The Standard Questions category includes the following types of format: Multiple Choice, Text Entry, Rank Order, Matrix Table, Slider, and Side by Side - which are all described below. The questions can be used to accomplish most of the features offered in the Yale Form Builder, including the Text box, Paragraph Text, Check Boxes, Option Buttons, Select List, Street Address, Birthdate, and Calendar formats. These questions provide you with a variety of tools to gain the answers to simple questions.
For more information, visit the Qualtrics Support Website.
The Multiple Choice question allows you to format a question in which the respondent is prompted by several answer options. Qualtrics allows you to format this question to accept either a single response - using radio buttons, dropdown list, or select box - or multiple responses - collected using check boxes or a combination box. This allows you to achieve the same functionality you wold have been offered using the "Check Boxes," "Option Buttons," or "Select List," in the retired Form Builder.
To view how this function might appear in your form, please see Question 3 in the Example Survey.
The Text Entry option allows you to collect user text input for the answer to your question. These fields can be used for short answers, mid-length answers, and paragraph answers. This question serves the same purpose as the "Text Box" and "Paragraph Text" features in the retired Form Builder.
To view how this function might appear in your form, please see Question 4 in the Example Survey.
The Rank Order question type can be used to gather user preference in a series of options. For example, if you wanted to see on which day of the week people preferred to meet, users could drag and drop the listed days of the week into the order that most suited them.
To view how this function might appear in your form, please see Question 5 in the Example Survey.
The Matrix Table Questions allows for you to gather the same type of response for several items at a time, gathered within a Matrix layout. The Matrix layout can be manipulated in several ways, including Likert, Bipolar (or Double Axis), Rank Order, Constant Sum, Text Entry, Profile, or MaxDiff. For more information about this question type, please visit the Qualtrics Support page about Matrix Tables.
To view how this function might appear in your form, please see Question 6 in the Example Survey.
To view how this function might appear in your form, please see Question 7 in the Example Survey.
Side-by-Side questions allow the respondent to answer nuanced questions in a single line requiring more than one type of response. For example, a respondent is able to evaluate their opinion of a particular item using a Matrix format indicating Satisfaction, and then specify why they hold that particular opinion in a Text Entry format.


"Salmon patties (AIP/Paleo) | Food to Heal Ourselves"
Recently I slow cooked salmon fillets and then used the cooked salmon to make AIP salmon patties. Delicious success!
These AIP salmon patties are wonderful hot from the skillet and also terrific as leftovers right out of the fridge. The consistency is great and they hold together really well.
When I first started the autoimmune protocol I tried a number of times to make salmon patties and none of them were very appealing. The worst ones were when I substituted roasted yam for the potatoes from a pre-AIP recipe and then eliminated the egg and added coconut flour to hold them together. The patties broke into tiny chunks and they had a really sweet taste.
If you've been following my blog you know that I am budget conscious so these didn't get chucked out. I did eat them all up but without any help from my husband who normally eats everything in sight! The only way that I could tolerate them was if I broke a pattie apart and added small amounts to a veggie stir fry.
Last week I took these AIP salmon patties on a long drive (12 hours spread over two days) and this week another 11 hour drive all in one long day. These patties are yummy cold or at room temperature and perfect for a picnic on the road or on a hike.
That reminds me, on this trip I found another benefit of being on the autoimmune protocol. Check out my discovery!
Delicious salmon patties that are great hot or cold.
In a medium sized bowl combine flaked salmon, lemon juice and lemon zest, minced red onion, minced green onions and/or chives, salt (dill if using) and cassava flour.
In a small pot sprinkle one tablespoon gelatin on top of 1/4 cup water. Bring to a simmer stirring until the gelatin is dissolved.
Gently incorporate the gelatin egg into the salmon mixture.
Using a 1/2 cup measure, pack the salmon mixture into the measuring cup, then tap the contents out onto a sheet of wax paper or parchment paper.
Press the patties down until about 1/2 inch thick.
Add enough oil to cover the bottom of a skillet to about 1/4 inch.
Fry on low/medium heat for 5-6 minutes on each side.
Keeps well in the fridge for 2-3 days (if they last that long).
Avocado oil is the best for frying these patties.
I'd love to hear all about a great AIP salmon pattie recipe that you have created … leave a comment.
I used arrowroot and coconut, had to add more water to get it moist enough to hold, microwaved the water for the gelatin, and fried them in bacon fat. It tasted so good! I will try the fridge next time and see if they hold together better. I ate mine crumbled on romaine with radishes, balsamic, and oil.
These salmon patties taste delicious no matter what but, the only way that I could get them to hold together was to follow the recipe exactly. Great on tacos too I imagine.
Hi! What do you think would work instead of cassava? I gluten cross-react. I have arrowroot and coconut on hand. I believe I remember you from AIP Support Group. I'm Jenn JP, but I closed my account. Also, what is the point of refrigerating them after mixing and shaping? I have cold leftover salmon.
I found that if I refrigerated the patties they stayed together much better than if I just formed them and cooked them ... then they tended to fall apart. Using cold leftover salmon is what I usually do. I'm not sure about other flours ... I haven't had luck in trying to sub anything else. Bummer that you have a problem with cassava. Yes, I do remember you from the AIP support group. I found that Facebook group really helpful, especially in the beginning. Sorry that I couldn't be more help.


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Viola Davis is the first black actress to win Tony ("Fences" & "King Hedley II"), Oscar ("Fences") and Emmy ("How to Get Away with Murder") awards.
This year, Davis received an Oscar nomination for her transformative performance as Ma Rainey in Netflix's "Ma Rainey's Black Bottom." She also won a SAG award for the role and received a Golden Globe Award nomination and Critic's Choice Award nomination.
Davis will next be seen portraying Michelle Obama in Showtime's "The First Lady." She will also executive produce the limited series. Davis will soon begin production on "The Woman King." Based on true events and produced by Davis' JuVee Productions, the film tells the story of a general of an all-female West African military.
In 2019, Davis wrapped the final season of the ABC series "How to Get Away with Murder," from ABC Studios and Shondaland. In 2015, Davis received the Screen Actors Guild Award for "Outstanding Performance by a Female Actor in a Drama Series," in addition to becoming the first African American actress to receive the Primetime Emmy Award for "Outstanding Lead Actress in a Drama Series." In 2016, she received her second Screen Actors Guild Award and her second Emmy nomination for portraying Annalise Keating and in 2019, received her third Emmy Nomination for the role. In 2019, Davis was also seen in Amazon Studio's family comedy, "Troop Zero," directed by Bert & Bertie.
In 2018, Davis was seen staring in Fox's "Widows." Directed by Steve McQueen and written by Gillian Flynn, the film follows a group of widows after their criminal husbands are killed during a heist.
In 2017, she was seen in "Fences" for Paramount Pictures. The film was directed and produced by Denzel Washington and was adapted from August Wilson's play. Davis garnered Critics Choice, Golden Globe, SAG, BAFTA and anAcademy awards in the category of "Best Supporting Actress" for her portrayal of 'Rose Maxson.' Davis also starred with Washington in the 2010 revival of the play on Broadway. Her performance earned her a Tony Award, as well as the Drama Critics' Circle Award, Outer Critics Circle Award and Drama Desk Award. "Fences" was also honored with the Tony Awardfor "Best Play Revival" and was the most profitable theater production of the year.
In 2016, Davis was seen in the highly anticipated "Suicide Squad" with Will Smith, Margot Robbie and Jared Leto. The film broke the all-time opening record for August with $135 million. In 2015, Davis starred opposite Jennifer Lopez in "Lila & Eve," the story of two mothers whose children are killed in drive-by shootings. Davis also took the reins as a producer on this film, along with her husband, Julius Tennon, for their company, JuVee Productions.
In 2013, film audiences saw Davis in four vastly different projects. In the film adaptation of the popular science fiction novel "Ender's Game," Davis portrayed military psychologist 'Major Gwen Anderson,' opposite Asa Butterfield, Hailee Steinfeld, Abigail Breslin and Harrison Ford. In Warner Bros.' "Prisoners," a dark thriller about two families shattered by the kidnapping of their daughters on Thanksgiving Day, she starred alongside Jake Gyllenhaal, Hugh Jackman, Maria Bello, Terrence Howard and Paul Dano. In "Beautiful Creatures," Davis joined Emma Thompson and Jeremy Irons in telling the tale of two teens confronting a multi-generational curse. Warner Brothers and Alcon produced the film.
In 2012, Davis and her husband founded JuVee, with its focus being to give a voice to the voiceless through strong, impactful and culturally relevant narratives. The Los Angeles-based and artist driven company produces film, television and digital content across all of entertainment.
In 2012, Davis received an Oscar nomination for "Best Actress" for her portrayal of the heart-broken but stoic 'Aibileen Clark' in "The Help." Emma Stone, Bryce Dallas Howard and Octavia Spencer also starred. The film adaptation of Kathryn Stockett's best-selling novel, directed by Tate Taylor, was set in Jackson, Mississippi, during the turbulent 1960s, and was also nominated for an Academy Award for "Best Picture." Davis won the Screen Actors Guildand Critics' Choice "Best Actress" Awards for her portrayal of 'Aibileen,' and was also nominated for a Golden Globe and British Academy Film Award. The film won a Screen Actors Guild Award for "Outstanding Performance by a Cast in a Motion Picture" and a Critics' Choice Award for "Best Acting Ensemble."
In 2008, Davis starred in the critically revered film "Doubt" based on John Patrick Shanley's Tony Award winning play, where she portrayed 'Mrs. Miller,' the mother of a young boy who piques the fascination of a Catholic priest. Davis shared the screen alongside Meryl Streep, Amy Adams and Phillip Seymour Hoffman. Davis was nominated for a Golden Globe Award, Screen Actors Guild Award and an AcademyAward for "Best Supporting Actress." The National Board of Review recognized Davis with the Breakthrough Award and she was also honored by the Santa Barbara Film Festival as a Virtuoso.
That same year, Davis had a six-episode arc in the Showtime's hit series "United States of Tara," written by Academy-Award winner Diablo Cody. Davis portrayed 'Lynda P. Frazier,' a wildly eccentric artist and friend to 'Tara' (Toni Collette).
Davis won the Independent Spirit Award for "Best Supporting Female" for her performance in "Antwone Fisher." Additional film credits include, "Get On Up," "Blackhat," "Extremely Loud and Incredibly Close." "Knight and Day," "Nights in Rodanthe," "Madea Goes to Jail," "Law Abiding Citizen," "Disturbia," "Eat, Pray, Love," "It's Kind of a Funny Story," "The Architect," "Never Back Down" and "Far From Heaven." She worked with director Steven Soderbergh on "Solaris," "Traffic" and "Out of Sight," and in "Syriana," which Soderbergh produced for director Stephen Gaghan.
In 2004, Davis starred in the stage in the Roundabout Theatre Company's production of Lynn Nottage's play, "Intimate Apparel," directed by Daniel Sullivan. She garnered the highest honors for an off-Broadway play, including "Best Actress" awards from the Drama Desk, the Drama League, the Obie and the Audelco Award. Davis was nominated for the Lucille Lortel Award as well. She reprised her role at the Mark Taper Forum in Los Angeles where she was recognized with the Ovation, Los Angeles Drama Critics and the Garland Awards.
In 2001, Davis was awarded a Tony for "Best Performance by a Featured Actress in a Play" for her portrayal of 'Tonya' in "King Hedley II." She commanded the attention of critics and audiences alike for her portrayal of "Tonya," a 35-year old woman who is forced to fight for the right to abort an unwanted pregnancy. Davis also received a Drama Desk Awardin recognition of her work.
A graduate of The Julliard School, Davis received an Honorary Doctorate during its 109th Commencement Ceremony and she also holds an Honorary Doctorate of Fine Arts degree from her alma mater, Rhode Island College. She resides in Los Angeles with her husband and daughter.
Peter and the Wolf with Viola Davis and Dudamel

