The message to ask a ZeroMQ socket for its affinity configuration.
The message to ask a ZeroMQ socket for its affinity configuration.
socket.ask(affinity())
the single instance of Affinity
The message to ask a ZeroMQ socket for its backlog configuration.
The message to ask a ZeroMQ socket for its backlog configuration.
socket.ask(backlog())
the single instance of Backlog
The message that is sent when an ZeroMQ socket disconnects.
The message that is sent when an ZeroMQ socket disconnects.
if (message == closed()) { // Socket disconnected }
the single instance of Closed
The message that is sent when an ZeroMQ socket connects.
The message that is sent when an ZeroMQ socket connects.
if (message == connecting()) { // Socket connected }
the single instance of Connecting
The message to ask a ZeroMQ socket for its file descriptor configuration.
The message to ask a ZeroMQ socket for its file descriptor configuration.
socket.ask(fileDescriptor())
the single instance of FileDescriptor
The message to ask a ZeroMQ socket for its identity configuration.
The message to ask a ZeroMQ socket for its identity configuration.
socket.ask(identity())
the single instance of Identity
The message to ask a ZeroMQ socket for its linger configuration.
The message to ask a ZeroMQ socket for its linger configuration.
socket.ask(linger())
the single instance of Linger
The message to ask a ZeroMQ socket for its max message size configuration.
The message to ask a ZeroMQ socket for its max message size configuration.
socket.ask(maxMessageSize())
the single instance of MaxMsgSize
The message to ask a ZeroMQ socket for its multicast hops configuration.
The message to ask a ZeroMQ socket for its multicast hops configuration.
socket.ask(multicastHops())
the single instance of MulticastHops
The message to ask a ZeroMQ socket for its multicast loop configuration.
The message to ask a ZeroMQ socket for its multicast loop configuration.
socket.ask(multicastLoop())
the single instance of MulticastLoop
The message to ask a ZeroMQ socket for its rate configuration.
The message to ask a ZeroMQ socket for its rate configuration.
socket.ask(rate())
the single instance of Rate
The message to ask a ZeroMQ socket for its receive bufferSize configuration.
The message to ask a ZeroMQ socket for its receive bufferSize configuration.
socket.ask(receiveBufferSize())
the single instance of ReceiveBufferSize
The message to ask a ZeroMQ socket for its receive high watermark configuration.
The message to ask a ZeroMQ socket for its receive high watermark configuration.
socket.ask(receiveHighWatermark())
the single instance of ReceiveHighWatermark
The message to ask a ZeroMQ socket for its reconnect interval configuration.
The message to ask a ZeroMQ socket for its reconnect interval configuration.
socket.ask(reconnectIVL())
the single instance of ReconnectIVL
The message to ask a ZeroMQ socket for its max reconnect interval configuration.
The message to ask a ZeroMQ socket for its max reconnect interval configuration.
socket.ask(reconnectIVLMax())
the single instance of ReconnectIVLMax
The message to ask a ZeroMQ socket for its recovery interval configuration.
The message to ask a ZeroMQ socket for its recovery interval configuration.
socket.ask(recoveryInterval())
the single instance of RecoveryInterval
The message to ask a ZeroMQ socket for its send buffer size configuration.
The message to ask a ZeroMQ socket for its send buffer size configuration.
socket.ask(sendBufferSize())
the single instance of SendBufferSize
The message to ask a ZeroMQ socket for its send high watermark configuration.
The message to ask a ZeroMQ socket for its send high watermark configuration.
socket.ask(sendHighWatermark())
the single instance of SendHighWatermark
The message to ask a ZeroMQ socket for its swap configuration.
The message to ask a ZeroMQ socket for its swap configuration.
socket.ask(swap())
the single instance of Swap
Java API for akka.zeromq


You can use Riemann sums to estimate the net area, where you approximate the enclosed region with rectangles using left or right endpoints or midpoints to determine the height,˙ or you can select Simpson’s Rule or the Trapezoidal Rule. In each case, the module will compute the estimated value and display a graph of the area computed. You can improve the approximation by increasing the number of subintervals, and you can see the error in the estimated value both graphically and numerically.
Colors are split into their red, green, blue and alpha dimensions. The operation is applied to each color dimension separately. E.g., if the user added two colors, then the green dimension of the result is equal to sum of green dimensions of input colors. If the user multiplied a color by a number, each color dimension will get multiplied. Note: arithmetic operation on alpha is not defined, because math operation on colors do not have standard agreed upon meaning. Do not rely on current implemention as it may change in later versions.
Less provides a variety of functions which transform colors, manipulate strings and do maths. They are documented fully in the function reference. Using them is pretty straightforward. The following example uses percentage to convert 0.5 to 50%, increases the saturation of a base color by 5% and then sets the background color to one that is lightened by 25% and spun by 8 degrees:
Here we are fetching an image across the network and inserting it into an image element. The simplest use of fetch() takes one argument — the path to the resource you want to fetch — and returns a promise containing the response (a Response object).


Class Client
The message bus client.
Inheritance
Inherited Members
Namespace: JetBlack.MessageBus.Adapters
Assembly: JetBlack.MessageBus.Adapters.dll
Syntax
public class Client
Methods
| Improve this Doc View SourceAddNotification(String)
Request notifications of subscriptions to a feed.
Declaration
public void AddNotification(string feed)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | feed | The feed name. |
AddSubscription(String, String)
Add a subscription to a feed and topic.
Declaration
public void AddSubscription(string feed, string topic)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | feed | The feed name. |
| System.String | topic | The topic name |
Authorize(Guid, String, String, Boolean, ISet<Int32>)
Authorize a client.
Declaration
public void Authorize(Guid clientId, string feed, string topic, bool isAuthorizationRequired, ISet<int> entitlements)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Guid | clientId | The client id. |
| System.String | feed | The feed name. |
| System.String | topic | The topic name |
| System.Boolean | isAuthorizationRequired | If true authorization is required. |
| System.Collections.Generic.ISet<System.Int32> | entitlements | The set of entitlements granted to the client. |
Create(String, Int32, Boolean, Boolean, IClientAuthenticator, Boolean)
Create a new client.
Declaration
public static Client Create(string server, int port, bool monitorHeartbeat = false, bool isSslEnabled = false, IClientAuthenticator authenticator = null, bool autoConnect = true)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | server | The server host name. |
| System.Int32 | port | The server port |
| System.Boolean | monitorHeartbeat | If true send heartbeats. |
| System.Boolean | isSslEnabled | If true use SSL. |
| IClientAuthenticator | authenticator | The client authenticator. |
| System.Boolean | autoConnect | If true automatically connect to the message bus. |
Returns
| Type | Description |
|---|---|
| Client |
Dispose()
Dispose of the client, closing the connection.
Declaration
public void Dispose()
Publish(String, String, Boolean, DataPacket[])
Publish a message to the subscribers of a feed and topic.
Declaration
public void Publish(string feed, string topic, bool isImage, DataPacket[] dataPackets)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | feed | The feed name. |
| System.String | topic | The topic name |
| System.Boolean | isImage | A boolean indicating if the message was an image. |
| DataPacket[] | dataPackets | The data packets to send. |
RemoveNotification(String)
Remove notification of subscriptions to a feed.
Declaration
public void RemoveNotification(string feed)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | feed | The feed name. |
RemoveSubscription(String, String)
Remove a subscription to a feed and topic.
Declaration
public void RemoveSubscription(string feed, string topic)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | feed | The feed name. |
| System.String | topic | The topic name |
Send(Guid, String, String, Boolean, DataPacket[])
Send a message to a specific client.
Declaration
public void Send(Guid clientId, string feed, string topic, bool isImage, DataPacket[] dataPackets)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Guid | clientId | The client id. |
| System.String | feed | The feed name. |
| System.String | topic | The topic name |
| System.Boolean | isImage | A boolean indicating if the message was an image. |
| DataPacket[] | dataPackets | The data packets to send. |
Start()
Start processing message bus data.
Declaration
public void Start()
Events
| Improve this Doc View SourceOnAuthorizationRequest
Raised when authorisation is requested.
Declaration
public event EventHandler<AuthorizationRequestEventArgs> OnAuthorizationRequest
Event Type
| Type | Description |
|---|---|
| System.EventHandler<AuthorizationRequestEventArgs> |
OnConnectionChanged
Raised when the connection state has changed.
Declaration
public event EventHandler<ConnectionChangedEventArgs> OnConnectionChanged
Event Type
| Type | Description |
|---|---|
| System.EventHandler<ConnectionChangedEventArgs> |
OnDataReceived
Raised when data has received from a subscribed feed and topic.
Declaration
public event EventHandler<DataReceivedEventArgs> OnDataReceived
Event Type
| Type | Description |
|---|---|
| System.EventHandler<DataReceivedEventArgs> |
OnForwardedSubscription
Raised when a requested notification is received.
Declaration
public event EventHandler<ForwardedSubscriptionEventArgs> OnForwardedSubscription
Event Type
| Type | Description |
|---|---|
| System.EventHandler<ForwardedSubscriptionEventArgs> |
OnHeartbeat
Raised when a heartbeat has been received.
Declaration
public event EventHandler<EventArgs> OnHeartbeat
Event Type
| Type | Description |
|---|---|
| System.EventHandler<System.EventArgs> |


