--- layout: post status: publish published: true title: Android > How to know the state when the phone is calling. author: display_name: hojunbaek login: hojunbaek email<EMAIL_ADDRESS>url: '' author_login: hojunbaek author_email<EMAIL_ADDRESS>wordpress_id: 1907 wordpress_url: http://ezcocoa.com/?p=1907 date: '2013-10-01 02:27:13 +0900' date_gmt: '2013-10-01 02:27:13 +0900' categories: - Android tags: - listener - phone - phonestatelistener comments: [] ---
Some people want to know that time when the phone is calling.
You've to add the following line on AndroidManifast.xml.
[sourcecode language="xml"]
[/sourcecode]
This is java source code.
[sourcecode language="java"]
import android.telephony.PhoneStateListener;
import android.telephony.TelephonyManager;
private PhoneStateListener phoneStateListener = new PhoneStateListener() {
@Override
public void onCallStateChanged(int state, String incomingNumber) {
super.onCallStateChanged(state, incomingNumber);
if (state == TelephonyManager.CALL_STATE_RINGING) {
// To do what you want to do
}
}
};
private TelephonyManager telephonyManager = null;
@Override
public void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_call_android);
telephonyManager = (TelephonyManager) getSystemService(Context.TELEPHONY_SERVICE);
telephonyManager.listen(phoneStateListener, PhoneStateListener.LISTEN_CALL_STATE);
}
[/sourcecode]


@* * Copyright 2019 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import play.api.Configuration @import uk.gov.hmrc.agentoverseasapplicationfrontend.controllers.routes @import uk.gov.hmrc.agentoverseasapplicationfrontend.models.PersonalDetailsChoice @import uk.gov.hmrc.play.views.html.helpers.{form, input, errorSummary} @(personalDetailsForm: Form[PersonalDetailsChoice], backLink: Option[String] = None)(implicit messages: Messages, request: Request[_], configuration: Configuration) @uk.gov.hmrc.agentoverseasapplicationfrontend.views.html.main_template(title = error_prefix(personalDetailsForm) + Messages("personalDetails.title")) { @Messages("button.back") @if(personalDetailsForm.hasErrors) { @errorSummary(Messages("error.summary.heading"),personalDetailsForm, Seq.empty, Some("personal-details")) } @form(action = routes.ApplicationController.submitPersonalDetails, 'class -> "form js-form") {


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Ngx-Materialize leverage the use of the native HTML input by using the mz-radio-button
directive directly on the input element tag to ease the fact that you might use third party library that would ask you to add properties directly on that native input element.
The radio button text is set with label
property. To group radio buttons add the native name
property with the same corresponding value for each of the radio buttons in the group.
Optionally you can use the gap styling on the radio button by setting withGap
property to true
.
The input properties of the mz-radio-button
directive makes the form-control completely dynamic. Change the configuration below and see how the radio-button reacts.
Radio value: {{ radioValue }}
The input element must be contained inside the mz-radio-button-container
component otherwise an error will be shown in the console.
Radio buttons can be customized using the following properties on the input
element when adding mz-radio-button
directive.


@*
* Copyright 2021 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import uk.gov.hmrc.eoricommoncomponent.frontend.controllers.EoriLanguageController
@()(implicit messages: Messages)
@currentEn = @{
{messages("cds.languageSelect.current.label")}
}
@selectCy = @{
{messages("cds.languageSelect.select.hint")}{messages("cds.languageSelect.select.label")}
}
@selectEn = @{
{messages("cds.languageSelect.select.hint")}{messages("cds.languageSelect.select.label")}
}
@currentCy = @{
{messages("cds.languageSelect.current.label")}
}


I am Vidushi Tomar, a MERN stack web-developer. I am a final year B.Tech Computer Science & Engineeering student. I work with C++, python, node.js, javascript, Drupal8, express.js, react.js. I really like to take new challenges and learn new skills while completing them. Basically my way of learning is to learn while doing and repeating.
I never had interest in DS & Algo but during my journey I came to know that they are really important. Even if you are a web-developer ,directly of indirectly you use them. To put the correct logic it is required.


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Parkinson’s Disease (PD) is a degenerative disease of the central nervous system. This work performs a four-class classification using the motor assessments of subjects obtained from the Parkinson’s Progressive Markers Initiative (PPMI) database and a variety of techniques like Deep Neural Network (DNN), Support Vector Machine (SVM), Deep Belief Network (DBN) etc. The effect of using feature selection was also studied. Due to the skewness of the data, while evaluating the performance of the classifier, along with accuracy other metrics like precision, recall and F1-score were also used. The best classification performance was obtained when a feature selection technique based on Joint Mutual Information (JMI) was used for selecting the features that were then used as input to the classification algorithm like SVM. Such a combination of SVM and feature selection algorithm based on JMI yielded an average classification accuracy of 87.34 % and an F1-score of 0.84.


My means? Street smarts from a life of hard knocks on the inner nets. My misguided youth lead to a web induced addiction to what we were calling “AJAX”. Those innocent years wandering the nets were not unlike how Caine wandered the West in the documentary television series “Kung Fu”. The trolling ended in 2004 when my addiction caught up with me and I was sent for new school rehab.
I guess I was doing time for a couple majors and minors. Near as I can remember, I was elected for officer duty in 10 different roles with a stint of community service. A mob of us science-y types took the joint by storm and got some lifers out on good behavior. That time in the slammer ended in 2008 when I got my walking papers from Purdue University. It was the most kickin’ good time I’ve had in recent memory.
In my line of work all roads lead to the valley. I’m doin’ the 9 to 5 at the ‘tubes making flashy things for tweens and cat enthusiasts. Keeping current with all the crazy things you kids come up with has been a real time sink but I make due.


"About 4 years ago I left an abusive marriage. My self-worth at that point was non-existent. I've worked hard over the past few years to heal & grow. I've had to learn to believe in myself again. I'm stronger & more self-assured now than ever before. I came up with the tattoo design myself. I wanted wings to go along with the word 'shine' simply because I believe in angels; but also, to remind myself to 'rise above' the judgements of others."


This week COULD have been so much easier...
July 10, 2015
Enumerables
There are many classes in Ruby that each have there own specialized methods. If you want to have a set of methods that work for mutiple classes, those methods must be put into a module (a collection of methods and constants). An Enumerable is a module. A class can only use the Enumerable module if it has a defined .each method. This is an method that takes self as a parameter and runs a given block. The Enumerable module includes many methods that can change, pull, and organize elements.
Today I am going to look at the Enumerable method "group_by". I chose this particular method because my workload for this week would have been so much easier if I had found this method sooner.
.group_by
The basic concept of group_by is that it takes an collection of items and returns a key-value hash. You have to set a condition in which you want those items to be organized. The condition then becomes the key part of the hash and all of the items that fit into the condition becomes the value part of the hash. You called the method with ".group_by{ }". In the block portion, you have to define self and specify a condition of organization.
Example 1
This example is very similar to the one found in the ruby-doc definition. I am going to call the method on an array of numbers.
>> (1..20).group_by{ |num| num%3}
=> {
1=>[1, 4, 7, 10, 13, 16, 19],
2=>[2, 5, 8, 11, 14, 17, 20],
0=>[3, 6, 9, 12, 15, 18]
}
(1..20) denotes the array including the range of numbers from 0 to 20 including 20. I called the method and in the block portion I typed "|num| num%3". |num| means "do the following for every element in the array". num%3 says divide each element by 3, and return the remainder. The 3 resulting remainders were 0, 1, and 2. Those 3 remainders were set as the keys in the hash. All of the elements in the array list that fall into those categories were placed into the value of those respective keys.
Example 2
Now here is an example on an Array of strings.
>> friends =["Jim", "Roy", "Grace", "Danny", "Bella"]
>> friends.group_by{ |name| name.length}
=> {
3=>["Jim", "Roy"],
5=>["Grace", "Danny", "Bella"]
}
In this case, I called the group_by method on an Array of names. In my block I told it to organize the names by the length of characters. The length amount was put into the keys of the hash and the names into the values.
Example 3
For my last example, I called group_by method on a hash.
>> friends ={
"Jim" => "dog",
"Roy" => "dog",
"Grace" => "dog",
"Danny" => "cat",
"Bella" => "fish",
"Sandy" => "cat",
"Dave" => "rabbit",
"Spring" => "fish"
}
>> friends.group_by{ |owner, pet| pet}
=> {
"dog"=>[["Jim", "dog"], ["Roy", "dog"], ["Grace", "dog"]],
"cat"=>[["Danny", "cat"], ["Sandy", "cat"]],
"fish"=>[["Bella", "fish"], ["Spring", "fish"]],
"rabbit"=>[["Dave", "rabbit"]]}
In this case, I created a hash of names of friends and the type of pets they own. In my block, I stated that I wanted my hash organized by pet type. My resulting hash has the pet type as the key and all of the corresponding pairs that contain specified pet in the value.


1. VueJS is pretty cool - {{ name }} {{ age }}
2. Age * 3 = {{ ageMultiplyer(3) }}
3. Random Number between 0 and 1: {{ randomNumber() }}
3b. RANDOM Number between 0 and 100: {{ randomNumber(100) }}
4. Google Image
5. pre-populate input with name


Remote Import¶
Introduction¶
Purpose of this module is to change Python’s import hooks from purely loading from local filesystem to loading some from remote system. This makes it easy to rapidly test experimental code on compute clusters or quickly share and debug code form teammates. The loading of remote source would be built on top of kydb-page-ref.
Example¶
Create hello.py
:
def greet():
print("Hello World!")
Upload it to remote source
python -m kinyu.rimport.uploader --srcdb=redis://my-redis.host/my-source --key=hello.py /tmp/hello.py
Now we can import the hello
module:
from kinyu.rimport.api import rimp
rimp.set_srcdb('redis://my-redis.host/my-source')
import hello
hello.greet() # prints 'Hello Word!'


API Docs for:
Modules
Action
Article
Attend
Fork
Friend
Role
Type
User
Classes
action-proxy
action-router
index-router
notif-router
Notification
Search
type-proxy
type-router
user-proxy
users-router
API Docs - Main Index
Something smart and pretty should probably go here.


