the hierarchy of orgchart
I want a color-coded chart
I want a multiple-layers chart
I want a hybrid(horizontal + vertical) chart


The first step in building a VRaniML console based program is to start with a new project. To do this complete the following steps from the MS Visual C++ IDE. Note: These instructions assume that you are using version 5.0 of MS Visual C++.
#include "stdlib.h" #include "stdio.h" void main(int argc, char *argv[]) { printf("VRaniML Pretty Printer\n"); printf("Version 1.0\n\n"); }
You should see the text "VRaniML Pretty Printer" show up on the DOS window. In the next step we will link to the VRaniML library and prepare for reading a VRML 2.0 file.


--- layout: archive title: Lean Prover Zulip Chat Archive permalink: archive/113488general/99821andinVSCode.html ---
I'm trying to learn the category theory library. Why do I not get told what the keyboard shortcuts for 𝟙
(the identity morphism) and 𝟭
(the identity functor) are, when I hover over them in VS Code? NB 𝟙
is \b1
but I can't seem to find the other one in https://github.com/leanprover/vscode-lean/blob/master/translations.json which is where I usually look on the rare occasion when this happens.
And another thing: hovering over ≈
in VS Code gives Type ≈ using ~~ ...
and it should be \~~


Option YES passed 17-0-1 at meeting of Mar-89.-----
Forum: Compiler
Issue: CONSTANT-CIRCULAR-COMPILATION
References: Issue CONSTANT-COLLAPSING
Issue QUOTE-SEMANTICS
Category: CLARIFICATION, ADDITION
Edit History: V1, 07 Nov 1988, Sandra Loosemore
V2, 14 Nov 1988, Cris Perdue
V3, 12 Dec 1988, Sandra Loosemore (merge versions 1 and 2)
V4, 03 Jan 1989, Sandra Loosemore (add PRESERVE-SHARING-ONLY)
V5, 06 Jan 1989, Sandra Loosemore (minor wording changes)
V6, 08 Feb 1989, Sandra Loosemore (replace FLAG with YES)
V7, 11 Mar 1989, Sandra Loosemore (error terminology)
V8, 18 Mar 1989, Sandra Loosemore (changes per Moon, Masinter)
Status: Ready for release
Problem Description:
CLtL does not specify whether constants containing circular or
recursive references may be compiled. It is also not clear whether
the compiler must preserve sharing of EQL substructures; that is, whether
subobjects that are EQL in the source code must remain EQL after being
compiled.
The proposals below apply to constants appearing in a file compiled by
COMPILE-FILE. If proposal QUOTE-SEMANTICS:SAME-AS-COMPILE-FILE
passes, then the same constraints would apply to all constants. The
minimal scope over which sharing would be required to be detected is
over a single call to EVAL or COMPILE.
In the proposals that follow, "preserving EQLness" means that
subobjects that are EQL in the source code must remain EQL after being
compiled; that is, things don't get "less EQL" after compilation.
(Note that coalescing of constants implies that things may get "more
EQL".)
Proposal CONSTANT-CIRCULAR-COMPILATION:NO
State that conforming programs must not contain circular objects
appearing as constants to be compiled. The consequences of compiling
a program containing such an object with COMPILE-FILE and/or LOADing
the output produced by COMPILE-FILE for such a program [and, if we
accept proposal QUOTE-SEMANTICS:SAME-AS-COMPILE-FILE, compiling it
with COMPILE or evaluating it interpretively] are undefined.
State that the compiler is not required to preserve EQLness of
substructures.
Rationale:
This proposal would not require any existing implementation to change.
Disallowing portable programs from containing circular constants
allows compiled file loaders to use somewhat simpler implementation
strategies (for example, to build constants in a strict bottom-up
fashion).
Proposal CONSTANT-CIRCULAR-COMPILATION:PRESERVE-SHARING-ONLY
State that conforming programs must not contain circular objects
appearing as constants to be compiled. The consequences of compiling
a program containing such an object with COMPILE-FILE and/or LOADing
the output produced by COMPILE-FILE for such a program [and, if we
accept proposal QUOTE-SEMANTICS:SAME-AS-COMPILE-FILE, compiling it
with COMPILE or evaluating it interpretively] are undefined.
State that the compiler is required to preserve EQLness of
substructures within a file compiled with COMPILE-FILE.
Rationale:
Disallowing portable programs from containing circular constants
allows compiled file loaders to use somewhat simpler implementation
strategies (for example, to build constants in a strict bottom-up
fashion).
Some programs (such as PCL) have come to depend on COMPILE-FILE
preserving the EQLness of uninterned symbols, and it is cleaner
to require sharing to be preserved in general instead of making
symbols be a special case. Requiring sharing to be preserved still
allows loaders to build constants bottom-up.
Proposal CONSTANT-CIRCULAR-COMPILATION:YES
State that objects containing circular references may legitimately
appear as constants to be compiled. State that the compiler is
required to preserve EQLness of substructures within a file compiled
with COMPILE-FILE.
Rationale:
Users seem to expect this functionality, and some implementations
already provide it.
Current Practice:
A-Lisp preserves EQLness of substructures (since it makes an effort to
collapse isomorphic structures) but signals an error if an attempt is
made to compile a circular constant. PSL and Utah Common Lisp both
get stuck in an infinite loop if an attempt is made to compile a
reentrant structure. The TI Explorer compiler is able to reproduce
recursive lists and arrays, but currently hangs in a loop on a
circular list. Neither the Explorer nor Symbolics Genera 7.x detects
EQLness of list CDRs. Lucid handles circular constants correctly.
Franz uses a flag to control whether or not to attempt to detect
circular constants. KCL handles circular structures, but only detects
sharing of top-level structure (it does not traverse constants to look
for shared substructure).
Cost to implementors:
We know of no implementation that would have to change under proposal
NO.
For proposal YES, some implementations would require sweeping
changes; in some cases a completely different dumper/loader strategy
would have to be implemented.
The cost of proposal PRESERVE-SHARING-ONLY would fall somewhere in
between.
Cost to users:
The situation now is that programs which depend upon circularity or
sharing of substructure being preserved by the compiler are already
nonportable. Proposal NO simply formalizes the status quo. Proposal
YES would offer users functionality that is currently not portable.
Portable CommonLoops reportedly requires EQLness of uninterned symbols
to be preserved across a file, and would break under proposal NO.
According to Cris Perdue:
I am told that support for PCL requires the kinds of guarantees about
uninterned symbols [that say EQLness will be preserved]. Jim Kempf
here at Sun tells me he believes that this is true. John Foderaro
said that Franz made their compiler give this behavior in order to
support PCL.
Jim Kempf says he believes that certain GENSYMs appear in multiple
pieces of initialization code across a file in PCL, and the
initialization code only works if symbols EQ at compile time map to
symbols that are EQ at load time.
Benefits:
An area of ambiguity in the language is removed.
Discussion:
The issue of compiler speed is largely a red herring on this issue;
the overhead of detecting circularities is generally quite small. The
main question is whether we should require some implementations to
completely redo their compiler/loader interface in order to support
circular constants.
It has been argued that any "serious" implementation will support
circular constants anyway, because of customer demand. However, since
there appears to be only one implementation (Lucid) that now
implements proposal YES in its full generality, perhaps the demand for
this feature is not really all that strong.
Earlier drafts of this writeup contained a proposal FLAG which would
have added a variable *COMPILE-CIRCLE*, similar to *PRINT-CIRCLE*.
However, there were unresolved problems about what would happen if the
value of this variable were altered within the file being compiled,
and it was generally agreed that this proposal didn't have any
particular advantages over proposal YES and just introduced
unnecessary hairiness.
Since it is usually fairly simple to detect circular constants,
Loosemore would support an amendment to proposals NO and
PRESERVE-SHARING-ONLY to change the word "undefined" to "unspecified",
adding:
Implementations must either correctly handle the circular reference
or signal an error.
This is similar to the language which is already used in proposal
CONSTANT-COMPILABLE-TYPES:SPECIFY.


