Brynn Mechem and Mary Freda // Story Kaiti Sullivan, Madeline Grosh and Rachel Ellis // Photography Michael Himes and Emily Wright // Development and Design Created April 26, 2018
Brynn Mechem and Mary Freda // Story Kaiti Sullivan, Madeline Grosh and Rachel Ellis // Photography Michael Himes and Emily Wright // Development and Design Created April 26, 2018
In 2018 alone, 610,000 Americans will die from cancer. For Cancer Control Month, The Daily News shadowed and sat down with Ball State professors to reflect on their past and continuing journeys with cancer.
In the brisk October morning, Stan Sollars quickly walked through the automatic sliding glass doors into the almost too-bright lobby. He took a seat in the pale blue and green chairs that are meant to be comforting, but the lumps didn’t help overcome the anxiety that comes with waiting. He put his arm around his nervous wife. “It’s going to be fine,” he said. “We’ve done this before.”
He walked through the labyrinth that was Indiana University Health University Hospital, never even looking where he’s going. He clutched the “football,” the giant accordion-style folder that houses all of his medical information to his side. He smiled and gave an animated hello to the receptionist, greeting her like he would an old friend. She smiled, the blue light from the computer screen reflecting in her glasses.
She knew him.
They all knew him.
They had seen him more than 40 times in the last three years.
He took a seat in the familiar chairs. He clutched a large water bottle that he purchased in anticipation for the day’s events. He explained what his body was about to endure.
“I have to drink a liter of water so that I can flush out all of the radioactive stuff as quickly as possible,” Sollars said.
A liter may seem like a lot, but to Sollars, a couple extra trips to the bathroom is a small price to pay for his life. The radiologist called him to the back. He walked past a patient left in the hallway, too weak to lift his head off the pillow. In a cold room, he showed off the machine that would soon send an army of X-ray waves and radiation into his body, scanning for any signs of a foreign object. Sollars lay still, the machine whirring around him, waiting for any signal of something that could again consume his life.
In the reception room, his wife fought back the tears that were perched just behind her lids, threatening to tumble over the edge.
“There's always a fear that it will come back,” Allison Pareis, Sollars’ wife, said. “That's why I always get really antsy on scan days. I mean, he's fine and I don't have any reason to really think that there's a problem, but it's a human thing just to have that little thing that says, ‘What if, what if, what if.’”
Sollars, a Ball State lecturer of telecommunications, isn’t alone in his fight. He is just one of the 1.8 million U.S. citizens who get a cancer diagnosis every year, according to the National Cancer Institute. Of those, 67 percent will still be alive five years down the road, up 17 percent from 1974.

In the waiting room, Pareis nervously thumbed through the pages of a book that she never got the chance to read. Her right knee was bouncing up and down as if she were calming an imaginary child, and even as she talked, she couldn’t peel her eyes from the door. When he finally came back, she went through her routine checklist.
“How did it go? How do you feel? Did you finish your water? We need to go upstairs now.”
As they made their way back through the never-ending maze, the couple explained life during treatment. Sollars initially had surgery to fix an issue with his esophagus, but during the surgery, doctors found a tumor. When Pareis found out the surgery meant something more, she didn’t handle it very well.
“That first day, he was so heavily drugged, we were all sitting in the room waiting for the ER doctor to come and explain to Stan because nobody had told Stan yet. I was just trying to hold myself together so I couldn't say it,” Pareis said.
“So they have the ER doc explain it to him, and at that point my parents and I are just like stunned. I’m really just upset, like, I had been screaming and everything. Stan was like, ‘It'll be OK. It'll be totally OK. I'll be fine, I already know that I'll be fine.’”
It is this mindset Sollars would later say got him through.
“You have to put on horse blinkers, like you would put on a race horse, because if you’re distracted one way or the other — ‘What about Fred’s cancer,’ ‘What about Jane’s cancer?’ — that spreads,” Sollars said. “I had to focus on my cancer and survival.”
A white and teal wedding
It was on her 30th birthday that Kendra Zenisek, lecturer of kinesiology and coordinator of physical fitness, got preoperative testing to see if she had cancer. She found out two weeks later she did.
“Thirty wasn't a great year,” Zenisek said. “I didn't really enjoy 30. Turning 30, most people are like, ‘Oh, it's just 30, it sucks,’ and I was like, ‘No, 30 is different for me.’”
For the newly-engaged woman, the diagnosis was worse than she could’ve imagined. She had adenocarcinoma, a more aggressive type of cervical cancer.
According to The National Cancer Institute, adenocarcinoma is when cancerous cells skip around to various parts of the body. This type of cancer is much harder to treat than squamous cell carcinoma, which tends to be much more isolated.
“The recommended path of treatment at that point in time coming from the doctor that I had was complete hysterectomy, which means ovaries, uterus — everything,” Zenisek said. “At that age, I was like, ‘What if I want kids or what if I want a family?’”
After being recommended to doctors in Indiana and Georgia, Zenisek found a doctor at Memorial Sloan Kettering Cancer Center in New York City that could perform a surgery that would still allow Zenisek to have a child through a C-section delivery. However, during surgery, doctors found the lesions had spread further than they first thought and Zenisek had a complete hysterectomy.
The complications didn’t stop there, though. A few days after the initial surgery, Zenisek developed a bowel restriction and underwent another surgery, keeping her in the hospital for another three weeks.
“The thoughts kind of at the time were, ‘What did I do wrong,’” Zenisek said. “I did everything that I was supposed to. I worked out regularly, I ate healthily, I tried to take really good care of myself — I did everything right.
“You start to think, ‘I'm a good person and I do everything I'm supposed to do,’ and then all the sudden you get hit with a diagnosis.”
After returning to Muncie, Zenisek’s doctor recommended she undergo six to eight weeks of chemotherapy, including daily radiation.
“During that second week of radiation, I had massive complications,” Zenisek said. “I couldn't keep anything down. I was really dehydrated. I ended up back in the hospital and what they determined at that point was that the radiation had burned my intestines to my abdominal wall.”