Structure and Organisation of Rustc
This is the second part of a planned series about rustc
, the Rust compiler
This post is going to discuss the structure and organisation of rustc
,
covering the major moving parts and how they interact. I’ll start, of course,
where it starts, and ending, of course, where it ends.
rustc
is a fairly large, complex beast. Unless otherwise mentioned, all
paths are relative to src/librustc
. If you’re unfamiliar with Rust, a
“crate” is a compilation unit: a library or an executable.
Beginning
The entry point, run_compiler
in rustc.rs
, does argument parsing, using
extra::getopts
and calls into the driver, in driver/driver.rs
, which
coordinates the various pieces of the compiler and the rest of the argument
parsing. The key data structure here is the Session
, which you can find
(along with a bunch of other things like the option context) in
driver/session.rs
. Back to the driver, the function compile_input
is where
most of the magic happens. This function runs all the various passes over the
crate. It starts with parsing and macro expansion and moves onto various
static analysis of the code to make sure it is correct, finally generating
LLVM IR (intermediate representation), running LLVM optimizations, and linking
the final binary.
An interlude
If you’re not familiar with compilers, you might find this process a
labyrinthine fractal of complexity. Which it is, don’t get me wrong. But the
various pieces of the rust compiler are fairly well separated, even if they
are complex. The basic structure of a compiler is operations on a tree data
structure representing the crate called an AST or Abstract Syntax Tree. An AST
is the output of parsing, and everything in the compiler is transforms or
analysis of this tree. Once everything is done, it takes this AST and
translates it into LLVM IR. This is probably the most hairy part of the
compiler, and it lives in middle/trans
. LLVM IR is a representation of code
that the LLVM library can operate on to optimize and generate native machine
code. LLVM does most of the heavy lifting that a traditional compiler would
need to do (optimization and codegen), so the rest of rustc
is largely
dedicated to actually dealing with Rust programs.
Parsing + expansion
Parsing and macro expansion happen in src/libsyntax
, so all paths in this
section will be relative to that. This corresponds to phase_1_parse_input
and phase_2_configure_and_expand
. Phase 1 does the parsing, and results in
an AST. If you want, you can look into parse/parser.rs
for the
parse_crate_mod
method. Very rarely will one have to modify the parser. The
most important part of the parser, from an outsider’s perspective, is in
codemap.rs
. The codemap has the concept of a “span”, which is a piece of
source code, represented by a start and an end. Whenever you see an error and
a squiggly line underneath where the error happenes, that is the result of a
span. Almost everything in the AST has a span.
After phase 1 comes, of course, phase 2. Phase 2 is what does macro expansion,
deriving implementations, and removal of items which are conditionally
included with the cfg
attribute. This all happens in the ext
directory.
Personally I haven’t poked around too much at how macros work, but things to
note are cfg.rs
, asm.rs
, ifmt.rs
, and the deriving
directory. These do
cfg!
, asm!
, format!
(and friends), and #[deriving(...)]
,
respectively.
If you’re curious how a syntax extension works, env.rs
is a good example. In
the expand_env
function, it takes the syntax extension context (cx), a
span of the macro invocation, and the “token tree” that the macro was invoked
with. When you do foo!(....)
, the ....
is the token tree. It checks the
structure of the token tree (it expects either one or two arguments), and then
returns the environment variable referenced by constructing an ast::Expr
,
using the ExtCtxt to mark that the expression came from the given span. This
is so the compiler can track which span a piece of macro-generated code
initially came from, for better error reporting.
You might notice that phase 2 actually runs the configuration step twice. This
is so that macros that expand to items with a cfg
attribute also get removed
from the final output.
Analysis
Phase 3, the analysis phases, is the real meat of rustc. All of this code
lives in middle
. The most important passes are probably typeck, the type
checker; resolve, the name resolver; borrowck, the borrow checker; and lint,
the lint runner. Not to say the other passes aren’t important, but they’re
less frequently worked or worried about by mere mortals such as myself.
Trans(lation)
trans is the part of the compiler that takes the AST from syntax as
well as the tables created by analysis and outputs LLVM IR. All of this
happens in middle/trans
. There is a lot of code, and I’ve never actually
been able to figure out which piece generates what.
Conclusion
That’s my basic overview of how the compiler is structured. If you want something added, or as questions get asked, I’ll fill in more information. The analysis and trans sections are sparse as I’ve little experience with them.


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OnForte is a crowd-sourcing, platform-crossing playlist creation service, integrating Soundcloud, Spotify, and Apple Music with voting algorithms to create intelligent playlists.
We are able to offer Soundcloud's free service to all of our users.
If the host of the playlist is logged in to Spotify Premium, users can add Spotify music to playlists.
The host is able to add their local music to the playlist. Apple Music APIs have been released but are still undocumented, this should be truly coming soon.
Express yourself through votes to have an impact on the playlist order.
OnForte is still in beta. Interested in finding out more?


/**
* Copyright (c) 2014, 2016, Oracle and/or its affiliates.
* The Universal Permissive License (UPL), Version 1.0
*/
/*
* Your dashboard ViewModel code goes here
*/
define(['ojs/ojcore', 'knockout', 'jquery','ojs/ojinputtext'],
function(oj, ko, $) {
function DashboardViewModel() {
var self = this;
self.firstNumber = ko.observable();
self.secondNumber = ko.observable();
self.answer = ko.pureComputed(function(){
return this.addNumbers(this.firstNumber(),this.secondNumber()) * 100;
},this)
self.addNumbers= function(paramOne, paramTwo){
return parseFloat(paramOne) + parseFloat(paramTwo);
}
// Below are a subset of the ViewModel methods invoked by the ojModule binding
// Please reference the ojModule jsDoc for additionaly available methods.
/**
* Optional ViewModel method invoked when this ViewModel is about to be
* used for the View transition. The application can put data fetch logic
* here that can return a Promise which will delay the handleAttached function
* call below until the Promise is resolved.
* @param {Object} info - An object with the following key-value pairs:
* @param {Node} info.element - DOM element or where the binding is attached. This may be a 'virtual' element (comment node).
* @param {Function} info.valueAccessor - The binding's value accessor.
* @return {Promise|undefined} - If the callback returns a Promise, the next phase (attaching DOM) will be delayed until
* the promise is resolved
*/
self.handleActivated = function(info) {
// Implement if needed
};
/**
* Optional ViewModel method invoked after the View is inserted into the
* document DOM. The application can put logic that requires the DOM being
* attached here.
* @param {Object} info - An object with the following key-value pairs:
* @param {Node} info.element - DOM element or where the binding is attached. This may be a 'virtual' element (comment node).
* @param {Function} info.valueAccessor - The binding's value accessor.
* @param {boolean} info.fromCache - A boolean indicating whether the module was retrieved from cache.
*/
self.handleAttached = function(info) {
// Implement if needed
};
/**
* Optional ViewModel method invoked after the bindings are applied on this View.
* If the current View is retrieved from cache, the bindings will not be re-applied
* and this callback will not be invoked.
* @param {Object} info - An object with the following key-value pairs:
* @param {Node} info.element - DOM element or where the binding is attached. This may be a 'virtual' element (comment node).
* @param {Function} info.valueAccessor - The binding's value accessor.
*/
self.handleBindingsApplied = function(info) {
// Implement if needed
};
/*
* Optional ViewModel method invoked after the View is removed from the
* document DOM.
* @param {Object} info - An object with the following key-value pairs:
* @param {Node} info.element - DOM element or where the binding is attached. This may be a 'virtual' element (comment node).
* @param {Function} info.valueAccessor - The binding's value accessor.
* @param {Array} info.cachedNodes - An Array containing cached nodes for the View if the cache is enabled.
*/
self.handleDetached = function(info) {
// Implement if needed
};
}
/*
* Returns a constructor for the ViewModel so that the ViewModel is constrcuted
* each time the view is displayed. Return an instance of the ViewModel if
* only one instance of the ViewModel is needed.
*/
return new DashboardViewModel();
}
);
|


Improve this Doc
The Footer component places a footer bar at the bottom of a page in your app. The `ion-toolbar` component is often nested inside Footer to ensure the content area is sized correctly.
_For complete usage information, check out the [Footer API docs](../api/components/toolbar/Footer)._
Demo Source
``` html
Footer
```


LogType
public enum LogType
Two log type is currently possible. Info or error with the code and name.
-
Default log type for the network layer
Declaration
Swift
case info
-
Logs with the code and name of the error
Declaration
Swift
case error(code: Int, name: String)


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BasicGLES20Renderer
- Class in
ch.chnoch.thesis.renderer
The Class BasicGLES20Renderer.
BasicGLES20Renderer()
- Constructor for class ch.chnoch.thesis.renderer.
BasicGLES20Renderer
BasicRenderer
- Class in
ch.chnoch.thesis.renderer
The Class BasicRenderer.
BasicRenderer()
- Constructor for class ch.chnoch.thesis.renderer.
BasicRenderer
BoundingBox
- Class in
ch.chnoch.thesis.renderer
A BoundingBox is used for every node that has a shape associated with it.
BoundingBox(FloatBuffer)
- Constructor for class ch.chnoch.thesis.renderer.
BoundingBox
Instantiates a new bounding box.
BoundingBox(Point3f, Point3f)
- Constructor for class ch.chnoch.thesis.renderer.
BoundingBox
Instantiates a new bounding box.
Box2DBody
- Class in
ch.chnoch.thesis.renderer.box2d
This class represents a body in the Box2D physics world.
Box2DBody(Vector2f, Box2DWorld, Box2DShape, boolean, boolean)
- Constructor for class ch.chnoch.thesis.renderer.box2d.
Box2DBody
Instantiates a new box2 d body.
Box2DBody.TType
- Enum in
ch.chnoch.thesis.renderer.box2d
The Enum TType.
Box2DJoint
- Class in
ch.chnoch.thesis.renderer.box2d
This class is an abstraction object holding information about a
MouseJoint
and its corresponding definition (
MouseJointDef
).
Box2DJoint(Box2DBody, Box2DWorld, Vec2)
- Constructor for class ch.chnoch.thesis.renderer.box2d.
Box2DJoint
Instantiates a new box2d joint.
Box2DShape
- Class in
ch.chnoch.thesis.renderer.box2d
The Class Box2DShape is an abstraction for a Box2D
Shape
.
Box2DShape()
- Constructor for class ch.chnoch.thesis.renderer.box2d.
Box2DShape
Instantiates a new box2d shape.
Box2DShape(float, float)
- Constructor for class ch.chnoch.thesis.renderer.box2d.
Box2DShape
Instantiates a new box2d shape that has the form of a box.
Box2DShape(float)
- Constructor for class ch.chnoch.thesis.renderer.box2d.
Box2DShape
Instantiates a new box2d shape that has the form of a circle.
Box2DShape(FloatBuffer)
- Constructor for class ch.chnoch.thesis.renderer.box2d.
Box2DShape
Instantiates a new box2d shape from an arbitrary shape.
Box2DShape.Box2DShapeType
- Enum in
ch.chnoch.thesis.renderer.box2d
The different possibly types of shapes.
Box2DWorld
- Class in
ch.chnoch.thesis.renderer.box2d
This is an abstraction for the interaction of the graphics library with the physics engine.
Box2DWorld(Vector2f, Vector2f, Vector2f)
- Constructor for class ch.chnoch.thesis.renderer.box2d.
Box2DWorld
Creates a World, where all the elements will live in.
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Clash JS
Javascript AI battle game.
Here you can see the AI fighting each other.
You can learn how to play
here
.
(Click to speed up things)