redpajama_set_name: RedPajamaCommonCrawl


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How Does Solar Energy Benefit The Environment Your Health And Your Wealth
How solar energy benefits the environment, your health and your wealth?
Published on November 14, 2018 by Zeeshan Hyder
Categories: Solar energy, Solar panels, Utilities
Everyone knows that using solar energy is good for the environment. However, many homeowners have no idea how big of a contribution to a greener world they are making.
According to the Pew Research Center, 92% of people who have already installed solar panels or plan to install them say saving money on utility bills is their primary motivator, but the environment comes in at a strong number two at 87% of those surveyed.
How is solar energy beneficial to the environment?
Burning fossil fuels releases toxic gasses into the atmosphere. This contributes to global warming. Solar panels produce electricity with zero emissions. Solar power reduces the "greenhouse gas effect" around the world by eliminating the need for the same power to be produced using fossil fuels.
Most electricity in the United States burning coal or natural gas. The downside to this is that it increases global warming. Global warming has many negative effects some of which we are already seeing such as the polar ice caps melting and increased intensity of storms caused by greater energy in the atmosphere.
Plus, using the energy from the sun doesn't involve drawing it out of the sun itself. The sun's energy is going to hit the earth anyway - so solar energy will last as long as the sun does. Fossil fuel resources are finite. As they become harder to extract, they are going to become more expensive.
What is global warming and how does it work?
Global warming is the warming of the earth caused by too much carbon dioxide and other harmful gasses in the atmosphere increasing its density. If you put a blanket over your body, it reduces how much of your body heat escapes causing you to become warmer. The concept is the same as the earth. The higher density of harmful gasses in the atmosphere reduces how much heat can escape from the earth.
How does widespread solar adoption improve my health?
Air pollutants from the burning of fossil fuels has been linked to a variety of respiratory problems, neurological damage, and heart attacks. Solar power reduces the need for these fuels in the first place. Every solar panel installed slightly reduces the amount of fossil fuels that need to be burned to generate electricity.
For a free estimate on the cost, savings and environmental benefits of installing solar on your home enter your zip code below.
How much climate change has happened already?
Here is a graph from the leading government meteorological agencies around the world showing that the globe has already warmed by around 33.08°F (0.6°C) since 1980.
It is no longer prediction — it is now recorded fact that the globe is warming.
Image source: NASA Climate 365
What all of this means for us is that at some point — greater action will be required on climate change than what is currently the case.
Given 39.2°F (4°C) is considered a global extinction event further action on climate change and a ban on the use of fossil fuels is coming. Power prices will go up as systems need to be reconfigured to balance energy demands with environmental impacts. The result? Your solar system will be even more valuable than it is now - your savvy investment will yield greater savings.
Research is still happening regarding the health issues associated with increased average temperatures and the other impacts of global warming. They could be far more wide-ranging as global warming starts to affect food production and air quality
How can solar panels reduce climate change?
Transportation accounts for 28% of greenhouse gas emission in the US. So a move to electric vehicles will be an important part of fighting climate change. The recent drop in Tesla prices with the release of the Tesla Model 3 has made electric vehicles more attractive to consumers. And other manufacturers are taking notice!
Over the coming years, there will be a whole host of new electric vehicles on the US market. The price of EV's will fall and, before long, you will be test driving one. However, electric vehicles on their own will do nothing to fight climate change if the electricity used to power them is still produced by burning fossil fuels. This where solar panels will come into their own. Electric vehicles use kilowatt hours of electricity, not gallons of gas, and the simple fact is that on a per kilowatt-hour basis solar is already much cheaper than grid power.
At a solar cost of $3.50 per watt, the levelized cost of solar power (cost of the system divided by total kWh's produced over the life of the system) is around 7 cents per kilowatt-hour.
Compare this to the average price of grid power in the US which is currently around 13 cents per kWh, and as high as 20 cents per kilowatt-hour in California and much of the Northeast! This price differential means it makes both economic and environmental sense to use solar energy to power both your house and your car. If you do this by installing solar panels and buying an electric car - you will be reducing the carbon footprint of your life by a whopping 74%!
There are two other trends that are changing the game when it comes to powering your home with solar panels. The first is the introduction of the Tesla solar roof. This innovative technology is a giant leap forward for people who want the benefits of solar without detracting from the aesthetics of their homes or commercial buildings.
These solar roof tiles are designed to look exactly like the non-active roof tiles except they will power your home! The downside is that currently, the Tesla solar roof cost is about triple the cost of regular Tesla solar panels. However, other companies are working on similar solar shingles, and competition always means technological advances and lower prices.
The other important trend that is tipping the decision for consumers to go solar is the fact that solar batteries are becoming more widely available and more affordable. Again this trend has been led by Tesla's release of the Powerwall 2, the latest Tesla battery.
Savings on power bills, reducing the impact on the environment and contributing to improved health - all of these factors are attracting people to solar panels. And, as fossil fuels become scarcer and more challenging to mine, solar is a way to future-proof your lifestyle as soon as possible. If this is adding up for you - save even more while you can! The 30% federal solar tax credit can still pay for a significant part of your system. However, this solar tax credit steps down at the end of 2019. Get started now - enter some details into the solar panels calculator and generating an online solar estimate tailored to your home.
Help reduce climate change
Find out how much it will cost to install solar panels in your area. See if you are eligible for the 30% federal solar tax credit


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Q: How can I set the test path in the config for blanket.js I currently have all my tests in the root/test folder. Using blanket.js for code coverage works 100%. I want to move some of my tests into sub folders for example root/test/domainTests. However when doing this blanket.js does not find the test to run the coverage on. Is there an option i can set to point blanket.js to my tests? I have had a look around their site but cannot find anything useful.
current code in package.json - pattern points to my app files, not to the test files.
"config": {
  "blanket": {
    "pattern": "/domain/",
    "data-cover-never": "node_modules"
  }

I call it from the command line as follows
'mocha -r blanket -R html-cov > coverage.html'

A: Put the proper paths in in package.json file. In your example you are pointing to a folder, I think it needs to be a path that can locate the files.
"config": {
    "blanket": {
        "pattern": [
            "test/test1.js"
            "test/domain/test2.js"
        ]
    }
}

Also notice that you can use array for pattern property value if you need to specify more than one value (i.e. you moved only some of the tests, not all). Also wildcards (*) should also work for pattern values.

redpajama_set_name: RedPajamaStackExchange


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Our portfolio companies share the passion for addressing some of the most challenging problems of our time through elegant, ambitious and intellectually daring solutions.
Cadence Health
Cadence Health is a US-based women's health pharma startup working to make a cost-effective birth control pill available over the counter in the United States, without a prescription which is currently universally required. Its goal is to disrupt the U.S. contraceptive market while creating easy and affordable access to contraceptives nationwide.
Whiz.ai
Whiz.ai is a pre-trained conversational AI platform that connects business users with their enterprise data, insights and workflows via speech and text on web and mobile. Its AI solutions pave the way for the workplace of the future where tech-mediated interactions will be as seamless and intuitive as unmediated human speech.
Mango Materials
Mango Materials - manufactures bio-based, biodegradable polymers using a patent-protected biological process that transforms methane into the bioplastic PHA for various use, including fibers (apparel and textiles), injection molding (packaging), sheeting (garbage bags) and 3D printing.
X2.AI
X2.AI is an AI-based platform providing 24/7 accessible help to those who need it, including people with depression and anxiety, or those in need of life coaching.
IPDx
IPDx enables to decipher the genetic code of our immune system and uses state of art technology in order to develop drugs and diagnostics that are truly personalised and patient specific.
Elemeno Health
Elemeno Health is a cloud-based solution for front-line healthcare teams. Its software-as-a-service platform helps organisations to scale training and democratises access to best practices by enabling every healthcare professional to provide the best care possible to every life they touch.
NextProtein
NextProtein uses insect-based protein to offer a sustainable animal feed solution with close to zero carbon footprint in a bid to boost sustainable agriculture and address resource scarcity.
The Shellworks
The Shellworks produces packaging made from a biopolymer found in shellfish waste. This packaging is biodegradable, home compostable and cost competitive, providing an alternative to plastic that is environment-friendly.
Shiok Meats
Shiok Meats produces sustainable and healthy cultured shrimp and crustaceans by harvesting it from cells instead of animals and offers clean alternatives to the shrimp farming industry.
Ecomedes
Ecomedes is a cloud-based software and sustainability as a service solution that provides clients access to a variety of information including building standards, product certifications, and energy and water use statistics. Ecomedes 'platform combines data, AI, and workflow components to deliver a three-sided marketplace for manufacturers, contractors and owners to help enterprises reduce costs and environmental impact through optimized sustainable product purchasing.
Cultivo is a land restoration investment platform bridging together corporations with land owners. It unlocks millions of hectares of land for restoration projects that will remove significant amounts of CO2 emissions from the atmosphere.
Xeal
Xeal is a cloud software management platform that provides drivers with smart scheduling solution to make charging as automated and effortless as possible.
NatureMetrics
NatureMetrics is a pioneer in the development and application of Environmental DNA sampling techniques which can be used to monitor and therefore protect biodiversity in habitats worldwide.
Reach52
Reach52 is a last-mile delivery company, providing accessible and affordable healthcare services in the Philippines, Cambodia, India and Kenya.
Anaphite
Anaphite is a battery materials company who have developed a way to integrate graphene into battery electrode materials, improving battery's performance and enabling acceleration of electric vehicle adoption.
Umami Meats
Umami Meats is an advanced materials company developing a growth media for cultured meat and seafood.
Copyright © 2019 www.impactventure.com. All rights reserved.