The info I collect on this form is purely so I can make the scoreboard work and contact you if you win a prize. If this bothers you please put a fake name and no contact information.
There are video cameras inside the challenge rooms so people can see what your doing and to make it interesting. I may be streaming this live online.
You have to step over a board when going through the doorways, and its possible to get splinters, please be careful.


Sadly, the dubious honor of "first to be slashed on Polkadot" belongs to me.
First the concerning bits: With all else being equal, where there are funding gaps on accounts placing trust, they will be made whole. This means that if your address appears in the linked table of affected nominators and the slash is applied, you will receive a top-up as soon as we have transfers.
So back to the crux of the issue. Like I said elsewhere, "I make mistakes. I try not to". This means that generally I take actions based on the information and knowledge available to me - and in hindsight this information, despite proven in practice, may be lacking. The short version:
During a slightly less than optimal period of stability, one of the restarts on a non-validating node yielded a corrupt database. To ensure the active validator nodes don't suffer the exact same corruption fate, I opted for freshly restored databases on the active nodes, moving from RocksDb to ParityDb in the process. However...
... Grandpa state is also stored in the database, so the restoration of blocks/state from a non-validating node removed the Grandpa state. The node started up, looked for valid state and ended up reporting itself and another active node (also restored) as misbehaving. The information was cleanly restored, no extra downtime, just not all of it.
The learnings here is that best practice is not just avoiding downtime, being vigilant in planning for failures, ensuring keys are not shared, but also ensuring that a full own-node database backup is used at all times. Restoring from "elsewhere" (as often advocated and employed by operators), may end up with unintended consequences.
- Do you believe it was clearly a software bug? Make no mistake I had "some" issues with stability in the last while, but the node didn't decide to slash itself.
- Do you believe it is operator error? The actions taken were made clear-headed, all based on steps taken before in testnets and other live networks, nothing that goes against documented approaches.
- So you blame knowledge? I cannot subscribe to a "I didn't know" defence and based on prior testing, the outcome was (seemingly) known.
Overall, it is gray - there are certainly improvements that can be made, but sometimes a combination ends up creating a small storm of effects. Dark unexplored corners sometimes (even if you've been there before) yield spiders. Be safe out there.


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{% for item in items %}
{% for i in item %}
{{ i }}
{% endfor %}
{% endfor %}


You should add at least one step to explain how to make your project.
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pitchRx: Tools for Harnessing 'MLBAM' 'Gameday' Data and Visualizing
'pitchfx'
With 'pitchRx', one can easily obtain Major League Baseball Advanced
Media's 'Gameday' data (as well as store it in a remote database). The
'Gameday' website hosts a wealth of data in XML format, but perhaps most
interesting is 'pitchfx'. Among other things, 'pitchfx' data can be used to
recreate a baseball's flight path from a pitcher's hand to home plate. With
pitchRx, one can easily create animations and interactive 3D 'scatterplots'
of the baseball's flight path. 'pitchfx' data is also commonly used to
generate a static plot of baseball locations at the moment they cross home
plate. These plots, sometimes called strike-zone plots, can also refer to a
plot of event probabilities over the same region. 'pitchRx' provides an easy
and robust way to generate strike-zone plots using the 'ggplot2' package.
| Version: |
1.8.2 |
| Depends: |
R (≥ 2.15.1), ggplot2 (≥ 0.9.3) |
| Imports: |
XML2R (≥ 0.0.6), plyr, MASS, hexbin, mgcv |
| Suggests: |
DBI, dplyr, RSQLite (≥ 1.0.0), parallel, knitr, animation, shiny, testthat, ggsubplot, rgl |
| Published: |
2015-12-09 |
| Author: |
Carson Sievert |
| Maintainer: |
Carson Sievert <cpsievert1 at gmail.com> |
| BugReports: |
http://github.com/cpsievert/pitchRx/issues |
| License: |
MIT + file LICENSE |
| URL: |
http://cpsievert.github.com/pitchRx |
| NeedsCompilation: |
no |
| Citation: |
pitchRx citation info |
| Materials: |
NEWS |
| CRAN checks: |
pitchRx results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=pitchRx
to link to this page.