So, Zenisek went through another surgery and quit chemotherapy and radiation.
“I had to eat through a tube in my arm, which is not a diet plan that I recommend,” she said. “I lost about 20 pounds during the whole process, but fortunately at that point, I was still in the process of getting ready for a wedding, and I was like, ‘I don't want to lose my hair,’ and I was fortunate enough that I didn't.”
Zenisek’s final surgery happened in October 2009 and she and her fiance were married the following May. Zenisak didn’t have to move her wedding day and even got inspiration for her journey — she added teal, the color for cervical cancer, to her wedding dress.
And while it has been nine years since she was diagnosed, Zenisek said there is still a fear that the cancer may come back.
 “You know the challenges and so every time you go back for that test, you question, ‘Am I that strong enough to do it again,’ and ‘Can I put my family and my loved ones and my support system through that again?’’
A routine check up
It was 1995 and Peggy Fisher was 36 years old.  Nine months later, her mother died.
After that, she and her three sisters made it a habit to get routine mammograms. But in 2001, Fisher’s routine turned into a months-long relationship with breast cancer.
“I kept doing everything that I had done before,” Fisher said.
She went through chemo, had three lumpectomies — a surgery where only the tumor and some surrounding tissue are removed — and the cancer was gone — or so she thought.
Fourteen years later, Fisher found herself in the doctor’s office for another routine mammogram. She got the call 10 minutes before her next class that day.
“I got the phone call on my phone and I recognized the number and I went over to Beth Messner’s office and put it on speakerphone,” Fisher said. “You just go into fight mode. It’s like, ‘OK, what do I do next? Let’s get this going so I can get it over with.’”
The cancer was back in the same breast. This time it was more aggressive and a completely different chemical makeup.
She had a simple mastectomy — the removal of an entire breast, including the nipple, the areola and skin according to American Cancer Society — and the doctors brought out the “big guns” with chemo again, and this time it was harder, she said.
“You lose a boob and what does that do to you? And I never really thought that it mattered. I was like ‘OK, well no big deal,’” Fisher said. “Physically it didn’t really matter to me that much.”
During her second round with cancer, Fisher was teaching a fall 2015 immersive learning course in collaboration with Little Red Door — a local nonprofit that provides cancer patients with resources.
Fisher recalled leaving class to throw up and sometimes not coming in at all because she physically couldn’t. So, she would Skype in and her students continued to show up.
“Those students got more than they bargained for that semester. They shaved my head, they came to chemo with me. They came to doctor's appointments with me. They helped me pick out a fake boob,” Fisher said.
Despite the support from her students, there were times she felt others thought she invited the disease into her body. “It’s your diet,” they would say.
“That frustrated me that people thought I was doing something lifestyle-wise to cause my cancer,” Fisher said.
In spring 2016, Fisher started exercising with her son, Adam, who was training to serve in the military.
 “So I did it, and the next time I did a little more and next time I did a little more.”
Adam, would complete training exercises on the patio, like push ups, and she would join him, but he never let her do too much, she said.
Now, Fisher said she still deals with, and will forever, some side effects like pain in her feet, legs and hips and “chemo brain,” which affects a person’s ability to remember certain things, complete tasks or learn something new, according to American Cancer Society.
The hardest part for Fisher, though, was and still is the emotional recovery.
In fall 2016, Fisher shared a PowerPoint presentation with her students, which showed a photo of her after with a newly-shaved head in the wig room at Little Red Door — she had to send her students home that day.
“I've looked at these slides 100 times. It was no big deal, but as soon as it popped up on a big screen and I saw [it] larger than life, I had to stop class. I just stood there and bawled,” she said.
One in a thousand
“I was the first person who noticed a lump,” said David Sumner, professor emeritus of journalism.
That lump was found after the then 64-year-old came home from a June 2010 run. Sumner waited two weeks before seeing a doctor. After a needle biopsy, his suspicion was confirmed: breast cancer.
It was a Thursday. By Monday, Sumner had an appointment for a mastectomy.
According to the American Cancer Society, 1 in 1,000 men will be diagnosed with breast cancer is his lifetime. In 2018, American Cancer Society estimates 2,550 men will be diagnosed with breast cancer.

“I think the experience of cancer is definitely not a death sentence …  “I don't want to call it a blessing, but you can use that experience of suffering to make yourself a better person and a more caring person.”
When he was diagnosed, Sumner was a magazine-track professor. He taught students how to improve their writing and the technicalities of publishing, but he also gave students an up-close-and-personal look into male breast cancer.
Every Wednesday during the fall 2010 semester, Sumner, who never missed a day of class, would go to his 5-hour chemotherapy appointment in the morning and teach a class in the afternoon — through the chemo, hair loss and diet changes.
“I just wanted people to continue to relate to me like they always had,” Sumner said. “I didn't want people [to] start seeing me as a victim or starting asking a lot of questions about my treatment, so I just continued acting like I always had.”
Instead of taking the diagnosis as a death sentence, Sumner changed his diet and continued running. He cut out red meat, dairy and took daily supplements to be an active part of his cure.
Since 2011, Sumner, now in his 70s, has committed to running a half-marathon every year including the Kentucky Derby Festival miniMarathon and the Indianapolis 500 Festival Mini-Marathon — a tradition he started nine days after he finished radiation therapy.
But an active part of his cure wasn’t just about physical well-being, Sumner continued to develop his Christian faith. A woman in her 40s from Sumner’s church was diagnosed around the same time he was. They shared stories and struggles, but she died in 2015, giving Sumner survivor’s guilt, the guilt one feels when they survive something another dies from, according to Memorial Sloan Kettering Cancer Center.
“You know, why, why me, God?”
“I feel fortunate that God allowed me to continue to live and I felt like, ‘Well, he must have more for me to do,’ but I'm just so sad that not only her, but there was another woman in our church who died just from breast cancer just a year before that,” Sumner said.
In the last seven years, Sumner said three women from his church have died from breast cancer.
“Sometimes I'm not sure I was the one who deserved [life],” Sumner said.