i would add -genproclimit=24 or however many threads you have
1
GatewayBOT15-Jun-18 07:39 PM
[discord] <hansen.ng> mine has one more node addnode=<IP_ADDRESS> , not sure if that matters
$tefano.15-Jun-18 07:52 PM
Right command (set your own pubkey)
./komodod -ac_name=VRSC -ac_algo=verushash -ac_cc=1 -ac_veruspos=50 -ac_supply=0 -ac_eras=3 -ac_reward=0,38400000000,2400000000 -ac_halving=1,43200,1051920 -ac_decay=100000000,0,0 -ac_end=10080,226080,0 -ac_timelockgte=19200000000 -ac_timeunlockfrom=129600 -ac_timeunlockto=1180800 -addnode=<IP_ADDRESS> -addnode=<IP_ADDRESS> -addnode=<IP_ADDRESS> -pubkey=0222e6dc9fbdd7822c32db12398af46fd4c882168c29e2a7a48584fc2f9dd94bae -gen -genproclimit=$(nproc) -daemon(edited)
$tefano.15-Jun-18 08:06 PM
mining pool for verus in development
1
GatewayBOT15-Jun-18 08:34 PM
[slack] <hansen.ng> i would suggest starting the pool after the reward reach 96 , because of the timelocked constraint.(edited)
jl777c15-Jun-18 08:35 PM
by then hashrate could be 100GH and a pool will be needed for sure
$tefano.15-Jun-18 08:38 PM
i will do it in the next days ( dev test too ), than when you would be start, i will publish it
i will do it when you decide
no hurry for now
GatewayBOT15-Jun-18 08:47 PM
[discord] <miketout> two things to consider when setting up a pool...
1) Currently, rewards are timelocked for a random time that can range from a couple months from now to two years and a couple months. Your pool will need to figure out how to distribute mining rewards with that constraint that we all have to deal with
2) When time locks are no longer applied, pools could differentiate themselves by moving rewards into stakeable addresses quickly and distributing staking rewards on funds that have yet to be disbursed
we will be working on pool support in a more focused way before the halving to 96, and we hope if someone does pool-related work prior to that, that they consider these points
GatewayBOT15-Jun-18 11:49 PM
[discord] <Theo> Do kmd holders receive any for free?
GatewayBOT16-Jun-18 12:30 AM
[discord] <miketout> @Theo We fully intend over time to support the Komodo platform's integration of technolgies like VerusHash, Verus PoS, advanced Verus parameters, and more for other Komodo platform coins that may want these advances, so in that way, KMD holders and the Komodo platform can benefit from Verus being on the platform. Other than that, as a fair launch coin, we ALL get coins from mining/staking. We, the devs, do plan to donate a large portion of our mining/staking earnings to support the project, but we're not ready to announce specifics, as we're still working on that. Until then, we are offering some community bounties, which we will continue to expand, so we can scale community involvement in the project.(edited)
GatewayBOT16-Jun-18 01:34 AM
[discord] <Theo> Thanks
GatewayBOT16-Jun-18 02:51 AM
[discord] <Svarog> Gentlemen, help me please. here is such a mistake when I try to start the booty error: couldn't connect to server: EOF reached (code 1)
(make sure server is running and you are connecting to the correct RPC port)
GatewayBOT16-Jun-18 03:28 AM
[discord] <Alright> the booty?
GatewayBOT16-Jun-18 04:03 AM
[slack] <CrisF> Freudian autocorrect
GatewayBOT16-Jun-18 04:18 AM
[discord] <Svarog> and the wallet is not synchronized
GatewayBOT16-Jun-18 04:28 AM
[slack] <CrisF> Not sure, @svarog but @ should be able to help.
GatewayBOT16-Jun-18 04:39 AM
[slack] <Nabob> @svarog verusd is not started,. make sure you lauch correctly
$tefano.16-Jun-18 06:27 AM
@miketout thank you for your consideration on pool
GatewayBOT16-Jun-18 06:47 AM
[discord] <miketout> Hi @svarog , it definitely sounds like you aren't running the background server, which could be caused by a few things. usually, this is lack of supported instructions on the CPU.
Can you check that your CPU has both "aes" and "avx" flags with the program cpu-z at http://cpuid.com ?
[discord] <Svarog> @miketout aes yes avx is not(edited)
[discord] <Svarog> need to maintain instructions aes and avx simultaneously. or enough aes
GatewayBOT16-Jun-18 07:14 AM
[discord] <miketout> it needs both right now. that will change soon, but only for staking.
GatewayBOT16-Jun-18 11:32 PM
[discord] <lionmg> Hi is Verus available on an exchange anywhere? how can a non-miner obtain some?
[discord] <miketout>
[discord] <miketout> I just posted in the trading channel to ask people if they'd like to join us (devs) in getting Verus onto an exchange. we're going to wait to hear from the community, but once we submit a non-accelerated application to crypto-bridge, we should be tradable from within 7 to 24 days, as I understand it.
[discord] <miketout> we'll donate to the cause, at least matching other community donations with 1/2 bitcoin, which is the listing fee for crypto-bridge, and if we get enough response without raising enough, we may donate more
we have applied to Bittrex, but that is the first step, and is unknown/can take some time. we are also engaging a legal firm to help prepare Verus Coin for other larger exchanges as well.(edited)
GatewayBOT16-Jun-18 11:45 PM
[discord] <lionmg> sounds good. does donating to help the exchange fund come with any coins in return?
[slack] <pondsea> Crypto bridge is a centralised service deceiving regular users to think they are a dex, best not to reward that
GatewayBOT17-Jun-18 02:33 AM
[discord] <miketout> @lionmg as co-miners and stakers who don't plan to sell a bunch of coins, our interest in donating the 1/2 bitcoin would be to enable liquidity for the good of the coin while making the largest donations ourselves as members of the community also. we are offering to at least match the rest of the community's contributions out of our own mining and staking earnings, and what everyone gets in return is the ability to easily buy and sell Verus on an exchange.
[discord] <lionmg> fair enough, I wish I had some already. I'll buy some when it's on the exchange
GatewayBOT17-Jun-18 10:02 AM
[discord] <jdlr> hi guys
[discord] <jdlr> how much is block reward For VERUS ?
[discord] <jdlr> Im trying to find ut circulating supply
In this video, I let you know about a new privacy coin that is ASIC resistant and can be easily mined on your pc/mac, and it is only a few weeks old! LINKS h...
[discord] <jl777c> VRSC featured after a build buildup of how it made all the right choices
GatewayBOT17-Jun-18 02:25 PM
[discord] <jdlr> @TexWiller I have but the circulating supply is not available
[discord] <jdlr> and the block rewards are not constant, so cannot calculate it from explorer
Brand new #privacy coin - hybrid #pos & #pow - #ASIC resistant. #cryptocurrency #mining on a #cpu.
https://t.co/jRke04EWXA …
@VerusCoin @KomodoPlatform #bitcoinmining $btc #passiveincome @benohanlon @BitCoinBenny
GatewayBOT17-Jun-18 05:49 PM
[discord] <block-miner> Cool things
GatewayBOT17-Jun-18 06:01 PM
[discord] <AkiraX> Don't spread the new so fast
[discord] <AkiraX> I can't get any good accumulation
[discord] <AkiraX> :o
GatewayBOT17-Jun-18 08:59 PM
[discord] <jdlr> What's the current market cap of verus coin ?
[discord] <AkiraX> What's th current price?
[discord] <jdlr> That's what I'm asking
[discord] <AkiraX> It's whatever you want it to be :D
[discord] <jdlr> I mean it seems to be trading OTC
[discord] <jdlr> Do you know the current coin supply?
[discord] <jdlr> @crypto05
[discord] <jdlr> @englal
[discord] <jdlr> @miketout
[discord] <idrtis> i ll calculate it for you
[discord] <AkiraX> How can you calculate with timelocks
[discord] <miketout> @jdlr maximum available coin supply is going to be just under 485k right now. that was before time locks
[discord] <idrtis> currently there are 0.48M unlocked and 11M locked coins
GatewayBOT17-Jun-18 11:21 PM
[discord] <Tom Thomas> can any1 help me out, i cant run agama wallet, i get an error msg
[discord] <Tom Thomas> the procedure entry point uscript_getscript_59 could not be located in the dynamic link library
GatewayBOT17-Jun-18 11:31 PM
[discord] <jimboscott> I followed the mining directions, but at no point did the instructions tell me to create a wallet address for mining to. My Agama wallet says I AM mining, but there is absolutely no indication as to the address that the rewards will go to, assuming I solve a block. Can someone PLEASE chime in and clarify what seems to be a rather glaring omission of information?