Barrier |
Node for an OPENQASM barrier statement. |
BinaryOp |
Node for an OPENQASM binary operation expression. |
BinaryOperator |
Node for an OPENQASM binary operator. |
Cnot |
Node for an OPENQASM CNOT statement. |
Creg |
Node for an OPENQASM creg statement. |
CustomUnitary |
Node for an OPENQASM custom gate statement. |
ExpressionList |
Node for an OPENQASM expression list. |
External |
Node for an OPENQASM external function. |
Format |
Node for an OPENQASM file identifier/version statement. |
Gate |
Node for an OPENQASM gate definition. |
GateBody |
Node for an OPENQASM custom gate body. |
Id |
Node for an OPENQASM id. |
IdList |
Node for an OPENQASM idlist. |
If |
Node for an OPENQASM if statement. |
IndexedId |
Node for an OPENQASM indexed id. |
Int |
Node for an OPENQASM integer. |
Measure |
Node for an OPENQASM measure statement. |
Opaque |
Node for an OPENQASM opaque gate declaration. |
Prefix |
Node for an OPENQASM prefix expression. |
PrimaryList |
Node for an OPENQASM primarylist. |
Program |
Node for an OPENQASM program. |
Qreg |
Node for an OPENQASM qreg statement. |
Real |
Node for an OPENQASM real number. |
Reset |
Node for an OPENQASM reset statement. |
UnaryOperator |
Node for an OPENQASM unary operator. |
UniversalUnitary |
Node for an OPENQASM U statement. |


I’ve releaed Carbon v0.2.6. Download it from BitBucket.
With this release, I’ve completed all documentation for Carbon’s 100+ functions. Sample scripts and the HTML help files are also now included in the download package. There’s also one bug fix. Here are the complete release notes:
Enhancements
Improved documentation for:
- Grant-ServiceControlPermission
- Install-Service
- Invoke-SubInAcl
- Remove-Service
- Restart-RemoteService
- Install-Share
- Add-GroupMembers
- Get-WmiLocalUserAccount
- Install-Group
- Install-User
- Remove-User
- Test-User
- Install-WindowsFeatureIis
- Install-WindowsFeatureMsmq
- Install-WindowsFeatures
- Test-WindowsFeature
- Uninstall-WindowsFeatures
- Assert-FirewallConfigurable
- Disable-FirewallStatefulFtp
- Enable-FirewallStatefulFtp
- Get-FirewallRules
- Test-FirewallStatefulFtp
- Find-ADUser
- Format-ADSpecialCharacters
- Get-Certificate
- Get-CertificateStore
- Set-HgrcDefaultPushUrl
Additional enhancements:
- Help files and example scripts now included in Carbon download package.
- Added test to help ensure all functions have proper documentation.
Bug Fixes
- Install-IisWebsite now validates the website bindings.


| 012:043 Khan | : |
And the king (of Egypt) said: "Verily, I saw (in a dream) seven fat cows, whom seven lean ones were devouring - and of seven green ears of corn, and (seven) others dry. O notables! Explain to me my dream, if it be that you can interpret dreams." |
| 012:043 Maulana | : |
And the King said: I have seen seven fat kine which seven lean ones devoured; and seven green ears and (seven) others dry. O chiefs, explain to me my dream, if you can interpret the dream. |
| 012:043 Pickthal | : |
And the king said: Lo! I saw in a dream seven fat kine which seven lean were eating, and seven green ears of corn and other (seven) dry. O notables! Expound for me my vision, if ye can interpret dreams. |
| 012:043 Rashad | : |
The king said, "I saw seven fat cows being devoured by seven skinny cows, and seven green spikes (of wheat), and others shriveled. O my elders, advise me regarding my dream, if you know how to interpret the dreams." |
| 012:043 Sarwar | : |
(Sometimes later), the King dreamt that seven lean cows were eating seven fat ones and that there were seven green ears of corn and seven dry ones. He asked the nobles to tell him the meaning of his dream if they were able to. |
| 012:043 Shakir | : |
And the king said: Surely I see seven fat kine which seven lean ones devoured; and seven green ears and (seven) others dry: O chiefs! explain to me my dream, if you can interpret the dream. |
| 012:043 Sherali | : |
And the King said to his courtiers, 'I see in a dream seven fat kine which seven lean ones were eating, and seven green ears of corn and seven others withered. O ye chiefs, explain to me the meaning of my dream if you can interpret dreams.' |
| 012:043 Yusufali | : |
The king (of Egypt) said: "I do see (in a vision) seven fat kine, whom seven lean ones devour, and seven green ears of corn, and seven (others) withered. O ye chiefs! Expound to me my vision if it be that ye can interpret visions." |
Concepts/Themes Covered:
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Space Concordia - Aleksandr
System Status
Send Commands
Battery Heaters Demo
Payload Experiment Data
Temp EPS Board
Temp EPS Board
Temp Payload Board
Magnetometer Data
Batteries Power
Batteries Current
Batteries Voltage


Welcome to livingTree’s documentation!¶
Introduction¶
Taxonomic data is generally considered as a tree-like structure. In more and more fields of bioinformatics, metagenomics, microbiology, etc., analysis is needed with the help of species classification trees (such as reading counting, abundance calculation, etc.)
Installation¶
The easy way to install livingTree is using pip package manager
$ pip install livingTree
or just run the setup script.
$ python setup.py install
Example¶
>>> import livingTree as lt
Constructing tree¶
Node by node¶
>>> tree = lt.SuperTree()
>>> tree.create_node(identifier = 'root')
>>> tree.create_node(identifier = 'taxa_1', parent = 'root')
>>> tree.create_node(identifier = 'taxa_2', parent = 'taxa_1')
>>> tree.create_node(identifier = 'taxa_3', parent = 'taxa_2')
>>> tree.create_node(identifier = 'taxa_4', parent = 'taxa_1')
>>> tree.create_node(identifier = 'taxa_5', parent = 'root')
>>> tree.create_node(identifier = 'taxa_6', parent = 'taxa_5')
>>> tree.create_node(identifier = 'taxa_7', parent = 'taxa_6')
>>> tree.show()
Using node id paths¶
>>> tree = lt.SuperTree()
>>> tree.create_node(identifier = 'root')
>>> paths = [['taxa_1', 'taxa_2', 'taxa_3'],
['taxa_1', 'taxa_4'],
['taxa_5', 'taxa_6', 'taxa_7']]
>>> tree.from_paths(paths)
>>> tree.show()
Restore from pkl file¶
>>> tree = lt.SuperTree()
>>> mm_tree = tree.from_pickle('trees/mammalia_living_tree.pkl')
>>> mm_tree.depth()
>>> mm_tree.size()
Using taxonomic data from NCBI taxonomy database¶
>>> builder = lt.TreeBuilder('Mammalia')
>>> builder.build()
>>> tree = builder.tree
>>> tree.show()
Deeling with abundance data¶
Initiate all nodes’ data at once¶
>>> tree.init_nodes_data(data = 0)
Fill node data in batch¶
>>> abundances = {'taxa_3': 10,
'taxa_4': 5,
'taxa_7': 3,
'taxa_6': 4}
>>> tree.fill_with(abundances)
>>> tree.get_node('root').data
>>> tree.update_value()
>>> tree.get_node('root').data
Get a numpy n-dimensional array from all taxonomy abundances¶
>>> import numpy as np
>>> tree.get_matrix(dtype = np.float32)


ANALYSIS OF CYCLOTRON-~TYPE ELECTRIC LENSES
AND THE EFFECTS OF POSTS
by Chang Sun Han
Thesis submitted to the Faculty of the Graduate School of the University of Maryland in partial fulfillment of the requirements for the degree of Doctor of Philosophy 1970
C 0 |
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.