Winning the war
In another waiting room, the couple is much more relaxed. They joke and tell stories about their dogs. Various people give side-eyed glances as they laugh, something Pareis said was not uncommon for them. On chemo days, Pareis said she, along with family and friends, would play games.
“We were always up there joking around and having fun and some people are like, ‘It's chemo, why are you laughing about chemo day?’ [But] why not,” Pareis said. “You've got to have fun to get through craziness like that, so why sit and be depressed about it because it's dark enough as it is.”
Despite 28 days of back and forth trips to the hospital, Sollars only missed a couple days as an anchor for Indiana Public Radio and physical class, but on those days, he had digital lessons for his students.
“We basically told him you have to stay home. If we were to have given him a choice, he would have been there,” Pareis said. “Right before chemo, we video recorded him teaching his students by himself in the studio. He was just like, ‘They won't miss anything.’”
The couple sits in an examination room, the doctor checking his vitals. He shivers as she presses a cold stethoscope to his chest. They hold their breath as the doctor tells them the news — he is clear, a diagnosis that still makes the couple wary.
“After I got the all-clear, I said, ‘Now what do we do?’ From the time of the diagnosis up through surgery to post-op, we had definite steps,” Sollars said. “I don’t know what it feels like to be in the armed services, but I can imagine that’s what a soldier feels after they get off the battlefield.
“I won the battle, but I had to figure out a way to win the war.”
They leave the examination room triumphant. Once more, they stroll through the network of hallways, saying goodbye to the receptionist, back through the sliding glass doors and into the adjacent parking garage.
His wife points out the spot where Sollars passed out after a nasty round of chemo. It is a reminder of a scarier time, a landmark of a past battle. The couple got into their car. He pulled out of the garage and onto University Boulevard. Their biggest worry was what they were going to eat for dinner.
And for today, that was enough.
Contact Brynn Mechem with comments at
Contact Mary Freda with comments<EMAIL_ADDRESS>or on Twitter@Mary_Freda1.


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This source file is subject to the new BSD license that is bundled with this package in the file LICENSE.txt. It is also available through the world-wide-web at this URL: http://framework.zend.com/license/new-bsd If you did not receive a copy of the license and are unable to obtain it through the world-wide-web, please send an email to<EMAIL_ADDRESS>so we can send you a copy immediately.
array
$_termStreams= ''
Array of term streams (Zend_Search_Lucene_Index_TermsStream_Interface objects)
__construct(
array $termStreams
)
:
void
Object constructor
| Name | Type | Description |
|---|---|---|
| $termStreams | array | array of term streams (Zend_Search_Lucene_Index_TermsStream_Interface objects) |
closeTermsStream(
)
:
void
Close terms stream
Should be used for resources clean up if stream is not read up to the end
currentTerm(
)
:
\Zend_Search_Lucene_Index_Term|null
Returns term in current position
| Type | Description |
|---|---|
| \Zend_Search_Lucene_Index_Termnull |
nextTerm(
)
:
\Zend_Search_Lucene_Index_Term|null
Scans term streams and returns next term
| Type | Description |
|---|---|
| \Zend_Search_Lucene_Index_Termnull |
skipTo(
\Zend_Search_Lucene_Index_Term $prefix
)
:
void
Skip terms stream up to the specified term preffix.
Prefix contains fully specified field info and portion of searched term
| Name | Type | Description |
|---|---|---|
| $prefix | \Zend_Search_Lucene_Index_Term |


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My name is Matias and I am a Software Engineer. I will never trust in somebody who cannot program ;)
Originally from Buenos Aires and currently living in Asia I am obsessed about travelling and technology.
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Meeting on pending Central Excise and Service Tax cases held
by Special CorrespondentA meeting to explain the procedure to be adopted for settling pending Central Excise and Service Tax-related cases under ‘Sabka Cishwas (Legacy Dispute Resolution) Scheme, 2019’ to taxpayers was held here on Saturday.
In order to clear pending cases, the advantages and other features of the scheme was explained to participants by officials of GST and Central Excise Department, led by the Assistant Commissioner, Thanjavur Division, A.T.Panneerselvam, at the meeting.
Taxpayers registered under GST in Thanjavur, Pudukottai, Kumbakonam, Tiruvarur and Nagapattinam and the chartered accountants and GST consultants from the areas attended the meeting.
The participants were also briefed about the simplified GST monthly return form to be introduced from the first quarter of the next financial year, a press release said.


The 'PayPal Mafia' formed in the early 2000s, and includes everyone from Elon Musk to the Yelp founders. Here's where the original members have ended up., Business Insider - Business Insider Singapore
by Tami Brehse, Business Insider US- Since working at PayPal in the early 2000s, these 23 entrepreneurs have all gone on to impact Silicon Valley and the startup world by founding companies and backing new ventures.
- Known for their entrepreneurial mindset and anti-establishment attitude, this elite group is known as the “PayPal Mafia.”
- Some are household names, like Elon Musk, but others who you might not have heard of have been impactful behind the scenes.
- Visit Business Insider’s homepage for more stories.
What do the founders of YouTube, Yelp, Tesla, and LinkedIn have in common? Apart from leading massively disruptive technology companies, their founders all share a common resume line item: employment at PayPal.
Known for their entrepreneurial mindset and anti-establishment attitude, this elite group is known as the “PayPal Mafia,” and they all put in time during the payment platform’s early days some 20 years ago.
Here’s a rundown of the most prominent members of this exclusive group and what they’re up to two decades later. You might want to break out your thumb tacks and red string, because the connections in this web run deep.
The so-called “don” of the PayPal Mafia is founder Peter Thiel.
Though it wouldn’t become a household name for years to come, PayPal was launched all the way back in 1999 as an offshoot of Thiel’s first company, Confinity. The platform was intended to give consumers an easier option for transferring money from handheld devices – the PalmPilot was one such device at the time.
Thiel served as CEO of PayPal until October of 2002, when eBay acquired it for $1.5 billion. Thiel’s 3.7% stake was worth a reported $55 million.