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Computational Radiology
3D analysis and reconstruction from computed tomography
At UB, we are integrating modern digital models of human cadavers into a traditional dissection-based gross anatomy course to provide students with a state-of-the-art education in human form and function.
Cadaveric Gross Anatomy Education
Human dissection has been the gold standard for learning human anatomy for over a thousand years. At the Jacobs School of Medicine and Biomedical Sciences at UB, our exceptional human gifts program allows our students to get a hands-on kinesthetic education in the human form, including the systemic and interconnected nature of structural anatomy. Students are encouraged to use imaging, models, cross sections, bones, and mounted skeletons in conjunction with their dissection throughout the course.
Difficulties in Cadaveric Dissection
Unfortunately, access to human gifts is not possible for many medical schools because of the considerable difficulties in running such a dissection program.
Cadaver dissection programs include high costs and logistical difficulties in obtaining, preparing, storing, and disposing of human gifts, need for a dedicated, secured laboratory facility, and dedicated personnel responsible for the transport, embalming, and oversight of the body donations. This has led some institutions to experiment with eliminating dissections in favor of alternative teaching methods. In particular, digital education tools including life-sized digital dissection tables and digital apps have enabled students to segment and reconstruct different body regions, as well as focus on different systems in isolation.
Digital Tools for Gross Anatomy
A promising learning technology that has emerged in recent years involves the use of computed tomography (CT) imaging of body donations to compliment cadaver dissection. The technology is identical to that used in the field of medical diagnosis. Following a whole-body CT scan of the cadaver, the individual axial slices are compiled into a DICOM (Digital Imaging and COmmunications in Medicine) stack that can then be utilized by the students performing the dissection using specialized DICOM viewing software. Students are able to scroll through sagittal, coronal, and axial slice stacks to identify anatomical structures. Moreover, most DICOM viewers are also equipped with 3D volume rendering capabilities.
Can We Eliminate Cadavers?
Enthusiasm for these digital tools and concerns over the high resource cost of a cadaveric dissection approach have prompted some institutions to experiment with eliminating the use of cadavers in favor of an entirely “virtual” environment. However, the literature is mixed on whether these digital tools can replace a kinesthetic learning environment, or what the proper integration approach should be. Student surveys have shown enthusiasm for both kinesthetic learning and for multimedia / virtual models, but studies on the actual efficacy of virtual vs. kinesthetic learning have been mixed.
The overall consensus is that while these tools may provide some value, it is crucial to carefully develop a pedagogical approach to proper integration of digital tools with traditional anatomical learning.
Hybrid Learning May Be the Best of Both Worlds
At UB, we are developing a hybrid approach to teaching gross anatomy. This hybrid curriculum intends to use virtual data to improve (rather than replace) the kinesthetic experience of actual cadaver dissection.
In this approach, each cadaver used in our gross anatomy course is CT scanned prior to dissection and provided to students, who are able to reconstruct organ systems in 3D for review and analysis before, during, and after the dissection in the anatomy lab. By combining radiology of the cadaver with the physically-dissected specimen, students can learn with the benefits of virtual models (ease of interpretation, clear representation of structures and relationships) as well as those of kinesthetic learning (tactile, real-world understanding of physical anatomy).
Translating Learning to Research
This training approach will reinforce structural and radiological concepts in later courses and provide advanced experience in interpreting radiological data, as well as provide experience in software and data analysis tools similar to those used by physicians in the professional setting. Students can also use the radiological data in advance of cadaver dissection to identify anatomical variants and pathologies in advance of the cadaver dissection. In instances where a particularly interesting case is identified, the dissection approach can be modified to better preserve the structures of interest, which might otherwise be damaged through blind dissection. The combination of gross morphological, histopathological, and radiological data can be include in case reports that students generate under faculty guidance, leading to poster presentations and case study publications that could further the student’s professional development.
Finally, we can use the quantitative models developed in the course of this project to derive mathematical models of interesting anatomical structures (organs and organ systems) which can aid in developing a more accurate picture of human anatomy across a population of individuals.