Initializing Electron¶
Now, on to finishing setting up Electron and to begin working on your game!
Step 5: Modifiy your main.js file¶
I’ve slightly modified main.js
from Electron’s tutorial, and it can be copied and pasted into your main.js
file:
// Importing the nessecary stuff
const electron = require( "electron" )
const { app, BrowserWindow } = require( "electron" )
// Code
app.whenReady().then( onReady )
app.on( "window-all-closed", function() {
if ( process.platform !== "darwin" ) {
app.quit()
}
} )
// Functions
function onReady () {
const window = new BrowserWindow( {
width: 1000,
height: 750,
webPreferences: {
nodeIntegration: true
}
} )
// Sets a minimum size for the window
window.setMinimumSize( 300, 300 );
// Removes dev options + menu titlebar
//window.removeMenu()
// Loads the main file
window.loadFile( "index.html" )
}
Step 6: Modify your package.json file¶
Now we’ve got to edit the file package.json
, which will tell NPM information like the name of your game. Again, this has been slightly modified from Electron’s documentation.
{
"name": "my-game",
"version": "1.0",
"author": "your name",
"description": "My Proton game",
"main": "main.js",
"scripts": {
"start": "electron ."
}
}


Using Mappings: Classes: Methods: Fields:
Please enter the stack trace or thread-dump in the following textfield:
Please select the Minecraft version:
Source on GitHub
Downloading mappings...


Match Play. 5-5 to 9. Should Red redouble? If redoubled, should White take?
pips Red=163 White=92
(CorrectAnswer= 0/0, ErrorScore= 0)
For money this is no redouble but at this score White’s take-point is 33% and gammons will be no good to him.
White is not going to win from here 33% of the time. Too much has to go right for him for that to happen. The rollout gives him 30% chances.
Therefore, this is a redouble and a huge drop. Taking is a quintuple blunder.
White is not going to win from here 33% of the time. Too much has to go right for him for that to happen. The rollout gives him 30% chances.
Therefore, this is a redouble and a huge drop. Taking is a quintuple blunder.
Cube analysis 2-ply cubeless equity +0.450 (Money: +0.435) 0.704 0.156 0.007 - 0.296 0.134 0.002 Cubeful equities: 1. Double, pass +1.000 2. Double, take +1.207 (+0.207) 3. No double +0.926 (-0.074) Proper cube action: Redouble, pass


Fixture count updated at: {{lastUpdate | date: 'HH:mm:ss dd-MM-yyyy'}}
The list of fixtures is not automatically updated - refresh the page or click on "Update list of fixture" to update it| Id | Start Time | Description | Competition (SSLNCompetitionId) | State | Seq. |
|---|---|---|---|---|---|
| {{fixture.Id}} | {{fixture.StartTime | date:'dd MMM yyyy HH:mm' }} | {{fixture.Description}} | {{fixture.Competition}} ({{fixture.CompetitionId}}) | {{fixture.State | fixtureStatus}} | {{fixture.Sequence}} |


This is an analysis of the amplitude and time variation of image ghosts. Odd and even ghosts are assessed by calculating the ratio of the average signal in a ${ghostroisize}x${ghostroisize}x1 ghost roi to the average amplitude in the center of the phantom. The ghost roi is placed at the edge of the field of view in the phase encode direction, outside the phantom, and in the center (even ghost) or at the edge (odd ghost) of the phantom position in the readout direction.
Odd ghost ROI plot
mean=${'%4.4f' % oddghostmean}
stddev=${'%4.4f' % oddghoststddev}
min=${'%4.4f' % oddghostmin}
max=${'%4.4f' % oddghostmax}
p-p=${'%4.4f' % oddghostpp}
Even ghost ROI plot
mean=${'%4.4f' % evenghostmean}
stddev=${'%4.4f' % evenghoststddev}
min=${'%4.4f' % evenghostmin}
max=${'%4.4f' % evenghostmax}
p-p=${'%4.4f' % evenghostpp}