Mira Rajput, a name that has not been heard of much in India or elsewhere, has suddenly become the talk of town after it was revealed that the 21-year-old resident of Delhi got married to Bollywood heartthrob Shahid Kapoor earlier today. Shahid Kapoor, who was reportedly dating Mira, who at 34 years old is 13 years older than the 21-year-old actor, had kept his relationship with Mira under wraps for a considerable time. However, media reports had started circulating about Shahid's relationship with Mira a few months ago. In fact, several reports also confirmed that the couple were planning to get married soon.
A Times of India report has earlier said that the couple were planning to get married in May. However, it seems Shahid and Mira wanted to give everyone a surprise with their sudden marriage in July.
The much talked-about Shahid-Mira wedding had an exclusive invitee list and only close family and friends were invited. Apparently, Mira, whose family does not have any Bollywood connections whatsoever, wished to keep the ceremony a strictly private affair. However, soon after the weddings, photos of the brand new Bollywood couple were flashed across social media websites. Several invitees at the wedding venue also helped the cause by posting pictures of Mira and Shahid together.
Until a few months ago, very little was known about Mira Rajput and her family. Several reports add that the marriage was a completely arranged affair and that the couple met via their parents and family. What we now know about Mira Rajput is that she is reportedly an English Honors student at the prestigious Delhi University. After the wedding was finalized, the couple had a small engagement ceremony in which Mira was reportedly gifted a ring worth Rs. 23 lakhs ($36,000) by Shahid. The couple also plan to move into a new sea-facing home in Mumbai. Mira Rajput has been apparently helping Shahid with the interiors of their new home as well. Meanwhile, it has also been reported that Mira does not happen to be a keen Bollywood fan. In fact, she hasn't even watched many movies in which Shahid has performed.
Shahid Kapoor has in the past been linked to several Bollywood actresses. He was also involved in a much talked-about five-year relationship with Kareena Kapoor. Following their break-up, he was linked to Priyanka Chopra, Vidya Balan, and of late, Sonakshi Sinha.
However, Shahid finally seems to have found the love of his life in Mira Rajput — who will from today be known in B-Town as Mira Rajput Kapoor.


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HomeNBA
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Shaquille O'Neal on How Tom Cruise and Eddie Murphy Inspired Him: "This Is What a Superstar Is Supposed to Be Like"
Published 01/02/2022, 10:08 PM EST
NEW YORK, NY – OCTOBER 05: Former professional basketball player/author Shaquille O'Neal speaks on stage during LIVE from the NYPL: Shaquille O'Neal held at the New York Public Library – Stephen A. Schwartzman Building on October 5, 2015 in New York City. (Photo by Brent N. Clarke/Getty Images)
By Chaitanya Dadhwal
Shaquille O'Neal has undoubtedly achieved legendary status in NBA history. Arguably from the very onset of his NBA career, the media and experts knew he was something special. However, it took a bit more than just being himself for him to get that status. He also needed a tad bit of inspiration from the greatest people to grace the entertainment industry.
Tom Cruise is undoubtedly one of the biggest names when it comes to action movies in Hollywood. Eddie Murphy is a world-renowned figure for his comedic genius and work in the industry. Surprisingly, it turns out that these two iconic celebrities had something to do with O'Neal's career. Apparently, it's all due to the Lakers' Legend Magic Johnson. This is what Shaq said about his inspiration from Cruise and Murphy.
Shaquille O'Neal on how Tom Cruise and Eddie Murphy inspired him
When Shaq appeared on GaryVee's latest podcast, he revealed what he learned from Magic Johnson's lifestyle. He said "I was a local star in San Antonio. I was a local star in Baton Rouge, but this guy is a superstar. Magic would take me to the clubs and I see Tom Cruise and Eddie Murphy and all these people pull up and I was like, this is what a superstar is supposed to be like."
Los Angeles Lakers Magic Johnson, right, is guarded by Michael Jordan of the Chicago Bulls in the 1991 NBA Finals in Los Angeles. (Photo by Al Seib/Los Angeles Times via Getty Images)
Shaquille O'Neal always knew that the fundamental difference between a star and a superstar was their mindset. Shaq acknowledged he knew he was just a local celebrity early in his career. He had to learn from the best, and the best back then was Magic Johnson.
Charles Barkley Once Dropped Truth Bombs as Michael Jordan Heard in Silence: "If I Had Dennis Rodman and Scottie Pippen, I Would Have Won Some Too"
What did this mean for Shaquille O'Neal's career?
To date, Shaq gives a lot of credit for his business ventures to Magic Johnson. It was Magic who got him thinking about what he wanted to do with his future.
LOS ANGELES, CA – MARCH 14: TV personality/retired basketball player Shaquille O'Neal onstage at The Comedy Central Roast of Justin Bieber at Sony Pictures Studios on March 14, 2015 in Los Angeles, California. The Comedy Central Roast of Justin Bieber will air on March 30, 2015 at 10:00 p.m. ET/PT. (Photo by Kevin Winter/Getty Images)
Since Magic Johnson was dominating the NBA, Shaquille O'Neal had no option but to listen. Indeed, his advice changed his life and elevated him to the level of superstardom that he has achieved today. To be a legend, you've got to follow what the legends do.
Watch This Story: NBA Players Taller than Lakers Giant Shaquille ONeal
Chaitanya Dadhwal
Chaitanya Dadhwal is an NBA writer at EssentiallySports. Even though his journalism is driven by his desire to understand both sides of an argument and give a neutral perspective, he is biased towards Houston Rockets and Arsenal, and he has shown exactly how loyal he can be through tough times. Currently a Law student at Jindal Global Law School, he aims to have his own sports management firm one day and enjoys rap music during his time off.


In this episode, we sit down with Rabbi Daniel Lapin. He enlightens us on the spiritual nature of money and the tragedy of resentment.
Rabbi Daniel Lapin is the author of Thou Shall Prosper and Business Secrets from the Bible, among many other works. He is the President of The American Alliance of Jews and Christians and is an exceptionally gifted communicator.
Money is proof you served another human being well.


You may have heard that Social Security is broke, bankrupt, insolvent, or some other variation. This is simply untrue. In fact, Social Security has trillions of dollars in reserves and is expected to run a surplus this year. Here's the truth about Social Security's finances and what to expect for the future.
This video was recorded on Nov. 27, 2017.
Michael Douglass: Let's head on over to myth No. 5. This one is kind of a different myth from the others, because if people believe it, it might be in some ways better for them. We'll get to that in a minute. Myth No. 5: Social Security is going broke.
Matt Frankel: Yeah. This is one that, in all honesty, I asked Michael to include in the show because it bothers me a lot, just because of the sheer number of people, especially the younger generation, who believe it. The reality is, Social Security has nearly $3 trillion in reserves. That's enough that if we stopped charging Social Security tax and did nothing else, it could sustain the program for several years. Not only that, Social Security is actually running a surplus right now. Social Security brought in, I think, about $35 billion more than it paid out last year, and it's expected to do the same over the next five or six years. It's beyond that where the problem is. So there's some truth to this myth in that eventually, the number of retirees is going to grow, and it's going to be paying out a little bit more. But Social Security is not close to broke right now. The latest forecasts say, by 2044 is when it will run out of reserves. And keep in mind, there's still going to be payroll tax flowing into it. So, worst-case scenario, you'll see Social Security benefits have to be cut by a quarter. Social Security is not going completely broke or bankrupt, or whatever people want to say these days. It's just not true. That's almost $3 trillion and growing in Social Security's reserves.
Douglass: Yeah. What's interesting about this issue is, as you pointed out, even in the worst-case scenario, which is that no one -- not Congress, not anybody else -- no one figures out a way to fix the system or do things differently, and no one does anything at all, eventually the reserves will run dry. That is true. That's definitely going to happen at some point. But, even so, that would just necessitate a benefit cut. Of course, a benefit cut is a bad thing, to be clear. But it's not nearly as bad as Social Security going completely insolvent and not paying out at all. That's just really not on the table.
What's interesting about this is, at least for me, and Matt, I think this is probably true for you, too, I actually try to invest as if Social Security were going broke. Basically, my idea is to have enough money for retirement so that I'm not dependent on Social Security. I'm not thinking about Social Security. So whatever amount they're paying in benefits is gravy. It's icing on the cake. It's "insert other food metaphor." Instead, I can merely focus on living through my golden years and really enjoying them, and not be concerned about what might happen with a funding mechanism or a cost of living adjustment for Social Security, but really have enough in my savings that that's just not a concern.
Frankel: Right. This was what Michael was talking about, how this could actually be a good myth, in many ways, if you believe it. I'm 35. If I believe Social Security isn't going to be there when I retire, then I'm going, "Oh no, I'd better save a lot more for my retirement and build up a nice nest egg," which is honestly what people should be doing anyway. So in many ways, this is a very good myth, in terms of the effect that it could have.


text:
Q: Winamp with gen_scripting in Jscript Using the gen_scripting plugin for Winamp you must instantiate the object. In VBS this is
Set winamp = CreateObject("gen_scripting.WinAmp")

However, I'm pretty sure that the same plug-in can be used to script Winamp using Jscript (or possibly even Javascript - a better choice for me). So, I've tried
var winamp = Object.create("gen_scripting.WinAmp");
x = winamp.GetVersion();
alert(x);
winamp.ButtonPlay;