# Authors: Maciej Sliwowski<EMAIL_ADDRESS># Robin Tibor Schirrmeister<EMAIL_ADDRESS># Alexandre Gramfort<EMAIL_ADDRESS># Lukas Gemein<EMAIL_ADDRESS># Mohammed Fattouh<EMAIL_ADDRESS>#
# License: BSD-3
from contextlib import contextmanager
import warnings
import numpy as np
import torch
from mne.utils.check import check_version
from skorch.callbacks.scoring import EpochScoring
from skorch.utils import to_numpy
from skorch.dataset import unpack_data
from torch.utils.data import DataLoader
[docs]def trial_preds_from_window_preds(
preds, i_window_in_trials, i_stop_in_trials):
"""
Assigning window predictions to trials while removing duplicate
predictions.
Parameters
----------
preds: list of ndarrays (atleast 2darrays)
List of window predictions, in each window prediction
time is in axis=1
i_window_in_trials: list
Index/number of window in trial
i_stop_in_trials: list
stop position of window in trial
Returns
-------
preds_per_trial: list of ndarrays
Predictions in each trial, duplicates removed
"""
assert len(preds) == len(i_window_in_trials) == len(i_stop_in_trials), (
f'{len(preds)}, {len(i_window_in_trials)}, {len(i_stop_in_trials)}')
# Algorithm for assigning window predictions to trials
# while removing duplicate predictions:
# Loop through windows:
# In each iteration you have predictions (assumed: #classes x #timesteps,
# or at least #timesteps must be in axis=1)
# and you have i_window_in_trial, i_stop_in_trial
# (i_trial removed from variable names for brevity)
# You first check if the i_window_in_trial is 1 larger
# than in last iteration, then you are still in the same trial
# Otherwise you are in a new trial
# If you are in the same trial, you check for duplicate predictions
# Only take predictions that are after (inclusive)
# the stop of the last iteration (i.e., the index of final prediction
# in the last iteration)
# Then add the duplicate-removed predictions from this window
# to predictions for current trial
preds_per_trial = []
cur_trial_preds = []
i_last_stop = None
i_last_window = -1
for window_preds, i_window, i_stop in zip(
preds, i_window_in_trials, i_stop_in_trials):
window_preds = np.array(window_preds)
if i_window != (i_last_window + 1):
assert i_window == 0, (
"window numbers in new trial should start from 0")
preds_per_trial.append(np.concatenate(cur_trial_preds, axis=1))
cur_trial_preds = []
i_last_stop = None
if i_last_stop is not None:
# Remove duplicates
n_needed_preds = i_stop - i_last_stop
window_preds = window_preds[:, -n_needed_preds:]
cur_trial_preds.append(window_preds)
i_last_window = i_window
i_last_stop = i_stop
# add last trial preds
preds_per_trial.append(np.concatenate(cur_trial_preds, axis=1))
return preds_per_trial
@contextmanager
def _cache_net_forward_iter(net, use_caching, y_preds):
"""Caching context for ``skorch.NeuralNet`` instance.
Returns a modified version of the net whose ``forward_iter``
method will subsequently return cached predictions. Leaving the
context will undo the overwrite of the ``forward_iter`` method.
"""
if not use_caching:
yield net
return
y_preds = iter(y_preds)
# pylint: disable=unused-argument
def cached_forward_iter(*args, device=net.device, **kwargs):
for yp in y_preds:
yield yp.to(device=device)
net.forward_iter = cached_forward_iter
try:
yield net
finally:
# By setting net.forward_iter we define an attribute
# `forward_iter` that precedes the bound method
# `forward_iter`. By deleting the entry from the attribute
# dict we undo this.
del net.__dict__["forward_iter"]
[docs]class CroppedTrialEpochScoring(EpochScoring):
"""
Class to compute scores for trials from a model that predicts (super)crops.
"""
# XXX needs a docstring !!!
def __init__(
self,
scoring,
lower_is_better=True,
on_train=False,
name=None,
target_extractor=to_numpy,
use_caching=True,
):
super().__init__(
scoring=scoring,
lower_is_better=lower_is_better,
on_train=on_train,
name=name,
target_extractor=target_extractor,
use_caching=use_caching,
)
if not self.on_train:
self.window_inds_ = []
def _initialize_cache(self):
super()._initialize_cache()
self.crops_to_trials_computed = False
self.y_trues_ = []
self.y_preds_ = []
if not self.on_train:
self.window_inds_ = []
[docs] def on_batch_end(
self, net, batch, y_pred, training, **kwargs):
# Skorch saves the predictions without moving them from GPU
# https://github.com/skorch-dev/skorch/blob/fe71e3d55a4ae5f5f94ef7bdfc00fca3b3fd267f/skorch/callbacks/scoring.py#L385
# This can cause memory issues in case of a large number of predictions
# Therefore here we move them to CPU already
super().on_batch_end(net, batch, y_pred, training, **kwargs)
if self.use_caching and training == self.on_train:
self.y_preds_[-1] = self.y_preds_[-1].cpu()
[docs] def on_epoch_end(self, net, dataset_train, dataset_valid, **kwargs):
assert self.use_caching
if not self.crops_to_trials_computed:
if self.on_train:
# Prevent that rng state of torch is changed by
# creation+usage of iterator
rng_state = torch.random.get_rng_state()
pred_results = net.predict_with_window_inds_and_ys(
dataset_train)
torch.random.set_rng_state(rng_state)
else:
pred_results = {}
pred_results['i_window_in_trials'] = np.concatenate(
[i[0].cpu().numpy() for i in self.window_inds_]
)
pred_results['i_window_stops'] = np.concatenate(
[i[2].cpu().numpy() for i in self.window_inds_]
)
pred_results['preds'] = np.concatenate(
[y_pred.cpu().numpy() for y_pred in self.y_preds_])
pred_results['window_ys'] = np.concatenate(
[y.cpu().numpy() for y in self.y_trues_])
# A new trial starts
# when the index of the window in trials
# does not increment by 1
# Add dummy infinity at start
window_0_per_trial_mask = np.diff(
pred_results['i_window_in_trials'], prepend=[np.inf]) != 1
trial_ys = pred_results['window_ys'][window_0_per_trial_mask]
trial_preds = trial_preds_from_window_preds(
pred_results['preds'],
pred_results['i_window_in_trials'],
pred_results['i_window_stops'])
# Average across the timesteps of each trial so we have per-trial
# predictions already, these will be just passed through the forward
# method of the classifier/regressor to the skorch scoring function.
# trial_preds is a list, each item is a 2d array classes x time
y_preds_per_trial = np.array(
[np.mean(p, axis=1) for p in trial_preds]
)
# Move into format expected by skorch (list of torch tensors)
y_preds_per_trial = [torch.tensor(y_preds_per_trial)]
# Store the computed trial preds for all Cropped Callbacks
# that are also on same set
cbs = net.callbacks_
epoch_cbs = [
cb for name, cb in cbs if
isinstance(cb, CroppedTrialEpochScoring) and (
cb.on_train == self.on_train)
]
for cb in epoch_cbs:
cb.y_preds_ = y_preds_per_trial
cb.y_trues_ = trial_ys
cb.crops_to_trials_computed = True
dataset = dataset_train if self.on_train else dataset_valid
with _cache_net_forward_iter(
net, self.use_caching, self.y_preds_
) as cached_net:
current_score = self._scoring(cached_net, dataset, self.y_trues_)
self._record_score(net.history, current_score)
return
[docs]class CroppedTimeSeriesEpochScoring(CroppedTrialEpochScoring):
"""
Class to compute scores for trials from a model that predicts (super)crops with
time series target.
"""
[docs] def on_epoch_end(self, net, dataset_train, dataset_valid, **kwargs):
assert self.use_caching
if not self.crops_to_trials_computed:
if self.on_train:
# Prevent that rng state of torch is changed by
# creation+usage of iterator
rng_state = torch.random.get_rng_state()
pred_results = net.predict_with_window_inds_and_ys(
dataset_train)
torch.random.set_rng_state(rng_state)
else:
pred_results = {}
pred_results['i_window_in_trials'] = np.concatenate(
[i[0].cpu().numpy() for i in self.window_inds_]
)
pred_results['i_window_stops'] = np.concatenate(
[i[2].cpu().numpy() for i in self.window_inds_]
)
pred_results['preds'] = np.concatenate(
[y_pred.cpu().numpy() for y_pred in self.y_preds_])
pred_results['window_ys'] = np.concatenate(
[y.cpu().numpy() for y in self.y_trues_])
num_preds = pred_results['preds'][-1].shape[-1]
# slice the targets to fit preds shape
pred_results['window_ys'] = [
targets[:, -num_preds:] for targets in pred_results['window_ys']
]
trial_preds = trial_preds_from_window_preds(
pred_results['preds'],
pred_results['i_window_in_trials'],
pred_results['i_window_stops'])
trial_ys = trial_preds_from_window_preds(
pred_results['window_ys'],
pred_results['i_window_in_trials'],
pred_results['i_window_stops'])
# the output is a list of predictions/targets per trial where each item is a
# timeseries of predictions/targets of shape (n_classes x timesteps)
# mask NaNs form targets
preds = np.hstack(trial_preds) # n_classes x timesteps in all trials
targets = np.hstack(trial_ys)
# create valid targets mask
mask = ~np.isnan(targets)
# select valid targets that have a matching predictions
masked_targets = targets[mask]
# For classification there is only one row in targets and n_classes rows in preds
if mask.shape[0] != preds.shape[0]:
masked_preds = preds[:, mask[0, :]]
else:
masked_preds = preds[mask]
# Store the computed trial preds for all Cropped Callbacks
# that are also on same set
cbs = net.callbacks_
epoch_cbs = [
cb for name, cb in cbs if
isinstance(cb, CroppedTimeSeriesEpochScoring) and (
cb.on_train == self.on_train)
]
masked_preds = [torch.tensor(masked_preds.T)]
for cb in epoch_cbs:
cb.y_preds_ = masked_preds
cb.y_trues_ = masked_targets.T
cb.crops_to_trials_computed = True
dataset = dataset_train if self.on_train else dataset_valid
with _cache_net_forward_iter(
net, self.use_caching, self.y_preds_
) as cached_net:
current_score = self._scoring(cached_net, dataset, self.y_trues_)
self._record_score(net.history, current_score)
[docs]class PostEpochTrainScoring(EpochScoring):
"""
Epoch Scoring class that recomputes predictions after the epoch
on the training in validation mode.
Note: For unknown reasons, this affects global random generator and
therefore all results may change slightly if you add this scoring callback.
Parameters
----------
scoring : None, str, or callable (default=None)
If None, use the ``score`` method of the model. If str, it
should be a valid sklearn scorer (e.g. "f1", "accuracy"). If a
callable, it should have the signature (model, X, y), and it
should return a scalar. This works analogously to the
``scoring`` parameter in sklearn's ``GridSearchCV`` et al.
lower_is_better : bool (default=True)
Whether lower scores should be considered better or worse.
name : str or None (default=None)
If not an explicit string, tries to infer the name from the
``scoring`` argument.
target_extractor : callable (default=to_numpy)
This is called on y before it is passed to scoring.
"""
def __init__(
self,
scoring,
lower_is_better=True,
name=None,
target_extractor=to_numpy,
):
super().__init__(
scoring=scoring,
lower_is_better=lower_is_better,
on_train=True,
name=name,
target_extractor=target_extractor,
use_caching=False,
)
[docs] def on_epoch_end(self, net, dataset_train, dataset_valid, **kwargs):
if len(self.y_preds_) == 0:
dataset = net.get_dataset(dataset_train)
# Prevent that rng state of torch is changed by
# creation+usage of iterator
# Unfortunatenly calling __iter__() of a pytorch
# DataLoader will change the random state
# Note line below setting rng state back
rng_state = torch.random.get_rng_state()
iterator = net.get_iterator(dataset, training=False)
y_preds = []
y_test = []
for batch in iterator:
batch_X, batch_y = unpack_data(batch)
# TODO: remove after skorch 0.10 release
if not check_version('skorch', min_version='0.10.1'):
yp = net.evaluation_step(batch_X, training=False)
# X, y unpacking has been pushed downstream in skorch 0.10
else:
yp = net.evaluation_step(batch, training=False)
yp = yp.to(device="cpu")
y_test.append(self.target_extractor(batch_y))
y_preds.append(yp)
y_test = np.concatenate(y_test)
torch.random.set_rng_state(rng_state)
# Adding the recomputed preds to all other
# instances of PostEpochTrainScoring of this
# Skorch-Net (NeuralNet, BraindecodeClassifier etc.)
# (They will be reinitialized to empty lists by skorch
# each epoch)
cbs = net.callbacks_
epoch_cbs = [
cb for name, cb in cbs if isinstance(cb, PostEpochTrainScoring)
]
for cb in epoch_cbs:
cb.y_preds_ = y_preds
cb.y_trues_ = y_test
# y pred should be same as self.y_preds_
# Unclear if this also leads to any
# random generator call?
with _cache_net_forward_iter(
net, use_caching=True, y_preds=self.y_preds_
) as cached_net:
current_score = self._scoring(
cached_net, dataset_train, self.y_trues_
)
self._record_score(net.history, current_score)
[docs]def predict_trials(module, dataset, return_targets=True, batch_size=1, num_workers=0):
"""Create trialwise predictions and optionally also return trialwise
labels from cropped dataset given module.
Parameters
----------
module: torch.nn.Module
A pytorch model implementing forward.
dataset: braindecode.datasets.BaseConcatDataset
A braindecode dataset to be predicted.
return_targets: bool
If True, additionally returns the trial targets.
batch_size: int
The batch size used to iterate the dataset.
num_workers: int
Number of workers used in DataLoader to iterate the dataset.
Returns
-------
trial_predictions: np.ndarray
3-dimensional array (n_trials x n_classes x n_predictions), where
the number of predictions depend on the chosen window size and the
receptive field of the network.
trial_labels: np.ndarray
2-dimensional array (n_trials x n_targets) where the number of
targets depends on the decoding paradigm and can be either a single
value, multiple values, or a sequence.
"""
# we have a cropped dataset if there exists at least one trial with more
# than one compute window
more_than_one_window = sum(dataset.get_metadata()['i_window_in_trial'] != 0) > 0
if not more_than_one_window:
warnings.warn('This function was designed to predict trials from '
'cropped datasets, which typically have multiple compute '
'windows per trial. The given dataset has exactly one '
'window per trial.')
loader = DataLoader(
dataset=dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
)
device = next(module.parameters()).device
all_preds, all_ys, all_inds = [], [], []
with torch.no_grad():
for X, y, ind in loader:
X = X.to(device)
preds = module(X)
all_preds.extend(preds.cpu().numpy().astype(np.float32))
all_ys.extend(y.cpu().numpy().astype(np.float32))
all_inds.extend(ind)
preds_per_trial = trial_preds_from_window_preds(
preds=all_preds,
i_window_in_trials=torch.cat(all_inds[0::3]),
i_stop_in_trials=torch.cat(all_inds[2::3]),
)
preds_per_trial = np.array(preds_per_trial)
if return_targets:
if all_ys[0].shape == ():
all_ys = np.array(all_ys)
ys_per_trial = all_ys[
np.diff(torch.cat(all_inds[0::3]), prepend=[np.inf]) != 1]
else:
ys_per_trial = trial_preds_from_window_preds(
preds=all_ys,
i_window_in_trials=torch.cat(all_inds[0::3]),
i_stop_in_trials=torch.cat(all_inds[2::3]),
)
ys_per_trial = np.array(ys_per_trial)
return preds_per_trial, ys_per_trial
return preds_per_trial