Senior Data Scientist - San Francisco, CA
Job Description
- Perform hands-on analysis of large volumes of web analytics, transaction, customer data, second and third-party data. Work with complex data structure, manipulate, cleanse data and perform statistical analysis
-
Expertise in predictive analytics/statistical modeling/data mining/machine learning algorithms and techniques (classification , clustering, regression, multivariate testing)
-
Proven capability to deliver end-to-end analyses by asking the right questions, extracting data, and building predictive models to ensure actionable results.
-
Strong passion for understanding key business problems, bringing together a team to understand data/ instrumentation needs and/or mine through data to unearth deep insights into customer experiences
-
Excellent communication & interpersonal skills with an ability to communicate ideas, insights and complicated analysis effectively at all organizational levels, include engineering leadership
-
Design and Implementation of refinery data product using Spark ML, Python, Map-Reduce, Hive, HDFS, Spring, Hibernate and Java
-
Experience with large scale distributed databases and computing systems like COSMOS, Hadoop, HDInsight or Databricks.
-
Expertise with statistical software packages like Python, MatLab, NumPy, SciPy, or SAS
-
Educate clients and fellow team members about implications of customer Insights.
-
Serve as an evangelist for data-driven decision making, accountability, and a commitment to continuous optimization.
-
Work with project teams to translate client requests into analytic work plans.
-
Participate in and support delivery of presentations to clients in senior positions
-
Identify new opportunities for Data Science projects and solutions.
-
Mentor junior data science team members.
-
Perform advanced statistical analysis and multivariate statistical analysis (regression and simple segmentation)
-
Extract data and actionable insights from a variety of client sources and systems, find probabilistic and deterministic matches across second and third party data sources, and complete exploratory data analysis.
-
Create engaging and meaningful data visualizations of findings linked to clear client business impact.
Qualifications
- MS in Computer Science, Math, Physics, or equivalent education/professional experience is required.
-
5+ years in a data science role with demonstrable experience
-
Deep experience in machine learning with Spark and Azure Machine Learning and Cognitive Services.
-
Knowledge of IBM Informix
-
Azure Cloud experience
-
Strong experience in at least one database technology (i.e. SQL Server, Redshift, Hive, PrestoDb etc.)
-
Experience with one or more web analytics tools (Google Analytics, etc.)
-
Strong experience in at least one programming language (i.e. C#, Python, R).
-
Experience implementing and automating models
-
Experience with data modeling and normalization concepts.
-
Experience working with different query languages (i.e. PL-SQL, T-SQL).
-
Understanding and experience working with cloud infrastructure services like Azure and Amazon Web Services. Azure preferred.
-
Experience working with code repositories and continuous integration (i.e. Git, Jenkins, etc.)
-
Graph database experience a plus.
-
Understanding of development and project methodologies.
-
Strong passion for understanding key business problems, bringing together the team to understand data/ instrumentation needs and/or mine through data to unearth deep insights into customer experience.
-
Excellent communication & interpersonal skills with an ability to communicate ideas, insights and complicated analysis effectively at all organizational levels, include engineering leadership
Additional Information
All your information will be kept confidential according to EEO guidelines.


by David Quintana
Posted on April 7,2016
What I think about Exponents and Logs that it's an easy concept and it was very easy to learn.
At first it was a complicated but I slowly learned the concept and it turned easy to me.
The problem is solved by using the Variable and exponents and basically solve it on a calculator.
Log is basically the opposite but the exponent is a negative and the answer always have a equation.
Overall this concept wasn't that hard to pick up but I haven't mastered the concept yet but I am looking forward to it.


| Active ingredients |
Purpose |
| Clotrimazole 1% (50 mg in each applicatorful) |
Vaginal Antifungal |
| Clotrimazole 1% (external cream) |
Vaginal Antifungal |
Uses
- treats vaginal yeast infections
- relieves external itching due to a vaginal yeast infection
Warnings
For vaginal use only
Do not use if you have never had a vaginal yeast infection diagnosed by a doctor
Ask a doctor before use if you have
- vaginal itching and discomfort for the first time
- lower abdominal, back or shoulder pain, fever, chills, nausea, vomiting, or foul smelling vaginal discharge. You may have a more serious condition.
- vaginal yeast infections often (such as once a month, or 3 in 6 months). You could be pregnant or have a serious underlying medical cause for your symptoms, including diabetes or a weakened immune system.
- been exposed to the human immunodeficiency virus (HIV) that causes AIDS
When using this product
- do not use tampons, douches, spermicides, or other vaginal products. Condoms and diaphragms may be damaged and fail to prevent pregnancy or sexually transmitted diseases (STDs).
- do not have vaginal intercourse
- mild increase in vaginal burning, itching or irritation may occur
- if you do not get complete relief ask a doctor before using another product
Stop use and ask a doctor if
- symptoms do not get better in 3 days
- symptoms last more than 7 days
- you get a rash or hives, abdominal pain, fever, chills, nausea, vomiting, or a foul smelling vaginal discharge
If pregnant or breast-feeding, ask a health professional before use.
Keep out of the reach of children. If swallowed, get medical help or contact a Poison Control Center right away.
Directions
- before using this product read the enclosed educational brochure for complete directions and information
- adults and children 12 years of age and over:
-
vaginal cream: insert one applicatorful of cream into the vagina at bedtime for 7 days in a row. Wash applicator after each use.
-
external cream: use the same tube of cream if you have itching and irritation on the skin outside the vagina. Squeeze a small amount of cream onto your fingertip. Apply to itchy, irritated skin outside the vagina. Use 2 times daily for up to 7 days as needed.
- children under 12 years of age: ask a doctor
Other information
- store between 20° to 25°C (68° to 77°F)
- see end panel of carton and tube crimp for lot number and expiration date
-
tamper-evident: do not use if seal embossed with "S" over tube opening is missing, open or broken
Inactive ingredients
benzyl alcohol, cetostearyl alcohol, cetyl esters wax, 2-octyldodecanol, polysorbate 60, purified water, sorbitan monosterate
Questions or comments?
If you have questions or need more information on this product, call toll free 1-800-317-2165 between 8:00 AM and 5:00 PM Central Standard Time, Monday through Friday.
© Copyright & Distributed by
Schering-Plough HealthCare Products, Inc., Memphis, TN 38151 USA.
PRINCIPAL DISPLAY PANEL - 45g Tube Carton
Cures most vaginal yeast infections
Gyne-Lotrimin®7
7-Day
Treatment
Clotrimazole Vaginal Cream (1%)
Vaginal Antifungal
Vaginal Cream
ONE 45g (1.5 oz) TUBE OF VAGINAL CREAM (1%)
AND APPLICATOR (7-Day Therapy)
EDUCATIONAL
PAMPHLET ENCLOSED
MSD Consumer Care, Inc.