--- layout: default title: Papers categories: - papers --- {% for paper in site.categories.papers %} {% cycle 'add rows': '
{{ paper.ref }}


Get to know me!
Hello, my name is Jastine. I am an emerging professional in Auburn. I'm a student in the software development and testing track in Year up. I have graduated Greenriver College with an associates in Bioengineering in Spring of 2020. I was also the former President of Circle K International in Greenriver and I am a part-time worker in Uniqlo Bellevue.
I have strong leadership skills, I'm driven, hardworking, a team player and I can adapt to any situation when needed. I do have my weakness in overworking, having high expectations for myself and being to stubborn to quit, but I think those weakness can be overcome and a strength in my professional journey. I hope to be working with you all soon .
Page Origin
The HTML/CSS comes from Sara Newman's WEB110 class as taught at Seattle Central. This page provides a basis for students to build a responsive portal page.
Study how this page is built, and use it as a foundation for your portal or other sites!
Copyright [2014-2020] [Sara Newman]. Licensed under the Apache License, Version 2.0


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Socrates' Secret
Our first event for the year!
Click here to register now!


We've included some classes which allow you to create signage mockups in any NHS colour.
You just need to start with a div
with the class of sign
and either one-line-horizontal
or three-line-rectangle
. You can optionally add the class destination-centre
to centre the sign text, and destination-right
to right-align it. There's no class needed to left align the text.
Inside this, just include the text of the sign, inside a span
with the class destination
.
You can add an arrow to the sign, using glyphicons. If you want it to appear on the left, just before the text, include the glyphicon span code inside the destination
span. If you want to display it on the right, add the class of arrow-right
to the destination
span and the class glyphicon-arrow-right
to the glyphicon span.
sign-blue
sign-dark-green
sign-green
sign-light-green
sign-aqua-green
sign-aqua-blue
sign-light-blue
sign-dark-blue
sign-purple
sign-dark-pink
sign-dark-red
sign-red
sign-orange
sign-yellow
sign-black
sign-white


This software is provided with no warranty.
City Hub does not currently collect any information about you as a person. This can change in the future, with new features available. A change to the privacy policy, will be announced.
Whenever you perform transactions, the transactions is store on a public chain of blocks (blockchain). You can never remove this information, they are locked in the chain.
 This ensures your instance of City Hub is downloading the full blockchain for you to validate, and for you to verify your own transactions on. Nobody can discover what addresses you own, if you use the full-mode. Other modes can potentially leak your wallet address to third parties.


University of Washington
September 2013 — Present
- Classes: Design Thinking, Information Systems Analysis and Management, Client Side Web Development, Capstone Project, Calling BS in the Age of Big Data
- Research Assistant for the Pew Charitable Trusts on projects assessing and visualizing scholarly impact
- Research Assistant for JSTOR building tools to visualize and explore their archives of scholarly publications


List<E> results
KeeperException.Code code
String path
int exitCode
ServerCnxn.DisconnectReason reason
Copyright © 2008–2020 The Apache Software Foundation. All rights reserved.


@Immutable @Utility public final class JRendererFactory extends java.lang.Object
Active rendering is used if the org.jdesktop.renderer.active property is defined. For example with the following passed to the Java interpreter.
-Dorg.jdesktop.renderer.active=true
| Modifier and Type | Field and Description |
|---|---|
static java.lang.String |
PROPERTY |
| Modifier and Type | Method and Description |
|---|---|
static JRenderer |
getDefaultRenderer(JRendererPanel on,
JRendererTarget<java.awt.GraphicsConfiguration,java.awt.Graphics2D> target,
boolean hasChildren)
Gets a renderer based upon the users preference for active or passive
rendering.
|
static boolean |
useActiveRenderer()
Gets if active rendering is desired.
|
public static final java.lang.String PROPERTY
public static JRenderer getDefaultRenderer(JRendererPanel on, JRendererTarget<java.awt.GraphicsConfiguration,java.awt.Graphics2D> target, boolean hasChildren)
Active rendering is used if the org.jdesktop.renderer.active property is defined. For example with the following passed to the Java interpreter.
-Dorg.jdesktop.renderer.active=trueThis method constructs and starts an
SwingTimerTimingSource
or a
ManualTimingSource
and sets it as the default for all animations. A
handle to this timing source can be obtained by invoking
JRenderer.getTimingSource()
, for example, if it needs to be
disposed.on
- the panel to render on.target
- the rendering implementation to callback to.hasChildren
- if on manages child Swing components. true
if it
does. false
if it does not. This helps the renderer
improve performance if no child components are ever used.public static boolean useActiveRenderer()
true
if active rendering is desired, false
otherwise.


Skynet Group of Companies WLL one of the growing business groups located in Doha, the golden place of Qatar. We are one of the service industry focusing customer demands, Most Popular Mobile shops in Qatar. Skynet Group of Companies WLL a Company with a number of outlets spread all over Qatar was established in the year 2003 with an object of providing services.
Skynet Solution the one of the leading pioneer IT Solution providers in Qatar.Skynet provides bespoke technological solution to many entrepreneurs, enterprises and start-up ventures, to help them gain maximum ROI for their business.Over the years,our committed professionals have provided solution par excellence.Some of these include bespoke application development, system migration, web marketing, technology audits, third party integration, e-learning tools etc. Today, with our experience and skills, we have been able to create products that help many Businesses across the country to run their business in a more productive way. Some of our clients include business from industry verticals like travel and tourism, finance, E-commerce, E-learning, IT solution & consultancy organizations, young entrepreneurs, etc..


60 Fifth Ave (New York University), Room 505, New York, NY10011
{{ with .Site.Copyright }}{{ . | markdownify}} Zhongshi Jiang · {{ end }} Powered by the Academic theme for Hugo.


- I think "Chrome plug-in" belongs to "productivity and quality-oriented technologies". The plug-ins are designed to add utility and convenience to the browser. The basic idea is to let developers add some minor scripts and utilities to the browser, like auto-paging, capturing the whole web page or extract Baidu Netdisk download link. This is obviously productivity-and-quality-oriented.
- A good point is that since there can be no more than 1 connection through port 80 (or 443, in case of https) at a certain time, closing the connection as soon as possible, the server can handle requests faster.
- A bad point is that since establishing and closing TCP or UDP connections costs extra traffic, it puts greater burden on the network.
- A server may be unable to know the exact number of browsers reading its content. Consider the case when several browsers of the same brand and version on different clients connected to a DHCP server send requests to the same server. Since their addresses and HTTP requests looks the same to the server, it's impossible for the server to decide whether these requests come from the same client.
You may go back to my Main Page.


Paddy Jewels
hand-crafted terracotta jewellery
Home
Orders
About
Track
Here are our creations so far...
You can choose and customize your order from one among these


Place Order Form
place order
| Order ID | Order Date | item name | Quantity | Total | |
|---|---|---|---|---|---|
| O001 | 2021-09-25 | Apple | 5 | Rs. 100.00 | |
| O001 | 2021-09-25 | Apple | 5 | Rs. 100.00 | |
| O001 | 2021-09-25 | Apple | 5 | Rs. 100.00 | |
| O001 | 2021-09-25 | Apple | 5 | Rs. 100.00 | |
| O001 | 2021-09-25 | Apple | 5 | Rs. 100.00 | |
| O001 | 2021-09-25 | Apple | 5 | Rs. 100.00 | |
| O001 | 2021-09-25 | Apple | 5 | Rs. 100.00 | |
| Net price | Rs. 1000.00 | ||||


{% extends "dashboard/layouts/base.html" %} {% block content %}
Top up {{amount}} USD to your Account


Time together.
这是标题
About us.
{{ site.description }}
Detail for our memory and love stories.
-
{% for item in site.categories.projects reversed %}
-
{{ item.project.type }}
{{ item.project.title }}
{% endfor %}
--- ---
这是标题
{{ site.description }}


[1]
The ability to stream netflix and pandora have been a lifesaver.; None so far
[2]
good quality picture and functions; Nightmare to set up you wifi connection