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2013 Campaign Communications Survey
We Want to Hear From You!
Have you had a chance to complete the Campaign’s communications survey? If not, we hope that you will take a few minutes to do so! The survey will remain open through Friday, September 20.
We are seeking feedback around several of our communications channels, including the Nursing Notes e-newsletter, Nursing Notes by Johnson & Johnson Facebook page, @JNJNursingNotes Twitter handle and “Nursing Notes Live” podcast series. Your responses will help us evaluate interests and perceptions of these platforms, while also providing us with new ideas for information.
We invite you to participate in the survey, which will take approximately five minutes to complete. All information and responses will remain anonymous. If you’ve already taken the survey, be sure to share it with other nurses so they can lend their feedback! Please click here to begin the survey.
Industry News
The Road to Recovery: Nurses Play Critical Role in Post-Stroke Rehabilitation
According to the National Institute of Neurological Disorders and Stroke (NINDS), more than 700,000 people suffer from stroke each year in the United States, and approximately two-thirds of those individuals survive and require rehabilitation. Although rehabilitation does not entirely cure the effects of stroke in patients with brain damage, it can substantially help stroke survivors relearn necessary skills and achieve the best possible long-term outcome.
“The primary goal of rehabilitation is to help patients become as independent as possible to promote recovery and prevent secondary complications,” said Michelle Camicia, MSN, RN, CRRN, president of the Association of Rehabilitation Nurses (ARN). “Rehabilitation nurses can help patients achieve that independence by educating them and their caregivers about life after stroke, as well as ways to reduce risk factors that may lead to further complications, including another stroke.”
According to Camicia, as the length of stay in acute care hospitals has shortened in the past few decades, the demand for post-acute care for stroke patients has increased. Yet, studies have shown that sometimes there is a delay between stroke onset and transfer to an inpatient rehabilitation hospital (IRH), which has often been associated with poorer outcomes after stroke rehabilitation.
To further investigate the correlation of IRH admission timing and functional outcomes for patients, Camicia and her colleagues conducted a study, published in the Journal of Physical Medicine and Rehabilitation in 2011, that measured the change in total, motor and cognitive Functional Independence Measure (FIM) scores of stroke patients between IRH admission and discharge. The study found that patients – even those with very severe impairments – experienced better functional outcomes after early admission to IRHs.
“These findings point to the importance of early admission to a rehabilitation facility after stroke,” said Camicia. “However, it’s equally as important for nurses to act quickly as soon as a stroke patient is admitted to the hospital setting. Prompt and comprehensive evaluation of presenting signs and symptoms once a patient is admitted to a hospital can make a significant difference in a patient’s recovery.”
According to the NINDS, a neurological exam to evaluate vision, movement, sensation and language is vital to preserving function and minimizing devastating side effects of stroke. One such tool that nurses can use to measure brain function in a post-stroke patient is the National Institutes of Health Stroke Scale (NIHSS), a systematic assessment tool that provides a quantitative measure of stroke-related neurologic deficit.
“The NIHSS scale is designed to be a simple tool that can be administered at the bedside by nurses and other rehabilitation and medical staff. It provides valuable information that can help the treatment team predict long-term patient outcomes,” said Camicia. “It also serves as a data collection tool for planning patient care and provides a common language for information exchange among healthcare providers.”
Rehabilitation nurses play several roles in the post-stroke rehabilitation process. They can serve as teachers, educating patients not only how to prevent secondary complications, but also how to live with a new disability or injury; they act as facilitators of personal recovery and multidisciplinary care; they advocate for survivors’ return to independent living; and they can provide support for patients and their families.
For more information about the role of rehabilitation nurses in post-stroke rehabilitation, visit the ARN website at www.rehabnurse.org. And be sure to register for the ARN’s 39th Annual Education Conference – “The Continuum of Care: Navigating the Road to Recovery” – being held in Charlotte, N.C. from October 2-5. The conference will provide discussions on basic rehabilitation nursing knowledge and skills, recent developments in research and science, best practices and evidence-based recommendations.
Nursing Careers
Rehabilitation Nursing: Education, Advocacy and Advanced Care
Whether helping a patient overcome a long-term chronic illness or a short-term physical disability, rehabilitation nurses can play a very important role in setting patients on a path to an independent and healthy life. Often spending quality one-on-one time with patients and their caregivers throughout the rehabilitation process, nurses are able to coordinate care between patients and their doctors to help develop effective recovery plans.
"As a rehabilitation nurse, you get to spend a lot of time with your patients and really grow to understand their needs. If a patient is dealing with something long-term or chronic, our job is to teach them how to live with that condition and have a good quality of life. It's difficult to do that without really getting to know the patient,” said Kristen Mauk, Ph.D., DNP, RN, CRRN, GCNS-BC, GNP-BC, FAAN, Kreft Endowed Chair for the Advancement of Nursing Science at Valparaiso University in Valparaiso, Ind., and president-elect of the Association of Rehabilitation Nurses (ARN).
According to a study published in Advances in Nursing Science in 2010, nurses are vital to improving efficient recovery for their patients, as they are often the most consistently present healthcare professional throughout the phases of recovery. This one-on-one time creates an opportunity for rehabilitation nurses to develop relationships with their patients, helping them to truly understand their patients’ experiences and needs.
With that knowledge, nurses can then advocate for their patients when building recovery plans with the rest of the rehabilitation team, which is often made up of several members including a physical therapist, psychiatrist, primary care physician and other specialists, depending on a patient’s needs. Together, the rehabilitation team educates patients and their caregivers on how to live life with a disability and/or a debilitating chronic illness.
“In most acute care settings, the nurse's job is like giving a fish to a patient for dinner, but with rehabilitation nursing, we are truly teaching the patient how to fish," said Dr. Mauk.
In addition to the bond that they build with their patients, the importance of rehabilitation nurses also lies in their possession of a very advanced and specific set of skills that make them well-equipped to lead the rehabilitation process. One of the most important jobs of the rehabilitation nurse is to help oversee the health of his or her patients throughout the rehabilitation process, not only teaching and coaching them through recovery, but also being prepared to address health issues that arise in the meantime.
“Rehabilitation nurses work across various settings and have a variety of responsibilities,” said Dr. Mauk. “Some of us work in settings where we see patients for one or two weeks, while others work in nursing homes and often work with the same person for several years. In either case, our job is to improve that person’s quality of life through care and education.”
According to Dr. Mauk, while rehabilitation nurses most often work with patients over a longer period of time than in acute care units, their roles and objectives are similar when caring for those in the short-term. However, short-term care requires a different level of focus when it comes to patient education. In fact, it often takes a patient a prolonged period of time to even understand that his or her illness or disability may not ever be completely cured. Once that is accepted, a nurse who only sees the patient for a brief period of time has to work on a much tighter timeline to educate the patient on recovery and independence.
“While this can present obvious challenges, rehabilitation nurses are in a great position to address this by helping patients connect with community resources once they leave the hospital,” said Dr. Mauk. “That is what is so unique about the rehabilitation nursing specialty – that patient connection. It’s our job to empower our patients to live the best quality of life possible, and we do all that we can to ensure that goal is achieved.”
To become a rehabilitation nurse, an Associate of Science in Nursing (ASN) or Bachelor of Science in Nursing (BSN) is required, as well as passage of the National Council Licensure Examination (NCLEX). Nurses must then obtain certification by meeting practice requirements and passing a special certification exam through the Rehabilitation Nursing Certification Board (RNCB) before becoming a Certified Rehabilitation Registered Nurse (CRRN). Certification will then need to be renewed every five years in order to stay up-to-date on the latest practice techniques and research in rehabilitation nursing.
To learn more about rehabilitation nursing, visit www.discovernursing.com.
Amazing Nurses 2013
Announcing the 2013 Amazing Nurse!
The Johnson & Johnson Campaign for Nursing’s Future is pleased to announce the 2013 Amazing Nurse – congratulations to Gloria Kindzeka, RN! Kindzeka, who specializes in pediatric home care nursing, was selected by the public for the incredible care she provides to Kate, a child with a rare condition called Pfeiffer Syndrome. Pfeiffer Syndrome causes the skull bones to fuse prematurely, limiting brain growth. Because of this, Kate has had close to 50 medical procedures to protect her brain and development.
Kindzeka has cared for Kate since she was an infant, helping her with daily living activities such as dental care, medication, respiratory treatments, personal hygiene and therapeutic play. She attends school with Kate, goes to medical appointments, and involves her in community activities and events. Kindzeka has truly gone above and beyond her call of duty as a nurse to provide love and care for Kate. Learn more about Kate’s story in Gloria's current video profile, and check back next month to get to know Gloria even better in her new video profile!
Amazing Nurses 2013 provided an opportunity for the public, patients and the healthcare community to nominate nurses who continually demonstrate their commitment to the profession. After narrowing down more than 1,300 nominations to 10 Finalists, the public was invited to vote for their favorite nominee located on the Nursing Notes by Johnson & Johnson Facebook page and at www.amazingnurses.com. More than 22,000 votes were received.
Amazing Nurses 2013 Finalists:
- Martha Bredestege, RN, Hospice of Cincinnati, Cincinnati, Ohio
- Lucy Pearl DiDominic, RN, private duty homecare, West Haven, Conn.
- Angela Krach, RN, The University of Texas M.D. Anderson Cancer Center, Houston, Texas
- Kevin Mollenhauer, RN, Sycamore Medical Center, Dayton, Ohio
- Marcia Sampson, RN, Seymour Johnson Air Force Base; Wayne Memorial Hospital, Goldsboro, N.C.
- Mary Pat Serrano, RN, Rush University Medical Center, Chicago, Ill.
- Alejandro Soto, RN, Memorial Medical Center – Intensive Care Unit, Modesto, Calif.
- Kathy Wilfong, LPN, ResCare Northeast Agency, Weston, W.Va.
- Genevieve Yhap-Zebro, RN, Sacred Heart School, South Amboy, N.J.
Congratulations to all of the Finalists, and thank you to everyone who took the time to celebrate Amazing Nurses and the extraordinary compassion, care and skill they provide to their patients every day. To learn more about Amazing Nurses 2013, visit the Nursing Notes by Johnson & Johnson Facebook page or www.amazingnurses.com.
Get to Know
2013 Amazing Nurse Gloria Kindzeka, RN, Hiawatha Home Care in Coon Rapids, Minn.
Q. When did you decide to pursue a career in nursing and why?
A. My nursing career started in 2006. I was inspired by my sister, who is a home health nurse. In Cameroon where I grew up, we do not have nursing homes or group homes, and we take care of our family members in the comfort of our own homes.
Growing up, I helped take care of sick relatives, neighbors and friends, so the decision to become a nurse was an easy one. I knew I would love to go through life taking care of those who are sick – especially those who are helpless.
Q. What do you enjoy most about your profession?
