Alexa I'm hot ...
Best Smart Thermostats for Amazon Echo in 2019
by Jeramy JohnsonBest Smart Thermostats for Amazon Echo Android Central 2019
There are several reasons why you might want to automate your home heating and air conditioning systems. Whether for convenience, energy efficiency, or that "cool" factor, connected smart thermostats are a great investment. If you are in the Amazon ecosystem, there are a growing number of great options. The ecobee SmartThermostat with Voice Control is our top choice because it has Alexa built-in, includes a room sensor, and is easy to use.
- Best Overall: ecobee SmartThermostat with Voice Control
- Next Best: ecobee4 Smart Thermostat
- Best Alternative Ecosystem: Honeywell RTH9585WF1004
- Best Nest: Google Nest Thermostat E
- Best Basic Box: Honeywell T5 Smart Thermostat
- Best Budget Box: iDevices Thermostat
Best Overall: ecobee SmartThermostat with Voice Control
Believe it or not, it's only been a couple of years since ecobee launched the ecobee4 with Alexa built-in. Before that, we had to talk to Alexa from another device, like cave people! But in all seriousness, the concept was a hit, and ecobee has followed up on the success of the ecobee4 with its newer SmartThermostat with Voice Control.
So what's new or better you may ask? It's got a newer, cleaner design with a more vivid touch display. It has a quad-core processor (yeah, in a thermostat!), and far-field voice recognition, so you don't have to shout to lower the AC. And with that built-in Alexa goodness, you can ask the ecobeee SmartThermostat for weather or news updates, or even play your music with Spotify connect. Are we living in the Jetsons' age or what?!
Pros:
- Faster processor
- Far-field voice recognition
- Alexa built-in
- Room sensor included
Cons:
- Pricey
- Complicated interface
Best Overall
ecobee SmartThermostat with Voice Control
Top-of-the-line thermostat
This device has pretty much everything you'd want in a connected thermostat. Alexa integration, music controls, and a smart sensor included!
Next Best: ecobee4 Smart Thermostat
The ecobee4 Smart Thermostat may be a few years old at this point, but it's still a great value. It was one of the first smart thermostats to include a built-in smart assistant (Alexa) so that you could control the temperature without the need for a separate Echo device. This is a really nice feature to have because it means you can control your entire smart home through your thermostat!
ecobee also includes a smart room sensor to help monitor room-by-room climate and more efficiently use your HVAC system. Like other ecobee thermostats, you can pair this with additional units and sensors for full-house coverage for homes large or small.
Pros:
- Comes with a sensor for your rooms
- Alexa built-in for voice commands
- Wires directly into your home
Cons:
- Getting long in the tooth
- Even on sale, there are cheaper alternatives
- A Nest's dial is easier to control the temperature
Next Best
ecobee4 Smart Thermostat
Alexa for less
Coming with built-in Alexa support and a spare sensor, this older model is a steal of a deal.
Best Alternative Ecosystem: Honeywell RTH9585WF1004
ecobee and Google Nest may get all of the headlines, but they are not the only game in town when it comes to smart thermostats. Honeywell has been building great thermostats for decades, so it stands to reason that they'd have several strong entries when it comes to smart devices. The RTH9585WF1004 (wow, that's a mouthful!) is a great alternative to ecobee and Nest and works with most of the major voice assistant platforms, including Alexa.
The design of the RTH9585WF1004 is more reminiscent of a traditional thermostat panel, albeit with a modern flair. It has a monochromatic touchscreen display, and you can change the background color to suit your tastes. At 4.5", you get more display area than other similar devices. This allows you to see more data at once, such as a five-day forecast and humidity levels for indoors and outdoors. It will send you intelligent alerts on screen and in-app to let you know when your system needs service. It also learns your heating and cooling preferences with its Smart Response Technology to optimize temperature controls at the right time.
Pros:
- Affordable
- Huge display
- Intelligent alerts
Cons:
- There are cheaper options by Honeywell
- Doesn't directly support geofencing
Best Alternative Ecosystem
Honeywell RTH9585WF1004
Modern conveniences from traditional player
This device builds on Honeywell's decades-long experience in the heating and cooling industry and adds all kinds of smart home functionality.
Best Nest: Google Nest Thermostat E
Even though the Nest brand is owned and controlled by Google, their smart thermostats still work with Alexa and the Echo. The Nest Thermostat E is one of the most affordable Google Nest products and is arguably the easiest to use. Nest thermostats work with additional sensors like ecobee's, but none are included with the device.
While Nest's separate sensors don't work as well as ecobee's, that's not really a concern if you are using them in a smaller home or apartment. Smart voice control is also not included in the device, so you'll need an Echo to control the thermostat remotely. This device doesn't include all of the bells and whistles of more expensive Nest thermostats, but it does a great job of controlling the temperature of your home. What else do you really need?
Pros:
- Affordable
- Simple, pleasing aesthetic
- The dial is easy to use
Cons:
- Sensors not included
- Sensors not as good as ecobee's
Best Nest
Google Nest Thermostat E
Nest lite
If you have a smaller home or apartment, the Nest E is perfect for yours. It's also one of the most affordable Google Nest products.
Best Basic Box: Honeywell T5 Smart Thermostat
The Honeywell T5 doesn't have the screen real estate or display functionality of its bigger brother on this list, but this newer, more compact model does have a few tricks up its sleeve that the RTH958WF1004 does not. For starters, it includes the ability to do geofencing out of the box. This means you can set programmable routines to control the temperature based on where you are. For example, you can have it set the air to lower once you leave from work or to turn on the heat as soon as you are getting close to your neighborhood.
There are several features that this device carries over from the RTH958WF1004. It has smart alerts for maintenance, as well as adaptive recovery to learn how long it takes to achieve the right temperature so it can automatically adjust its schedule to suit your preferences. It is also compatible with various utility rebate programs.
Pros:
- Allows for geofencing
- Compatible with Alexa, Google Assistant, and Siri
- Smart alerts
Cons:
- Alexa not built-in
- Basic touchscreen
Best Basic Box
Honeywell T5 Smart Thermostat
Gets the job done
The Honeywell T5 Smart Thermostat gets the basics right, with a touch screen, smart alerts, geofencing, and Alexa functionality.
Best Budget Box: iDevices Thermostat
If you are just looking to get into smart home controls, or you don't want to fuss about with fancy touchscreens and features you may not really need, then this is the iDevice for you. The iDevices Thermostat looks like a traditional thermostat box, with familiar button controls and a small window that displays the temperature.
Well, even if you like things low-key, it is nice to be able to ask Alexa to lower the temperature by 5 degrees, or turn on the heater in an app on your phone rather than getting up off the couch. For these users, the iDevices Thermostat works great, and it sits at a very comfortable price point as well!
Pros:
- Very affordable
- Easy installation
- Works with Alexa, Siri, and Google Assistant
Cons:
- Basic box
- No touchscreen controls
- No onscreen info beyond temperature
Best Budget Box
iDevices Thermostat
Back to basics
The iDevices Thermostat is a basic Alexa-enabled smart thermostat that can handle all of the smart things you need at an affordable price.
Bottom line
The ecobee SmartThermostat with Voice Control is the lastest and greatest, top-of-the-line, best option if you want a thermostat that works with Alexa. There's a room sensor included to get a better feel for your home's temperature, and you can give voice commands directly to the thermostat since Alexa is built-in. It's a bit expensive, but it's a great investment to help you save money in your home.
There is an ever-growing number of smart thermostats that work with voice assistants like Alexa, and the Amazon ecosystem, in particular, is perhaps the largest and most robust. When weighing your options, we recommend looking for devices that can work across multiple platforms, as you never know if/when you might change assistants or sell your home to someone comfortable with a different ecosystem. Thankfully, the ones on this list have you covered!
Credits — The team that worked on this guide
Jeramy Johnson is proud to help to Keep Austin Weird and loves hiking in the hill country of central Texas with a breakfast taco in each hand. When he's not writing about Amazon products and services, he's defending his relationship with his side-chick Alexa to his family. You can follow him on Twitter at @jeramyutgw.
Tom Westrick is a U.S.-based freelancer. He's been poking and prodding at Android phones since 2011, and covering various topics for Android Central since 2017. He can often be found with multiple laptops in his backpack and a cup of iced tea in his hand. Follow him on Twitter.
Phil Nickinson is the editor of CordCutters.com, the Head Dad behind Modern Dad and Editor Emeritus of AndroidCentral.com. He's been a journalist since starting at his hometown newspaper in 1998, and with Mobile Nations since 2009. Follow him on Twitter: @Mdrndad.
We may earn a commission for purchases using our links. Learn more.


Introducing Suma
December 30, 2015
I'm excited to announce the release of the first version of Suma. Suma stands for Short URL Managment App. The role of Suma to manage external links and extract data from them. Suma is a small web service to do easily the followings:
- Creating short URL for external link within your application
- Extracting Title
- Capturing Screenshot from URL
- Blocking URL's
- Collecting clicks
If your application needs to display external links then it's probably important that you don't redirect the user directly to the URL so you can fight spam, phishing attacks or inappropriate links.
Use cases
If you don't understand directly what Suma is for, let's illustrate it with few use cases:
- Public Feeds (eg: Twitter or FB like app) which allow user to post link publicly
- Reviews or comments allowing external links
- Display link title or screenshot to preview an external link within your application (eg: slack like app)
To summarize: if your application allows external links from users, then Suma can be useful.
Technology
Suma is built in Python using Pyramid. If you used to deploy a WSGI app then it should not take you more than few minutes to run it. Otherwise you may have to do some digging until I improve the documentation on deployment.
What's next
There are a few things that I would like to add to this project:
- Download the favicon
- Customize the behavior of 404 (eg: redirect to a specific url)
- Add option to choose a 403 when a link has been banned
- Add alternative to Goose for extracting content using diffbot or libextract
- Supporting css selector on the html via the API
License
Suma is licensed under Apache V2 license, the full license text can be found here
Motivations
I had the need for a similar project in past and also recently for a new side project.
Suma is at an early stage of development, but the goal of this project is to provide a microservice which covers the basic need for a company to protect their users from external links within their products. You can read more about the project in the README


We are going to enter a username with a button that will redirection us to an api whose username has been entered previously.