Andrew Mc Fulton was committed to the Athens Hospital
for the insane by his father, Andrew Fulton, in February 1911


[1]
Suction cup worked well.; -Rampant incorrect POIs.. -Unsuccessful searches for POIs. -GUI not intuitive
[2]
Large, easy to read display; Poorly constructed, poor documentation, minimal customer support


Soldier Tenis:
Ha! You say you defeated Dius? You claim to have succeeded where I failed? Where's the proof?


This page contains the notes for the second part of R Workshop Module 5: Statistical modeling with R, which is part of the R Workshop series prepared by ICJIA Research Analyst Bobae Kang to enable and encourage ICJIA researchers to take advantage of R, a statistical programming language that is one of the most powerful modern research tools.
Click here to go to the workshop home page.
Click here to go to the workshop Modules page.
Click here to view the accompanying slides for Module 5, Part 2.
Navigate to the other workshop materials:
In the previous Part of Module 5, we have explored how to conduct basic statistical analysis with R. Now we would like move toward the realm of advanced statistical modeling. In order to facilitate such progress in knowledge, this Part offers a crude introduction to some advanced modeling topics (survival analysis, time series analyis, spatial regression analysis, and machine learning) as well as a list of relevant R packages.
Please note that the current Part is barely scratching the surface of any of the introduced topics. Think of this Part as taking the very first step, and refer to the suggested resource materials to learn more about each topic.
Source: Wikimedia Commons
“Survival analysis is used to analyze data in which the time until the event is of interest. The response variable is the time until that event and is often called a failure time, survival time, or event time.”
- Harrell Jr. (2015).
In survival analysis, the response variable is a non-negative discrete/continuous random variable, which represents the time to event for each subject/observation.
We start this section by introducing some basic concepts in survival anaylsis.
Failure, or event, refers to an event of interest where the time, \(T\), from the beginning of observation to the occurence of an event is measured and available for modeling.
Examples of event include:
Observations are called censored when the information about their survival time is incomplete.
The most common form of censoring is right-censoring, where the final endpoint is only known to exceed a particular value. Right censored data most likely occurs due to the end of the study/observation period.
The survival function \(S(t)\) is the probability that the time of event/failure is later than some specified time \(t\). Using mathematical notation:
\[S(t) = \text{Pr}(T > t),\quad 0 < t < \infty.\]
The hazard function \(\lambda(t)\) is the event rate at time \(t\), conditional on survival (i.e. no event) until time \(t\) or later:
\[\lambda(t) = \lim_{dt\to0} \frac{\text{Pr}(t \leq T < t + dt)}{S(t)dt} = \frac{S'(t)}{S(t)}\]
R ecosystem offers a number of packages to offer tools and functionalities pertaining to survival analysis.
Among many, the survival
package is the most essential, which makes available the Surv
class that is fundamental to a survival analysis tools in R ecosystem. For more details, you may visit its GitHub repository and read the package vignettes available on the package CRAN page
survminer
package helps visualization of survival models. Take a look at the package website or the GiHub repository for more information.
In the following, we will introduce two basic models for survival analysis:
The Kaplan-Meier (KM) estimator is perhaps the most widely used nonparametric estimator of the survival function. Mathematically, the KM estiamor \(\hat{S}(t)\) is the product over the failure times of the conditional probabilities of surviving to the next failure time, expressed as follows:
\[\hat{S}(t) = \prod_{t_i \leq t} \Big( 1 - \frac{d_i}{n_i} \Big),\]
where \(d_i\) is the number of subjects who fail at time \(t\) and \(n_i\) is the number of subjects at risk at time \(t\).
Surv
function and Surv
classsurvival::Surv(time, time2, event, type, ...)
survival
package has Surv()
function to create objects of Surv
class, which is used in many survival analysis tools in R.
In Surv()
, time
is the follow up time for the right censored data. For interval censored data, time
is the starting time and time2
is the ending time.
event
is the status indicator, where 0 = alive (i.e., event not occured), 1 = dead (i.e., event occured).
type
is a character string of the censoring type. Available inputs include “right” for right censoring, “left” for left censoring, and “interval” for interval data.
Surv()
returns a Surv
class object, used to fit survival models.
survfit
functionsurvival::survfit(formula/model, data, ...)
survfit()
function returns a survival curve. The function uses KM estimator if with formula
. If model
is given, the survival curve is based on the input model (e.g. Cox model).
When formula
is used for the KM estimator, it must have the Surv
object as the response variable and might look like: Surv(time, status) ~ x
.
data
is optional, and if provided, the columns of the input data frame can be used in the formula.
Cox proportional hazrads model, or simply Cox (regression) model, models the effect of a set of explanatory variables on the hazard or risk of failure over time.
The model assumes that the unique effect of a change in a covariate is multiplicative with respect to the hazard rate. The model is given as follows:
\[\lambda(t|\boldsymbol{\text{x}}_i) = \lambda_0(t)\psi_i = \lambda_0(t)\text{exp}(\boldsymbol{\text{x}}_i^{\text{T}}\beta),\] where \(\boldsymbol{\text{x}}_i\) denotes the covariate values for subject \(i\). The estimated coefficient is interpreted in releative terms.
coxph
functionsurvival::coxph(formula, data, ...)
survival
package offers coxph
function to fit Cox models. As in the case of survfit()
, formula
for coxph()
must have the Surv
object as the response variable. Again, data
is optional and if provided, the columns of the input data frame can be used in the formula.
plot(survfit)
survminer::ggsurvplot(survfit, data, ...)
There are two ways to plot survival curves.
First, we can simply use the bas R plotting function, plot()
, since the survival
package provides a plot
method for survfit
objects.
Alternatively, we can used ggsurvplot()
from the survminer
package to plot survival curves. ggsurvplot()
has ggplot2
-like API and makes ggplot2
themes available. The output of ggsurvplot()
might look like this:
Obviously, what we have covered above is only the first step toward survival analysis in R. Please refer to the following resources. Also, for specific functions, remember to take advantage of the reference manuals using ?
or help()
.
Source: Wikimedia Commons