Stock Investing For Dummies
ISBN-13:<PHONE_NUMBER>033
ISBN-10:<PHONE_NUMBER>
Author: Paul Mladjenovic
Edition: 2
Binding: Paperback
Publisher: For Dummies
Published: January 2006
List Price: $21.99
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-
AngularJS (+route and animate) v1.6.9
-
AngularJS Loading Bar v0.9.0
-
jQuery v3.3.1
-
Bulma.CSS v0.7.1
-
Font: Rigid Square from Adobe Typekit.
About Grammarer
We've all used flashcard tools like Quizlet or StudyBlue to revise vocab for Greek, Latin and other languages. They are perfect for helping us effectively learn words as well as making the process enjoyable. However, it's not possible to efficiently learn grammar with them. That's why I (Pal Kerecsenyi) made Grammarer.
Users receive a simple 4-digit code on a piece of paper with which they can access the system and track their progress. They can select a grammar list, look at the table, press 'Go!' and test themselves.
How to use
Once you've signed in with your 4-digit access code, find a grammar list that you want to revise. Click 'Study' in the relevant box, and memorise the table that appears. Once you've done so, press the 'Go!' button below the table. The endings will disappear and a blue square will highlight your position. Once you think you know the ending shown by this highlighter, press space to reveal the answer. If you got it right, press the right arrow or if you got it wrong, press the left arrow. The box will be highlighted green or red (depending on whether you got it right or wrong) and the highlighter will move to the next box. For some grammar lists (like the Greek definite article), the highlighter will move sideways but most of the time it will move downwards.
Once you've completed the grammar list, the Results box will appear. This shows how many endings you got right, and how many wrong. Click 'Save!' to finish studying the list. If you don't do this, you'll lose your score for that session.
How it works
Grammarer is hosted with Heroku, a freemium container-based hosting system that's based on Git pushes. Everytime you push code, it rebuilds and deploys (zero downtime). You don't get access to the OS, it's just a hosting system for code. It works with any language.
Grammarer's backend works with NodeJS and Express, like most simple websites. It stores Lists, Users and Results using MongoDB. This is hosted using MongoDB Atlas (Mongo-as-a-service freemium DB hosting using AWS). In the frontend, it uses AngularJS and a heavily customised version of Bulma.CSS. The actual table navigation and marking system is written entirely by me (😄). Surprisingly enough, Greek characters work perfectly without any special accomodation. All authentication happens on the backend, so you can't hijack another account by changing a variable. No data besides the code is stored about users. Nice and simple.
Feedback
If you've found an issue with Grammarer, you want me to add another checklist, you've lost your code or you want to tell me how amazing Grammarer is (I already know!), feel free to email me at<EMAIL_ADDRESS>Copyright and attribution
This website is created and operated by Pal Kerecsenyi. All rights are reserved. Copyright © 2018 Pal Kerecsenyi.
Grammarer uses:
AngularJS (+route and animate) v1.6.9
AngularJS Loading Bar v0.9.0
jQuery v3.3.1
Bulma.CSS v0.7.1
Font: Rigid Square from Adobe Typekit.
You can also find me on GitHub.
Your privacy
Grammarer is made to be as simple as possible and to avoid data protection complications, we store as little data as possible.
| Data | Given by | Purpose | Shared with |
|---|---|---|---|
| Code | Admin or your teacher | Used for signing you in and storing your results | You, the admin (Pal Kerecsenyi) and your teacher (if assigned by teacher) |
| Deploy code | Admin or your teacher | Used to identify what batch your code came from (usually one deploy is assigned to an A4 sheet of codes, 16) | You (written on the back of your card), the admin and your teacher (if assigned by teacher) |
| Last access date/time | Automatically on your visit | Used to identify codes that aren't being used and could be deleted to save database space and maintain clarity | The admin (through DB access) and no-one else |
| Your results for Lists | Automatically when you save results | Used to report your progress to you, make nice graphs and calculate averages | The admin (through DB access), you - never with your teacher. |
| Prefix code | Admin - given to teacher accounts to uniquely identify. This is a two-character string (e.g. 'hq'). | Used to identify teachers and their assigned students. Student codes begin with this prefix if they are assigned by the respective teacher. | Same as your code |


|
geopm
2.0.0+dev770g55e83fbc
GEOPM - Global Extensible Open Power Manager
|
#include <mpi.h>
#include <stdio.h>
#include <stdint.h>
#include <pthread.h>
Go to the source code of this file.
Functions | |
| int | geopm_ctl_create (MPI_Comm comm, struct geopm_ctl_c **ctl) |
| creates a geopm_ctl_c object, ctl which is an opaque structure that holds the state used to execute the control algorithm with one of the other functions described in this header file. More... | |
| int | geopm_ctl_destroy (struct geopm_ctl_c *ctl) |
| destroys all resources associated with the ctl structure which allocated by a previous call to geopm_ctl_create(). More... | |
| int | geopm_ctl_run (struct geopm_ctl_c *ctl) |
| steps the control algorithm continuously until the application signals shutdown. More... | |
| int | geopm_ctl_pthread (struct geopm_ctl_c *ctl, const pthread_attr_t *attr, pthread_t *thread) |
| creates a POSIX thread running the control algorithm continuously until the application signals shutdown. More... | |
| int geopm_ctl_create | ( | MPI_Comm | comm, |
| struct geopm_ctl_c ** | ctl | ||
| ) |
creates a geopm_ctl_c object, ctl which is an opaque structure that holds the state used to execute the control algorithm with one of the other functions described in this header file.
The control algorithm relies on feedback about the application profile.
| comm | The user provides an MPI communicator, which must have at least one process running on every compute node under control. |
| ctl | The out parameter pointer to the created geopm_ctl_c object |
| int geopm_ctl_destroy | ( | struct geopm_ctl_c * | ctl | ) |
destroys all resources associated with the ctl structure which allocated by a previous call to geopm_ctl_create().
| int geopm_ctl_pthread | ( | struct geopm_ctl_c * | ctl, |
| const pthread_attr_t * | attr, | ||
| pthread_t * | thread | ||
| ) |
creates a POSIX thread running the control algorithm continuously until the application signals shutdown.
With this method of launch the supporting MPI implementation must be enabled for MPI_THREAD_MULTIPLE using MPI_Init_thread()
| ctl | is an opaque structure that holds the state used to execute the control algorithm |
| attr | is used to create the POSIX thread |
| thread | is used to create the POSIX thread |
| int geopm_ctl_run | ( | struct geopm_ctl_c * | ctl | ) |
steps the control algorithm continuously until the application signals shutdown.
steps the control algorithm continuously until the application signals shutdown.


rr.event
Classes
AccessEvent
AcquireEvent
ArrayAccessEvent
ClassInitializedEvent
Event
FieldAccessEvent
InterruptEvent
JoinEvent
MethodEvent
NotifyEvent
ReleaseEvent
SleepEvent
StartEvent
VolatileAccessEvent
WaitEvent
Enums
AccessEvent.Kind


Being that you're done with your makeup today, it's officially time to put some clothes on.
What kind of outfit do you want to wear today?


class: center, middle, title # Zero Config React .subtitle[[ExchangeJS](https://www.exchangejs.com/) – Dec 1, 2016] --- class: left, middle ### You'll need: - nodejs (https://nodejs.org) - npm (https://www.npmjs.com) - a web browser --- class: left, middle ### You'll find useful: - Familiarity with ES6 syntax – [I wrote a quick primer](https://github.com/ExchangeJS/zero-config-react/blob/master/appendix-a-brief-es6-primer.md) if you're looking for somewhere to start - [React Developer Tools](https://chrome.google.com/webstore/detail/react-developer-tools/fmkadmapgofadopljbjfkapdkoienihi?hl=en) (Chrome) - Clone the [example project](https://github.com/ExchangeJS/zero-config-react), run the `git checkout` commands found at the beginning of each section, view the code and app locally - [The official React docs](https://facebook.github.io/react/docs/hello-world.html) --- class: center, middle ## Part 1: Create React App ```terminal git checkout tags/v0.1.0 ``` --- class: left, middle ### Step 1: Install ```terminal npm install -g create-react-app ``` --- class: left, middle ### Step 2: Use ```terminal cd ~/Projects ``` ```terminal create-react-app myappname ``` --- class: left, middle ### Step 3: Start ```terminal cd myappname ``` ```terminal npm start ``` .footnote[starts a server on localhost:3000 and opens default browser] --- class: center, middle ## Uhhh, that's it. 😳 --- class: left, middle ### Check out your new project folder ``` myappname/ ├── README.md ├── node_modules/ │ ... ├── package.json ├── public/ │ ├── favicon.ico │ └── index.html └── src/ ├── App.css ├── App.js ├── App.test.js ├── index.css ├── index.js └── logo.svg ``` --- class: center, middle ## Start Hacking! Check out the [example project repo](https://github.com/ExchangeJS/zero-config-react) for more. --- ### Credits - [This tweet](https://twitter.com/dan_abramov/status/773535743310462977) - The very excellent [create-react-app](https://github.com/facebookincubator/create-react-app) project - Presented by Christian Naths, Co-owner/CTO [MicroHero Inc](https://microhero.com) - Presented at ExchangeJS ([exchangejs.com]((https://www.exchangejs.com/), [@EdmontonJS](https://twitter.com/EdmontonJS)) - Making it through an entire talk in 2016 without using a single gif 😅 ### Totally Shameless Plug
Check out [MicroHero](https://microhero.com)! An app for iOS that lets you raise money for charity by answering survey questions. On the flip side, MicroHero gets you answers to your survey questions super quick! Visit https://microhero.com/surveys for more.