Only that doesn't work. Any help to get me started would be appreciated

A: JScript analog of VBScript CreateObject() is new ActiveXObject().
Also, alert is a browser-specific function; in Windows shell scripts you should use WScript.Echo.
var winamp = new ActiveXObject("gen_scripting.WinAmp");
var x = winamp.GetVersion();
WScript.Echo(x);
winamp.ButtonPlay();


text:
Opened Book: Women's Stories by Washington Area Artists
by Felix Gillette April 13th, 2001 August 29th, 2020
Drunken Cookies, a sculpture by Zoe Leoudaki, looks like the leftover junk from an apartment renovation: It's basically strips of jagged wood bristling with nails, hung in a cross-shaped formation. And it's uninviting—until you hear a voice speaking softly from behind the spiky façade. Once your face comes within impaling range of the nails, you hear a recording of a woman you don't know, reading a recipe for cookies you've never eaten, in a language you don't understand. It's intriguing. A good sculpture, like a good book, tells a story you can't ignore—in this case, one of a guarded Greek matriarch who lets her defenses down only when she's cooking. "Opened Book: Women's Stories by Washington Area Artists," an exhibition by 16 women, presents a set of narrations, like that of Leoudaki's grandmother (the woman whose voice beckons you toward the sculpture), which are told with tactile structures. The exhibit is on view from 11 a.m. to 4 p.m. at the University of Maryland's Art Gallery, University Boulevard and Adelphi Road, College Park. Free. (301) 405-2763. (Felix Gillette)


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In this episode: Greg suddenly loses his voice and Jennifer steps in just in time to help out and shares her thoughts on living just for "this day." This podcast is courtesy of SiriusXM 129 (The Catholic Channel). You can get a full three hours of The Catholics Next Door, Monday through Friday from 1 to 4PM EST, only from Sirius/XM. Sign up today!


text:
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redpajama_set_name: RedPajamaGithub


text:
NAKO and Ukroboronprom Have Reestablished Dialogue on the International Audit of Ukroboronprom
18 June 2018 the NAKO met with management and the Supervisory Board of the state defence establishment Ukroboronprom to discuss further dialogue on the international audit of Ukroboronprom. The Independent Defence Anti-Corruption Committee is in favour of the revised tender for an independent audit and strategic analysis of Ukroboronprom, said NAKO Co-Chair Drago Kos. Drago Kos, NAKO Co-Chair Independent audit of Ukroboonprom due to OECD standards is in the interest of the Ukrainian society and the Ukrainian army as it will help make company's activity transparent but first and foremost in the interest of Ukroboronprom itself as it will help the state defence establishment to regain trust and attract investors. The independent international tender is supposed to bring light to inefficiencies, corruption risks and structural deficits at Ukroboronprom.
The NAKO provided recommendations to Ukroboronprom's Supervisory Board about what should be included in the tender, and the Supervisory Board revised it in line with these recommendations. For the time being, auditors companies "Baker McKenzie", "Baker Tilly", "Ernst & Young" have already submitted their bids for the tender. However, during the tender call some technical problems have appeared, due to this the call for bidding was extended till 28 September 2018. Ukroboronprom also made some changes to the tender documentation to enlarge the number of potential applicants. The NAKO has monitored the Ukroboronprom's way towards reform since 2017 as an independent observer. Because by the end of 2017 the Supervisory Board was not established, NAKO withdrew from engagement with UOP citing stalled reform efforts. At the beginning of 2018, the Supervisory Board was reestablished and the NAKO recommendations were included to the tender documentation. This made further dialogue between the NAKO and Ukroboronprom possible.


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text:
Home » Science & Mathematics » On the Origin of Species By Means of Natural Selection
On the Origin of Species By Means of Natural Selection
by: Charles Darwin
On the Origin of Species By Means of Natural Selection ebook epub/pdf/prc/mobi/azw3 download for Kindle, Mobile, Tablet, Laptop, PC, e-Reader. Author: Charles Darwin
in: Science & Mathematics
On the Origin of Species By Means of Natural Selection – Charles Darwin
Download On the Origin of Species By Means of Natural Selection ebook. On the Origin of Species (or more completely, On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life), published on 24 November 1859, is a work of scientific literature by Charles Darwin which is considered to be the foundation of evolutionary biology. Darwin's book introduced the scientific theory that populations evolve over the course of generations through a process of natural selection. It presented a body of evidence that the diversity of life arose by common descent through a branching pattern of evolution. Darwin included evidence that he had gathered on the Beagle expedition in the 1830s and his subsequent findings from research, correspondence, and experimentation.
Various evolutionary ideas had already been proposed to explain new findings in biology. There was growing support for such ideas among dissident anatomists and the general public, but during the first half of the 19th century the English scientific establishment was closely tied to the Church of England, while science was part of natural theology. Ideas about the transmutation of species were controversial as they conflicted with the beliefs that species were unchanging parts of a designed hierarchy and that humans were unique, unrelated to other animals. The political and theological implications were intensely debated, but transmutation was not accepted by the scientific mainstream.
The book was written for non-specialist readers and attracted widespread interest upon its publication. As Darwin was an eminent scientist, his findings were taken seriously and the evidence he presented generated scientific, philosophical, and religious discussion. The debate over the book contributed to the campaign by T. H. Huxley and his fellow members of the X Club to secularise science by promoting scientific naturalism. Within two decades there was widespread scientific agreement that evolution, with a branching pattern of common descent, had occurred, but scientists were slow to give natural selection the significance that Darwin thought appropriate. During "the eclipse of Darwinism" from the 1880s to the 1930s, various other mechanisms of evolution were given more credit. With the development of the modern evolutionary synthesis in the 1930s and 1940s, Darwin's concept of evolutionary adaptation through natural selection became central to modern evolutionary theory, and it has now become the unifying concept of the life sciences.
Charles Darwin was born in England in 1809 and attended the University of Edinburgh to study medicine. When he decided against that vocation, he enrolled at Cambridge where he earned a degree in theology. During an expedition to Africa and South America, Darwin continued his studies in natural science and began writing about his theories of natural selection. His work led to the publication of On the Origin of Species, a book that changed the world.
Charles Darwin: Original Thinking
Each generation of students comes to Darwin's epoch-making works, several of which are the basis of our publishing program in biology and related fields: The Essential Darwin, 2006; The Descent of Man, 2010; The Expression of the Emotions in Man and Animals, 2006; and On the Origin of the Species, 2006.
In the Author's Own Words:
"A mathematician is a blind man in a dark room looking for a black cat which isn't there."
"I feel most deeply that this whole question of Creation is too profound for human intellect. A dog might as well speculate on the mind of Newton! Let each man hope and believe what he can."
"Ignorance more frequently begets confidence than does knowledge: it is those who know little, not those who know much, who so positively assert that this or that problem will never be solved by science."
"There is grandeur in this view of life, with its several powers, having been originally breathed into a few forms or into one; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved."
"Man with all his noble qualities, with sympathy which feels for the most debased, with benevolence which extends not only to other men but to the humblest living creature, with his god-like intellect which has penetrated into the movements and constitution of the solar system — with all these exalted powers — Man still bears in his bodily frame the indelible stamp of his lowly origin." — Charles Darwin
On the Origin of Species By Means of Natural Selection ebook pdf, epub, mobi, prc
Free Download On the Origin of Species By Means of Natural Selection ebook pdf, epub, mobi, prc
1 On the Origin of Species By Means of Natural Selection – Charles Darwin
3 Charles Darwin: Original Thinking
4 In the Author's Own Words:
Keywords: AZW3, download free ebook, ebook, epub, free books online, free ebook, mobi, PDF
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text:
\section{Introduction}
Networks are ubiquitous, with prominent examples including social networks (\emph{e.g.}, Facebook, Twitter) or citation networks of research papers (\emph{e.g.}, arXiv).
When analyzing data from these real-world networks,
traditional methods often represent vertices (nodes) as one-hot representations (containing the connectivity information of each vertex with respect to all other vertices), usually suffering from issues related to the inherent sparsity of large-scale networks.
This results in models that are not able to fully capture the relationships between vertices of the network \cite{perozzi2014deepwalk, tu2016max}.
Alternatively, network embedding (\emph{i.e.}, network representation learning) has been considered, representing each vertex of a network with a \emph{low-dimensional vector} that preserves information on its similarity relative to other vertices.
This approach has attracted considerable attention in recent years \cite{tang2009relational, perozzi2014deepwalk, tang2015line, grover2016node2vec, wang2016structural, chen2016incorporate, wang2017community, zhang2018diffusion}.

Traditional network embedding approaches focus primarily on learning representations of vertices that preserve local structure, as well as internal structural properties of the network.
For instance, Isomap \cite{tenenbaum2000global}, LINE \cite{tang2015line}, and Grarep \cite{cao2015grarep} were proposed to preserve first-, second-, and higher-order proximity between nodes, respectively.
DeepWalk \cite{perozzi2014deepwalk}, which learns vertex representations from random-walk sequences, similarly, only takes into account structural information of the network.
However, in real-world networks, vertices usually contain rich textual information (\emph{e.g.}, user profiles in Facebook, paper abstracts in arXiv, user-generated content on Twitter, \emph{etc}.), which may be leveraged effectively for learning more informative embeddings.

\begin{figure}[!t]
	\centering
	\includegraphics[width=.3\textwidth]{figure/example_2.pdf}
	\caption{Example of the text information (abstracts) associated to two papers in a citation network. Key words for matching are highlighted.}
	\label{fig:example}
	\vspace{-3mm}
\end{figure}

To address this opportunity, \citet{yang2015network} proposed text-associated DeepWalk, to incorporate textual information into the vectorial representations of vertices (embeddings).
\citet{sun2016general} employed deep recurrent neural networks to integrate the information from vertex-associated text into network representations.
Further, \citet{tu2017cane} proposed to more effectively model the semantic relationships between vertices using a mutual attention mechanism.

Although these methods have demonstrated performance gains over structure-only network embeddings, the relationship between text sequences for a pair of vertices is accounted for solely by comparing their sentence embeddings.
However, as shown in Figure~\ref{fig:example}, to assess the similarity between two research papers, a more effective strategy would compare and align (via local-weighting) individual important words (keywords) within a pair of abstracts, while information from other words (\emph{e.g.}, stop words) that tend to be less relevant can be effectively ignored (down-weighted).
This alignment mechanism is difficult to accomplish in models where text sequences are first embedded into a common space and then compared in pairs \cite{he2016pairwise, parikh2016decomposable, Wang2016ACM, wang2017bilateral, shen2018context}.