A time series is a set of observations measured sequentially through time. Examples of time series include annual crime count for multiple years and fluctuations in stock prices.
Time series analysis then involves modeling such time series data, often for making forecasts, i.e., predicting what comes next based on the information from the historical records.
Before we take a look at specific time series analysis tools and models, let’s take a brief look at some basic concepts
Decomposition refers to the work of separating a time series into trend, seasonal effects, and remaining variability.
The following plot provides a visual illustration of decomposing a time series into its components:
The notion of seasonality, also called seasonal variation, pertains to similar and recurrent patterns of behavior in data at particular times of the given period. Seaonality is generally annual in period, but could be monthly, weekly, daily, or any other kind of periods.
Stationarity is a common assumption underlying any time series analysis techniques. A stationary process is a stochastic process of which the mean, covariance and autocorrelation structure do not change over time.
The following figure illustrates the difference between a stationary time series and a non-stationary one:
Source: “Stationary process”, Wikipedia
Differencing refers to a time sereis analysis technique to make the series stationary, de-trend, and control the auto-correlations.
stats
package, which is part of R “base packages”, provide a wide range of useful tools for time series analysis.
There is, of course, no shortage of third-party packages to offer additional functionalities relevant to time series analysis. Among many, forecast
package seems to be a popular choice. Visit the package website] to find out more. There also exists a full-fledged online textbook using forecast
package: Hyndman, R. & Athanasopoulos, G. (2018). Forecasting: Principles and Practice.
Let’s take a look at some basic time series analysis techniques and how to implement them in R. Specifically, we will take a look at the following:
A time series \(y_t\) can be decoposed into three components:
There are two models for decomposing a time series. One is additive and the other is multiplicative.
decompose
and stl
functionsdecompose(x, type = c("additive", "multiplicative"), ...)
stl(x, s.window, ...)
stats
offers two functions for time series decomposition using different methods for time series decomposition.
decompose()
uses moving averages to decompose a time series. Here, type
input specifices whether seasonal components are additive or multiplicative.
stl()
uses LOESS (local regression), which is a more modern technique for time series decomposition. For that reason, stl
is often recommended for time series decomposition. s.window
input controls the span of the LOESS window for seasonal extraction (must be odd and at least 7). The decomposion is additive by default, but taking natural log of the data will account for multiplicative decomposition.
Autocorrelation function (ACF) and Partial autocorrelation function (PACF) plots of a differenced time series offer a heuristic way to choose specifications for ARIMA models, which we will explore shortly.
It is beyond the scope of the current Part to offer a full description of how each function is computed and how to use them as a guide to selecting a proper time series model. However, you can read this “Guide” for how to use ACF and PACF plots for selecting models to fit.
Roughly:
acf
and pacf
functionsacf(x, lag.max = NULL, plot = TRUE,
type = c("correlation", "covariance", "partial"), ...)
pacf(x, lag.max = NULL, plot = TRUE, ...)
acf()
and pacf()
from stats
package can generate ACF and PACF plots from the input x
, a univariate time series.
In acf()
, type = "correlation"
is the default for an ACF plot. Also, pacf()
is equivalent to acf()
with type = "partial"
.
Here we will take a brief look at the following basic time series models:
\(\{X_t\}\) is an autoregressive process of order \(p\), \(\text{AR}(p)\):
\[X_t = Z_t + \sum_{i=1}^p \phi_i X_{t-i},\] \[\text{rewritten as }\phi(B)X_t = Z_t\] Here, \(Z_t\) is a white noise (stationary) with mean 0 and the constant variance, and \(\phi_i\) is the autoregressive parameter for the \(i^{th}\) order. Finally, \(\phi(B)X_t\) can be unpacked: \(\phi(B)X_t = 1 - \phi_1B - ... - \phi_pB^p\).
\(\{X_t\}\) is an moving average process of order \(q\), \(\text{MA}(q)\):
\[X_t = Z_t + \sum_{i=1}^q \theta_i X_{t-i},\] \[\text{rewritten as }X_t =\theta(B)Z_t\] Again, \(Z_t\) is a white noise. \(\theta(B)\) can be unpacked as: \(\theta(B) = 1 + \theta_1B + ... + \theta_qB^q\)
Combining \(\text{AR}(p)\) and \(\text{MA}(q)\), we get \(\text{ARMA}(p, q)\):
\[\phi(B)X_t =\theta(B)Z_t\]
We can also incorporate differencing to non-stationary data in order to generate stationarity needed to fit AR, MA, and ARMA models. With \(d^{th}\) differences, we get \(\text{ARIMA}(p, d, q)\):
\[\phi(B)(1-B)^dX_t =\theta(B)Z_t\]
arima
functionarima(x, order = c(0L, 0L, 0L), seasonal = list(order, period) ...)
arima()
from the stats
package is the function used to fit ARIMA univariate time series models. The first input of arima()
is x
, a vector of a univariate time series.
Then, order
input is used to specify the (p, d, q) for ARIMA model, in that order. If d and q are both 0, it is a \(\text{AR}(p)\) model. If p and d are both 0, it is a \(\text{MA}(q)\) model. If d equals 0, it is \(\text{ARMA}(p, q)\) model.
seasonal
is a specification of the seasonal part of ARIMA model, consists of order
and period
.
Refer to the reference manual documentation using ?armia
for more details.
The following resource materials can help you to dive deeper into the time series analysis with R:
stats
and tseries
packagesSource: R Spatial
Spatial regression analysis is for modeling data with spatial dependence, which refers to the spatial relationship of variable values or locations. Spatial regression then seeks to capture the effect of spatial dependence in the statistical modeling efforts.
Neighbors of a spatial data point are other data points that are proximate to the focal data point. There are various ways to define neighbors:
x
are neighbors of x
.x
to be the neighbors of x
.Try this Shiny application by Klye E. Walker to see how the neighboring structure changes with different definitions.
Spatial weights represent the significance of neighbors. This reflects an intuition that not all neighbors are equally important. However, in practice, weights are often binary: 1 if neighbors and 0 otherwise.
For geospaital data expressed in polygons (e.g. counties), contiguity-based neighbors are used to construct spatial weights.
Currently, there are two major packages to create and manipulate spatial objects: sp
and sf
.
sp
is a mature package that is first release on CRAN in 2005. Currently, over 130 packages on CRAN are dependent on and built on top of sp