45.104 Responsibility and liability for Government property.
45.105 Contractors’ property management system compliance.
45.106 Transferring accountability.
Subpart 45.2—Solicitation and Evaluation Procedures
Subpart 45.3—Authorizing the Use and Rental of Government Property
45.302 Contracts with foreign governments or international organizations.
45.303 Use of Government property on independent research and development programs.
Subpart 45.4—Title to Government Property
45.401 Title to Government-furnished property.
45.402 Title to contractor-acquired property.
Subpart 45.5—Support Government Property Administration
45.501 Prime contractor alternate locations.
45.502 Subcontractor and alternate prime contractor locations.
45.503 Support property administrator findings.
Subpart 45.6—Reporting, Reutilization, and Disposal
45.602 Reutilization of Government property.
45.602-1 Inventory disposal schedules.
45.602-2 Reutilization priorities.
45.602-4 Interagency property transfer costs.
45.603 Abandonment or destruction of personal property.
45.604 Sale of surplus personal property.
45.604-2 Use of GSA sponsored sales centers.
45.604-3 Proceeds from sales of surplus property.
45.604-4 Sale of property pursuant to the exchange/sale authority.
45.605 Inventory disposal reports.
45.606 Contractor scrap procedures.


importance: 4
The following function returns true
if the parameter age
is greater than 18
.
Otherwise it asks for a confirmation and returns its result.
function checkAge(age) {
if (age > 18) {
return true;
} else {
return confirm('Did parents allow you?');
}
}
Rewrite it, to perform the same, but without if
, in a single line.
Make two variants of checkAge
:
?
||


OpenTrails at the Summit!
Code for America Summit is next week, and it’s going to be awesome. OpenTrails will be making several appearances at the Summit, and we want to make sure you know about them. We can’t wait to see everyone and share the latest news and ideas for OpenTrails!
Tuesday, September 23rd
Trailhead Labs at the Civic Tech Fair
6-8pm @ Yerba Buena Center Forum
Come meet the team behind Trailhead Labs, the first commercial implementation of the OpenTrails standard. We’re OpenTrails Course technical facilitators and generally nice folks to chat with!
Wednesday, September 24th
Publishing Data in OpenTrails Workshop
9-9:45am @ Intercontinental Hotel
OpenTrails rockstars Lyzi Diamond and Alan Williams, with help from Jereme Monteau and Ryan Branciforte, will introduce and lead you through the process of adopting and making OpenTrails work for your citizens!
Thursday, September 25th
Trailhead Labs at the Accelerator Roundtable
11-11:45AM @ Yerba Buena Center
Come hear Ryan Branciforte, CEO of Trailhead Labs, tell the story of making open data work for trails, the challenges involved and the solutions we are working on.
Defaulting to Open
2-2:45 @ Yerba Buena Center
Code for America Fellowship alum Alan Williams and Trailhead Labs CEO Ryan Branciforte will expound on the value of parks, good design and the user experience that is enabled with open data as a foundation.
Meet the OpenTrails Community
3:30-4: @ Yerba Buena Garden (Directly in front of YBCA)
Grab a coffee and drink it outside while meeting and greeting all of the fantastic folks that are making OpenTrails a reality. After, head back inside to check out one of our favorite projects, Transitmix!


Software Engineer at UpbeatPR - San Francisco, USA
May 2017 - current
Working in a small startup which automates public relations. I'm building a platform to tens of thousands of businesses and hundreds of thousands of journalists and bloggers by using machine learning to disrupt a very old-school industry.
Software Engineer at Gofore OY - Helsinki, Finland
June 2016 - May 2017
I was working as IT-consultant at Finnish Institute of Occupational Health (FIOH). I was the main developer of the Solmu project. Solmu is a self-assessment questionnaire, for measuring the work ability and functional capacity of the participants. The work requires different web-technologies.
Part-time Teacher at Aalto University - Espoo, Finland
January 2013 - June 2016
I worked as a part-time teacher in Basic C-programming, Data structures and algorithms and C++ programming -courses. The work included active teaching during the exercise sessions, keepping lectures, programming projects’ guiding and evaluation, and grading the final exams. The work required good communication skills and deep understanding of C and C++ programming languages.
Software Specialist at Futurice Oy - Helsinki, Finland
May 2015 - August 2015
I worked as IT consultant at Sanoma Oy. Basically, I was responsible of the new Android and Windows Phone Iltasanomat news -applications development during the summer. I improved the view and information gathering properties of the software. The work was challenging and extended my insights of the design and marketing. The customer was really satisfied about my work and offered to me extended working opportunity.
Software Developer at Fortum Power and Heat Oy - Espoo, Finland
May 2014 - August 2014
I developed, implemented, and maintained Fortum's District Heating Intranet Site. The work required different web-technologies and good database knowledge.
Junior Software Developer at Oy LM Ericsson AB - R&D Center Finland
June 2012 - August 2012 and May 2013 - August 2013
I improved Ericsson's test automation tools. I participated to global Open Source projects and teached other consultants how to use Jenkins test automation tools.


Link: https://en.wikipedia.org/wiki/Blog
Seq: 2
Date: 10 Feb 2021
Status: 9 - Published
Date Added: 2021-02-10 13:16:52 -0800
Timestamp:<PHONE_NUMBER>1652
Teaser:
My second blog post has even more fascinating info.
Body:
Blog posts are typically displayed in reverse chronological order, so that the most recent post appears first, at the top of the web page.


{% load static %}
If you want discount notification, new templates or custom work, just fill out your name and email below.
If not... scroll down to the QuillPro Demo
See QuillPro Demo
From the very first line of code, we made sure that QuillPro is extremely easy for anyone integrate into their project.
All our code is made so that you can just copy and paste it into your project even without reading the documentation.
Speaking about documentation, the documentation covers eveything you need to get started with QuillPro. If it's not covered in the documentation, then it means it is a default Bootstrap 4 feature. So also check the Bootstrap 4 documentation.
Bootstrap is an experienced player in the CSS Framework game and Bootstrap 4 takes it's game to a whole new level. It allows you build responsive, mobile-first projects on the web with the world's most popular front-end component library.
MDBootstrap is a powerful and UI KIT with 400+ material UI elements, 600+ material icons, 74 CSS animations, SASS files, templates, tutorials and many more.
QuillPro comes with icons for almost evey situation. From CRM related to Git related, plus lots of general icons for everyday use.
There is a demo file to cover every feature that you might need in developing your project.
We've made it dead easy for you to make CSS changes by using Sass to write our CSS code. Change in one place, change everywhere.
It's a great template. What makes it even greater is excellent support. No matter what I have asked, got answered. - kalyanc
- kalyanc
I love this template, it works right out the box, beautiful UI, elegante code, great support by the seller. I highly recommend it. - carlosmorasalguero
- carlosmorasalguero
Just purchased, what a beautiful theme. - SSSikora
- SSSikora


Please complete the following form:
Interest Rate
DTI
60 Month Loan (1 or 0)
Annual Income
Total open to buy on revolving bankcards
Revolving line utilization rate
Monthly payment owed by borrower
Average current balance of all accounts
Total high credit/credit limit
Total credit revolving balance
Total current balance of all accounts
Loan Amount
Months since oldest revolving account opened
Total credit balance excluding mortgage
The total number of credit lines currently in the borrower's credit file
Months since oldest bank installment account opened
Months since most recent bankcard account opened.
Number of trades opened in past 24 months.
Months since most recent account opened
Months since most recent revolving account opened
The number of months since the borrower's last delinquency.
Months since most recent inquiry
Number of bankcard accounts
The upper boundary range the borrowers FICO at loan origination belongs to
The lower boundary range the borrowers FICO at loan origination belongs to
Ratio of total current balance to high credit/credit limit on all install acct
Maximum current balance owed on all revolving accounts
This flask webpage was created for Project McNulty @ Metis.
You can find my github repo here.


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That's Grow in just about ten minutes. To get a better grip of the system, we recommend you to poke around changing configs, customizing templates, adding and removing content all within the codelab pod that you've downloaded to your computer.
Once you're comfortable with how the different pieces of pods fit together – and your ability to customize and config each piece – you should be able to start using Grow to rapidly build and architect a web site from scratch!
Grow comes with a few pod themes built-in that can help you get started with site architecture really quickly. You can try any of the following commands:
grow init base # A nice base template that has a more complex js and css compiling and partials. grow init scaffold # Just plain config scaffolding. Comes with gulp and bower. grow init product # A "product" site with a few categories of products. grow init cards # A "card-style" blog.
Themes are published to the growthemes GitHub organization.
This codelab did not cover all of Grow's features. Make sure to check out the rest of the documentation, including the samples, the reference material, and the template API to learn more about the rest of the system's capabilities. You should be able to read the entire documentation in just about 30 minutes or less.