We propose to learn a semantic-aware Network Embedding (NE) that incorporates word-level alignment features abstracted from text sequences associated with vertex pairs.
Given a pair of sentences, our model first aligns each word within one sentence with keywords from the other sentence (adaptively up-weighted via an attention mechanism), producing a set of fine-grained matching vectors.
These features are then accumulated via a simple but efficient aggregation function, obtaining the final representation for the sentence.
As a result, the word-by-word alignment features (as illustrated in Figure~\ref{fig:example}) are explicitly and effectively captured by our model.
Further, the learned network embeddings under our framework are adaptive to the specific (local) vertices that are considered, and thus are context-aware and especially suitable for downstream tasks, such as link prediction.
Moreover, since the word-by-word matching procedure introduced here is highly parallelizable and does not require any complex encoding networks, such as Long Short-Term Memory (LSTM) or Convolutional Neural Networks (CNNs), our framework requires significantly less time for training, which is attractive for large-scale network applications.

We evaluate our approach on three real-world datasets spanning distinct network-embedding-based applications: link prediction, vertex classification and visualization.
We show that the proposed word-by-word alignment mechanism efficiently incorporates textual information into the network embedding, and consistently exhibits superior performance relative to several competitive baselines.
Analyses considering the extracted word-by-word pairs further validate the effectiveness of the proposed framework.

\section{Proposed Methods}
\subsection{Problem Definition}
A network (graph) is defined as $\boldsymbol{G} = \{\boldsymbol{V},\boldsymbol{E}\}$, where $\boldsymbol{V}$ and $\boldsymbol{E}$
denote the set of $N$ vertices (nodes) and edges, respectively, where elements of $\boldsymbol{E}$ are two-element subsets of $\boldsymbol{V}$.
Here we only consider undirected networks, however, our approach (introduced below) can be readily extended to the directed case.
We also define $\boldsymbol{W}$, the symmetric $\mathbb{R}^{N \times N}$ matrix whose elements, $w_{ij}$, denote the weights associated with edges in $\boldsymbol{V}$, and $\boldsymbol{T}$, the set of text sequences assigned to each vertex.
Edges and weights contain the structural information of the network, while the text can be used to characterize the semantic properties of each vertex.
Given network $\boldsymbol{G}$, with the \emph{network embedding} we seek to encode each vertex into a low-dimensional vector $\hv$ (with dimension much smaller than $N$), while preserving structural and semantic features of $\boldsymbol{G}$.

\subsection{Framework Overview}
To incorporate both structural and semantic information into the network embeddings, we specify two types of (latent) embeddings: ($i$) $\hv_s$, the \emph{structural embedding}; and ($ii$) $\hv_t$, the \emph{textual embedding}.
Specifically, each vertex in $\boldsymbol{G}$ is encoded into a low-dimensional embedding $\hv = [\hv_s; \hv_t]$.
To learn these embeddings, we specify an objective that leverages the information from both $\boldsymbol{W}$ and $\boldsymbol{T}$, denoted as
\begin{align}\label{eq:obj}
	\mathcal{L} = \sum_{\boldsymbol{e} \in \boldsymbol{E}} \mathcal{L}_{\textrm{struct}}(\boldsymbol{e}) + 	\mathcal{L}_{\textrm{text}}(\boldsymbol{e}) + \mathcal{L}_{\textrm{joint}}(\boldsymbol{e}) \,,
\end{align}
where $\mathcal{L}_{\textrm{struct}}$, $\mathcal{L}_{\textrm{text}}$ and $\mathcal{L}_{\textrm{joint}}$ denote structure, text, and joint structure-text training losses, respectively.
For a vertex pair $\{v_i,v_j\}$ weighted by $w_{ij}$, $\mathcal{L}_{\textrm{struct}}(v_i, v_j)$ in \eqref{eq:obj} is defined as \cite{tang2015line}
\begin{align}\label{eq:str}
	\mathcal{L}_{\textrm{struct}}(v_i, v_j) = w_{ij} \log p(\hv^i_s|\hv^j_{s}) \,,
\end{align}
where $p(\hv^i_s|\hv^j_{s})$ denotes the conditional probability between structural embeddings for vertices $\{v_i,v_j\}$.
To leverage the textual information in $\boldsymbol{T}$, similar text-specific and joint structure-text training objectives are also defined
\begin{align}\label{eq:text}
	\mathcal{L}_{\textrm{text}}(v_i, v_j) & = w_{ij} \alpha_1 \log p(\hv^i_t|\hv^j_{t}) \,, \\
	\mathcal{L}_{\textrm{joint}}(v_i, v_j) & = w_{ij} \alpha_2 \log p(\hv^i_t|\hv^j_{s}) \\
	& + w_{ij}\alpha_3 \log p(\hv^i_s|\hv^j_{t}) \,,
\end{align}
where $p(\hv^i_t|\hv^j_t)$ and $p(\hv^i_t|\hv^j_s)$ (or $p(\hv^i_s|\hv^j_t)$) denote the conditional probability for a pair of text embeddings and text embedding given structure embedding (or \emph{vice versa}), respectively, for vertices $\{v_i,v_j\}$.
Further, $\alpha_1$, $\alpha_2$ and $\alpha_3$ are hyperparameters that balance the impact of the different training-loss components.
Note that
structural embeddings, $\hv_s$, are treated directly as parameters, while the text embeddings $\hv_t$ are learned based on the text sequences associated with vertices.

For all conditional probability terms, we follow \citet{tang2015line} and consider the second-order proximity between vertex pairs.
Thus, for vertices $\{v_i,v_j\}$, the probability of generating $\hv_i$ conditioned on $\hv_j$ may be written as
\begin{align}\label{eq:def}
	p(\hv^i|\hv^j) = \frac{\exp\left({\hv^j}^T \hv^i\right)}{\textstyle{\sum}_{k=1}^{N}\exp\left({\hv^j}^T \hv^k\right)} \,.
\end{align}
Note that \eqref{eq:def} can be applied to both structural and text embeddings in \eqref{eq:str} and \eqref{eq:text}.

Inspired by \citet{tu2017cane}, we further assume that vertices in the network play different roles depending on the vertex with which they interact.
Thus, for a given vertex, the text embedding, $\hv_t$, is adaptive (specific) to the vertex it is being conditioned on.
This type of context-aware textual embedding has demonstrated superior performance relative to context-free embeddings \cite{tu2017cane}.
In the following two sections, we describe our strategy for encoding the text sequence associated with an edge into its adaptive textual embedding, via word-by-context and word-by-word alignments.

\subsection{Word-by-Context Alignment}
We first introduce our base model, which re-weights the importance of individual words within a text sequence in the context of the edge being considered.
Consider text sequences associated with two vertices connected by an edge, denoted $\boldsymbol{t}_a$ and $\boldsymbol{t}_b$ and contained in $\boldsymbol{T}$.
Text sequences $\boldsymbol{t}_a$ and $\boldsymbol{t}_b$ are of lengths $M_a$ and $M_b$, respectively, and are represented by $\Xmat_a\in\mathbb{R}^{d\times M_a}$ and $\Xmat_b\in\mathbb{R}^{d\times M_b}$, respectively, where $d$ is the dimension of the word embedding.
Further, $\boldsymbol{x}^{(i)}_a$ denotes the embedding of the $i$-th word in sequence $\boldsymbol{t}_a$.

Our goal is to encode text sequences $\boldsymbol{t}_a$ and $\boldsymbol{t}_b$ into \emph{counterpart-aware} vectorial representations $\hv_a$ and $\hv_b$.
Thus, while inferring the adaptive textual embedding for sentence $\boldsymbol{t}_a$, we propose re-weighting the importance of each word in $\boldsymbol{t}_a$ to explicitly account for its alignment with sentence $\boldsymbol{t}_b$.
The weight $\alpha_i$, corresponding to the $i$-th word in $\boldsymbol{t}_a$, is generated as:
\begin{align}\label{eqn:alpha}
\alpha_i = \frac{\exp(\tanh(\Wmat_1 \cv_b + \Wmat_2 \boldsymbol{x}^{(i)}_a))}{\sum_{j = 1}^{M_a} \exp(\tanh(\Wmat_1 \cv_b + \Wmat_2 \boldsymbol{x}^{(j)}_a))} \,,
\end{align}
where $\Wmat_1$ and $\Wmat_2$ are model parameters and $\cv_b = \sum_{i = 1}^{M_b} \boldsymbol{x}^b_i$ is the context vector of sequence $\boldsymbol{t}_b$, obtained by simply averaging over all the word embeddings in the sequence, similar to fastText \cite{joulin2016bag}.
Further, the \emph{word-by-context} embedding for sequence $\boldsymbol{t}_a$ is obtained by taking the weighted average over all word embeddings
\begin{align}\label{eqn:weighted}
	\boldsymbol{h}_a = \textstyle{\sum}_{i = 1}^{M_a} \alpha_i \boldsymbol{x}^{(i)}_a \,.
\end{align}
Intuitively, $\alpha_i$ may be understood as the relevance score between the $i$th word in $\boldsymbol{t}_a$ and sequence $\boldsymbol{t}_b$.
Specifically, keywords within $\boldsymbol{t}_a$, in the context of $\boldsymbol{t}_b$, should be assigned larger weights, while less important words will be correspondingly down-weighted.
Similarly, $\boldsymbol{h}_b$ is encoded as a weighted embedding using \eqref{eqn:alpha} and \eqref{eqn:weighted}.

\begin{figure}[!t]
	\centering

	\includegraphics[width=0.45\textwidth]{figure/illustration_3.pdf}
	\caption{Schematic of the proposed \emph{fine-grained word alignment module} for incorporating textual information into a network embedding. In this setup, word-by-word matching features are explicitly abstracted to infer the relationship between vertices.}
	\label{fig:model}

\end{figure}

\subsection{Fine-Grained Word-by-Word Alignment}
With the alignment in the previous section, word-by-context matching features $\alpha_i$ are modeled; however, the \emph{word-by-word} alignment information (\emph{fine-grained}), which is key to characterize the relationship between two vertices (as discussed in the above), is not explicitly captured.
So motivated, we further propose an architecture to explicitly abstract word-by-word alignment information from $\boldsymbol{t}_a$ and $\boldsymbol{t}_b$, to learn the relationship between the two vertices.
This is inspired by the recent success of Relation Networks (RNs) for relational reasoning \cite{santoro2017simple}.