Thiel went on to cofound Founders Fund, a venture capital firm that has helped launch companies like SpaceX and Airbnb.
Thiel cofounded the big data firm Palantir in 2003. He was the first major outside investor in Facebook and contributed early funding to Yelp and LinkedIn, along with a number of other ventures launched by his PayPal peers. Thiel continues to serve as a partner of Founders Fund.
Thiel has also drawn criticism in recent years for his support of President Donald Trump and for secretly funding Hulk Hogan’s lawsuit against Gawker Media, which resulted in the company shutting down Gawker and selling the company’s assets.
In October, The Wall Street Journal reported Founders Fund is raising nearly $3 billion across two funds, one of which will pump money into late-stage startups that want to delay an IPO. Thiel is also dabbling in the growing cryptocurrency market. Fortune recently reported he was part of a $50 million investment in Layer1, a San Francisco-based Bitcoin-mining startup.
PayPal cofounder Max Levchin served as the company’s chief technology officer.
Levchin is sometimes called the “consigliere” of the PayPal Mafia – in “The Godfather,” a consigliere is an advisor to the boss.
Levchin made significant contributions to PayPal’s anti-fraud efforts. Together with PayPal technical architect David Gausebeck, he helped create the Gausebeck-Levchin test, an early version of a CAPTCHA for commercial applications.
Levchin served as the chairman of Yelp from its founding in 2004 until July 2015.
Levchin now serves as the CEO of Affirm, a payment platform he cofounded.
Affirm works like an instant layaway program, allowing consumers to finance online purchases at the point of sale and pay for them over time. In April, Reuters reported Affirm had raised $300 million in its latest funding round to support hiring and expansion.
Levchin is also the chairman of Glow, a fertility-tracking app that helps users improve their odds of conceiving.
Ken Howery is one of PayPal’s cofounders and served as its CFO from 1998 to 2002.
After eBay bought PayPal, Howery stayed on as eBay’s director of corporate development until 2003. In 2005, he entered into another venture with Thiel, serving as cofounder and partner of Founders Fund.
Howery is the current US ambassador to Sweden.
He was appointed by President Trump in January and confirmed in September.
Howery is active in several nonprofits and serves as a founding advisor to Kiva, an organization that facilitates loans to low-income entrepreneurs. Kiva was founded in part by Premal Shah, PayPal’s former product manager.
Elon Musk founded a payments company called X.com, which merged with Thiel’s Confinity in 2000. He was named CEO of PayPal, but it didn’t last.
Musk was ousted from his role as PayPal CEO that same year. But as the company’s largest shareholder, he still walked away from the PayPal sale to eBay with a cool $165 million.
Musk is currently No. 23 on Forbes’ list of the world’s richest people.
Musk’s estimated net worth is $20.8 billion. The SpaceX and Tesla Motors founder aims to revolutionize transportation on Earth and beyond. In October, the Tesla CEO made headlines for sending a tweet from a SpaceX Starlink satellite, which is part of a program working to provide fast and accessible internet around the world.
In 2017, Musk purchased the X.com domain name back from PayPal, citing its sentimental value.
PayPal cofounder Luke Nosek served as the company’s vice president of marketing and strategy.
He’s reportedly the person who clued in Peter Thiel to cryogenic preservation, which Thiel has since invested in heavily. In 2005, Nosek joined Thiel and Howery as a partner at Founders Fund.
In 2017, Nosek left Founders Fund to launch investment firm Gigafund, which helped raise money for SpaceX.
Nosek sits on the board of Musk’s SpaceX as well as ResearchGate, a platform where scientists and researchers can ask questions, follow topics, and review one another’s papers.
Roelof Botha started as PayPal’s director of corporate development.
Botha went to school to be an actuary. He said he never planned to get into tech, but when he saw the opportunity in Silicon Valley, his intuition told him it was where he needed to be.
He went on to become PayPal’s vice president of finance, and in 2001 was named CFO.
Now a partner of venture capital firm Sequoia Capital, Botha is considered one of the top tech investors in the world.
When combined, the companies Sequoia Capital has invested in have a total market value of over $1 trillion, and the firm has funded tech giants like Apple, Google, YouTube, and Instagram.
Botha sits on the board of more than a dozen companies, including Square, EventBrite, and Weebly.
LinkedIn cofounder Reid Hoffman served on the board of directors when PayPal was founded.
He eventually joined the company full-time at PayPal’s COO. In a New York Times interview, Peter Thiel referred to Hoffman as PayPal’s “firefighter in chief,” noting that there were many fires that needed putting out in the company’s early days.
When PayPal was acquired by eBay, Hoffman was the company’s executive vice president. He cofounded LinkedIn in 2002.
Hoffman is one of Silicon Valley’s most prolific angel investors.
He was an early investor in Facebook, Flickr, Care.com, and many more. In 2017 he joined the board of Microsoft.
Hoffman has coauthored several books on startups and professional development. He hosts the “Masters of Scale” podcast, on which he interviews founders about how they launched and scaled their companies, and is a partner at VC firm Greylock Partners.
Previously a management consultant for McKinsey & Company, David Sacks joined PayPal in 1999.
He, like Hoffman, served as COO for the company.
After PayPal was bought by eBay, Sacks produced and financed the box office hit “Thank You For Smoking,” which would go on to be nominated for two Golden Globes. In 2006 he founded Geni.com, an online tool for building family trees.
In 2016, Sacks was briefly interim CEO at Zenefits, an HR software firm that was plagued by scandal, including allowing unlicensed brokers to sell insurance to its customers.
In 2017, Sacks cofounded the early-stage investment firm Craft Ventures.
Sacks is a serial entrepreneur and investor, with angel investments in Airbnb, Postmates, Slack, and many more. In October, TechCrunch reported that Craft Ventures closed its second funding round with $500 million in capital commitments.
Jawed Karim, Chad Hurley, and Steve Chen met at PayPal during its early days.
Karim and Chen were engineers, while Hurley was a web designer.