Who are we?
We are the ACM Student Chapter for UMass Amherst. We're one of the largest computer science organizations on campus, and we hold events and competitions to enrich the computer science student body here at UMass. We're here to provide resources and information to CS students at UMass, and our events are often great networking opportunities and learning experiences.
Regardless of your previous background, UMass ACM exists to help you develop your technical interests. We hold tech talks, workshops, programming competitions, and other events to provide UMass students with the resources they need to be successful in Computer Science. We feature speakers from Google, Amazon, Microsoft, Facebook, any many more! Our events are a great place to network, make friends, and learn from your peers. Whether you're looking for someone to co-found your startup, or just need help with your CS250 homework, chances are you'll find what you're looking for by being a part of UMass ACM.
Events
We hold a number of events during the school year, such as:
Cambridge Campus Tours
We've held tours at Google and Microsoft, and hope to reach the rest of the Cambridge tech companies in the near future. Here are snippets of some we've done in the past:


§71. Attorney-general. That there shall be an attorney-general, who shall have the powers and duties of the attorney-general and those of the powers and duties of the minister of the interior which relate to prisons, prisoners, and prison inspectors, notaries public, and escheat of lands under the laws of Hawaii, except as changed by this Act and subject to modification by the legislature.
Referred to in 16 H. 245; 16 H. 773, 779; 26 H. 570.


Introduction to Probability Models
Lecture 13
Qi Wang, Department of Statistics
Sep 19, 2018
Revision
- Permutation: Ordered arrangement of r distinct objects from a set of n objects. $$_nP_r = P_r^n = \frac{n!}{(n - r)!}$$
- Combination:Unordered arrangement of r distinct objects from a set of n objects. $$_nC_r = C_r^n = \frac{n!}{(n - r)! r!}$$
- Multinomial Coefficient:m objects are in k distinct groups, size of groups are $m_1, m_2, \cdots, m_k$, number of ways to order these are: $$\binom{m}{m_1, m_2, \cdots, m_k} = \frac{m!}{m_1!m_2!\cdots m_k!}$$
Properties of Expected Value
X, Y are random variables, c and d are constant
- $E[c] = c$
- $E[cX] = cE[X]$
- $E[X + Y] = E[X] + E[Y]$
- $E[cX + dY] = cE[X] + dE[Y]$
Properties of Variance
X, Y are random variables, c and d are constant
- $Var(X) = E[(X - E[X])^2] = E[X^2]-E[X]^2$
- $Var(c) = 0$
- $Var(cX) = c^2 Var(X)$
- If X and Y are independent, $Var(X + Y) = Var(X) + Var(Y)$
- If X and Y are independent, $Var(cX + dY) = c^2Var(X) + d^2Var(Y)$
Example 1
Suppose X and Y are random variables with $E[X] = 3, E[Y] = 4$ and $Var(X) = 2, Var(Y) = 1$. Find
- $E[2X + 1]$
- $E[X – Y] $
- $E[X^2]$
- $E[X^2 – 4]$
- $E[(X – 4)^2]$
- $Var(2X – 4Y)$


It looks like you have to run composer install first. Note: this may take a while to finish.
composer install


Section 6 NSCI 175: Introduction to Neuroscience / PSYC 220 Biopsychology
Essential Question: What are some essential DEI topics I can teach in NSCI 175/PSYC 220?
Abstract: Up through the 2000s, scientists conducting neuroscience and biopsychology research were largely white men of European descent. This means that the foundational knowledge featured in textbooks does not represent the work of diverse scientists. Likewise, the (WEIRD) samples used in neuroscience and biopsychology research does not encompass the diversity of research participants as a whole. For example, until recently most animal research was conducted on male animals. Likewise, until recently, most clinical trials recruited only male subjects. This is problematic because certain populations may have increased risk for disease/disorder, rates of disease/disorder diagnosis, or poor treatment outcomes due to systemic inequities.
Class Topics and Learning Objectives
- Topic: Racial Disparities in Pain Management
- Objective: Discuss racial and ethnic disparities in pain management. Differences in pain management negatively affect groups of people who have systematically experienced greater social and/or economic obstacles to health.  Emphasizes mechanisms underlying pain disparities: bias, socioeconomic status, language barriers, and access to care.
- Slide Deck: Here.
- Resources
- Mossey, J. M. (2011). Defining Racial and Ethnic Disparities in Pain Management. Clinical Orthopaedics and Related Research, 469(7), 1859–1870. https://doi.org/10.1007/s11999-011-1770-9
- Paulson, M. R., Dekker, A. H., & Aguilar-Gaxiola, S. (2007). Eliminating disparities in pain management. The Journal of the American Osteopathic Association, 107(9 Suppl 5), ES17-20.
- Topic: Racial Disparities in Diagnosis of Schizophrenia
- Objective: Racial disparities exist in schizophrenia and little research has addressed these disparities. Our current understanding is that these disparities are likely due to both diagnostic bias and differences in environmental risk factors. Illustrates how systemic and structural racism can result in biased diagnosis. Also emphasizes the contribution of environmental risk and stressors.
- Slide Deck: Here.
- Resources
- Metzl, J. M. (2009). The protest psychosis: How schizophrenia became a black disease (pp. xxi, 246). Beacon Press.
- Topic: Genetics - GWAS Studies
- Objective: Understanding the disparities in genetic tests, specifically GWAS studies. How applicable are any genetic studies to date? If certain GWAS studies are not using a diverse set of individuals, then can these be reliable? Currently, there is a push in the genetics field to engage minority communities to enroll in genetics or genomic research studies and on investigators to reach out to underrepresented communities. Important to mention to students if they are reading a GWAS study, they need to check to see if they’ve included a diverse set of individuals.
- Slide Deck: Here.
- Resources
- Haga, S. B. (2010). Impact of limited population diversity of genome-wide association studies. Genetics in Medicine, 12(2), 81–84. https://doi.org/10.1097/GIM.0b013e3181ca2bbf
- A Call For Equity In Genomics Research: Short Wave. (n.d.). NPR.Org. https://www.npr.org/2019/09/10/759582674/a-call-for-equity-in-genomics-research
- Topic: War on Drugs
- Objective: Introducing a bit of history on the War on Drugs and emphasizing that drug policies need to be shifted to a health-based approach (based on peer-reviewed studies). It’s important for students to see that many of the past or even current policies to combat substance use disorder created a large racial disparity. Oftentimes the policies are enforced despite the lack of scientific evidence of its effectiveness.
- Slide Deck: Here.
- Resources
- Rosino, M. L., & Hughey, M. W. (2018). The War on Drugs, Racial Meanings, and Structural Racism: A Holistic and Reproductive Approach. American Journal of Economics and Sociology, 77(3–4), 849–892. https://doi.org/10.1111/ajes.12228
- Topic: Glia are More Than Glue
- Objective: For a long time, glia were considered simply as support cells for neurons. Work by Ben Barres and others demonstrated that glia can influence a variety of functions in the brain. Dr. Barres work specifically showed that glia cells can influence synapse formation and elimination.
- Slide Deck: Here.
- Resources
- See website for Project Biodiversify
- Topic: Human Brain Compared to Other Mammalian Brains
- Objective: Describe how the human brain is similar and different from other mammalian brains highlighting work by researcher, Suzana Herculano-Huouzel.
- Slide Deck: Here.
- Resources
- Communications, V. D. of. (n.d.). Brainiac: With her innovative ‘brain soup,’ Suzana Herculano-Houzel is changing neuroscience one species at a time. Vanderbilt University. https://news.vanderbilt.edu/2017/09/07/brainiac-with-her-innovative-brain-soup-suzana-herculano-houzel-is-changing-neuroscience-one-species-at-a-time/
- Herculano-Houzel, S. (n.d.). What is so special about the human brain? https://www.ted.com/talks/suzana_herculano_houzel_what_is_so_special_about_the_human_brain
- Topic: Scientist Spotlight! Dr. Bennet Omalu
- Objective: Highlighting Dr. Bennet Omalu for discovering CTE in the NFL player. Dr. Bennet Omalu is a nigerian physician, forensic pathologist and neuropathologist. He published in 2005 “Chronic Traumatic Encephalopathy in the NFL Player” and the NFL called for its retraction.
- Slide Deck: Here.
- Resources
- How the Discovery of CTE Shifted Thinking Behind Concussion Protocol. (n.d.). https://www.brainandlife.org/articles/when-bennet-omalu-md-identified-a-degenerative-brain-disease-in/
Diversity, Equity, and Inclusion in Teaching by UNC Psychology & Neuroscience DEI Education Subcommittee is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Wayanad is a rural district in Kerala state, southwest India. In the east, the Wayanad Wildlife Sanctuary is a lush, forested region with areas of high altitude, home to animals including Asiatic elephants, tigers, leopards and egrets. In the Ambukuthi Hills to the south, Edakkal Caves contain ancient petroglyphs, some dating back to the Neolithic age
Nagarhole National Park
Bandipur Tiger Reserve
Mudumalai Tiger Reserve
Wayanad Wildlife sanctuary