As illustrated in Figure~\ref{fig:model}, given two input embedding matrices $\Xmat_a$ and $\Xmat_b$, we first compute the affinity matrix $\Amat\in\mathbb{R}^{M_b\times M_a}$, whose elements represent the affinity scores corresponding to all word pairs between sequences $\boldsymbol{t}_a$ and $\boldsymbol{t}_b$
\begin{align}\label{eqn:affinity}
	\Amat = \Xmat^T_b\Xmat_a \,.
\end{align}
Subsequently, we compute the context-aware matrix for sequence $\boldsymbol{t}_b$ as
\begin{align}\label{eqn:matrix}

		\Amat_b = \textrm{softmax}(\Amat) \,, \qquad \widetilde{\Xmat}_b = \Xmat_b\Amat_b \,,

\end{align}
where the $\textrm{softmax}(\cdot)$ function is applied column-wise to $\Amat$, and thus $\Amat_b$ contains the attention weights (importance scores) across sequence $\boldsymbol{t}_b$ (columns), which account for each word in sequence $\boldsymbol{t}_a$ (rows).
Thus, matrix $\widetilde{\Xmat}_b \in\mathbb{R}^{d\times M_a}$ in \eqref{eqn:matrix} constitutes an attention-weighted embedding for $\Xmat_b$.
Specifically, the $i$-th column of $\widetilde{\Xmat}_b$, denoted as $\widetilde{\boldsymbol{x}}^{(i)}_b$, can be understood as a weighted average over all the words in $\boldsymbol{t}_b$, where higher attention weights indicate better alignment (match) with the $i$-th word in $\boldsymbol{t}_a$.

To abstract the word-by-word alignments, we compare $\boldsymbol{x}^{(i)}_a$ with $\widetilde{\boldsymbol{x}}^{(i)}_b$, for $i=1,2,...,M_a$, to obtain the corresponding matching vector
\begin{align}\label{eqn:m}
	\mv^{(i)}_a=f_{\textrm{align}}\left(\boldsymbol{x}^{(i)}_a,\widetilde{\boldsymbol{x}}^{(i)}_b\right) \,,
\end{align}
where $f_{\textrm{align}}(\cdot)$ represents the alignment function.
Inspired by the observation in \citet{Wang2016ACM} that simple comparison/alignment functions based on element-wise operations exhibit excellent performance in matching text sequences, here we use a combination of element-wise subtraction and multiplication as
\begin{align*}
f_{\textrm{align}}(\boldsymbol{x}^{(i)}_a,\widetilde{\boldsymbol{x}}^{(i)}_a) = [\boldsymbol{x}^{(i)}_a - \widetilde{\boldsymbol{x}}^{(i)}_a; \boldsymbol{x}^{(i)}_a \odot  \widetilde{\boldsymbol{x}}^{(i)}_a] \,,
\end{align*}
where $\odot$ denotes the element-wise Hadamard product, then these two operations are concatenated to produce the matching vector $\mv^{(i)}_a$.
Note these operators may be used individually or combined as we will investigate in our experiments.

Subsequently, matching vectors from \eqref{eqn:m} are aggregated to produce the final textual embedding $\hv_t^a$ for sequence $\boldsymbol{t}_a$ as
\begin{align}\label{eqn:hvt}
	\hv_t^a=f_{\textrm{aggregate}}\left(\mv^{(1)}_a,\mv^{(2)}_a,...,\mv^{(M_a)}_a\right) \,,
\end{align}
where $f_{\textrm{aggregate}}$ denotes the aggregation function, which we specify as the max-pooling pooling operation.
Notably, other commutative operators, such as summation or average pooling, can be otherwise employed.
Although these aggregation functions are simple and invariant to the order of words in input sentences, they have been demonstrated to be highly effective in relational reasoning \cite{parikh2016decomposable, santoro2017simple}.
To further explore this, in Section~\ref{sec:ablation}, we conduct an ablation study comparing different choices of alignment and aggregation functions.

The representation $\hv_b$ can be obtained in a similar manner through \eqref{eqn:affinity}, \eqref{eqn:matrix}, \eqref{eqn:m} and \eqref{eqn:hvt}, but replacing \eqref{eqn:affinity} with $\Amat = \Xmat^T_a\Xmat_b$ (its transpose).
Note that this word-by-word alignment is more computationally involved than word-by-context; however, the former has substantially fewer parameters to learn, provided we no longer have to estimate the parameters in \eqref{eqn:alpha}.

\subsection{Training and Inference}
For large-scale networks, computing and optimizing the conditional probabilities in \eqref{eq:obj} using \eqref{eq:def} is computationally prohibitive, since it requires the summation over all vertices $\boldsymbol{V}$ in $\boldsymbol{G}$.
To address this limitation, we leverage the negative sampling strategy introduced by \citet{mikolov2013distributed}, \emph{i.e.}, we perform computations by sampling a subset of negative edges.
As a result, the conditional in \eqref{eq:def} can be rewritten as:
\begin{align*
	\begin{aligned}
	p(\hv^i|\hv^j) & = \log \sigma\left({\hv^j}^T \hv^i\right) \\
	& + \sum_{i=1}^{K} \mathbb{E}_{\hv^i\sim P(v)}\left[\log \sigma\middle(-{\hv^j}^T \hv^i\middle)\right] \,,
	\end{aligned}
\end{align*}
where $\sigma(x) = 1/(1+\exp(-x))$ is the sigmoid function.
Following \citet{mikolov2013distributed}, we set the noise distribution $P(v) \propto d_v^{3/4}$, where $d_v$ is the out-degree of vertex $v\in\boldsymbol{V}$.
The number of negative samples $K$ is treated as a hyperparameter.
We use Adam \cite{kingma2014adam} to update the model parameters
while minimizing the objective in \eqref{eq:obj}.

\section{Related Work}
Network embedding
methods can be divided into two categories:
(\emph{\romannumeral1}) methods that solely rely on the structure, \emph{e.g.}, vertex information; and (\emph{\romannumeral2}) methods that leverage both the structure the network and the information associated with its vertices.

For the first type of models, DeepWalk \cite{perozzi2014deepwalk} has been proposed to learn node representations by generating node contexts via truncated random walks; it is similar to the concept of Skip-Gram \cite{mikolov2013distributed}, originally introduced for learning word embeddings.
LINE \cite{tang2015line} proposed a principled objective to explicitly capture first-order and second-order proximity information from the vertices of a network.
Further, \citet{grover2016node2vec} introduced a biased random walk procedure to generate the neighborhood for a vertex, which infers the node representations by maximizing the likelihood of preserving the local context information of vertices.
However, these algorithms generally ignore rich heterogeneous information associated with vertices.
Here, we focus on incorporating \emph{textual information} into network embeddings.

To learn semantic-aware network embeddings, Text-Associated DeepWalk (TADW) \cite{yang2015network} proposed to integrate textual features into network representations with matrix factorization, by leveraging the equivalence between DeepWalk and matrix factorization.
CENE (Content-Enhanced Network Embedding) \cite{sun2016general} used bidirectional recurrent neural networks to abstract the semantic information associated with vertices, which further demonstrated the advantages of employing textual information.
To capture the interaction between sentences of vertex pairs, \citet{tu2017cane} further proposed Context-Aware Network Embedding (CANE), that employs a mutual attention mechanism to adaptively account for the textual information from neighboring vertices.
Despite showing improvement over structure-only models, these semantic-aware methods cannot capture word-level alignment information, which is important for inferring the relationship between node pairs, as previously discussed.
In this work, we introduce a Word-Alignment-based Network Embedding (WANE) framework, which aligns and aggregates word-by-word matching features in an explicit manner, to obtain more informative network representations.

\section{Experimental Setup}

\paragraph{Datasets}
We investigate the effectiveness of the proposed WANE model on two standard network-embedding-based tasks, \emph{i.e.}, link prediction and multi-label vertex classification.
The following three real-world datasets are employed for quantitative evaluation:
(\emph{\romannumeral1}) \emph{Cora}, a standard paper citation network that contains 2,277 machine learning papers (vertices) grouped into 7 categories and connected by 5,214 citations (edges)
(\emph{\romannumeral2}) \emph{HepTh}, another citation network of 1,038 papers with abstract information and 1,990 citations;
(\emph{\romannumeral3}) \emph{Zhihu}, a network of 10,000 active users from Zhihu, the largest Q\&A website in China, where 43,894 vertices and descriptions of the Q\&A topics are available.
The average lengths of the text in the three datasets are 90, 54, and 190, respectively.
To make direct comparison with existing work, we employed the same preprocessing procedure\footnote{https://github.com/thunlp/CANE} of \citet{tu2017cane}.

\begin{table*}  \small
	\centering

	\begin{tabular}{c||c|c|c|c|c|c|c|c|c}
		\toprule[1.2pt]
		\textbf{\%Training Edges} &  	\textbf{15\%} & \textbf{25\%} & 	\textbf{35\%} & \textbf{45\%} & \textbf{55\%} & \textbf{65\%} & \textbf{75\%} & \textbf{85\%} & \textbf{95\%} \\
		\hline
		\textbf{MMB}      & 54.7 & 57.1 & 59.5 & 61.9 & 64.9 & 67.8 & 71.1 & 72.6 & 75.9  \\
		\textbf{DeepWalk}    & 56.0 & 63.0 & 70.2 & 75.5 & 80.1 & 85.2 & 85.3 & 87.8 & 90.3   \\
		\textbf{LINE}           & 55.0 & 58.6 & 66.4 & 73.0 & 77.6 & 82.8 & 85.6& 88.4& 89.3  \\
		\textbf{node2vec}        & 55.9 & 62.4 & 66.1 & 75.0 & 78.7 & 81.6 & 85.9 & 87.3 & 88.2   \\
		\hline
		\textbf{Naive combination}       & 72.7 & 82.0 & 84.9 & 87.0 & 88.7 & 91.9 & 92.4 & 93.9 & 94.0  \\
		\textbf{TADW}            & 86.6 & 88.2 & 90.2 & 90.8 & 90.0 & 93.0 & 91.0 & 93.4 & 92.7  \\
		\textbf{CENE} & 72.1 & 86.5 &84.6& 88.1& 89.4 &89.2& 93.9 &95.0 &95.9  \\
		\textbf{CANE}  & 86.8& 91.5 &92.2 &93.9 &94.6 &94.9 &95.6 &96.6& 97.7  \\
		\hline
		\textbf{WANE}  & 86.1 & 90.9 & 92.3 & 93.1 & 93.4 & 94.5 & 95.1 & 95.4 & 95.9 \\
		\textbf{WANE-\emph{wc}}  & 88.7 & 92.1 & 92.9 &94.4 & 94.8 & 95.1 & 95.7 & 96.5 & 97.4 \\
		\textbf{WANE-\emph{ww}}  & \textbf{91.7} & \textbf{93.3} & \textbf{94.1} & \textbf{95.7} & \textbf{96.2} & \textbf{96.9} & \textbf{97.5} & \textbf{98.2} & \textbf{99.1}  \\
		\bottomrule[1.2pt]
	\end{tabular}
	\caption{AUC scores for link prediction on the \emph{Cora} dataset.}
	\label{tab:cora}