In 2005, the trio launched the video-sharing platform YouTube. Karim uploaded the platform’s very first video, “Me at the zoo,” an 18-second clip of Karim in front of the San Diego Zoo’s elephant exhibit. It’s been viewed nearly 78 million times.
Today, Karim, Hurley, and Chen remain active entrepreneurs and investors with a hand in projects from finance to music.
In October, Karim contributed to a $15 million funding round for Truebill, a personal finance application.
News recently broke that Hurley is behind an in-the-works mobile gaming startup called GreenPark, which is reportedly set to launch in 2020.
Chen is an investor in actor Joseph Gordon-Levitt’s musical collaboration platform HitRecord, which in February secured $6.4 million in Series A funding.
Andrew McCormack joined PayPal in 2001.
He worked as an assistant to Peter Thiel as the company prepared for its IPO.
In 2003, McCormack started a restaurant group in San Francisco. In 2008, he joined Thiel Capital and worked there for 5 years.
McCormack and Thiel partnered up once again in 2010 to launch Valar Ventures.
Valar Ventures has invested in technology startups well beyond Silicon Valley, including some in Europe and Canada. In August, Crunchbase reported the firm had closed on a $150 million funding round for a new venture capital fund, Valar Fund V.
McCormack continues to serve as a general partner of the firm.
Entrepreneur Keith Rabois served as PayPal’s executive vice president from 2000 to 2002.
He would go on to join his PayPal colleague Reid Hoffman at LinkedIn as its vice president for business and corporate development from 2005 to 2007. He was an early investor in startups like Square, where he spent two-and-a-half years as COO.
In February, Rabois joined Thiel, Howery, and Nosek as a partner at Founders Fund.
In October, TechCrunch reported that Rabois was part of a $2 million investment in Vise AI, which uses machine learning to automate portfolio management.
He has served on the board of directors for Yelp, Xoom, and Reddit.
Russel Simmons and Jeremy Stoppelman worked on technology at PayPal.
Simmons was an engineer and Stoppelman was the vice president of technology after joining PayPal from X.com.
In 2004, the pair came up with the idea for a platform where users could leave recommendations about businesses in their area. They pitched the idea to Levchin, who provided an early investment of $1 million, and Yelp was born.
Simmons left his official role at Yelp in 2010, while Stoppelman continues to serve as Yelp’s CEO.
Stoppelman takes a salary of $1. This, of course, is in addition to his 11% stake in the company.
Jack Selby served as PayPal’s vice president of corporate and international development.
After leaving PayPal, Selby partnered with Thiel to start Clarium Capital Management.
In 2017, Selby was revealed to be the generous tipper behind “Tips for Jesus.”
Starting in 2013, Selby began anonymously leaving tips for unsuspecting waitstaff, ranging into the thousands, and signing them “Tips for Jesus.” His identity was confirmed by a New York City bartender who served him prior to receiving a $5,000 tip.
Selby serves as managing director at Thiel Capital.
Dave McClure served as PayPal’s director of marketing for four years beginning in 2001.
According to McClure’s LinkedIn, he began a program called the PayPal Developer Network, which consisted of about 300,000 developers that were using PayPal.
McClure left PayPal in 2004. He had a brief stint at Founders Fund before launching 500 Startups, an early stage venture fund.
McClure stayed at 500 Startups until June 2017, when he was accused of “inappropriate behavior with women” in a New York Times report and stepped down from his role at the firm.
“After being made aware of instances of Dave having inappropriate behavior with women in the tech community, we have been making changes internally,” 500 Startups told The New York Times. “He recognizes he has made mistakes and has been going through counseling to work on addressing changes in his previous unacceptable behavior.”
There are several more former PayPal employees who went on to have careers both in and out of technology.
- Yishan Wong was an engineering manager who later served as CEO of Reddit from 2011 to 2014.
- Jason Portnoy worked in finance at PayPal, and went on to work at Clarium Capital and Palantir. He’s now a partner at VC firm Oakhouse Partners.
- Premal Shah was a product manager at PayPal beginning in 2000, then went on to work at technology nonprofit Kiva. He’s currently working at financial-services startup Branch.
- David Gausebeck was a technical architect at PayPal. Now, he serves as chief technology officer at 3D modeling company Matterport.
- Joe Lonsdale started his career as a finance intern at PayPal before moving into venture capital – he’s worked at VC firms Clarium Capital, Formation 8, and 8VC. Lonsdale also cofounded Palantir.
- Eric Jackson was director of marketing at PayPal and went on to write a book about the company called “The PayPal Wars.” He’s currently the CEO of two companies, CapLinked and TransitNet.


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This example demonstrates how to create a DICOM Segmentation object.
Sample data is from the dicom4qi connectathon event to be held at RSNA 2017. This is test dataset #2


---
title: List of all Math Problems For Online Practice | FromDev
layout: post
---
{% include common-meta %}
{% feed_meta %}
{{ page.title }}
{% include theme-css %}
{% include ie-fixes %}
{% include theme-header %}
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The number input is just the bootstrap default:
The range input has some styling optimization for touch devices:
In many cases you'd like to give especially the touchscreen user a combination of these input elements, and as sliding may be too imprecise you also need large buttons for single step changes. That's what the slider widget does. It also adds the option of a checkbox and a default value to reflect if the user control shall be applied.
Sliders can easily be created from minimal markup using the .slider()
plugin,
with configuration given by data attributes and/or dynamic options:
…and slider state, limits and value can be controlled using the same method:
If you need even more control, you can create the slider markup yourself as well:
Events & values:
Checkbox, buttons and unit are optional. You can reduce the widget to just a number or
just a range input, the input then needs to have the slider-input
class.
The checkbox element defines the default value to be set on unchecking in
data-default
. By default, the checkbox will restore the previous user
value when re-checked, to disable this, set data-reset
to "true".
To reset the value to the default from a script, call the .slider()
method with value: null
(this resets both the actual and the stored
user value).
Values and checkbox status need to be consistent on init, or be set by your script.
To hook into value changes, attach event handlers to events input
and/or change
as usual. Read the checked
property to get the
checkbox state.