. However its objects have un-tidy structures, making it often difficult to work with.
sf
is a new package that is first released on CRAN in 2016. It is an R implementation of the “Simple Features Access” standard (ISO 19125) for geospatial data. Its spatial objects are better suited for tidy framework and its user base is growing. So far, however, sp
remains to be the dominant framework for spatial data structure and analysis.
spdep
packagespdep
package is, to my knowledge, the most comprehensive package for spatial regression analysis in R. Its functions use sp
spatial objects. To use spdep
with sf
objects, they must be first coerced into sp
spatial objects.
Take a look at the following resource materials for spdep
and spatial data analysis with R:
spdep
reference manual{target=“_blank“.The first step of spatial regression analysis involves creating neighbors and weights matrix for the model specification.
spdep
offers *2nb()
functions to create neighbors (nb
object):
poly2nb()
for continuity-based neighborsknn2nb()
for distance-based neighborstri2nb()
for grid-based neighborscell2nb()
for grid neighborsThen, nb2listw()
function is used to generate a list of spatial weights (listw
object) from an nb
object.
poly2nb
functionspdep::ploy2nb(pl, row.names = NULL, queen = TRUE, ...)
Let’s take a look at the poly2nb
function interface as an example of *2nb()
functions.
Here, pl
is a list of polygons (e.g. SpatialPolygons
class). If queen
is TRUE
, polygons with a single shared boundary point are considered neighbors; if FALSE
, more than two shared poitns are needed.
nb2listw
functionspdep::nb2listw(neighbours, ...)
The nb2listw
function takes a neighbours object of class nb
and returns a spatial weights list (listw
)
Now we will explore the basics of spatial modeling, which include the following:
Moran’s I is one of the most commonly used statistic for spatial autocorrelation. It is given in the following format:
\[I = \frac{\boldsymbol{\text{e}}^\text{T}\boldsymbol{\text{W}}\boldsymbol{\text{e}}/S_0}{\boldsymbol{\text{e}}^\text{T}\boldsymbol{\text{e}}/n} = \frac{\boldsymbol{\text{e}}^\text{T}\boldsymbol{\text{W}}\boldsymbol{\text{e}}/S_0}{\hat{\sigma}^2_{ML}},\]
where \(S_0\) is the sum of the weights, and \(\boldsymbol{\text{e}}\) is a vector of OLS residulas.
It is a non-constructive test, which means that no specific model is implied as an alternative hypothesis. The test is formulated as follows:
\[I_z = \frac{I - \text{E}[I]}{\sqrt{\text{Var}[I]}} \sim N(0, 1)\]
moran.test
and lm.morantest
spdep::moran.test(x, listw, ...)
spdep::lm.morantest(model, listw, ...)
spdep
offers two functiosn for conducting Moran’s I test.
moran()
takes a numeric vector of data and a spatial weights list (listw
) created by nb2listw
. On the other hand, lm.morantest()
takes a lm
object and a spatial weights list.
A small p-value
for the test statistic suggests the presence of special autocorrelation that is not accounted for in the ordinary least squares (OLS) model.
Unlike Moran’s I test, Lagrange multiplier (LM) tests for spatial lag model and spatial error model have specific models implied in their alternative hypothesis.
In either type of LM test, the null hypothesis is no spatial autocorrelation
There are also “Robust” LM test, which are used when LM tests for both lag model and error model reject the null.
lm.LMtests
functionspdep::lm.LMtests(model, listw, test = "LMerr")
The lm.LMtests()
function is used for conducting LM tests for various types of spatial models. lm.LMtests()
takes a lm
model and a spatial weights list (listw
).
Then, test
input specifies which test to implement. Available inputs for test
include:
"LMerr"
and "LMlag"
for spatial error and spatial lag model"RLMerr"
and "RLMlag"
for robust LM tests"SARMA"
for spatial ARMA modelSpatial lag model models spatial autoregression of the response variable. The model is formulated as follows:
\[\boldsymbol{\text{y}} = \rho\boldsymbol{\text{W}}\boldsymbol{\text{y}} + \boldsymbol{\text{X}}\beta + \boldsymbol{\text{u}},\]
where \(\boldsymbol{\text{W}}\boldsymbol{\text{y}}\) is a spatial lag term, \(\rho\) is a spatial autoregressive parameter, and \(\boldsymbol{\text{u}}\) is an error term.
Spatial error model models spatial dependence on the error term, and is expressed in the following form:
\[\boldsymbol{\text{y}} = \boldsymbol{\text{X}}\beta + \boldsymbol{\text{u}}, \text{where}\] \[\boldsymbol{\text{u}} = \lambda\boldsymbol{\text{W}}\boldsymbol{\text{u}} + \varepsilon\] Here, \(\boldsymbol{\text{W}}\) is a weights matrix, \(\lambda\) is a spatial autoregressive parameter for the error term, and \(\varepsilon\) is an idiosyncratic error term.
lagsarlm()
and errorsarlm()
spdep::lagsarlm(formula, data, listw, ...)
spdep::errorsarlm(formula, data, listw, ...)
These two are functions to fit lag and error models. formula
and data
works like in lm()
, and listw
is a spatial weights matrix.
To know more about spatial regression analysis in general and its implementation in R, refer to the following materials:
Source: Wikimedia Commons
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”
-Tom M. Matchell
Machine learning (ML) is a key factor of the recent success of artificial intelligence (AI) algorithms and applications. ML can be roughly thought as the automated optimization of model parameters through iteratively reducing the difference between the model outputs and the input data.
The field of machine learning is largely divided into the following three subfields:
The basic machine learning workflow can be roughly divided into the following steps:
Here is a brief list of commonly used machine learning algorithms:
It is beyond the scope of the current Part to offer a full description of each algorithm and how it works. However, feel free to leverage the power of the Internet and search online for each algorithm. There is a wealth of resources (articles, books, written and video tutorials, online courses, etc.) available on the subject of machine learning.
R has many packages that offer machine learning algorithms and relevant tools. The following two are among the most comprehensive packages providing a wide range of machine learning functionalities.
caret
packagecaret
is a shorthand for “Classification And REgression Training”, and one of the most mature machine learning package out there. Check out the package vignettes on the CRAN page and the package GitHub repository. There is also an online textbook on using caret
for machine learning.
mlr
packagemlr
is an abbreviation of “Machine Learning in R”. Take a look at the vignettes on the package CRAN page and its GitHub repository. Also visit the package website, which provide tutorials on the mlr
API.
To get started with machine learning, you may want to try the following materials. Also, don’t forget to search online for other machine learning materials.