Every day, hundreds of thousands of visitors from around the world collectively make tens of thousands of edits and create thousands of new articles to augment the knowledge held by the Wikipedia encyclopedia (see also Wikipedia:Statistics.)
People of all ages, cultures and backgrounds can add or edit article prose, references, images and other media here. What is contributed is more important than the expertise or qualifications of the contributor. What will remain depends upon whether it fits within Wikipedia's policies, including being verifiable against a published reliable source, so excluding editors' opinions and beliefs and unreviewed research, and is free of copyright restrictions and contentious material about living people. Contributions cannot damage Wikipedia because the software allows easy reversal of mistakes and many experienced editors are watching to help and ensure that edits are cumulative improvements. Begin by simply clicking the edit link at the top of any editable page!
Wikipedia is a live collaboration differing from paper-based reference sources in important ways. Unlike printed encyclopedias, Wikipedia is continually created and updated, with articles on historic events appearing within minutes, rather than months or years. Older articles tend to grow more comprehensive and balanced; newer articles may contain misinformation, unencyclopedic content, or vandalism. Awareness of this aids obtaining valid information and avoiding recently added misinformation (see Researching with Wikipedia).


NoSQL Databases
Not Only SQL Databases
June 21, 2015
Sometimes relational databases are too complex or unsuitable for given website. Another object a developer can look into is NoSQL databases which include a variety of flavors. Overall, these databases provide a simpler design, horizontal scaling (e.g., scaling from one web server to three), and more granular control over availability. NoSQL databases are increasingly common in real-time web development where the consumer expects real-time feeds instead of periodic updates.
At a high level there are 4 flavors of NoSQL databases:
The table below summarizes the performances of different flavor:
| Data Model | Performances | Scalability | Flexibility | Complexity |
| Key-Value | high | high | high | none |
| Column | high | high | moderate | low |
| Document | high | variable(high) | high | low |
| Graph | variable | variable | high | high |
| Relational | variable | variable | low | moderate |
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我喜歡一個人看電影、看電視、聽音樂、去打擊練習場打棒球
I like seeing movies, watching tv, listening to music, batting oneself
| 時間 | 事件 | 說明 |
|---|---|---|
| 2007.3 | 校模範生 | 每班票選的兩位模範生中以第二名入選 |
| 2010.10~2012.4 | 學校糾察隊 | 擔任幫忙學校規勸違反校規學生的職務 |
| 時間 | 事件 | 說明 |
|---|---|---|
| 2012.10 | 人生初次班級大隊接力入選 | 改變跑步方法成功進入男生10名的大隊接力名單 |
| 2013.9~2014.6 | 擔任總務股長 | 管理班級班費、班級公物及各種物理性問題 |
| 2014.10 | 運動會班級得名 | 班級在運動會創意進場獲得年級第2名，精神總錦標榮獲第1名 |
| 時間 | 事件 | 說明 |
|---|---|---|
| 2016.9~2017.6 | 學校回收場場長 | 在班級負責的回收場外掃區領導班上無事度過一年 |
| 2017.5 | 畢旅晚會焦點 | 提議班上在畢業旅行晚會上表演現代芭蕾在現代流行舞蹈的其他班級中成為受矚目及留下深刻印象 |
| 2018.1 | 學測 | 考試 |
| 2018.7 | 指考 | 全力考試 |
| 時間 | 事件 | 說明 |
|---|---|---|
| 2018.9 | 世新大學入學 | 開始大學生活 |
為了慶祝２０１９年的到來，我設計了一張新年賀卡，左邊有新年第一天的日出，右邊是施放煙火的台北１０１大樓，結合從晚上到白天的活動，呈現跨年的感覺。

During the math exam. He didn't observe that the exam was divided into two different units. He then tried lots of ridiculous things to copy the answer by the man beside him. It shows that Mr. Bean is a careless man and also a person likes to play tricks.
On the beach. When Mr. Bean wanna put on his swimsuit, he found a man sitting near him. To put on the swimming suit without the man's attention, he did many movements to hide the truth that he wanted to put on the swimsuit. But we found the man a blind person in the end. It shows that Mr. Bean is a shy and introverted person. And he is also a silly man but also can imagine lots of clever actions.
In the church. He first used his pocket to blow his nose. Then tried to eat a candy without other's attention but failed. In the end he especially sang out loud at his only-knowed-lyrics “halleluya”. It's inappropriate to do those things during cononical hour. Which shows Mr. Bean is a bad-mannered man.
In the supermarket. He tried to show his credit card to everyone. When the man who had the same card took the wrong one, Mr. Bean said nothing to the man and tried to changed the card back. Then his hand went stocked in the man’s pocket until they went into the toilet.
In the restaurant. He did lots of terrible actions to hide the meal. In the end, the manager of the restaurant prepared him a new one because he blamed the fault to the waiter. It shows that Mr. Beans is a very terrible man.
In short, Mr. Bean is a funny, silly, careless, shy, introverted, bad-mannered, weird, and sometimes a very terrible man.
音樂、散步、單車、吃、棒球、排球、看電視


NETCALL
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A B C D E F G H I J L M N O P R S T U V
All Classes All Packages
All Classes All Packages
All Classes All Packages
S
- save(Relationship) - Method in class edu.cnm.deepdive.tunefull.service.RelationshipService
-
Saves the relationship to the repository.
- SecurityConfiguration - Class in edu.cnm.deepdive.tunefull.configuration
-
Provides security functions for the TuneFull Application, and provides for authentication between the client app and the server.
- SecurityConfiguration(UserService) - Constructor for class edu.cnm.deepdive.tunefull.configuration.SecurityConfiguration
-
Constructor for
SecurityConfiguration
. - setAlbum(String) - Method in class edu.cnm.deepdive.tunefull.model.entity.Clip
-
Sets the name of the clip's album
- setArtist(String) - Method in class edu.cnm.deepdive.tunefull.model.entity.Clip
-
Sets the name of the clip's artist
- setBeginTimestamp(long) - Method in class edu.cnm.deepdive.tunefull.model.entity.Clip
-
Sets the timestamp for the beginning of the clip
- setEmail(String) - Method in class edu.cnm.deepdive.tunefull.model.entity.User
-
Sets the user's email address.
- setEndTimestamp(long) - Method in class edu.cnm.deepdive.tunefull.model.entity.Clip
-
Sets the timestamp for the beginning of the clip
- setFriendAccepted(Boolean) - Method in class edu.cnm.deepdive.tunefull.model.entity.Relationship
-
Sets the Boolean indicating whether a friend relationship has been accepted, denied, or not yet responded to.
- setFriendRelationship(boolean) - Method in class edu.cnm.deepdive.tunefull.model.entity.Relationship
-
Sets the boolean indicating whether the relationship is a friendship or simply a follow relationship.
- setFriendshipAccepted(Relationship, boolean) - Method in class edu.cnm.deepdive.tunefull.service.RelationshipService
-
Sets the boolean that determines whether a relationship is a friendship or not.
- setFriendshipAccepted(Authentication, long, boolean) - Method in class edu.cnm.deepdive.tunefull.controller.RelationshipController
-
Updates a relationship between two users.
- setGenre(User.Genre) - Method in class edu.cnm.deepdive.tunefull.model.entity.User
-
Sets the user's favorite genre.
- setOauth(String) - Method in class edu.cnm.deepdive.tunefull.model.entity.User
-
Sets the user's OAuth 2.0 key.
- setRequested(User) - Method in class edu.cnm.deepdive.tunefull.model.entity.Relationship
-
Sets the
User
entity associated with the user who received the relationship request. - setRequester(User) - Method in class edu.cnm.deepdive.tunefull.model.entity.Relationship
-
Sets the
User
entity associated with the user who requested the relationship. - setSongTitle(String) - Method in class edu.cnm.deepdive.tunefull.model.entity.Clip
-
Sets the title for the song the clip is from
- setTrackKey(String) - Method in class edu.cnm.deepdive.tunefull.model.entity.Clip
-
Sets the track key associated with the song on Spotify.
- setUser(User) - Method in class edu.cnm.deepdive.tunefull.model.entity.Clip
-
Sets the
User
who posted the clip - setUsername(String) - Method in class edu.cnm.deepdive.tunefull.model.entity.User
-
Sets the user's username.
- startFollowing(User, User) - Method in class edu.cnm.deepdive.tunefull.service.RelationshipService
-
Creates a relationship between two users in which one is following the other.
- startFollowing(Authentication, User) - Method in class edu.cnm.deepdive.tunefull.controller.RelationshipController
-
Lets the current user start following another user.
- SWING - edu.cnm.deepdive.tunefull.model.entity.User.Genre
All Classes All Packages