Designing Adaptive Experiments to Study Working Memory¶
In most of machine learning, we begin with data and go on to learn a model. In other contexts, we also have a hand in the data generation process. This gives us an exciting opportunity: we can try to obtain data that will help our model learn more effectively. This procedure is called optimal experimental design (OED) and Pyro supports choosing optimal designs through the pyro.contrib.oed
module.
When using OED, the data generation and modelling works as follows:
Write down a Bayesian model involving a design parameter, an unknown latent variable and an observable.
Choose the optimal design (more details on this later).
Collect the data and fit the model, e.g. using
SVI
.
We can also run multiple ‘rounds’ or iterations of experiments. When doing this, we take the learned model from step 3 and use it as our prior in step 1 for the next round. This approach can be particularly useful because it allows us to design the next experiment based on what has already been learned: the experiments are adaptive.
In this tutorial, we work through a specific example of this entire OED procedure with multiple rounds. We will show how to design adaptive experiments to learn a participant’s working memory capacity. The design we will be adapting is the length of a sequence of digits that we ask a participant to remember. Let’s dive into the full details.
The experiment set-up¶
Suppose you, the participant, are shown a sequence of digits
which are then hidden. You have to to reproduce the sequence exactly from memory. In the next round, the length of the sequence may be different
The longest sequence that you can remember is your working memory capacity. In this tutorial, we build a Bayesian model for working memory, and use it to run an adaptive sequence of experiments that very quickly learn someone’s working memory capacity.
A model of working memory¶
Our model for a single round of the digits experiment described above has three components: the length \(l\) of the sequence that the participant has to remember, the participant’s true working memory capacity \(\theta\), and the outcome of the experiment \(y\) which indicates whether they were able to remember the sequence successfully (\(y=1\)) or not (\(y=0\)). We choose a prior for working memory capacity based on the (in)famous “The magical number seven, plus or minus two” [1].
Note: \(\theta\) actually represents the point where the participant has a 50/50 chance of remembering the sequence correctly.
[2]:
import torch
import pyro
import pyro.distributions as dist
sensitivity = 1.0
prior_mean = torch.tensor(7.0)
prior_sd = torch.tensor(2.0)
def model(l):
# Dimension -1 of `l` represents the number of rounds
# Other dimensions are batch dimensions: we indicate this with a plate_stack
with pyro.plate_stack("plate", l.shape[:-1]):
theta = pyro.sample("theta", dist.Normal(prior_mean, prior_sd))
# Share theta across the number of rounds of the experiment
# This represents repeatedly testing the same participant
theta = theta.unsqueeze(-1)
# This define a *logistic regression* model for y
logit_p = sensitivity * (theta - l)
# The event shape represents responses from the same participant
y = pyro.sample("y", dist.Bernoulli(logits=logit_p).to_event(1))
return y
The probability of successfully remembering the sequence is plotted below, for five random samples of \(\theta\).
[4]:
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams.update({'font.size': 22})
# We sample five times from the prior
theta = (prior_mean + prior_sd * torch.randn((5,1)))
l = torch.arange(1, 16, dtype=torch.float)
# This is the same as using 'logits=' in the prior above
prob = torch.sigmoid(sensitivity * (theta - l))
plt.figure(figsize=(12, 8))
for curve in torch.unbind(prob, 0):
plt.plot(l.numpy(), curve.numpy(), marker='o')
plt.xlabel("Length of sequence $l$")
plt.ylabel("Probability of correctly remembering\na sequence of length $l$")
plt.legend(["Person {}".format(i+1) for i in range(5)])
plt.show()
Inference in the model¶
With the model in hand, we quickly demonstrate variational inference in Pyro for this model. We define a Normal guide for variational inference.
[5]:
from torch.distributions.constraints import positive
def guide(l):
# The guide is initialised at the prior
posterior_mean = pyro.param("posterior_mean", prior_mean.clone())
posterior_sd = pyro.param("posterior_sd", prior_sd.clone(), constraint=positive)
pyro.sample("theta", dist.Normal(posterior_mean, posterior_sd))
We finally specify the following data: the participant was shown sequences of lengths 5, 7 and 9. They remembered the first two correctly, but not the third one.
[6]:
l_data = torch.tensor([5., 7., 9.])
y_data = torch.tensor([1., 1., 0.])
We can now run SVI on the model.
[7]:
from pyro.infer import SVI, Trace_ELBO
from pyro.optim import Adam
conditioned_model = pyro.condition(model, {"y": y_data})
svi = SVI(conditioned_model,
guide,
Adam({"lr": .001}),
loss=Trace_ELBO(),
num_samples=100)
pyro.clear_param_store()
num_iters = 5000
for i in range(num_iters):
elbo = svi.step(l_data)
if i % 500 == 0:
print("Neg ELBO:", elbo)
Neg ELBO: 1.6167092323303223
Neg ELBO: 3.706324815750122
Neg ELBO: 0.9958380460739136
Neg ELBO: 1.0630500316619873
Neg ELBO: 1.1738307476043701
Neg ELBO: 1.6654635667800903
Neg ELBO: 1.296904444694519
Neg ELBO: 1.305729627609253
Neg ELBO: 1.2626266479492188
Neg ELBO: 1.3095542192459106
[9]:
print("Prior: N({:.3f}, {:.3f})".format(prior_mean, prior_sd))
print("Posterior: N({:.3f}, {:.3f})".format(pyro.param("posterior_mean"),
pyro.param("posterior_sd")))
Prior: N(7.000, 2.000)
Posterior: N(7.749, 1.282)
Under our posterior, we can see that we have an updated estimate for the participant’s working memory capacity, and our uncertainty has now decreased.
Bayesian optimal experimental design¶
So far so standard. In the previous example, the lengths l_data
were not chosen with a great deal of forethought. Fortunately, in a setting like this, it is possible to use a more sophisticated strategy to choose the sequence lengths to make the most of every question we ask.
We do this using Bayesian optimal experimental design (BOED). In BOED, we are interested in designing experiments that maximise the information gain, which is defined formally as
where \(KL\) represents the Kullback-Leiber divergence.