The E3 ubiquitin ligase AIP4 mediates ubiquitination and sorting of the G protein-coupled receptor CXCR4.
Abstract
Ubiquitination of the chemokine receptor CXCR4 serves as a targeting signal for lysosomal degradation, but the mechanisms mediating ubiquitination and lysosomal sorting remain poorly understood. Here we report that the Nedd4-like E3 ubiquitin ligase AIP4 mediates ubiquitination of CXCR4 at the plasma membrane, and of the ubiquitin binding protein Hrs on endosomes. CXCR4 activation promotes CXCR4 colocalization with AIP4 and Hrs within the same region of endosomes. Endosomal sorting of CXCR4 is dependent on Hrs as well as the AAA ATPase Vps4, the latter involved in regulating the ubiquitination status of both CXCR4 and Hrs. We propose a model whereby AIP4, Hrs, and Vps4 coordinate a cascade of ubiquitination and deubiquitination events that sort CXCR4 to the degradative pathway.


- What is the SAFE Stack?
- Details about the components
- Model-View-Update pattern
- A code example
- A cross-platform set of components for creating web applications in F#
- Includes everything from the server up to the client
- Stands for: Saturn Azure Fable Elmish
- Components can be replaced
- Dotnet CLI template exists
- Is a layer of abstraction on top of Giraffe
- Framework for creating ASP.NET Core Web applications in F#
- F# to JavaScript compiler
- Designed to produce readable and standard JavaScript code
- Enables access to JavaScript libraries
- Used for creating UIs
- SAFE uses the Elmish React module
- Uses the Model-View-Update pattern
Model-View-Update pattern
- Model
- State of the program
- Is passed to the view/update function
- Program
- Displays the data and has functionality in it (view/update function etc.)
- Similar to a ViewModel + View
- Init function
- Starting point of a program
- Update function
- Changing the Model
- No mutations only creates an updated version of the Model
- View function
- Command
- Fetches data from the server, writing data to the database etc.
- Only option to cause side-effects
- Message
- Is used for determination of what to do in the update function


{% extends('base.html') %}
{% set active_page="boats" %}
{% set page_title = _("Navette Boats") %}
{% set meta_description = "Practical multi-purpose boats with emphasis on ergonomics. Need a custom project done? Let us know." %}
{% block main %}
{% endblock %}


Keep in mind, these are just projects. They are not finished products, but mostly just stuff I am working on that looked cool enough to upload at the time. The code is not optimised at all, but the concepts are cool.
These links open the projects directly in your browser.
Noise Topography - Generates 1D value noise as well as a 2D value noise infinite terrain. Performance can be greatly improved.
Simplex Noise - Generates 1D and 2D simplex noise, which is much smoother than value noise. Again, I threw this together quickly and it can be improved.
Isometric Tile - Super cool pseudo 3D tile plotter that will crash your PC if it isn't lightning fast.
Orthographic 3D - An orthographic 3D engine written completely from scratch in JavaScript, running without WebGL or any other libraries. It does not use a Z buffer, so there is a little bit of z fighting between surfaces. This is not meant for use in production.
Rasterizer - A Pure JS rasterizer. It plots points in 2D shapes. Essentially it is a graphics drawing API that gives you access to each and every pixel in a shape before it is drawn. This is the basis for z buffering.
These links will download Powershell Scripts to your computer.
Stay Awake - A PS script that enters a keystroke every several seconds to keep your computer from entering sleep mode.
Idle Alert - A PS script that will cause a terminal window to flash red and white if there is no mouse activity for over 10 minutes, reminding you to move the mouse before your screen falls asleep.