Train (update) a discrete scale
train_discrete(new, existing = NULL, drop = FALSE, na.rm = FALSE)
new
New data to add to scale
existing
Optional existing scale to update
drop
TRUE, will drop factor levels not associated with data
TRUE
na.rm
If TRUE, will remove missing values


Sylvanian Families Fielding Field Mouse Family
UK Biography Information
Father Darcy Fielding is a cobbler and makes everything from clogs and boots to slippers and sandals. He is a very skilled craftsman, but he is also very shy and rarely says more than "Thank you, madam," or "Ready next week, sir,"when Sylvanians come into his shop.
Mother Bridget Fielding is a great help to her husband. She not only buys all the shiny materials and colourful laces that he needs to make his fine shoes, but she also assists him with his many customers. The only problem with this is that she is very shy too and rarely speaks above a whisper. Sometimes she has to repeat herself three or four times before people realise that she's even talking to them!
Brother Daniel Fielding is as intelligent as can be but it took his teachers a long time to discover this as he never says more than two words in class. It was only when he got top marks in every subject that they realised just how clever he was.
Sister Helen Fielding is as quiet as the rest of her family, except when it comes to netball. She walks out onto the netball court and begins to order everyone around as loudly as possible.


| Vendor Name | IMBIBITIVE TECHNOLOGIES CANADA INC. / IMBTEC CANADA |
|---|---|
| Reference Number | W0046-10Q494/001/KIN |
| Contract Date | 26-3-2010 |
| Description of Work | ABSORBENT MATERIAL FOR OIL AND OTHER PETROLEUM PRODUCTS - REPELS WATER |
| Delivery Date | 30-6-2010 |
| Contract Value | $14,865.00 |
| Comments | This contract was competitively sourced |
| Additional Comments |


Data events
Querys
User list
Actions between two times
Used services
Used services with special parameter
Count events from an user
Top user
By event
User name
From (YYYY-MM-DD)
To (YYYY-MM-DD)
Event name
Parameter key
Parameter value
Scan


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FLAGS FLAGS AND MORE FLAGS
Poland
Luxembourg
Thailand
Romania
Mexico
Greece
Challenge (These are optional)
South Africa
Seychelis
North Macedonia
Finland


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| lib/ | 85.82% | 115/134 | 75% | 45/60 | 73.68% | 14/19 | 85.82% | 115/134 | |
| lib/aop/ | 100% | 103/103 | 87.5% | 7/8 | 97.37% | 37/38 | 100% | 103/103 | |
| lib/aop/autoproxy/ | 100% | 70/70 | 100% | 12/12 | 100% | 12/12 | 100% | 70/70 | |
| lib/aop/framework/ | 100% | 240/240 | 97.22% | 70/72 | 98% | 49/50 | 100% | 240/240 | |
| lib/beans/ | 99.1% | 440/444 | 94.24% | 131/139 | 98.25% | 56/57 | 99.1% | 440/444 | |
| lib/beans/support/ | 77.11% | 384/498 | 64.09% | 116/181 | 87.5% | 91/104 | 77.11% | 384/498 | |
| lib/context/ | 69.5% | 294/423 | 44.97% | 76/169 | 81.69% | 58/71 | 69.5% | 294/423 | |
| lib/model/ | 85.38% | 432/506 | 73.45% | 130/177 | 87.8% | 72/82 | 85.32% | 430/504 | |
| lib/model/constraints/ | 60.38% | 64/106 | 13.16% | 5/38 | 64.29% | 9/14 | 60.38% | 64/106 | |
| lib/resource/ | 79.2% | 198/250 | 67.92% | 72/106 | 58.06% | 18/31 | 79.2% | 198/250 | |
| lib/util/ | 75.38% | 551/731 | 66.75% | 263/394 | 67.5% | 54/80 | 75.9% | 551/726 |