ng-boilerplate
is an opinionated kickstarter for web
development projects. It's an attempt to create a simple starter for new
web sites and apps: just download it and start coding. The goal is to
have everything you need to get started out of the box; of course it has
slick styles and icons, but it also has a best practice directory structure
to ensure maximum code reuse. And it's all tied together with a robust
build system chock-full of some time-saving goodness.
Because my team and I make web apps, and last year AngularJS became our client-side framework of choice. We start websites the same way every time: create a directory structure, copy and ever-so-slightly tweak some config files from an older project, and yada yada, etc., and so on and so forth. Why are we repeating ourselves? We wanted a starting point; a set of best practices that we could identify our projects as embodying and a set of time-saving wonderfulness, because time is money.
There are other similar projects out there, but none of them suited our
needs. Some are awesome but were just too much, existing more as reference
implementations, when we really just wanted a kickstarter. Others were just
too little, with puny build systems and unscalable architectures. So we
designed ng-boilerplate
to be just right.
This is not an example of an AngularJS app. This is a kickstarter. If
you're looking for an example of what a complete, non-trivial AngularJS app
that does something real looks like, complete with a REST backend and
authentication and authorization, then take a look at angular-app
,
which does just that, and does it well.
This section is just a quick introduction to all the junk that comes
pre-packaged with ng-boilerplate
. For information on how to
use it, see the project page at
GitHub.
The high-altitude view is that the base project includes Twitter Bootstrap styles to quickly produce slick-looking responsive web sites and apps. It also includes UI Bootstrap, a collection of native AngularJS directives based on the aforementioned templates and styles. It also includes Font Awesome, a wicked-cool collection of font-based icons that work swimmingly with all manner of web projects; in fact, all images on the site are actually font- based icons from Font Awesome. Neat! Lastly, this also includes Angular Placeholders, a set of pure AngularJS directives to do client-side placeholder images and text to make mocking user interfaces super easy.
And everything just works (assuming there were no breaking changes in the latest release of Bootstrap). Boom. Like that.
And, of course, ng-boilerplate
is built on AngularJS,
by the far the best JavaScript framework out there! But if you don't know
that already, then how did you get here? Well, no matter - just drink the
Kool Aid. Do it. You know you want to.
You already know about this, right? If not, hop on over and check it out; it's pretty sweet. Anyway, all that wonderful stylistic goodness comes built in. The LESS files are available for you to import in your main stylesheet as needed - no excess, no waste. There is also a dedicated place to override variables and mixins to suit your specific needs, so updating to the latest version of Bootstrap is as simple as:
$ cd vendor/twitter-bootstrap
$ git pull origin master
Boom! And victory is ours.
What's better than Bootstrap styles? Bootstrap directives! The fantastic UI Bootstrap library contains a set of native AngularJS directives that are endlessly extensible. You get the tabs, the tooltips, the accordions.  And more. How about a quick demo?
Oh, and don't include jQuery; you don't need it. This is better. Don't be one of those people. *
* Yes, there are exceptions.
Font Awesome has earned its label. It's a set of open (!) icons that come distributed as a font (!) for super-easy, lightweight use. Font Awesome works really well with Twitter Bootstrap, and so fits right in here.
There is not a single image on this site. All of the little images and icons are drawn through Font Awesome! All it takes is a little class:

And you get one of these: . Neat!
Angular Placeholders is a simple library for mocking up text and images. You can automatically throw around some "lorem ipsum" text:

Which gives you this:
Even more exciting, you can also create placeholder images, entirely client-side! For example, this:
Gives you this:
Which is awesome.
This is a project that is not broad in scope, so there's not really much of a wish list here. But I would like to see a couple of things:
I'd like it to be a little simpler. I want this to be a universal starting point. If someone is starting a new AngularJS project, she should be able to clone, merge, or download its source and immediately start doing what she needs without renaming a bunch of files and methods or deleting spare parts ... like this page. This works for a first release, but I just think there is a little too much here right now.
I'd also like to see a simple generator. Nothing like Yeoman, as there already is one of those, but just something that says "I want Bootstrap but not Font Awesome and my app is called 'coolApp'. Gimme." Perhaps a custom download builder like UI Bootstrap has. Like that. Then again, perhaps some Yeoman generators wouldn't be out of line. I don't know. What do you think?
Naturally, I am open to all manner of ideas and suggestions. See the "Can I Help?" section below.
There isn't much of a demonstration of UI Bootstrap. I don't want to pollute the code with a demo for demo's sake, but I feel we should showcase it in some way.
ng-boilerplate
should include end-to-end tests. This should
happen soon. I just haven't had the time.
Lastly, this site should be prettier, but I'm no web designer. Throw me a bone here, people!
Yes, please!
This is an opinionated kickstarter, but the opinions are fluid and evidence-based. Don't like the way I did something? Think you know of a better way? Have an idea to make this more useful? Let me know! You can contact me through all the usual channels or you can open an issue on the GitHub page. If you're feeling ambitious, you can even submit a pull request - how thoughtful of you!
So join the team! We're good people.


Background is avaliable to set like 'transparent', 'red', '#f0f0f0'. Maybe I'll add the function of setting background image.
Left and right gene colors are used when the genes are different. The left gene use the left color. So do the right gene. If the genes are the same, the basic gene color is used. The line under the gene text show the ratio.
CJK characters are avaliable to input for dragons' name. But please change the name font or they may be ugly.