In words, the information gain is the KL divergence from the posterior to the prior. It therefore represents the distance we “move” the posterior by running an experiment with length \(l\) and getting back the outcome \(y\).
Unfortunately, we will not know \(y\) until we actually run the experiment. Therefore, we choose \(l\) on the basis of the expected information gain [2]
Because it features the posterior density \(p(y|\theta,l)\), the EIG is not immediately tractable. However, we can make use of the following variational estimator for EIG [3]
Optimal experimental design in Pyro¶
Fortunately, Pyro comes ready with tools to estimate the EIG. All we have to do is define the “marginal guide” \(q(y|l)\) in the formula above.
[10]:
def marginal_guide(design, observation_labels, target_labels):
# This shape allows us to learn a different parameter for each candidate design l
q_logit = pyro.param("q_logit", torch.zeros(design.shape[-2:]))
pyro.sample("y", dist.Bernoulli(logits=q_logit).to_event(1))
This is not a guide for inference, like the guides normally encountered in Pyro and used in SVI
. Instead, this guide samples only the observed sample sites: in this case "y"
. This makes sense because conventional guides approximate the posterior \(p(\theta|y, l)\) whereas our guide approximates the marginal \(p(y|l)\).
[11]:
from pyro.contrib.oed.eig import marginal_eig
# The shape of `candidate_designs` is (number designs, 1)
# This represents a batch of candidate designs, each design is for one round of experiment
candidate_designs = torch.arange(1, 15, dtype=torch.float).unsqueeze(-1)
pyro.clear_param_store()
num_steps, start_lr, end_lr = 1000, 0.1, 0.001
optimizer = pyro.optim.ExponentialLR({'optimizer': torch.optim.Adam,
'optim_args': {'lr': start_lr},
'gamma': (end_lr / start_lr) ** (1 / num_steps)})
eig = marginal_eig(model,
candidate_designs, # design, or in this case, tensor of possible designs
"y", # site label of observations, could be a list
"theta", # site label of 'targets' (latent variables), could also be list
num_samples=100, # number of samples to draw per step in the expectation
num_steps=num_steps, # number of gradient steps
guide=marginal_guide, # guide q(y)
optim=optimizer, # optimizer with learning rate decay
final_num_samples=10000 # at the last step, we draw more samples
# for a more accurate EIG estimate
)
We can visualize the EIG estimates that we found.
[12]:
plt.figure(figsize=(10,5))
matplotlib.rcParams.update({'font.size': 22})
plt.plot(candidate_designs.numpy(), eig.detach().numpy(), marker='o', linewidth=2)
plt.xlabel("$l$")
plt.ylabel("EIG($l$)")
plt.show()
[13]:
best_l = 1 + torch.argmax(eig)
print("Optimal design:", best_l.item())
Optimal design: 7
This tells us that the first round should be run with a sequence of length 7. Note that, while we might have been able to guess this optimal design intuitively, this same framework applies equally well to more sophisticated models and experiments where finding the optimal design by intuition is more challenging.
As a side-effect of training, our marginal guide \(q(y|l)\) has approximately learned the marginal distribution \(p(y|l)\).
[14]:
q_prob = torch.sigmoid(pyro.param("q_logit"))
print(" l | q(y = 1 | l)")
for (l, q) in zip(candidate_designs, q_prob):
print("{:>4} | {}".format(int(l.item()), q.item()))
l | q(y = 1 | l)
1 | 0.9849993586540222
2 | 0.9676634669303894
3 | 0.9329487681388855
4 | 0.871809720993042
5 | 0.7761920690536499
6 | 0.6436398029327393
7 | 0.4999988079071045
8 | 0.34875917434692383
9 | 0.22899287939071655
10 | 0.13036076724529266
11 | 0.06722454726696014
12 | 0.03191758319735527
13 | 0.015132307074964046
14 | 0.00795808993279934
The elements of this fitted tensor represent the marginal over \(y\), for each possible sequence length \(l\) in candidate_designs
. We have marginalised out the unknown \(\theta\) so this fitted tensor shows the probabilities for an ‘average’ participant.
The adaptive experiment¶
We now have the ingredients to build an adaptive experiment to study working memory. We repeat the following steps:
Use the EIG to find the optimal sequence length \(l\)
Run the test using a sequence of length \(l\)
Update the posterior distribution with the new data
At the first iteration, step 1 is done using the prior as above. However, for subsequent iterations, we use the posterior given all the data so far.
In this notebook, the “experiment” is performed using the following synthesiser
[15]:
def synthetic_person(l):
# The synthetic person can remember any sequence shorter than 6
# They cannot remember any sequence of length 6 or above
# (There is no randomness in their responses)
y = (l < 6.).float()
return y
The following code allows us to update the model as we gather more data.
[16]:
def make_model(mean, sd):
def model(l):
# Dimension -1 of `l` represents the number of rounds
# Other dimensions are batch dimensions: we indicate this with a plate_stack
with pyro.plate_stack("plate", l.shape[:-1]):
theta = pyro.sample("theta", dist.Normal(mean, sd))
# Share theta across the number of rounds of the experiment
# This represents repeatedly testing the same participant
theta = theta.unsqueeze(-1)
# This define a *logistic regression* model for y
logit_p = sensitivity * (theta - l)
# The event shape represents responses from the same participant
y = pyro.sample("y", dist.Bernoulli(logits=logit_p).to_event(1))
return y
return model
Now we have everything to run a 10-step experiment using adaptive designs.