\end{table*}

\begin{table*}[!ht]  \small
	\centering

	\begin{tabular}{c||c|c|c|c|c|c|c|c|c}
		\toprule[1.2pt]
		\textbf{\%Training Edges} &  	\textbf{15\%} & \textbf{25\%} & 	\textbf{35\%} & \textbf{45\%} & \textbf{55\%} & \textbf{65\%} & \textbf{75\%} & \textbf{85\%} & \textbf{95\%} \\
		\hline
		\textbf{MMB}      &54.6& 57.9 &57.3 &61.6& 66.2 &68.4 &73.6 &76.0 &80.3  \\
		\textbf{DeepWalk}    & 55.2& 66.0 &70.0& 75.7& 81.3 &83.3 &87.6 &88.9 &88.0   \\
		\textbf{LINE}            & 53.7 &60.4 &66.5 &73.9 &78.5 &83.8 &87.5 &87.7 &87.6  \\
		\textbf{node2vec}        & 57.1 &63.6 &69.9 &76.2 &84.3 &87.3& 88.4 &89.2 &89.2  \\
		\hline
		\textbf{Naive combination}       & 78.7 &82.1& 84.7& 88.7 &88.7 &91.8 &92.1 &92.0 &92.7  \\
		\textbf{TADW}            & 87.0 &89.5 &91.8& 90.8 &91.1 &92.6 &93.5 &91.9 &91.7  \\
		\textbf{CENE} & 86.2 &84.6 &89.8 &91.2 &92.3 &91.8 &93.2 &92.9 &93.2  \\
		\textbf{CANE}  & 90.0 & 91.2 &92.0 &93.0 &94.2 &94.6 &95.4 &95.7 &96.3  \\
		\hline
		\textbf{WANE}  & 88.5 & 90.7 & 91.1 & 92.6 & 93.5 & 94.2 & 94.9 & 95.3 & 95.8 \\
		\textbf{WANE-\emph{wc}}  & 90.1 & 91.4 & 91.9 &94.1 & 95.3 & 95.9 & 96.5 & 96.9 & 97.2 \\
		\textbf{WANE-\emph{ww}}  & \textbf{92.3} & \textbf{94.1} & \textbf{95.7} & \textbf{96.7} & \textbf{97.5} & \textbf{97.5} & \textbf{97.7} & \textbf{98.2} & \textbf{98.7}  \\
		\bottomrule[1.2pt]
	\end{tabular}
	\caption{AUC scores for link prediction on the \emph{HepTh} dataset.}
	\label{tab:hepth}

	\vspace{-2mm}
\end{table*}

\paragraph{Training Details}
For fair comparison with CANE \cite{tu2017cane}, we set the dimension of network embedding for our model to 200.
The number of negative samples $K$ is selected from $\{1, 3, 5\}$ according to performance on the validation set.
We set the batch size as 128, and the model is trained using Adam \cite{kingma2014adam}, with a learning rate of $1 \times 10^{-3}$ for all parameters.
Dropout regularization is employed on the word embedding layer, with rate selected from $\{0.5, 0.7, 0.9\}$, also on the validation set.
Our code will be released to encourage future research.

\paragraph{Baselines}
To evaluate the effectiveness of our framework, we consider several strong baseline methods for comparisons, which can be categorized into two types: (\emph{\romannumeral1}) models that only exploit \emph{structural} information: MMB \cite{Airoldi2008MixedMS}, DeepWalk \cite{perozzi2014deepwalk}, LINE \cite{tang2015line}, and node2vec \cite{grover2016node2vec}.
(\emph{\romannumeral2}) Models that take both \emph{structural} and \emph{textual} information into account: Naive combination (which simply concatenates the structure-based embedding with CNN-based text embeddings, as explored in \cite{tu2017cane}, TADW \cite{yang2015network}, CENE \cite{sun2016general}, and CANE \cite{tu2017cane}.
It is worth noting that unlike all these baselines, WANE explicitly captures word-by-word interactions between text sequence pairs.

\paragraph{Evaluation Metrics}
We employ AUC \cite{hanley1982meaning} as the evaluation metric for link prediction, which measures the probability that vertices within an existing edge, randomly sampled from the test set, are more similar than those from a random pair of non-existing vertices, in terms of the inner product between their corresponding embeddings.

For multi-label vertex classification and to ensure fair comparison, we follow \citet{yang2015network} and employ a linear SVM on top of the learned network representations, and evaluate classification accuracy with different training ratios (varying from $10\%$ to $50\%$).
The experiments for each setting are repeated 10 times and the average test accuracy is reported.

\section{Experimental Results}
We experiment with three variants for our WANE model: (\emph{\romannumeral1}) WANE: where the word embeddings of each text sequence are simply average to obtain the sentence representations, similar to \cite{joulin2016bag, Shen2018Baseline}.
(\emph{\romannumeral2}) WANE-\emph{wc}: where the textual embeddings are inferred with word-by-context alignment.
(\emph{\romannumeral3}) WANE-\emph{ww}: where the word-by-word alignment mechanism is leveraged to capture word-by-word matching features between available sequence pairs.

\begin{table*} [!ht]  \small
	\centering

	\begin{tabular}{c||c|c|c|c|c|c|c|c|c}
		\toprule[1.2pt]
		\textbf{\%Training Edges} &  	\textbf{15\%} & \textbf{25\%} & 	\textbf{35\%} & \textbf{45\%} & \textbf{55\%} & \textbf{65\%} & \textbf{75\%} & \textbf{85\%} & \textbf{95\%} \\
		\hline
		\textbf{MMB}      & 51.0 & 51.5 & 53.7 & 58.6 & 61.6 & 66.1 & 68.8 & 68.9 & 72.4  \\
		\textbf{DeepWalk}    & 56.6 & 58.1 & 60.1 & 60.0 & 61.8 & 61.9 & 63.3 & 63.7 & 67.8   \\
		\textbf{LINE}            & 52.3 & 55.9 & 59.9 & 60.9 & 64.3 & 66.0 & 67.7 & 69.3 & 71.1  \\
		\textbf{node2vec}        & 54.2 & 57.1 & 57.3 & 58.3 & 58.7 & 62.5 & 66.2 & 67.6 & 68.5  \\
		\hline
		\textbf{Naive combination}       & 55.1 & 56.7 & 58.9 & 62.6 & 64.4 & 68.7 & 68.9 & 69.0 & 71.5  \\
		\textbf{TADW}            & 52.3 & 54.2 & 55.6 & 57.3 & 60.8 & 62.4 & 65.2 & 63.8 & 69.0  \\
		\textbf{CENE} & 56.2 & 57.4 & 60.3 & 63.0 & 66.3 & 66.0 & 70.2 & 69.8 & 73.8 \\
		\textbf{CANE}  & 56.8 & 59.3 & 62.9 & 64.5 & 68.9 & 70.4 & 71.4 & 73.6 & 75.4  \\
		\hline
		\textbf{WANE}  & 52.1 & 56.6 & 60.7 & 64.2 & 67.5 & 69.1 & 71.3 & 72.8 & 73.9 \\
		\textbf{WANE-\emph{wc}}  & 55.2 & 59.9 & 64.2 & 68.1 & 71.3 & 73.4 & 75.6 & 76.3 &78.8 \\
		\textbf{WANE-\emph{ww}}  & \textbf{58.7} & \textbf{63.5} & \textbf{68.3} & \textbf{71.9} & \textbf{74.9} & \textbf{77.0} & \textbf{79.7} & \textbf{80.0} & \textbf{82.6}  \\
		\bottomrule[1.2pt]
	\end{tabular}
	\caption{AUC scores for link prediction on the \emph{Zhihu} dataset.}
	\label{tab:zhihu}

\end{table*}

\begin{figure*}[!ht] \centering

	\begin{tabular}{ccc}
		\includegraphics[width=4.0cm]{figure/compare.pdf} & \hspace{2mm}
		\includegraphics[width=4.0cm]{figure/aggregate.pdf} &  \hspace{ 2mm}
		\includegraphics[width=4.0cm]{figure/ne_cora_acc.pdf}
		\\
		\hspace{-0mm}
		(a) $f_{\textrm{align}}$ \vspace{0mm}  &
		\hspace{-0mm}
		(b) $f_{\textrm{aggregate}}$ \hspace{-0mm}&
		(c) vertex classification \hspace{-0mm}

	\end{tabular}
	\vspace{-2mm}
	\caption{(a, b) Ablation study on the choice of different alignment and aggregation functions. (c) Test accuracy of \emph{supervised} vertex classification on the \emph{Cora} dataset.}
	\label{fig:classification}
	\vspace{-3mm}
\end{figure*}

\subsection{Link Prediction}
Table~\ref{tab:cora} presents link prediction results for all models on Cora dataset, where different ratios of edges are used for training.
It can be observed that when only a small number of edges are available, \emph{e.g.}, $15\%$, the performances of structure-only methods is much worse than semantic-aware models that have taken textual information into consideration
The perfromance gap tends to be smaller when a larger proportion of edges are employed for training.
This highlights the importance of incorporating associated text sequences into network embeddings, especially in the case of representing a relatively sparse network.
More importantly, the proposed WANE-\emph{ww} model consistently outperforms other semantic-aware NE models by a substantial margin, indicating that our model better abstracts word-by-word alignment features from the text sequences available, thus yields more informative network representations.