The East African jumping spider Evarcha culicivora feeds indirectly on vertebrate blood by preferentially preying upon blood-fed Anopheles mosquitoes, the vectors of human malaria 1, using the distinct resting posture and engorged abdomen characteristic of these specific prey as key elements for their recognition. To understand perceptual categorization of objects by these spiders, we investigated their predatory behavior toward different digital stimuli - abstract ‘stick figure’ representations of Anopheles constructed solely by known key identification elements, disarranged versions of these, as well as non-prey items and detailed images of alternative prey. We hypothesized that the abstract images representing Anopheles would be perceived as potential prey, and would be preferred to those of non-preferred prey. Spiders perceived the abstract stick figures of Anopheles specifically as their preferred prey, attacking them significantly more often than non-preferred prey, even when the comprising elements of the Anopheles stick figures were disarranged and disconnected from each other. However, if the relative angles between the elements of the disconnected stick figures of Anopheles were altered, the otherwise identical set of elements was no longer perceived as prey. These data show that E. culicivora is capable of making discriminations based on abstract concepts, such as the hypothetical angle formed by discontinuous elements. It is this inter-element angle rather than resting posture that is important for correct identification of Anopheles. Our results provide a glimpse of the underlying processes of object recognition in animals with minute brains, and suggest that these spiders use a local processing approach for object recognition, rather than a holistic or global approach. This study provides an excellent basis for a comparative analysis on feature extraction and detection by animals as diverse as bees and mammals.
Object recognition is the ability to perceive the physical properties (such as shape, color and texture) of an object and apply semantic/cognitive attributes to the object [1], such as an understanding of its use, or classification of the object as prey, predator or irrelevant. The process leading to recognition is typically, though not exclusively, viewed as a bottom-up hierarchy in which information is processed sequentially with increasing complexity. In vertebrates, the idea is that lower-level cortical processors, such as the primary visual cortex, process the basic object components such as color, depth and form, while higher-level cortical processors, such as the inferotemporal cortex in humans, are ultimately responsible for recognition [2]. Historically, perhaps one of the best-known attempts at explaining perception and recognition is that of Gestalt psychology.
The central tenet of Gestalt psychology is that the whole differs from the sum of its parts. The theoretical framework underlying Gestalt ideas is holism, which states that systems and their properties should be viewed as wholes, not as collections of parts [3]. This contrasts with earlier structuralist hypotheses, which state that perceptions can be derived by identifying the elementary parts [4]– [6]. Modern research into visual processing has changed its focus from gestaltism vs. structuralism to global vs. local processing [6], [7], with an expanded focus from the psychological processes of perception to include physiological processes [8]. The global processing framework results in the notion that an object is recognized only when its elements form the whole image, while the local processing framework requires the identification of correct elements, points and edges, but not necessarily the image as a whole. This distinction also suggests potential differences in the neurobiological processes underlying object recognition [8].
For a predator that relies on vision, visual ability to classify an object as predator or prey will be under strong selection. However, the extent to which visual predators further classify items can vary considerably. Some predators make rapid decisions and do minimal classifying of prey into particular types, relying instead on key features, such as seeing an object of a specific size range moving in a specific orientation, as identifiers of prey [9]– [12]. Examples of this approach can be found among amphibians [12], [13] and mantises [14], which adopt remarkably similar approaches despite possessing very different nervous systems. Many jumping spiders (Salticidae) also rapidly categorize objects as prey or non-prey based on only a few key features [15]– [18]. However, it is also amongst the salticids that some of the most precise prey identification and prey preference behaviors among animals is found.
An extreme case of such preference is that of Evarcha culicivora. Uniquely, this East African salticid feeds indirectly on vertebrate blood by selectively preying upon female mosquitoes (particularly Anopheles, famous as the vectors of malaria) that have recently fed on blood. These spiders are capable of using vision alone to discriminate between their preferred prey, blood-fed female Anopheles, and similar looking male Anopheles, female Anopheles that have not fed on blood, non-anopheline mosquitoes, as well as various similar-sized non-mosquito prey [19]– [21]. These experiments have also shown that for correct identification E. culicivora uses a complex non-linear process involving specific elements of the prey, including an engorged abdomen, resting posture and antennae [20], [22].
Like other salticids, E. culicivora has exceptional eyesight, which is used to locate, stalk and finally pounce on its prey [23]. Salticids have large forward-facing principal eyes that are specialized for high resolution vision but within a very narrow (ca. <5°) field of view [24]– [28] which is compensated for with complex movements that scan up to ca. 28° to either side of the body axis [27]. Additionally, salticids have three pairs of motion-sensitive secondary eyes with wide fields of view and which collectively encompass up to 360° [29]– [32].
E. culicivora’s unique dietary preferences, which can be expressed using vision as the sole sensory modality for prey classification [20], [22], make this species an excellent subject for the study of recognition and classification of prey. Here we presented the spiders with abstract representations of potential prey (‘stick figures’) differing in their level of simplicity to determine whether predatory behavior and prey classification was elicited by biologically unrealistic prey containing only key elements (local processing). Stimuli included stick figures of Anopheles mosquitoes in their resting posture, as well as non-prey items and alternative prey items. We used single-choice predatory behavior experiments to determine whether or not E. culicivora ‘viewed’ abstract representations of prey as potential prey, and two-choice predatory behavior experiments to test for specific preference between stimuli. Due to E. culicivora’s known ability to discern specific elements of prey, we predicted that these specialized visual hunters would stalk and pounce on abstract representations of prey. We also predicted that E. culicivora would choose simplified representations of its preferred prey over realistic images of alternative non-preferred prey, showing that it categorizes these images as its preferred prey item.
A total of 195 successful sessions were run in the single-choice predatory behaviour experiment: 85 with adult females, 50 with adult males and 60 with juvenile spiders. When spiders initiated stalking behavior, this almost always resulted in pouncing on the abstract prey ( Table S1). The type of stimulus had no effect on whether the spiders noticed it (χ 2 = 6.71, df = 6, p = 0.349, Table S2). Stimulus type did affect the propensity to stalk the prey once it was noticed (χ 2 = 37.87, df = 6, p<0.001), but did not affect the amount of time it took the spiders to ‘decide’ to stalk the prey (time between the spider first noticing the stimulus and initiation of stalking behavior; χ 2 = 3.928, df = 6; p = 0.686, Table S2). Once stalking was initiated, stimulus type had no effect on the propensity to pounce (χ 2 = 4, df = 6, p = 0.677, Table S2). We therefore considered stalking to be a true sign of predatory behavior by the spiders. The spiders stalked the abstract images of mosquitoes (stimuli 1, 2, 3 and 4) significantly more often than the images of non-prey items (stimuli 6 and 7; Table 1). However, while the image of the fly (stimulus 5) was stalked significantly more often than the altered, disarranged abstract image of the blood-fed mosquito (stimulus 7), it wasn’t stalked more often than the image of the circle (stimulus 6).
| left11Stimulus | left111 | left112 | left113 | left114 | left115 | left116 | left117 |
| left11N | left1127 | left1128 | left1127 | left1130 | left1129 | left1127 | left1127 |
| left11Stalk (%) | left1174 | left1164 | left1174 | left1177 | left1156 | left1133 | left1124 |
| left11 1 | left11- | left110.508 | left111 | left111 | left110.267 | left11<0.001 | left11<0.001 |
| left11 2 | left11- | left11- | left110.549 | left110.549 | left110.774 | left11<0.05 | left11<0.005 |
| left11 3 | left11- | left11- | left11- | left111 | left110.267 | left11<0.05 | left11<0.001 |
| left11 4 | left11- | left11- | left11- | left11- | left110.302 | left11<0.005 | left11<0.001 |
| left11 5 | left11- | left11- | left11- | left11- | left11- | left110.118 | left11<0.05 |
| left11 6 | left11- | left11- | left11- | left11- | left11- | left11- | left110.219 |
Upper section of the table contains the number of spiders that noticed each stimulus (N) and the percentage of stalking instances. The bottom section of the table contains the crosswise comparisons of the stalking responses to the different stimuli, using McNemar tests with a binomial distribution. See Figure 1 for stimulus images. Note all image sizes are equivalent, see Table 3.
GLMs on the propensity to stalk showed significant main effects of stimulus type (χ 2 = 22.315, df = 6, p<0.005) and spider sex (χ 2 = 7.413, df = 2, p<0.05), but not their interaction (χ 2 = 9.270, df = 11, p = 0.597). The effects of the relative contrast of the stimuli or its interaction with spider sex were also not significant (respectively, χ 2 = 0.039, df = 1, p = 0.843; χ 2 = 0.431, df = 2, p = 0.806). Females and juveniles were more prone to stalk stimuli (56.5% of 85 and 70% of 60 respectively) than males (38% of 50; females vs male: U = 1732.5, p<0.05; juveniles vs males: U = 1020, p<0.001, Mann-Whitney U test, Data, Tables S9, S11), while there was no significant difference between females and juveniles (U = 2205, p = 0.099, Mann-Whitney U test, Data, Table S11). Similarly, stimulus type had a significant effect on the propensity of females and juveniles to stalk (respectively,:: Cochran’s Q = 14.195, p<0.05; Cochran’s Q = 14.261, p<0.01, Data, Tables S3, S4, S7, S8) but not on that of the males (Cochran’s Q = 5.636, p = 0.465, Data, Tables S5, S6). While there were no significant differences in how often the different sexes noticed different stimuli (χ 2 = 5.762, df = 2, p = 0.056, Kruskal-Wallis test, Data, Table S10), there were significant differences in the distances at which the they noticed the stimuli (χ 2 = 14.021, df = 2, p<0.005, Kruskal-Wallis test, Data, Tables S10), with the females noticing the stimuli from significantly further away than males or juveniles (respectively, U = 1471, p<0.005; U = 1751, p<0.0.005, Mann-Whitney U tests, Data, Table S11).  See Data, Tables S1– S11 for the full datasets.
A total of 123 successful sessions were run in the two-choice predatory behaviour experiments, 61 with females, 34 with males and 28 with juveniles. Spiders never exhibited a side bias (experiments 1 through 5, respectively: p = 0.23; p = 0.83; p = 0.35; p = 0.54; p = 0.54, Binomial test). When given a choice between abstract representations of their preferred prey and a realistic image of non-preferred prey (a house fly), E. culicivora chose the preferred prey significantly more often (experiments 1 and 2 respectively, p<0.001; p<0.01, Binomial test, Table 2). Spiders also chose a disarranged abstract representation of their preferred prey significantly more often than they chose a realistic image of non-preferred prey (experiment 3, p<0.05, Binomial test, Table 2), or a disarranged non-prey item (experiment 5, p<0.05, Binomial test, Table 2). However, spiders showed no preference when presented with an abstract representation of their preferred prey and a disarranged version of that same image (experiment 4, p = 0.84, Binomial test, Table 2).
| left11Experiment | left11N | left11Image 1 | left11Image 2 | left11Chose Image 2 | left11p |
| left111 | left1122 | left112 | left115 | left119% | left11<0.001 |
| left112 | left1128 | left113 | left115 | left1125% | left11<0.05 |
| left113 | left1128 | left114 | left115 | left1129% | left11<0.05 |
| left114 | left1124 | left113 | left114 | left1146% | left110.84 |
| left115 | left1121 | left114 | left118 | left1119% | left11<0.05 |
Pairs of images used in the two-choice predatory behavior experiments, percentage of pounce choices for the second image, and results of Binomial tests. See Figure 1 for images.
This study shows that for E. culicivora, discrimination and categorization can be achieved using only visual representations of the basic elements of its preferred prey. By using stick figure drawings of their preferred prey – Anopheles mosquitoes, we have created stimuli constructed only of key elements of their prey that have been found to be important for recognition [20], [22]. As hypothesized, we have shown that not only do these spiders view these stimuli as potential prey (by initiating predatory behavior), but they also prefer these abstract images of prey to detailed images of alternative non-preferred prey. These results show that the various elements that have been found to be necessary for prey discrimination in previous studies are [20], [22] also sufficient for recognition. This was the case regardless of whether or not the spiders had encountered their preferred prey before. Our controls have ruled out external cues, such as side preference, number of elements of the stimulus, and the relative contrast of the stimuli. Interestingly, the propensity to pounce was not affected by the different stimuli, and was seen in almost all cases where stalking was initiated. It would seem that the decision to pounce relies on other cues not singled out in this study, or, perhaps more likely, that pouncing is a follow-up behavior akin to a ‘fixed action pattern’.
Our confidence in these results is strengthened by the behavior of the naïve juveniles in the single-choice predatory behavior experiment. When hunting Anopheles, but no other type of prey, juveniles of E. culicivora perform an innate prey-specific predatory behavior involving a detour to approach the prey from behind [33]. This detouring approach to the prey was evident in 57% of the trials involving a stimulus representing an Anopheles (stimuli 1–4; N = 31; stimulus 1 (detours/attacks): 7/11; stimulus 2: 4/9; stimulus 3: 6/10; stimulus 4: 1/1) with juveniles, but only once with the fly stimulus (stimulus 5; N = 8) and never with the circle stimulus (stimulus 6; N = 3). Despite these small sample sizes, it is apparent that they recognize the stick-figure stimuli specifically as Anopheles mosquitoes.
The low level categorization of the abstract stimuli into prey and non-prey items is also seen in other invertebrates such as the praying mantis, where basic features of the stimuli, including size and speed, are the main cues [14], [34]. However, E. culicivora’s discriminations use much finer details of an image, such of the size and shape of mosquito antennae, when making decisions regarding preference [20], [22], and thus require a considerably higher level of feature detection. The most notable instance of such discrimination in this study was the ability of the spiders to discriminate between the two disarranged stimuli in the single-choice predatory behavior experiment, where the only difference between the stimuli were the relative angles between the elements and yet one was categorized as prey, while the other was not. Nelson and Jackson [20], [22] have shown that the resting posture of a mosquito is an important cue for recognition. Our findings fine-tune those conclusions by suggesting that it is not the angle of the body compared to a surface or horizon, but rather the relative angles between the body elements that is crucial for recognition.
Discrimination of orientation has been shown in honeybees ( Apis mellifera), which can distinguish different orientations even when these are produced through illusory contours [35] and without clear edge detection [36]. Horridge [37], [38] proposed that the generalization ability of the honeybee uses different parameters of an image to form local cues. These discrimination mechanisms were based on physical aspects of an image, but Avargues-Weber et al. [39], [40] demonstrated that honeybees are even capable of abstract concepts such as above-below and left-right. Unlike in the bee studies, we used unlearned stimuli and untrained animals, and show that E. culicivora is capable of discrimination using a significantly more complex abstract concept - angles between disconnected elements.
One way of achieving such discrimination ability is by storing the ‘correct’ orientation of the various elements and comparing each element to stored memory. However, the spiders occasionally pounced upon the stimulus while standing on the sides or the ceiling of the starting chamber (analogous to behavior common in a natural setting, XJN pers. obs.), suggesting that orientation effects do not play a role in these decisions. While it is tempting to consider this type of object consistency in recognition to be superior to that seen in human recognition of faces (where face recognition is degraded significantly more than other objects when viewed upside-down [41]– [44]), there is an inherent difference between the two - faces often have a prototypical orientation, while in the spider’s natural three-dimensional environment prey is often viewed from different orientations.