Congratulations on reaching the final project of the Robotics Nanodegree!
Previously, you worked on the Semantic Segmentation lab where you built a deep learning network that locates a particular human target within an image. For this project, you will utilize what you implemented and learned from that lab and extend it to train a deep learning model that will allow a simulated quadcopter to follow around the person that it detects!
Most of the code below is similar to the lab with some minor modifications. You can start with your existing solution, and modify and improve upon it to train the best possible model for this task.
You can click on any of the following to quickly jump to that part of this notebook:
We have provided you with a starting dataset for this project. Download instructions can be found in the README for this project's repo. Alternatively, you can collect additional data of your own to improve your model. Check out the "Collecting Data" section in the Project Lesson in the Classroom for more details!
import os
import glob
import sys
import tensorflow as tf
from scipy import misc
import numpy as np
from tensorflow.contrib.keras.python import keras
from tensorflow.contrib.keras.python.keras import layers, models
from tensorflow import image
from utils import scoring_utils
from utils.separable_conv2d import SeparableConv2DKeras, BilinearUpSampling2D
from utils import data_iterator
from utils import plotting_tools
from utils import model_tools
In the Classroom, we discussed the different layers that constitute a fully convolutional network (FCN). The following code will introduce you to the functions that you need to build your semantic segmentation model.
The Encoder for your FCN will essentially require separable convolution layers, due to their advantages as explained in the classroom. The 1x1 convolution layer in the FCN, however, is a regular convolution. Implementations for both are provided below for your use. Each includes batch normalization with the ReLU activation function applied to the layers.
def separable_conv2d_batchnorm(input_layer, filters, strides=1):
output_layer = SeparableConv2DKeras(filters=filters,kernel_size=3, strides=strides,
padding='same', activation='relu')(input_layer)
output_layer = layers.BatchNormalization()(output_layer)
return output_layer
def conv2d_batchnorm(input_layer, filters, kernel_size=3, strides=1):
output_layer = layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides,
padding='same', activation='relu')(input_layer)
output_layer = layers.BatchNormalization()(output_layer)
return output_layer
The following helper function implements the bilinear upsampling layer. Upsampling by a factor of 2 is generally recommended, but you can try out different factors as well. Upsampling is used in the decoder block of the FCN.
def bilinear_upsample(input_layer):
output_layer = BilinearUpSampling2D((2,2))(input_layer)
return output_layer
In the following cells, you will build an FCN to train a model to detect and locate the hero target within an image. The steps are:
encoder_block
decoder_block
Create an encoder block that includes a separable convolution layer using the separable_conv2d_batchnorm()
function. The filters
parameter defines the size or depth of the output layer. For example, 32 or 64.
def encoder_block(input_layer, filters, strides):
# TODO Create a separable convolution layer using the separable_conv2d_batchnorm() function.
output_layer = separable_conv2d_batchnorm(input_layer, filters, strides)
return output_layer
The decoder block is comprised of three parts:
def decoder_block(small_ip_layer, large_ip_layer, filters):
# TODO Upsample the small input layer using the bilinear_upsample() function.
upsample_layer = bilinear_upsample(small_ip_layer)
# TODO Concatenate the upsampled and large input layers using layers.concatenate
if large_ip_layer != None:
merge_layer = layers.concatenate([upsample_layer, large_ip_layer])
else:
merge_layer = upsample_layer
# TODO Add some number of separable convolution layers
intermediate = separable_conv2d_batchnorm(merge_layer, filters, strides=1)
output_layer = separable_conv2d_batchnorm(intermediate, filters, strides=1)
return output_layer
Now that you have the encoder and decoder blocks ready, go ahead and build your FCN architecture!
There are three steps:
def fcn_model(inputs, num_classes):
# TODO Add Encoder Blocks.
# Remember that with each encoder layer, the depth of your model (the number of filters) increases.
# input: 160x160x3
en1 = encoder_block(inputs, filters=48, strides=2) # en1: 80x80x24
en2 = encoder_block(en1, filters=96, strides=2) # en2: 40x40x48
en3 = encoder_block(en2, filters=192, strides=2) # en3: 20x20x192
# TODO Add 1x1 Convolution layer using conv2d_batchnorm().
mid = conv2d_batchnorm(en3, filters=96, kernel_size=1, strides=1) # mid: 20x20x96
# TODO: Add the same number of Decoder Blocks as the number of Encoder Blocks
de3 = decoder_block(mid, en2, filters=192) # de3: 40x40x72
de2 = decoder_block(de3, en1, filters=96) # de2: 80x80x48
de1 = decoder_block(de2, None, filters=48) # de1: 160x160x12
# The function returns the output layer of your model. "x" is the final layer obtained from the last decoder_block()
return layers.Conv2D(num_classes, 1, activation='softmax', padding='same')(de1)
The following cells will use the FCN you created and define an ouput layer based on the size of the processed image and the number of classes recognized. You will define the hyperparameters to compile and train your model.
Please Note: For this project, the helper code in data_iterator.py
will resize the copter images to 160x160x3 to speed up training.
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
image_hw = 160
image_shape = (image_hw, image_hw, 3)
inputs = layers.Input(image_shape)
num_classes = 3
# Call fcn_model()
output_layer = fcn_model(inputs, num_classes)
Define and tune your hyperparameters.
learning_rate = 0.001
training_set_size = 7779
validation_set_size = 1184
batch_size = 100
num_epochs = 50
steps_per_epoch = training_set_size / batch_size + 1
validation_steps = validation_set_size / batch_size + 1
workers = 2
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
# Define the Keras model and compile it for training
model = models.Model(inputs=inputs, outputs=output_layer)
model.compile(optimizer=keras.optimizers.Adam(learning_rate), loss='categorical_crossentropy')
print(model.summary())
# Data iterators for loading the training and validation data
train_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join('..', 'data', 'train-ext'),
image_shape=image_shape,
shift_aug=True)
val_iter = data_iterator.BatchIteratorSimple(batch_size=batch_size,
data_folder=os.path.join('..', 'data', 'validation'),
image_shape=image_shape)
logger_cb = plotting_tools.LoggerPlotter()
reduceLR = keras.callbacks.ReduceLROnPlateau(monitor='loss',
factor=0.5,
patience=3,
verbose=1,
mode='auto',
min_lr = 0.0001)
#callbacks = [logger_cb]
callbacks = [reduceLR, logger_cb]
model.fit_generator(train_iter,
steps_per_epoch = steps_per_epoch, # the number of batches per epoch,
epochs = num_epochs, # the number of epochs to train for,
validation_data = val_iter, # validation iterator
validation_steps = validation_steps, # the number of batches to validate on
callbacks=callbacks,
workers = workers)
# Save your trained model weights
weight_file_name = 'model_weights_model5_run1_e50'
model_tools.save_network(model, weight_file_name)
Now that you have your model trained and saved, you can make predictions on your validation dataset. These predictions can be compared to the mask images, which are the ground truth labels, to evaluate how well your model is doing under different conditions.
There are three different predictions available from the helper code provided:
# If you need to load a model which you previously trained you can uncomment the codeline that calls the function below.
#weight_file_name = 'model_weights_run3_e50'
#restored_model = model_tools.load_network(weight_file_name)
The following cell will write predictions to files and return paths to the appropriate directories.
The run_num
parameter is used to define or group all the data for a particular model run. You can change it for different runs. For example, 'run_1', 'run_2' etc.
run_num = 'model5_run_1'
val_with_targ, pred_with_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_with_targ', 'sample_evaluation_data')
val_no_targ, pred_no_targ = model_tools.write_predictions_grade_set(model,
run_num,'patrol_non_targ', 'sample_evaluation_data')
val_following, pred_following = model_tools.write_predictions_grade_set(model,
run_num,'following_images', 'sample_evaluation_data')
Now lets look at your predictions, and compare them to the ground truth labels and original images. Run each of the following cells to visualize some sample images from the predictions in the validation set.
# images while following the target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','following_images', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# images while at patrol without target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','patrol_non_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# images while at patrol with target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data','patrol_with_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
Evaluate your model! The following cells include several different scores to help you evaluate your model under the different conditions discussed during the Prediction step.
# Scores for while the quad is following behind the target.
true_pos1, false_pos1, false_neg1, iou1 = scoring_utils.score_run_iou(val_following, pred_following)
# Scores for images while the quad is on patrol and the target is not visable
true_pos2, false_pos2, false_neg2, iou2 = scoring_utils.score_run_iou(val_no_targ, pred_no_targ)
# This score measures how well the neural network can detect the target from far away
true_pos3, false_pos3, false_neg3, iou3 = scoring_utils.score_run_iou(val_with_targ, pred_with_targ)
# Sum all the true positives, etc from the three datasets to get a weight for the score
true_pos = true_pos1 + true_pos2 + true_pos3
false_pos = false_pos1 + false_pos2 + false_pos3
false_neg = false_neg1 + false_neg2 + false_neg3
weight = true_pos/(true_pos+false_neg+false_pos)
print(weight)
# The IoU for the dataset that never includes the hero is excluded from grading
final_IoU = (iou1 + iou3)/2
print(final_IoU)
# And the final grade score is
final_score = final_IoU * weight
print(final_score)


Across: 1. Gain recognition (12) 8. "Low Lola" may be a fine yarn! (3 4) 9. Impressionable pals twitch (7) 11. Impressive Elgar composed a sequence of notes (5-5) 12. Ruler of earth space station (4) 14. Happy with market interference (8) 16. Religion is a bit of a mishmash in Tokyo (6) 17. One hundred on large island (3) 19. Put one inside residence (6) 21. Worked in the theatre? (8) 24. Gain from near-disaster? (4) 25. Top solicitor grabbed journalist and set off (7 3) 27. Cold-hearted Hermes is a plotter (7) 28. Will any second-hand golf club do? (3 4) 29. Mixture of berries and liver cannot be undone (12) Down: 1. Attractions everyone may be sure about (7) 2. Show Granny has Anglican background (10) 3. Choose about one hundred and ninety-nine, being selective (8) 4. Spike rascal's beer? (6) 5. What is melting the ice? (4) 6. Turkish box (7) 7. Commuters' paper? (5 7) 10. Ask someone out twice to find out age? (6 6) 13. Bashed Bert to get sweet! (7 3) 15. Cold ducks sound like doves (3) 18. Dodgy club is out of debt (8) 20. Wobbly dog? (7) 22. I agree, with regret, after warning (3 4) 23. Averts arrangement to deprive of food (6) 26. Bosun is in captain's meeting (4)


Soldiers. Scientists. Adventurers. Oddities.
In a time of global crisis, an international task force of heroes banded together to restore peace to a war-torn world: OVERWATCH.
Overwatch ended the crisis, and helped maintain peace in the decades that followed, inspiring an era of exploration, innovation, and discovery. But, after many years, Overwatch’s influence waned, and it was eventually disbanded.
Now, conflict is rising across the world again, and the call has gone out to heroes old and new. Are you with us?


| MATLAB 文件帮助: cv.NormalBayesClassifier/predictProb | 查看 cv.NormalBayesClassifier/predictProb 的代码 | Index |
predictProb Predicts the response for sample(s)
outputs = model.predictProb(inputs)
[outputs,outputProbs,f] = model.predictProb(inputs)
[...] = model.predictProb(inputs, 'OptionName',optionValue, ...)
nsamples-by-nclasses
). The probabilities are not
normalized to the 0..1 range.The method estimates the most probable classes for input
vectors. Input vectors (one or more) are stored as rows of the
matrix inputs. In case of multiple input vectors, there should
be one output vector outputs
. The matrix outputProbs
contains the output probabilities corresponding to each element
of result.
The function is parallelized with the TBB library.
| Access | public |
| Sealed | false |
| Static | false |


We’ll be back soon!
Sorry for the inconvenience but we’re performing some maintenance at the moment.