Further, WANE-\emph{ww} also outperforms WANE or WANE-\emph{wc} on a wide range of edge training proportions.
This suggests that:
(\emph{\romannumeral1}) adaptively assigning different weights to each word within a text sequence (according to its paired sequence) tends to be a better strategy than treating each word equally (as in WANE).
(\emph{\romannumeral2}) Solely considering the context-by-word alignment features (as in WANE-\emph{wc}) is not as efficient as abstracting word-by-word matching information from text sequences.
We observe the same trend and the superiority of our WANE-\emph{ww} models on the other two datasets, HepTh and Zhihu datasets, as shown in Table~\ref{tab:hepth} and \ref{tab:zhihu}, respectively.

\subsection{Multi-label Vertex Classification}
We further evaluate the effectiveness of proposed framework on vertex classification tasks with the Cora dataset.
Similar to \citet{tu2017cane}, we generate the global embedding for each vertex by taking the average over its \emph{context-aware} embeddings with all other connected vertices.
As shown in Figure~\ref{fig:classification}(c), semantic-aware NE methods (including naive combination, TADW, CENE, CANE) exhibit higher test accuracies than semantic-agnostic models, demonstrating the advantages of incorporating textual information.
Moreover, WANE-\emph{ww} consistently outperforms other competitive semantic-aware models on a wide range of labeled proportions, suggesting that explicitly capturing word-by-word alignment features is not only useful for vertex-pair-based tasks, such as link prediction, but also results in better global embeddings which are required for vertex classification tasks.
These observations further demonstrate that WANE-\emph{ww} is an effective and robust framework to extract informative network representations.

\paragraph{Semi-supervised classification}
We further consider the case where the training ratio is less than $10\%$, and evaluate the learned network embedding with a semi-supervised classifier.
Following \citet{yang2015network}, we employ a Transductive SVM (TSVM) classifier with a linear kernel \cite{joachims1998making} for fairness.
As illustrated in Table~\ref{tab:semi}, the proposed WANE-\emph{ww} model exhibits superior performances in most cases.
This may be due to the fact that WANE-\emph{ww} extracts information from the vertices and text sequences jointly, thus the obtained vertex embeddings are less noisy and perform more consistently with relatively small training ratios \cite{yang2015network}.

\subsection{Ablation Study} \label{sec:ablation}
Motivated by the observation in \citet{Wang2016ACM} that the advantages of different functions to match two vectors vary from task to task, we further explore the choice of alignment and aggregation functions in our WANE-\emph{ww} model.
To match the word pairs between two sequences, we experimented with three types of operations: \emph{subtraction}, \emph{multiplication}, and \emph{Sub \& Multi} (the concatenation of both approaches).
As shown in Figure~\ref{fig:classification}(a) and \ref{fig:classification}(b), element-wise \emph{subtraction} tends to be the most effective operation performance-wise on both Cora and Zhihu datasets, and performs comparably to \emph{Sub \& Multi} on the HepTh dataset.
This finding is consistent with the results in \citet{Wang2016ACM}, where they found that simple comparison functions based on \emph{element-wise} operations work very well on matching text sequences.

In terms of the aggregation functions, we compare (one-layer) \emph{CNN}, \emph{mean-pooling}, and \emph{max-pooling} operations to accumulate the matching vectors.
As shown in Figure~\ref{fig:classification}(b), \emph{max-pooling} has the best empirical results on all three datasets.
This may be attributed to the fact that the \emph{max-pooling} operation is better at selecting important word-by-word alignment features, among all matching vectors available, to infer the relationship between vertices.

\subsection{Qualitative Analysis}
\paragraph{Embedding visualization}
To visualize the learned network representations, we further employ $t$-SNE to map the low-dimensional vectors of the vertices to a 2-D embedding space.
We use the \emph{Cora} dataset because there are labels associated with each vertex and WANE-\emph{ww} to obtain the network embeddings.

As shown in Figure~\ref{fig:tsne} where each point indicates one paper (vertex), and the color of each point indicates the \emph{category} it belongs to, the embeddings of the same label are indeed very close in the 2-D plot, while those with different labels are relatively farther from each other.
Note that the model is not trained with any label information, indicating that WANE-\emph{ww} has extracted meaningful patterns from the text and vertex information available.

\begin{figure}
	\centering
	\vspace{-4mm}
	\includegraphics[width=.38\textwidth]{figure/tsne_20.pdf}
	\vspace{-2mm}
	\caption{$t$-SNE visualization of the learned network embeddings on the Cora dataset.}

	\vspace{-4mm}
	\label{fig:tsne}
\end{figure}

\begin{table}
	\centering

	\small
	\begin{tabular}{c||c|c|c|c}

		\textbf{Baseline Models} &  1\% & 3\% & 7\% & 10\%   \\

		\hline
		\textbf{Text Only} & 33.0 & 43.0 & 57.1 & 62.8 \\
		\textbf{Naive Combination}   & 67.4 & 70.6 & 75.1 & 77.4    \\
		\textbf{TADW}   & 72.1 & 77.0 & 79.1 & 81.3    \\
		\textbf{CENE}  & \textbf{73.8} & 79.1 & 81.5 & 84.5     \\
		\textbf{CANE}  & 72.6 & 78.2 & 80.4 & 83.4    \\
		\hline
		\textbf{WANE-\emph{ww}} (ours)    & 73.4 & \textbf{79.6} & \textbf{82.7} & \textbf{85.1}     \\

	\end{tabular}
	\vspace{-2mm}
	\caption{\emph{Semi-supervised} vertex classification results on the \emph{Cora} dataset.}

	\vspace{-4mm}
	\label{tab:semi}
\end{table}

\paragraph{Case study}
The proposed word-by-word alignment mechanism can be used to highlight the most informative
words (and the corresponding matching features) wrt the relationship between vertices.
We visualize the norm of matching vector obtained in \eqref{eqn:m} in Figure~\ref{fig:attention} for the Cora dataset.
It can be observed that matched key words, \emph{e.g.}, `MCMC', `convergence', between the text sequences are indeed assigned higher values in the matching vectors.
These words would be selected preferentially by the final \emph{max-pooling} aggregation operation.
This indicates that WANE-\emph{ww} is able to abstract important word-by-word alignment features from paired text sequences.

\begin{figure}[!h]
	\centering
	\begin{tabular}{c}
	\hspace{-3mm}
	\includegraphics[width=7.6cm]{figure/attention_1.pdf} \vspace{-2mm} \\
	\small {(a) Sentence pairs associated with Edge \#1}  \vspace{2mm}  \\

	\hspace{-3mm}
	\includegraphics[width=7.6cm]{figure/attention_2.pdf} \vspace{-1mm}  \\
	\small {(b) Sentence pairs associated with Edge \#2}   \hspace{-0mm}
	\end{tabular}

	\caption{Visualization of the word-level \emph{matching vectors}. Darker shades represent larger values of the norm of $\mv^{(i)}$ at each word position.}

	\label{fig:attention}
	\vspace{-4mm}
\end{figure}

\section{Conclusions}
We have presented a novel framework to incorporate the semantic information from vertex-associated text sequences into network embeddings.
An \emph{align-aggregate} framework is introduced, which first aligns a sentence pair by capturing the word-by-word matching features, and then adaptively aggregating these word-level alignment information with an efficient \emph{max-pooling} function.
The semantic features abstracted are further encoded, along with the structural information, into a shared space to obtain the final network embedding.
Compelling experimental results on several tasks demonstrated the advantages of our approach.
In future work, we aim to leverage abundant unlabeled text data to abstract more informative sentence representations \cite{dai2015semi, zhang2017deconvolutional,  shen2017deconvolutional, tang2018multi} .
Another interesting direction is to learn \emph{binary} and compact network embedding, which could be more efficient in terms of both computation and memory, relative to its continuous counterpart \citep{shen2018nash}.

\paragraph{Acknowledgments}
This research was supported in part by DARPA, DOE, NIH, ONR and NSF.

redpajama_set_name: RedPajamaArXiv


text:
require_relative '../test_helper'
require 'pages/welcome'

class WelcomeTest < Minitest::Test
  def setup
    @config_options = {
      :locales => {:path => '/tmp'},
      :page    => {},
    }
    @page = RWebLib::Pages::Welcome.new(Helper.get_config(@config_options))
  end

  def teardown
    @page = nil
  end

  def test_initialize
    assert_equal @page._("Welcome to RWebLib"), @page.welcome_text
  end
end


text:
Q: WSO2: passing the caller's IP address to the backend Say an instance of WSO2's API Manager receives API calls from an external client, it is possible to pass the caller's IP address to the backend (in the header for example, other solutions are welcome too)? If yes how?

A: HTTP Header is the way to do it. You can create a sequence like this and attach to API.
<?xml version="1.0" encoding="UTF-8"?>
<sequence xmlns="http://ws.apache.org/ns/synapse" name="sample">
    <property name="client_ip" expression="get-property('axis2', 'REMOTE_ADDR')" scope="transport"/>
</sequence>

If you have a proxy before API Manager, you may not be able to identify the real client using REMOTE_ADDR. It will be the IP of the proxy. In such cases you can use X-Forwarded-For http header.


"editor.findMatchBackground": "#32fc32", "editor.findMatchHighlightBackground" : "#c2fcc2"
"console.log(error + \" line:\"+ error.lineNumber);"
"description": "Public static main method"
"description": "Print to standard out"
"description": "Print to standard err"
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I just received a letter with a check for $975.45. The letter states that I have received A $10, 000.00 Grant and the check needed to be cashed or deposited and I need to obtain a Greendot MoneyPak for 5% which is $500.00. Then I needed to go on line to www.LisaProcter.BIZ or www.LisaProcter.NET and enter a client ID#. It also states that if I cash or deposit the check and not send the 5% commission in 5 days that I would not receive the remainder balance of $9, 025.00 and that I would be liable to payback this money and I would be reported to collections. The Check number is [protected], routing number is 000151 and account number is [protected]. What scares me is that they have my SSN on this letter. Everybody needs to watch out for people like this.
Key Services Corp - CASH GRANT SCAM!!!