An alternative mechanism of achieving the discrimination ability seen in this study is by ‘calculating’ the relative difference of the angles and comparing that to stored angles that represent prey. While discrimination of orientation has been well studied in vertebrates and invertebrates [45]– [49], relative angle discrimination in non-human animals remains largely unstudied. In humans, however, this ability has been well studied (e.g., [50]– [52]) and there is some evidence for a neural mechanism that encodes angles in humans [53], as well as in macaques [54] and cats [55].
Our results demonstrate that E. culicivora not only categorizes the simplified abstract stimuli as prey, but recognizes them as its preferred prey, exhibiting higher level categorization or within-category discrimination. This was the case even for the disarranged version of the blood-fed Anopheles, a capability not dissimilar to that of humans with visual expertise when viewing fragmented images of cars or faces [56], although in our case the images were abstract and dispersed rather than fragmented. E. culicivora not showing any preference between the blood-fed Anopheles stimulus and its disarranged version was perhaps the most surprising finding of this study. While it is possible that E. culicivora’s response to the image of the disarranged Anopheles was due to its resemblance to some other unknown prey rather than Anopheles, this is unlikely as the dietary preferences of these spiders has been well studied [19], [20], [22], [33]. We should note that experiments using stimuli 4 and 7 were both run at a later date. While this too might have affected the results, this also seems unlikely, as the laboratory conditions were constant and the spiders were healthy. Another alternative explanation is that the specific arrangement of the elements of the disarranged Anopheles exploits a sensory bias in the E. culicivora’s visual pathways, while the altered version of this stimulus does not. Regrettably, we could not test the spider’s responses to other alternative arrangements of these stimuli. Nonetheless, either through a sensory bias in the visual pathways, or by higher level visual analysis, the spiders evidently categorized both the blood-fed Anopheles stimulus and its disarranged version as their preferred prey. This suggests that they do not use a global, or holistic approach to recognition [4], [44], but rely instead on the analysis of specific elements at a local level to recognize an object [6]– [8]. This type of analysis functions much like distributed feature extraction algorithms of object recognition in computer vision based upon the vertebrate visual cortex [57], [58], in which low-level areas of the nervous system are delegated to recognizing different elements which are then fed to higher order centers [59]. A closer look at how these spiders visually analyze what it is they are seeing will provide a deeper understanding of what specific features these spiders are looking for when they are looking for prey.
All spiders used in this study were at least second generation laboratory reared individuals, and no juveniles tested had ever encountered mosquitos. Testing was carried between 0730 and 1200 h in a ÿharacteriz-controlled laboratory set to 24° with a photoperiod of 12L∶12D, lights on at 07∶00. Test spiders were unmated adults (body length, 4.5–5.5 mm) and juveniles (1.5–2.5 mm). Standard rearing and maintenance was as in earlier studies (for details, see [19], [20]). Spiders were caged individually and were fed to satiation once a week on Drosophila spp. Two h prior to their use as prey, Drosophila were given a honey and human blood (obtained from a blood bank) meal by inserting a cotton dental wick dipped in the mixture into their rearing container. Test spider hunger levels were standardized by a 5–7 day pretrial fast. Test spider predatory behaviors (noticing, stalking and/or pouncing) and their timing were recorded during all experiments. Noticing ÿharacte is ÿharacterized by the spider performing an optomotor response to face the stimulus with its AM eyes and subsequently staring continuously at the stimulus for a few seconds. Stalking behavior is ÿharacterized by the salticid slowly stepping toward the prey while visually fixated on the prey. Both are reliably identifiable behaviors.
Stimuli consisted of videos of repeated sporadic movement of different images ( Figure 1, Table 3), created using Adobe Photoshop CS5 in greyscale. Image 1 was a realistic line drawing of a blood-fed female Anopheles gambiae mosquito in typical resting posture, while the simplified images 2 (not blood-fed) and 3 (blood-fed) were similar but used only straight lines and ovals, with the latter depicting a blood-fed mosquito with an engorged abdomen, known as an important prey-identification cue [22]. Image 4 was a disarranged version of image 3, created so as to not alter the respective angles of any of the elements of image 3, while ensuring the elements were disconnected and, to humans, no longer resembling a mosquito. Image 5 was created by rendering a photograph of a housefly ( Musca domestica) to grayscale and removing the background. Image 6, a circle the size of a housefly was created as a control, as were images 7 and 8. Image 7 was an altered version of image 4 where the angles of each of the elements of the image were altered and image 8 was a disarranged version of image 6, broken into 4 unequal sections. All images were created on a background of 250, 250, 250 RGB and had black pixel counts between ca. 200 and 550 pixels ( Table 3). Screen size was set to 1024×768 pixels. All images were sized similarly and were presented at biologically relevant sizes (to the nearest 0.5 mm).
Images 1–4 are based on Anopheles mosquitoes. 1 is based on [60]. Image 4 is a disarranged version of image 3. Image 8 is a disarranged version of image 6. Image 7 is based on image 4 where the angles of the various elements have been altered.
| left11Image | left111 | left112 | left113 | left114 | left115 | left116 | left117 | left118 |
| left11 Relative Contrast | left11250 | left11230 | left11518 | left11545 | left11500 | left11211 | left11545 | left11211 |
| left11 Width (mm) | left117 | left116 | left116 | left116 | left117 | left116 | left117 | left117 |
For the actual images used see Figure 1.
To create the stimuli, one (in single-choice predatory behavior experiments) or two (in two-choice predatory behavior experiments) images were rendered into videos of repeated, horizontal (single-choice predatory behavior experiments) or vertical (two-choice predatory behavior experiments) motion (two bouts of back and forth movement every 10 s). Motion speed was 9°/s, at a viewing distance of 10 cm, and movement distance was set to be 8° visual angle. These parameters were selected to maximize the attention of the spiders [31], [32] (see Video S1 for a sample stimulus video).
Videos were projected onto a screen using an AAXA M2 Micro Projector connected to a computer, and placed 100 mm from the screen. The videos were played on a continuous loop using VLC player software. The screen was made of two protective sheets of glass (each 2 mm thick, 5 cm wide×5 cm long) with LCD screen polarizers from a Toshiba Tecra A9 PTS52C-MH409C laptop cut to size between them. This setup was used as we have found that the screen polarizers effectively reduce the brightness of the projected videos and did not result in a polarized image, while the glass sheets prevented the screen polarizers from getting damaged while being handled and cleaned. Due to the high spatial resolution of salticid principal eyes (ca. 11 minutes of arc, [25]) images projected directly onto a screen will appear pixelated once the spider gets close. To overcome this, while maintaining life size images at high resolution, larger than life size stimuli were back-projected through a lens placed between the projector and a screen, which reduced the projected image by ca. a factor of 10. Fine tuning the size of the projected stimuli was achieved by varying the size of the VLC player window on the computer monitor.
To answer this question, we tested the predatory responses of the spiders to individual stimuli (single-choice predatory behavior experiment). An angled wooden ramp supported by a wooden pole glued to a wooden base was placed in front of the screen and projector (see Figure 2a for dimensions). The apparatus was painted with two coats of polyurethane, but the top face of the ramp had a sticker marked with a 5 mm grid to allow accurate measurement of the spider’s distance from the stimulus when a particular behavior was observed. The ramp was wiped with 80% ethanol and allowed to dry for 15 min between each test to eliminate possible chemotactile traces from spiders in previous tests.
a) Apparatus used in single-choice predatory behavior experiment. Spiders (not to scale) were placed either 10 cm (adults) or 6 cm (juveniles) away from stimulus screen, and behavior recorded. b) Apparatus used in the two-choice predatory behavior experiment. Projector and reducing lens placed inside glass chamber 100 mm from screen and ramp complex.
For each test, a spider was placed on the ramp and covered with a petri dish, at a distance of 6 (juveniles) or 10 (adults) cm from the center of the petri dish to the screen. These distances were used as they are far enough from the screen so that the spiders couldn’t ‘wald’ directly onto the stimuli, while being close enough to enhance the chances of the spiders reacting to the stimuli (juveniles were less responsive to stimuli at a distance compared to adults). The screen was covered with a piece of black cardboard until test spiders were released to prevent them seeing the stimulus until tests began. Once the spiders were relaxed (staying stationary or grooming) the screen was uncovered, the petri dish was removed and timing started. Tests ended when the spiders pounced on an image or walked/jumped off the ramp. If a spider noticed the image, the session was considered successful and tests were not repeated with the same spider. A spider that failed to notice the stimulus was tested up to twice in one day, or up to a total of 4 times in the following 3 days.
In this experiment we relied on E. culicivora’s proven preference for Anopheles mosquitoes and presented them with a two-choice test. All spiders used in this test were laboratory reared and had no prior experience with mosquitoes. For these tests, rendered movies contained two images ( Table 1) which moved identically and simultaneously. In each test, which image was on the right and which was on the left was randomized. The movies were projected as above, but experiments were held within a specialized apparatus containing a stainless steel ramp (15 mm wide×150 mm long; angled up by 25 °) in front of the screen. The ramp was inside a glass chamber (diameter 300 mm, length 525 mm long) with removable sealing steel end plates (diameter 200 mm, kept off during this set of experiments). Welded to the ramp was a bracket onto which the screen was attached with a gap of 5 mm from the ramp. The ramp/screen unit (‘ramp complex’) sat mounted within holes on a stainless steel platform spanning the length of the cylinder ( Figure 2b). In this way it could be removed for cleaning with 80% ethanol after each test and returned to the same place, while ensuring that the distance between the screen and the reducing lens and projector was always the same (and thus stimulus size was constant).
At a distance of 22 mm from the end of the ramp, a stainless steel ‘starting box’ (11 mm wide×19 mm high×22 mm deep; i.e., furthest point 44 mm from top end of ramp) was welded to the ramp complex ( Figure 2b). The box had a transparent Plexiglas cover wired to an external controller so that it could be opened remotely. The spider was placed into the starting box and the door was closed. After 2 min, the ramp complex was put in place. Once the spider was away from the door of the starting box, after ca. 20 s, the door was opened and tests began. Tests ended with the spider pouncing on one of the two images on the screen or to jumping/walking off the ramp. Failing these two conditions, tests were stopped after 15 min. In this experiment we were interested in pouncing ÿehavior rather than instalking ÿehavior, as the former constitutes a more distinct choice by the spiders. For this reason, both adult and juvenile spiders were released a short distance from the screen (see Video S2 for a sample of the spider behavior in this experiment).
All analyses were done using SPSS Statistics v.20. For the single-choice predatory behavior experiment, GLM analyses were performed to check for the main effects of stimulus type, ‘sex’, relative contrast (number of black pixels against a white background) which was either in the ca. 200 or ca. 500 pixels) and their interaction on the spider’s choice to stalk the stimuli. Interactions between stimulus relative contrast and stimulus type were not analyzed, as these are nested. Sexes were divided into three – female, male and juvenile as their sex cannot be discerned and their behavior differs [22], [33]. In this model the dispersion parameter was set at 1, and type III sums of squares were used, though there was no qualitative difference from type I. Kruskal-Wallis tests were used to compare the predatory responses between the different sexes, with Mann-Whitney U tests for pairwise analysis. Cochran’s Q tests were used to test how the different stimuli affected the chances of the spiders noticing the stimulus and the propensity to stalk and pounce. Friedman tests were used to test the effects of the different stimuli on stalking initiation distance, as well as their effects on the amount of time it took the spiders to start stalking. When these effects were found to be significant, McNemar tests were used for pairwise comparisons. For the two-choice predatory behavior experiments, Binomial tests were used to test the spider’s choices, as well as possible side-bias.
Results from the single-choice predatory behavior experiment (all spiders). M = Median, IQR = interquartile range. The percentages of the spiders that stalked/pounced are nested within the percent of spiders that noticed/stalked, respectively. See Figure 1 for stimulus images.
(DOC)
Click here for additional data file.
Statistics comparing between the different stimuli for the single-choice predatory behavior experiment (results from all spiders; data in Table S1). *Cochran’s Q; **Friedman’s test (χ 2); df = 6 in all tests.
(DOC)
Click here for additional data file.
Results from the single-choice predatory behavior experiment (female spiders). M = Median, IQR = interquartile range. The percentages of the spiders that stalked/pounced are nested within the percent of spiders that noticed/stalked respectively. See Figure 1 for stimulus images.
(DOC)
Click here for additional data file.
Statistics comparing between the different stimuli for the single-choice predatory behavior experiment (results from female spiders; data in Table S3). *Cochran’s Q; **Friedman’s test (χ 2); df = 6 in all tests.
(DOC)
Click here for additional data file.
Results from the single-choice predatory behavior experiment (male spiders). M = Median, IQR = interquartile range. The percentages of the spiders that stalked/pounced are nested within the percent of spiders that noticed/stalked, respectively. *Insufficient cases for IQR. See Figure 1 for stimulus images.
(DOC)
Click here for additional data file.
Statistics comparing between the different stimuli for the single-choice predatory behavior experiment (results from male spiders; data in Table S5). *Cochran’s Q test; **Friedman’s test, χ2; ***Insufficient cases for analysis; in all tests, df = 6.
(DOC)
Click here for additional data file.
Results from the single-choice predatory behavior experiment (juvenile spiders). M = Median, IQR = interquartile range. The percentages of the spiders that Stalked/Pounced are nested within the percent of spiders that Noticed/Stalked respectively. *Insufficient cases for IQR. **No juveniles tested with this stimulus. See Figure 1 for stimulus images.
(DOC)
Click here for additional data file.
Statistics comparing between the different stimuli for the single-choice predatory behavior experiment (results from juvenile spiders; data in Table S7). *Cochran’s Q test; **Friedman’s test (χ 2); in all tests, df = 5.
(DOC)
Click here for additional data file.
Results from the single-choice predatory behavior experiment for each sex/age group. M = Median, IQR = interquartile range, F = female, M = male, Juv = juvenile. The percentages of the spiders that stalked/pounced are nested within the percent of spiders that noticed/stalked, respectively.
(DOC)
Click here for additional data file.
Statistics comparing between different sex/age groups for all stimuli in the single-choice predatory behavior experiment; data in Table S9. Kruskal-Wallis tests (df = 2). In pairwise analysis females noticed the stimuli from significantly further away than other groups ( Table S11).
(DOC)
Click here for additional data file.
Differences between the sex/age groups in notice distance, stalking propensity and pouncing propensity for all stimuli in the single-choice predatory behavior experiment (data in Table S9). Pairwise analysis of differences in noticing distance, stalking propensities and pouncing propensities once stalking was initiated. Mann-Whitney U tests. F = female, M = male, J = juvenile.
(DOC)
Click here for additional data file.
Sample stimulus video for the two-choice predatory behavior experiment presenting images 3 and 4.
(AVI)
Click here for additional data file.
Sample session video from the two-choice predatory behavior experiment.
(MP4)
Click here for additional data file.
We thank Aynsley McNab for technical assistance, Robert Jackson and Stephen Abok Aluoch for the collection of spiders, and Jason Tylianakis for constructive feedback on this manuscript.