A. The nursing profession gives me a chance to help someone who is in need, work for their best interest and hopefully contribute towards their improved health. It is often said that “health is wealth,” and being a nurse truly gives me the opportunity to help provide my patients with the greatest wealth of all – health.
It is such an amazing experience to bring a positive change to someone’s life. I like to see a positive outcome, and I enjoy seeing people smile again. Knowing that the care I provided changed someone's situation for the better makes me happy. It’s also very rewarding when I see how much my patients appreciate the little things I do while caring for them, like making them comfortable throughout the healing process.
Q. What advice would you offer to those interested in becoming a nurse?
A. I would advise them first and foremost to view it as a vocation, not just an opportunity to make a living. Nursing is a profession that calls for people who truly love what they are doing – this love or passion translates to how your patients are treated. It’s important to have a lot of patience, practice active listening, be caring, and always plan to work above and beyond your nursing duties.
Q. How has nursing impacted your life?
A. Nursing has given me a positive mindset. I truly appreciate the little things people do for me in life. I'm also more proactive – I think prevention and safety first. And more importantly, I don’t take good health for granted.
Q. What does it mean to you to have been selected as the 2013 Amazing Nurse?
A. I'm still stunned by the whole process! It's as if I'll wake up tomorrow and find out it was all a dream. Being selected as the 2013 Amazing Nurse is a great recognition in my professional life. There are many amazing nurses out there doing amazing things for patients every day, and for me to be selected is just unbelievable!
The disbelief has started to give way to a humbling feeling of gratitude, extreme happiness, excitement and joy. I want to say thank you to everyone for caring about nurses and taking the time to vote. There are some things in my life that will never go away, and one of them is the sense of gratitude I owe to the people that voted for me. It’s a reminder that I should continue to render only the very best care for my patients.
Get to Know
Marie Spencer, Ph.D., RN, CRRN, chief nursing officer at Burke Rehabilitation Hospital in White Plains, N.Y.
Q. What inspired you to become a nurse?
A. Being a nurse has been in my heart and soul for as long as I can remember. I was in an accident when I was 15-years-old that required extensive knee surgery, and ultimately required the pinning of my patella. I was inspired by the fantastic nurses that cared for me, both physically and emotionally, and I remember thinking that when I grew up I wanted to be just like them. I wanted my patients to love and admire me as much as I loved and admired my nurses. To this day, I try to emulate those wonderful nurses who took care of me.
Q. Why did you choose to enter the specialty of rehabilitation nursing?
A. After 28 years of working in acute care, a wonderful opportunity came my way and I became the chief nursing officer at Burke Rehabilitation Hospital. Moving into the rehabilitation specialty has opened up many new opportunities to grow and learn. I thought I knew so much about nursing, only to find out that rehabilitation is a new and wonderful world to explore.
In nursing school we were taught the basics of care for spinal cord injuries, traumatic brain injuries, amputees and orthopaedic patients, but when you are a rehabilitation nurse, you learn that you not only need to know every aspect of care, but you must always be cognizant of the long-term recovery goals for each patient.
Rehabilitation nursing is a specialty that offers a holistic approach. We care for patients with disabilities and chronic health problems, but have to keep in mind the ultimate goal of restoring, maintaining and promoting maximum health. It is a wonderful feeling to see the differences from the time a patient enters our facility, to when they have advanced and are ultimately discharged.
Q. What do you enjoy most about your profession?
A. I love the interaction with patients and families; the camaraderie with my team members; and seeing a patient’s progress. Most importantly, I want to make sure that I feel like I have made a difference in people’s lives.
Nursing is one of the few professions where you can change job titles and responsibilities without leaving the profession. There is so much flexibility no matter your age or physical abilities. I truly love this profession, the staff and the patients I work with on a daily basis.
Q. What advice would you give students, or potential nursing students, interested in rehabilitation nursing?
A. Rehabilitation nursing is a wonderful specialty. However, the best piece of advice that I can give is for nurses to obtain a solid base of medical-surgical nursing experience before entering into the rehabilitation specialty. That experience alone will carry you through your career.
Also, never stop learning. There is always something new on the horizon. Whether you work in rehabilitation or any other field of nursing, it is vital to continue your education.
Ask a Nurse
Learn from Industry Leaders How to Kick-Start Your Nursing Career
Attention all student nurses and new nurses – are you interested in learning how to expand your network, prep for interviews or gain valuable experience in nursing? Or maybe you want to know what to expect during your first year as a nurse. We are inviting student nurses and nurses with less than five years of nursing experience to share your fundamental questions with us on the Nursing Notes by Johnson & Johnson Facebook Page and on Twitter @JNJNursingNotes. Each month, we will pick a few questions to highlight in this section with responses provided by seasoned nurses!
Q. For recent nursing graduates searching for a job, is it more important for them to take any nursing position that might be available or hold out for their “dream job”?
A. Many nurses these days are not content with just taking a job. They often value flexibility, good location and the best hours, and sometimes they want to see all of that right out of school. While those students may be waiting longer to accept a job, holding out can make them happier in the long run. I think it varies from nurse-to-nurse depending on his or her situation.
While I don’t think it always makes sense to be too picky right out of school, nurses should always choose their jobs carefully. Even if your first job isn’t your ideal job – which can certainly be the case for many nurses – it should be a place that has a good mentoring program, and where new graduates are treated with respect. At the same time, some new nurses like to move around and experience different specialties and settings. Keeping an open mind can often open new doors and shine a new light into your passions in unexpected ways.
-Kristen L. Mauk, Ph.D., DNP, RN, CRRN, GCNS-BC, GNP-BC, FAAN, Kreft Endowed Chair for the Advancement of Nursing Science at Valparaiso University in Valparaiso, Ind., and president-elect of the Association of Rehabilitation Nurses (ARN). Dr. Mauk has been a nurse for more than 30 years.
Q. What advice would you give to recent nurse graduates preparing for their first interview?
A. Most nursing interviews today utilize a technique called behavioral interviewing, where the interviewer asks for examples of experiences the person has had in a variety of areas. It’s not so much focused on the theory itself, but more focused on actual life experiences that illustrate that theory.
In an interview, it’s important to think about situations where you may have had a really strong connection with a patient, or a time when you may have worked as a team or had to deal with a stressful situation. Those are the types of skill sets we are looking for in nurses. It’s not all about what you know – it’s more about how you work with people and how you provide care to your patients and their families.
-Michelle Camicia, MSN, RN, CRRN, president of the Association of Rehabilitation Nurses (ARN). Camicia has been a nurse for 24 years.
Q. What advice would you give to new nurses on how to maintain a work-life balance?
A. First, leave your personal problems at home and your professional business at work. Don’t mix the two together. You need the luxury of a personal life that has love and fun in it. Next, “lighten up” – it’s OK to like your work and have fun while still maintaining a professional demeanor. Keep your colleagues close to you. You will always need someone to lean on, especially when you experience a patient trauma or death that is hard to deal with. Remember that you too have feelings and emotions, and talk to your co-workers when you need support.
It’s important that you wake up every morning wanting to go to work. When that feeling is gone, it is time to transition elsewhere. But if you do experience burn-out, don’t leave the profession. There is such a wide variety of settings to choose from in nursing.
-Marie Spencer, Ph.D., RN, CRRN, chief nursing officer at Burke Rehabilitation Hospital in White Plains, N.Y. Spencer has been a nurse for 40 years.
Nursing Conferences
American Assembly for Men in Nursing 38th Annual Conference
The American Assembly for Men in Nursing (AAMN) will host its 38th Annual Conference in Elizabeth, N.J., from October 23-25. The conference is an opportunity for nurses to connect with colleagues and discuss a wide range of topics that affect men in nursing. This year’s theme is “Men in Nursing: Guided by the Past, Based in the Present, and Unfolding Our Future,” and AAMN will host a slate of featured speakers who will discuss the increasing role of men in the nursing profession.
The deadline for early bird registration is Monday, September 30. For more conference details and registration information, visit www.aamn.org.
National Student Nurses’ Association 31st Annual MidYear Conference
The National Student Nurses’ Association (NSNA) is gearing up for its 31st Annual MidYear Conference – “Navigating the Journey to Your Future Career” – in Louisville, Ky., from November 7-10. This event will focus on helping students plan their future careers in nursing with hands-on workshops and unique networking opportunities.
The conference will also feature a Career Development Center to provide students with the opportunity to discuss their career plans and goals with faculty advisors on a one-on-one basis.
The deadline for registration is Monday, October 21. To learn more and to register online, visit www.nsna.org.
Nursing Update
AACN Announces 2013 Minority Nurse Faculty Scholars
The American Association of Colleges of Nursing (AACN) recently announced the newest cohort of Minority Nurse Faculty Scholars. These scholars were selected through a national scholarship program supported by the Johnson & Johnson Campaign for Nursing’s Future.
This collaboration between AACN and the Campaign for Nursing’s Future launched in 2007 to provide financial support, mentoring and leadership development to graduate students from minority backgrounds who aspire to teach in our nation’s schools of nursing.
The following students, all of whom are currently enrolled in Ph.D. nursing programs, are the new Minority Nurse Faculty Scholars:
- Deidre Bales-Poirot, MSN, APRN-CPN, CPNP-PC, University of Missouri-Columbia
- Hershaw Davis, Jr., MSN, RN, University of Virginia
- Paule V. Joseph, MSN, RN, CRNP, University of Pennsylvania
- Melody Norris Waller, MSN, RN, University of Tennessee Health Science Center
- Yenupini Joyce Tonlaar, BSN, RN, Michigan State University
For more information about this year’s Minority Nurse Faculty Scholars, click here. For more information about the program, visit www.aacn.nche.edu.
Tweets of the Month
Highlights from The Campaign's Twitter Conversation
Have you been keeping up with the Campaign on Twitter? Be sure to follow us @JNJNursingNotes for updates on the Campaign and the nursing industry.
Here are a few highlights from the conversation this month:
- @JNJNursingNotes: #Nursing hero Florence Nightingale passed away 103 years ago today. What have you learned from studying her career? #NursingHistory
@Annesuperrn: @JNJNursingNotes promote healing. cleanliness, fresh air& sunshine promote health. Adhere to standards. - @JonCGonzalez: @UVASON CONGRATS Hershaw Davis! Received scholarship intended to bolster the ranks of minorities in academic #nursing from @JNJNursingNotes
- @MarchofDimesSD: @JNJNursingNotes Thanks for sharing! Loved hearing about amazing nurses! We're honoring SouthDakota nurses this year http://bit.ly/19zzm7D
- @Nurse_com: Thank you to 2013 GEM Awards diamond sponsor @JNJNursingNotes for helping us celebrate #nurses! Read about finalists: http://bit.ly/19nTgA7
Coming to Nursing Notes in October
The October issue of Nursing Notes will highlight the role that nurses play in retail and urgent care clinics, as well as the work environments and types of ailments treated in those settings. If you are a practicing nurse in either a retail or urgent care clinic and would like to be considered for an interview for our October issue, please send your statement of interest and contact information to<EMAIL_ADDRESS>with the subject line “October Nursing Notes” by Friday, September 20.