Nodes
Node {{ node + 1 }}
|
|
|
| RSSI | |
| Trigger | |
| Peak |
- RSSI
- Peak
- Trigger
- Trigger Threshold
- Calibration Threshold
Heats
Don't change a heat after it has a saved race.
-
{% for heat in heats.query.with_entities(heats.heat_id).distinct() %}
-
Heat {{ heat.heat_id }}
-
{% for node in range(num_nodes) %}
-
{% endfor %}
-
Pilots
-
{% for pilot in pilots.query.all() %} {% if pilot.pilot_id != 0 %}
-
Voice Selection
Race Format
-
-
Used for fixed-time races
-
Prevents phantom passes
Sensor Tuning
-
-
-
Trigger is Peak Rssi minus Calibration Offset (Default 8)
-
Trigger minus Calibration Threshold ends calibration pass (Default 95)
-
Trigger minus Trigger Threshold ends normal pass (Default 40)
-
RSSI Smoothing: .0001 smoothest, 1.0 unfiltered. (Default 0.01)
Calibration Guide
| Issue | Resolution |
|---|---|
| Missing passes | Increase Calibration Offset |
| Pass registers on other parts of course | Decrease Calibration Offset |
| First pass triggers before crossing the gate | Increase Calibration Threshold |
| Not getting a pass record for the first pass | Decrease Calibration Threshold |
| Several pass records while passing the start gate | Increase Trigger Threshold |
| Pass registering is significantly delayed | Decrease Trigger Threshold |
| RSSI too noisy | Decrease Filter Ratio |
| RSSI reacts slowly to changes | Increase Filter Ratio |
Database
Copy out any saved races first!
System
(Safely shutdown raspberry pi)


Let's be at Inauguration for impactful {{WEB}} Technologies that can fortify you better realization...
Let's Start
Need reasons to love Bootstrap? Look no further.
Built at Twitter by @mdo and @fat, Bootstrap utilizes LESS CSS, is compiled via Node, and is managed through GitHub to help nerds do awesome stuff on the web.


Web accessibility demo
Capturing navigation using JavaScript focus event
Indian Canyons
Indian Canyons Golf Resort
Elrod House
Palm Canyon Drive
Lulu California Bistro
Starbucks
Palm Springs Art Museum
The Museum Trail
Content from
Palm Springs Map Tour


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Video Heatmap
Overview
Segmented Heatmaps
Dataset
Segmentation
Eye
Mouse
Both
Set Based
Growth
Vertical
Horizontal
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Eye


<addtocalendar start-date="20150704T190000" end-date="20150704T210000" title="Fourth of July Fireworks" location="Battery Park City, New York, NY" class-name="btn btn-sm btn-default dropdown-toggle" description="Celebrate the independence of the United States with fireworks in one of the greatest cities in the world." ></addtocalendar>jshor/angular-addtocalendar


Installation
The best option to install TEASER is to clone the repository by using:
git clone [SSH-Key/Https]
and then run:
pip install -e [Path/to/your/Teaser/Clone]
You need to have pip installed (see dependencies).
Deinstallation
The best option is not to uninstall TEASER at all:-). However, if you want to
uninstall it, just use:
Dependencies
TEASER uses Python 2.7 <= v >=3.3. Further using a WinPython distribution is
recommended (https://winpython.github.io/). WinPython comes along with a lot of
Python packages (e.g. SciPy, NumPy, pip, PyQT, etc.) that are used in the
TEASER code.
In addition, TEASER requires some specific Python packages:
- Mako: template Engine
install on a python-enabled command line with pip install -U mako
- PyXB: XML binding Engine
install on a python-enabled command line with pip install -U pyxb
- pytest: Unit Tests engine
install on a python-enabled command line with pip install -U pytest


we make your dream
The below video speaks on our behalf...
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WITNESS LIST State Affairs Committee (Subcommittee on Lobby Influence) April 27, 2000-10:00A Overview of Lobbying Laws On: Harrison, Tom (Texas Ethics Commission) Lundquist, Karen (Texas Ethics Commission) Perspectives on the Lobby On: Floyd, Robert A. (Texas Society of Association Executives) Harrison, Tom (Texas Ethics Commission) McDonald, Craig L. (Texans for Public Justice) Smith, Tom "Smitty" (Public Citizen) Revolving Door Laws On: Hinkle, Ron (Public Utility Commission of Texas) Lundquist, Karen (Texas Ethics Commission) Phillips, Jim (Texas Natural Resources Conservation Commission)


HTML5 Hacks
Inline
Canvas
AI
Images
JSONP
Shared
Transfer
HTML5 WebWorkers API
Connect to a Shared Worker Simultaneously from Multiple Pages
Log:


Your machine can do amazing things using Ruby. Ruby can store large amounts of data that can be looked up at any given moment. The way Ruby can do this is through arrays and hashes. Though they operate similarly there are some key differences.
One way to think of arrays is as an ordered list. By ordered list I mean items within an array are ordered by index number. Each object within an array is assigned to an index number, the first item starting at [0], proceeding down the line of objects. As you can see from the example below, we have our array called grocery_list. I know what you are thinking, "Who only survives on ice cream, candy, and toilet paper?" For the sake of learning, lets look past that for a moment. As you can see our array consist of three objects. The first item, 'ice cream' is assigned the index number [0], the second [1], and the third [2].
grocery_list = ["ice cream", "candy", "toilet paper"]
As you can see the name of our array is grocery_list which contains the three objects: ice cream, candy, and toilet paper within []. You can call these objects by typing out the name of the array with the corresponding index number within [], you can see this from the image above, just below our array we are calling all three objects.
You can almost list anything as an object within an array: Strings, Integers, Symbols, other array objects, and even hashes. Arrays can have repeating objects as well, which is an important thing to remember when talking about hashes, but we will get to that in a moment.
An easy way to think about hashes is a list of pairs. These pairs will consist of a key name and value. You will use the key name to return the value, much like you would return an object within an array with its corresponding index number. One rule to keep in mind is that the key name must be unique. As you can see from the example listed below, we have a hash called grocery_list. Our hash has three key names corresponding to their value with a =>. In our example "ice cream" is the key name with the value of "vanilla". The key names and values go between {}. As mentioned earlier, the key name will be used to return the value. In the example we return each value by their key name below our hash.
grocery_list = {"ice cream" => "vanilla", "candy" => "chocolate", "toiletries" => "toilet paper"}
Keep in mind that hash keys must be unique, as there cannot be duplicate keys within a hash. This can be useful if you have a long list of objects. Rather than having to remember what was the index number associated to an object, you can return it by remembering the key name.
Arrays and hashes are both powerful and versatile ways of storing and retrieving data. They are so versatile they can actually be used within each other, but that should probably be discussed in another conversation. For now, I hope this has been helpful in understanding these great tools!


MOVIES Jonathan Richards
BLAME IT ON SYNCHRONIZED SWIMMING
WOMAN ON TOP
Directed by Fina Torres
Screenplay by Vera Blasi
With Penelope Cruz, Murilo Benicio
De Vargas R 91 min.
Blame it on the Olympics. No distributor wants to risk opening anything good this week, because they figure everyone's home watching synchronized swimming and beach volleyball from down under. NBC, meanwhile, is taking a ratings beating. Where is everybody? Abducted by aliens?
Fox Searchlight's sacrificial lamb is a relatively inoffensive bit of derivative fluff called "Woman On Top", starring Penelope Cruz ("All About My Mother") as Isabella, a Brazilian chef with motion sickness. She gets nauseous when she's riding or being ridden; for everything from cars to sex she has to be in the driver's seat.
When her husband Toninho (Murilo Benicio) craves the missionary position, she catches him in flagrante delictu, and promptly decamps for San Francisco. She bunks in with her old transvestite friend Monica (Harold Perrineau Jr.), and solicits the sea goddess (by phone) to cast a spell to remove the cheating bastard from her affections. She lands a cooking show on San Francisco TV. People fall in love with her. Toninho comes to Frisco to get her back. Nobody cares. Her show goes national, but the creeps from network mess with the formula. Hum-de-ho-de-hum.
The story is full of moments that reveal a careless attitude toward the material and the audience. When Isabella walks down the street in San Francisco, men follow her like cats after a fish, but only once; the rest of the time they pretty much leave her alone. When Monica accompanies her to the beach for an offering to the sea goddess he carries a dog, but there's no dog at their apartment. When Isabella runs after Toninho and gets sick because she had to take the elevator, she is foiled because he's turning the corner by the time she reaches the street. Everybody speaks English with a Brazilian accent.
"Woman On Top" is a by-the-numbers attempt at a "Like Water for Chocolate" confection of cookery and magic realism that comes out more like last year's half-baked Sarah Michelle Gellar culinary vehicle, "Simply Irresistible". The screenplay is a first try by Vera Blasi, who was a film student when she wrote it, but director Fina Torres ("Celestial Clockwork") has no such excuse. They seem to be under the delusion that by calling something magic you make it magical. The movie tries to survive on the wattage of Cruz's considerable appeal, but it's just not considerable enough. It does get some lift from a lively and knowing drag performance from Harold Perrineau Jr. (Mercutio in "William Shakespeare's Romeo + Juliet"), and it gets plenty of help from Luis Bacalov's wonderful Bossa Nova soundtrack featuring various Brazilian artists. But its charm never rises above the calculated level of the gift shop at Disney World.
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