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The Virtual University, Pakistan’s first University based completely on modern Information and Communication Technologies, was established by the Government as a public sector,
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follow its rigorous programs regardless of their physical locations. It thus aims at alleviating the lack of capacity in the existing while simultaneously tackling the acute shortage of qualified professors in the country.
By identifying the top Professors of the country, regardless of their institutional affiliations, and requesting them to develop and deliver hand-crafted courses, the Virtual University aims
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******************


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There doesn't appear to be anything here. Sorry about that.
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|
1-Wire API for .NET Version 4.00
|
The OneWireAccessProvider class manages the Maxim adapter class derivatives of DSPortAdapter
.
More...
Static Public Member Functions | |
| static String | getVersion () |
| Returns a version string, representing the release number on official releases, or release number and release date on incrememental releases. | |
| static void | main (String[] args) |
| Main method returns current version info, and default adapter setting. | |
| static Enumeration | enumerateAllAdapters () |
Gets an Enumeration of all 1-Wire adapter types supported. | |
| static DSPortAdapter | getAdapter (String adapterName, String portName) throws OneWireIOException, OneWireException |
| Finds, opens, and verifies the specified adapter on the indicated port. | |
| static DSPortAdapter | getDefaultAdapter () throws OneWireIOException, OneWireException |
| Finds, opens, and verifies the default adapter and port. | |
| static String | getProperty (String propName) |
| Gets the specfied onewire property. | |
| static void | setUseOverridingAdapter (DSPortAdapter adapter) |
| Sets an overriding adapter. | |
| static void | clearUseOverridingAdapter () |
| Clears the overriding adapter. |
The OneWireAccessProvider class manages the Maxim adapter class derivatives of DSPortAdapter
.
An enumeration of all available adapters can be accessed through the member function EnumerateAllAdapters
. This enables an application to be adapter independent. There are also facilities to get a system appropriate default adapter/port combination.
Get an instance of the default 1-Wire adapter. The adapter will be ready to use if no exceptions are thrown.
try
{
DSPortAdapter adapter = OneWireAccessProvider.getDefaultAdapter();
System.out.println("Adapter: " + adapter.getAdapterName() + " Port: " + adapter.getPortName());
// use the adapter ...
}
catch(Exception e)
{
System.out.println("Default adapter not present: " + e);
}
Enumerate through the available adapters and ports.
DSPortAdapter adapter;
String port;
// get the adapters
for (Enumeration adapter_enum = OneWireAccessProvider.enumerateAllAdapters();
adapter_enum.hasMoreElements(); )
{
// cast the enum as a DSPortAdapter
adapter = ( DSPortAdapter ) adapter_enum.nextElement();
System.out.print("Adapter: " + adapter.getAdapterName() + " with ports: ");
// get the ports
for (Enumeration port_enum = adapter.getPortNames();
port_enum.hasMoreElements(); )
{
// cast the enum as a String
port = ( String ) port_enum.nextElement();
System.out.print(port + " ");
}
System.out.println();
}
Display the default adapter name and port without getting an instance of the adapter.
System.out.println("Default Adapter: " +
OneWireAccessProvider.getProperty("onewire.adapter.default"));
System.out.println("Default Port: " +
OneWireAccessProvider.getProperty("onewire.port.default"));
| static void clearUseOverridingAdapter | ( | ) | [static] |
Clears the overriding adapter.
The operation of getAdapter and getDefaultAdapter will be returned to normal.
| static Enumeration enumerateAllAdapters | ( | ) | [static] |
Gets an Enumeration
of all 1-Wire adapter types supported.
Using this enumeration with the port enumeration for each adapter, a search can be done to find all available hardware adapters.
Enumeration
of DSPortAdapters
in the system | static DSPortAdapter getAdapter | ( | String | adapterName, |
| String | portName | ||
| ) | throws OneWireIOException, OneWireException [static] |
Finds, opens, and verifies the specified adapter on the indicated port.
| adapterName | string name of the adapter (match to result of call to getAdapterName() method in DSPortAdapter) |
| portName | string name of the port used in the method selectPort() in DSPortAdapter |
DSPortAdapter
if adapter present| OneWireIOException | when communcation with the adapter fails |
| OneWireException | when the port or adapter not present |
| static DSPortAdapter getDefaultAdapter | ( | ) | throws OneWireIOException, OneWireException [static] |
Finds, opens, and verifies the default adapter and port.
Looks for the default adapter/port in the following locations:
DSPortAdapter
if default adapter present| OneWireIOException | when communcation with the adapter fails |
| OneWireException | when the port or adapter not present |
| static String getProperty | ( | String | propName | ) | [static] |
Gets the specfied onewire property.
Looks for the property in the following locations:
| propName | string name of the property to read |
String
representing the property value or null
if it could not be found (onewire.adapter.default
and onewire.port.default
may return a 'smart' default even if property not present) | static String getVersion | ( | ) | [static] |
Returns a version string, representing the release number on official releases, or release number and release date on incrememental releases.
| static void main | ( | String[] | args | ) | [static] |
Main method returns current version info, and default adapter setting.
| args | cmd-line arguments, ignored for now. |
| static void setUseOverridingAdapter | ( | DSPortAdapter | adapter | ) | [static] |
Sets an overriding adapter.
This adapter will be returned from getAdapter and getDefaultAdapter despite what was requested.
| adapter | adapter to be the override |