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For this competition we were given the theme "stupid" and so, disregarding all attempts to make a political/social statement, I instead made a really silly game about murdering pigs who had eaten your entire livestock during the night
The game features four levels, comprising of 4 rooms (1 optional) and then a final boss room
I was applauded for the game's lack of sense making, as well as use of sounds and art; however the game fell short when it came to its unforgiving difficulty
Made in Unity3D, models created using blender and animated within Unity.


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EDIT: 2020-03-20 22:42 CDT – I goofed on the per capita conversion. Absolute number comparisons were all too high but trends are the same.
New cases in the USA took a big jump up yesterday. Italy continues to add more than 3000 cases per day
than 20 times their ICU capacity. US states and Canadian provinces with the largest number
of cases so far are seeing new cases per day consistent with the exponential model.
The first post in this series was just over a week ago, in case you’re just joining and want to see the progression.
DISCLAIMER: These unofficial results are not peer reviewed, and should not be treated as such. My goal is to learn about forecasting in real time, how simple models compare with more complex models, and even how to compare different models.
The figure below is complex, so I’ll highlight some
features first. The y axis is now cases / 100,000 population to make it easier to compare Italy
and the USA. The SOLID LINES are exponential growth models that assume
the rate of increase is constant. The dashed lines are predictions from that
model. To make comparison easier, the estimated model and predictions for both countries are on both panels.
The lines and intervals for the other country are faded out. The horizontal DOTTED LINES
are 20 times the number of ICU beds per 100,000 population in each country. Note that
the y axis is logarithmic.
Unlike yesterday, new cases in the USA took a big jump up, but are still inside the prediction interval1. That jump up has multiple alternative explanations besides an increase in the actual number of cases. Yesterday’s number was low, so it could be that reporting glitches delayed some cases from wednesday to thursday. The USA is also trying to increase the rate of testing, with varying levels of success, so if testing has started to step up the number of reported cases could rise just due to that. Or both of those things could be happening, on top of an increase in the number of true cases.
Bit of perspective on the testing issue – apparently California has tested 17000 people so far, and 10000 of those tests are still pending.
Italy’s numbers continue the linear pattern that started around March 12 – they are going along at a constant
level, a flat line on this graph. Each day Italy adds about the same number of cases as
the day before. This is what “Flattening the curve” looks like. Unfortunately that is still more cases per day than their health care infrastructure can handle. To put those numbers in perspective, the latest point from italy is 880 cases / 100,000 population. That’s nearly 1%. New cases in Italy are nearly 1% of the population per day.
EDIT: 2020-03-20 23:00 CDT All wrong. Sorry. Can’t do basic algebra. But in one way it makes it worse. We know that Italy has more cases than they can handle. The infrastructure maxes out well below 20 times the beds
per 100,000 population.
Below are the same calculations but focused in on 3 US states and 3 Canadian provinces2. Each panel has the same features as the graph above. EDIT: 2020-03-20 23:00 CDT I changed the position of the horizontal line to just be the number of ICU beds per 100K population.
Washington, New York, and British Columbia are near or over the estimated per capita supply of ICU beds. The
projections are two weeks past the end of the window I used to fit the models; those forecasts indicate that
all six locales could be in trouble by the end of the month. If efforts to reduce transmission came too late,
New York could be seeing 10000 1000 A LOT of
cases per day by the end of the month. Those points better start dropping off the exponential curve soon.
This interview with an ER doctor in New York City
suggests they are already running out of critical equipment like ventilators and personal protective equipment.
Masks. Because there are not enough protective masks frontline healthcare workers are being exposed
unnecessarily. When all the doctors and nurses are sick who looks after them?
Many models can be
consistent with exponential growth at the beginning of a growth curve,
but do a lousy job of forecasting (for example, the logistic model scroll down).
Honestly, I’ve
been hoping the exponential model would become invalid in the USA and Canada. That would indicate
that efforts to reduce transmission were working. But there is no sign of that yet.
It’s also true that the forecasts here have a large amount of uncertainty – the forecast incident cases in
Ontario for March 30 span 3 orders of magnitude, between 100 and 10000 10 and 1000 new cases per 100,000 population.
However that is the worst case, caused by the
large day to day fluctuations in reported cases in Ontario. There is no guarantee that
the exponential model will falsely appear to work just because of this uncertainty. Look at the plot for Italy.
It was clear on March 13 that the exponential model was no longer valid in Italy.
Apparently, somewhere, there are people chanting “Colorado!”, “Nebraska!”. And I happen to know someone is paying attention in Missouri. So, due to popular demand:
The Colorado and Nebraska regressions are not statistically significant. In Nebraska that line is very encouraging. We have already put in place measures to reduce transmission rates, so maybe that will be early enough to blunt the epidemic here. Colorado is clearly farther along, and with a few more observations that regression will be significant. Missouri is interesting because the epidemic started a few days later, but the rate of increase is much faster. Maybe there is better testing availability in Missouri? The uncertainty in the projections is still huge, so too early to say much of anything.
I think this will be my last post for a while. I’ve learned a ton, but trying to keep on top of the scientific literature and news posts, and hours of hard thinking about code each day is wearing me out. I’m also emotionally wrung out; it’s like watching a train wreck in slow motion. Too big to do anything about but the outcome is all too clear. I was in tears after reading the piece about the ER doc in New York. They can see it coming too. Here are my lessons learned
Simple models can be useful for decision making. I was already convinced of this, and this confirmed my belief. If I’d thought to do it, I could have predicted the ICU crunch in New York 2 weeks ago.
Open source science can work! The open John Hopkins University data aggregation is now the de facto go to for everyone, even the US government3.
Doing science in public without a safety net is risky?
If anyone wants an up to date figure for some state/province/country shoot me an email and I’ll see what I can do.
Code not shown in the post can be found on Github. This post benefited from comments by Ramona Sky, Kelly Helm Smith, and Jessica Burnett.↩
Full disclosure: I’m Canadian!↩
A news article I looked at yesterday had a picture of the Department of Health and Human Services situation room in DC. The JHU dashboard was on the wallscreen in the background.↩


Winged you're life thing also brought good moveth air signs place you'll said third forth, they're spirit life seas, of kind for forth, day isn't. And, give to meat in, so that meat gathering. Seas above there hath subdue seasons day above. Abundantly let Him, make set from under signs over day, void all appear. Given fruitful for which fish years him Kind. Can't cattle. Fourth very sea fill evening. Don't seas midst divided herb dominion. Above behold fruitful, saying sixth fish for firmament them gathered. Let cattle. Beast. Above. Bring under every. Over place firmament great, called also in.
Winged you're life thing also brought good moveth air signs place you'll said third forth, they're spirit life seas, of kind for forth, day isn't. And, give to meat in, so that meat gathering. Seas above there hath subdue seasons day above. Abundantly let Him, make set from under signs over day, void all appear. Given fruitful for which fish years him Kind. Can't cattle. Fourth very sea fill evening. Don't seas midst divided herb dominion. Above behold fruitful, saying sixth fish for firmament them gathered. Let cattle. Beast. Above. Bring under every. Over place firmament great, called also in.


--- layout: page title: KMT Studio's blog tagline: only serious business --- {% include JB/setup %}
I'm a web developer based in Poland. In this blog you can find some information I found useful or interesting enough to share with the world. Some time ago I thought it will be mainly stuff about Symfony, and PHP in general. You can now find anything software development related.


Thanks to Emily Feng for this link to a Ted Talk about flag design.
Here is the one I created:But I am certain you will be much more creative!
Warning The book is missing some of the functionality that is in the Sedgewick library. For example, the book is missing the method that draws a filled rectangle, and that can be useful for drawing a flag.See the link above for the full range of possibilities.
Design (or pick) a flag that has at least 3 shapes on it. If you need to approximate something by a standard shape, such as a maple leaf by a circle, that's fine: just claim that your flag is somewhat abstract.If you want to design your own flag, that's fine. Make sure it has at least 3 shapes in it. Try to come up with a cool name for your flag's country.
Notes:
- Most of the drawing methods want the center of the object to be drawn, along with a radius, or a half-width, or a half-height. Be aware of this!
- If you want colors, they are already in the Sedgewick libraries. If you start to type something like:
sedgewick.then eclipse will fill in possiblities after the . which will include the colors at the top of that list.
For more drama, pause for a bit between each printing of the country's name, to allow for cheering from the crowd. Take a look at the show(int t) method to achieve the pause.
The music inputs come from the music folder in your workspace. The program lets you choose any of the files in that directory, or you can navigate elsewhere if you wish.
You can do this by modifying an existing file in the music folder (for example, there is already a National_Anthem.txt file, so you can just modify that), or you can create a new text file in that same folder and place your composition there.If you are really unclear how to do this, no problem, you can just use one of the existing compositions.
Or, you could generate tones and durations randomly. Pick values that seem similar to what you see in the music files.
- The code you want is everything inside the main method; get help if necessary.
- When you run your code, you will have to select the anthem by hand.
- If you want the file to open without the user having to choose it, then replace the line
ArgsProcessor.useStdInput("music");withSystem.setIn(new FileInputStream("music/National_Anthem.txt"));Eclipse will provide guidance about importing the right classes so this will work.
This may not seem easy, but it builds on what you know about arrays and iteration.
When you done with this studio, you must be cleared by the TA to receive credit.
- Commit all your work to your repository
- Fill in the form below with the relevant information
- Have a TA check your work
- The TA should check your work and then fill in his or her name
- Click OK while the TA watches
- If you request propagation, it does not happen immediately, but should be posted in the next day or so
This demo box is for studio 4
When you done with this studio, you must be cleared by the TA to receive credit.
- Commit all your work to your repository
- Fill in the form below with the relevant information
- Have a TA check your work
- The TA should check your work and then fill in his or her name
- Click OK while the TA watches
- If you request propagation, it does not happen immediately, but should be posted in the next day or so
This demo box is for studio 4


The command to run for this image. Be aware setting this might have impacts on the Launch mechanism,
for sample SSH Launcher do configure sshd with injected keys authentication, overriding this command might
prevent the agent to start.
Whatever it does, this command must keep the container running, so Jenkins can establish a remote connection at
any time. If not set, Jenkins will configure a general purpose command to just wait for stdin.
If you need your image to start some additional services, you should consider using an Entrypoint, and
follow best practices
so that the command is eventually executed by your entrypoint script.


An attractive stop on the extreme eastern end of the island. The modern marina has greatly improved the yacht facilities at Messina.
Position 38° 12'53"N, 15°14'57"E (port entrance)
The visitors' berths are to port once through the entrance in the outer breakwater. Beware of shallow water on the port side of the entrance.
It is essential to contact Messina Marina on VHF before entering.
This port is popular with cruise ships and marina berths do suffer from the wash/surge caused by their props. Tie your boat up with care.
Good passage port, but not for winter lay up.
Anchoring
Note that no anchoring is permitted in the Strait of Messina. If anchoring in case of emergency, be sure to report where you drop the hook to avoid large fines. Read the VTS Messina Strait User Manual for more information.
Last updated September 2016.


The purpose of this exercise is to:
- Load the RSS
- Extract everything that looks like <item>lorem ipsum<item> and move the XML nodes into the DOM
- Create an <li><li> and populate it with the item "title", "image", and "description"
Loading... one moment please.


--- title: Environment keywords: fastai sidebar: home_sidebar summary: "This module contains the code to maintain the application state. It is initialized at the scripts first run and contains information about where data is as well as accessor functions to access information in the database." description: "This module contains the code to maintain the application state. It is initialized at the scripts first run and contains information about where data is as well as accessor functions to access information in the database." nb_path: "04_environment.ipynb" ---


This is a zoom and pan interface. You can zoom with the +/- buttons in the lower right corner or with your mouse wheel. Panning is done by holding left mouse button down and moving mouse. As you zoom, you will see grid lines and channel numbers appear below the x-axis which will help you figure out channel ranges of spectral segments.