MySmartHome
Monday
March 2018
19:30
Living Room Temperature
Make new rule set for:
"Living Room"
Current Temperature
0
°C
New Temperature
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+
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Eastern Cape cop kills himself after allegedly shooting girlfriend’s sister
The incident is under investigation by the Independent Police Investigative Directorate.
by News24 WireImage: iStock.
A Middleburg police officer has committed suicide after allegedly shooting his girlfriend’s sister with his service weapon.
Independent Police Investigative Directorate (Ipid) spokesperson Sontaga Seisa confirmed that Mzwamadoda Fuzani had discharged his firearm while off-duty on Friday night, allegedly killing the 30-year-old woman in Thembalethu, George.
It is understood that the police sergeant then went searching for his girlfriend, who is also an officer at Middleburg Police Station.
“Failing to trace his girlfriend, the suspect realized that the police were looking for him. He then decided to flee the area in his private car and managed to commit suicide by fatally shooting himself with the firearm some 200 kilometres away from Middleburg,” said Seisa.
The incident is under investigation by Ipid.


This demo was originally created by Andrej Karpathy for the project ConvNetJS, but was modified to use neurojs as the neural network back-end. In this case ConvNetJS was replaced by a DDPG agent.


Scaling Edit Tool for RoboFont is a mouse and keyboard controlled version of what are better known
as Interpolated Nudge tools, made famous by Christian Robertson
When moving oncurve points, offcurve points are scaled in relation to neighboring oncurve points.
The tool works on both bicubic and quadratic bezier curves, and open and closed contours.
All the built-in Edit Tool functionality should work as expected.
Angles of offcurve points are retained by default. Command-key overrides the angle keeping when mouse is down. In contextual menu (right mouse click) you can choose whether or not smooth and non-selected points are affected by the override. Simplified mode offers a more traditional operation without angle keeping.


Well it was around 9:30, and I was chilling at a friends house planning to try dimenhydrinate. I took a total of 450mg. Below are the events that unfolded.
T: 9:35 pm -> Took 350mg (7 pills) with a bottle of water.
T: 10:35 -> Feeling sight body high, very similar to DXM. Limbs are extremely heavy. I had to go to the bathroom VERY badly, I don't know if this was from the bottle of water or the pills. When I stood up it hit me fairly hard. It felt as if my body weight tripled, and although trying my hardest to walk normal, I did a zombie walk arcoss the hall to the toilet. When I sat back down I looked at the upper cornor of the wall in the room. It looked like is was pulsing and being distorted like a subwoofer does when you crank the bass up. Still visuals were very minor and I was fairly disappointed so I took 100mg (2 pills) more for a total of 450mg.
T: 12:05 -> At this point I'm pissed. I've felt nothing beyond what was described above. The only explination I can think of at this point is that I was watching a movie, and I was so focused on the movie I killed my trip. I decide to go to sleep. What happens next was extremely interesting, and I didn't understand it until the next day. When I was lying in bed, I thought I was dozing in and out of sleep. I would close my eyes, but I could still see. To prove to myself that this was not possible I pulled my eyelids down with my fingers and held them shut. I saw black for about 5 seconds, then like magic, I could see the room again.
I was still holding my eyes shut. I looked around the room and saw my cell phone sitting on the nightstand.  I scrolled through it and made sure I had set a alarm on it for the morning, then set it back down on the nightstand. I closed my eyes for a couple of seconds and opened them. There was no nightstand in front of me, and my cell phone was sitting in the pocket of my shorts. 'That's some weird shit' I thought, and I went back to sleep. The entire night was filled with extremely vivid dreams of things happening, I was talking to people, wandering around my friends house, wandering around in my house. When I woke up I just thought those were some wierd dreams, and drove home @ 7:30am.
T: 8:00 am -> Driving home was normal, no problems or intoxicated feeling at all, just a bit groggy and and mild neausa. I get home and go back to sleep.  As soon as I doze off I can see again. It was like my brains was making up situations and rooms from how I remember them. I'm starting understand this now, and freely allow myself to doze on and off into these 'renderings' my brain has created. Nothing about this was weird as all these 'dreams' (or where they?) were just of me chilling in my room. I woke feeling tired, but completely normal.
T: 11:00 am -> I woke up and am trying to make sense of what happened. It's very weird because that morning I remember driving home with no problems; but now I can't remember driving home at all, or getting in my bed.
This was a very awkward trip. I plan on trying this again except using about 550mg-650mg, and having a sitter to watch exactly what I'm doing. My suggestions to people who would like to try this is start with a small dose. If I had done this during the day it's VERY possible that the results would be different. This is not a 'cool' trip like LSD or shrooms. I don't get these heavy visuals like that, I see normal things and people, then just watch them disappear. It's interesting, but frightening.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Publications
† Equal Contribution, * Corresponding Author(s)
2023
-
Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean TeacherIn International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023
-
-
Multi-Modal Mutual Attention and Iterative Interaction for Referring Image SegmentationIEEE Transactions on Image Processing (TIP), 2023
-
Global Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
-
CVPRWNTIRE 2023 Quality Assessment of Video Enhancement ChallengeIn Computer Vision and Pattern Recognition Workshops (CVPRW), 2023
2022
-
-
Recent Advances on Neural Network Pruning at InitializationIn International Joint Conferences on Artificial Intelligence (IJCAI Survey), 2022
2021
-
-
Multi-Scale Selective Feedback Network with Dual Loss for Real Image Denoising.In International Joint Conferences on Artificial Intelligence (IJCAI Oral), 2021
2020
2019
-
LRDNN: Local-refining based Deep Neural Network for Person Re-Identification with Attribute Discerning.In International Joint Conferences on Artificial Intelligence (IJCAI Oral), 2019


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Lorem ipsum dolor sit amet, consectetur adipisicing elit. Nostrum quibusdam iste sit ratione voluptatibus vero, cumque dolores magni voluptates quos aut omnis suscipit voluptatem veritatis doloribus sed fuga alias architecto!
Preheat oven to 350°. Grease a medium baking dish with cooking spray. Cook spaghetti according to package directions until al dente, drain, and set aside.
Meanwhile, in a large skillet over medium heat, heat oil. Add onions and cook until soft and translucent, about 5 minutes. Stir in garlic and cook until fragrant, 1 minute more. Add ground beef, season with salt, pepper, and Italian seasoning, and cook until no longer pink, about 8 minutes. Drain fat in a bowl lined with paper towels and return to pan. Pour in crushed tomatoes and basil and simmer until slightly reduced, about 10 minutes. Season with more salt and pepper to taste.
Toss with spaghetti, then transfer to baking dish and top with mozzarella and Parmesan. Bake until cheese is melted and pasta is warmed through, about 20 minutes.
Garnish with parsley and serve.


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The Tragedy of MacBeth
by William Shakespeare
Act 5, Scene 5
Additional Information
- Year Published: 1607
- Language: English
- Country of Origin: England
- Source: Richard Grant White, ed. The Complete Works of William Shakespeare (New York: Sully and Kleinteich)
-
Readability:
- Flesch–Kincaid Level: 11.0
- Word Count: 467
- Genre: Tragedy
- Keywords: betrayal, envy, murder, power, revenge, tragedy
- ✎ Cite This
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SCENE. Dunsinane. Within the castle.

(A cry of women within)
What is that noise?
SEYTON
It is the cry of women, my good lord.
(Exit)
MACBETH
I have almost forgot the taste of fears;
The time has been, my senses would have cool'd
To hear a night-shriek; and my fell of hair
Would at a dismal treatise rouse and stir
As life were in't: I have supp'd full with horrors;
Direness, familiar to my slaughterous thoughts
Cannot once start me.
(Re-enter SEYTON)
Wherefore was that cry?
SEYTON
The queen, my lord, is dead.
MACBETH
She should have died hereafter;
There would have been a time for such a word.
To-morrow, and to-morrow, and to-morrow,
Creeps in this petty pace from day to day
To the last syllable of recorded time,
And all our yesterdays have lighted fools
The way to dusty death. Out, out, brief candle!

(Enter a Messenger)
Thou comest to use thy tongue; thy story quickly.
Messenger
Gracious my lord,
I should report that which I say I saw,
But know not how to do it.
MACBETH
Well, say, sir.
Messenger
As I did stand my watch upon the hill,
I look'd toward Birnam, and anon, methought,
The wood began to move.
MACBETH
Liar and slave!
Messenger
Let me endure your wrath, if't be not so:
Within this three mile may you see it coming;
I say, a moving grove.
MACBETH
If thou speak'st false,
Upon the next tree shalt thou hang alive,
Till famine cling thee: if thy speech be sooth,
I care not if thou dost for me as much.
I pull in resolution, and begin
To doubt the equivocation of the fiend
That lies like truth: 'Fear not, till Birnam wood
Do come to Dunsinane:' and now a wood
Comes toward Dunsinane. Arm, arm, and out!
If this which he avouches does appear,
There is nor flying hence nor tarrying here.
I gin to be aweary of the sun,
And wish the estate o' the world were now undone.
Ring the alarum-bell! Blow, wind! come, wrack!

(Exeunt)