[17]:
ys = torch.tensor([])
ls = torch.tensor([])
history = [(prior_mean, prior_sd)]
pyro.clear_param_store()
current_model = make_model(prior_mean, prior_sd)
for experiment in range(10):
print("Round", experiment + 1)
# Step 1: compute the optimal length
optimizer = pyro.optim.ExponentialLR({'optimizer': torch.optim.Adam,
'optim_args': {'lr': start_lr},
'gamma': (end_lr / start_lr) ** (1 / num_steps)})
eig = marginal_eig(current_model, candidate_designs, "y", "theta", num_samples=100,
num_steps=num_steps, guide=marginal_guide, optim=optimizer,
final_num_samples=10000)
best_l = 1 + torch.argmax(eig).float().detach()
# Step 2: run the experiment, here using the synthetic person
print("Asking the participant to remember a sequence of length", int(best_l.item()))
y = synthetic_person(best_l)
if y:
print("Participant remembered correctly")
else:
print("Participant could not remember the sequence")
# Store the sequence length and outcome
ls = torch.cat([ls, best_l.expand(1)], dim=0)
ys = torch.cat([ys, y.expand(1)])
# Step 3: learn the posterior using all data seen so far
conditioned_model = pyro.condition(model, {"y": ys})
svi = SVI(conditioned_model,
guide,
Adam({"lr": .005}),
loss=Trace_ELBO(),
num_samples=100)
num_iters = 2000
for i in range(num_iters):
elbo = svi.step(ls)
history.append((pyro.param("posterior_mean").detach().clone().numpy(),
pyro.param("posterior_sd").detach().clone().numpy()))
current_model = make_model(pyro.param("posterior_mean").detach().clone(),
pyro.param("posterior_sd").detach().clone())
print("Estimate of \u03b8: {:.3f} \u00b1 {:.3f}\n".format(*history[-1]))
Round 1
Asking the participant to remember a sequence of length 7
Participant could not remember the sequence
Estimate of θ: 5.788 ± 1.636
Round 2
Asking the participant to remember a sequence of length 6
Participant could not remember the sequence
Estimate of θ: 4.943 ± 1.252
Round 3
Asking the participant to remember a sequence of length 5
Participant remembered correctly
Estimate of θ: 5.731 ± 1.043
Round 4
Asking the participant to remember a sequence of length 6
Participant could not remember the sequence
Estimate of θ: 5.261 ± 0.928
Round 5
Asking the participant to remember a sequence of length 5
Participant remembered correctly
Estimate of θ: 5.615 ± 0.859
Round 6
Asking the participant to remember a sequence of length 6
Participant could not remember the sequence
Estimate of θ: 5.423 ± 0.888
Round 7
Asking the participant to remember a sequence of length 6
Participant could not remember the sequence
Estimate of θ: 5.092 ± 0.763
Round 8
Asking the participant to remember a sequence of length 5
Participant remembered correctly
Estimate of θ: 5.371 ± 0.717
Round 9
Asking the participant to remember a sequence of length 5
Participant remembered correctly
Estimate of θ: 5.597 ± 0.720
Round 10
Asking the participant to remember a sequence of length 6
Participant could not remember the sequence
Estimate of θ: 5.434 ± 0.640
Now let’s visualize the evolution of the posterior over \(\theta\):
[19]:
import numpy as np
from scipy.stats import norm
import matplotlib.colors as colors
import matplotlib.cm as cmx
matplotlib.rcParams.update({'font.size': 22})
cmap = plt.get_cmap('winter')
cNorm = colors.Normalize(vmin=0, vmax=len(history)-1)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cmap)
plt.figure(figsize=(12, 6))
x = np.linspace(0, 14, 100)
for idx, (mean, sd) in enumerate(history):
color = scalarMap.to_rgba(idx)
y = norm.pdf(x, mean, sd)
plt.plot(x, y, color=color)
plt.xlabel("$\\theta$")
plt.ylabel("p.d.f.")
plt.show()
(Blue = prior, light green = 10 step posterior)
By contrast, suppose we use a simplistic design: try sequences of lengths 1, 2, …, 10.
[20]:
pyro.clear_param_store()
ls = torch.arange(1, 11, dtype=torch.float)
ys = synthetic_person(ls)
conditioned_model = pyro.condition(model, {"y": ys})
svi = SVI(conditioned_model,
guide,
Adam({"lr": .005}),
loss=Trace_ELBO(),
num_samples=100)
num_iters = 2000
for i in range(num_iters):
elbo = svi.step(ls)
[22]:
plt.figure(figsize=(12,6))
matplotlib.rcParams.update({'font.size': 22})
y1 = norm.pdf(x, pyro.param("posterior_mean").detach().numpy(),
pyro.param("posterior_sd").detach().numpy())
y2 = norm.pdf(x, history[-1][0], history[-1][1])
plt.plot(x, y1)
plt.plot(x, y2)
plt.legend(["Simple design", "Optimal design"])
plt.xlabel("$\\theta$")
plt.ylabel("p.d.f.")
plt.show()
Although both design strategies give us data, the optimal strategy ends up with a posterior distribution that is more peaked: that means we have greater confidence in our final answer, or may be able to stop experimenting earlier.
Extensions¶
In this tutorial we used variational inference to fit an approximate posterior for \(\theta\). This could be substituted for an alternative posterior inference strategy, such as Hamiltonian Monte Carlo.
The model in this tutorial is very simple and could be extended in a number of ways. For instance, it’s possible that as well as measuring whether the participant did or did not remember the sequence, we might collect some other information as well. We could build a model for the number of mistakes made (e.g. the edit distance between the correct sequence and the participant’s response) or jointly model the correctness and the time taken to respond. Here is an example model where we model the response time using a LogNormal distribution, as suggested by [4].
[18]:
time_intercept = 0.5
time_scale = 0.5
def model(l):
theta = pyro.sample("theta", dist.Normal(prior_mean, prior_sd))
logit_p = sensitivity * (theta - l)
correct = pyro.sample("correct", dist.Bernoulli(logits=logit_p))
mean_log_time = time_intercept + time_scale * (theta - l)
time = pyro.sample("time", dist.LogNormal(mean_log_time, 1.0))
return correct, time
It would still be possible to compute the EIG using marginal_eig
. We would replace "y"
by ["correct", "time"]
and the marginal guide would now model a joint distribution over the two sites "correct"
and "time"
.
Our model also made a number of assumptions that we may choose to relax. For instance, we assumed that all sequences of the same length are equally easy to remember. We also fixed the sensitivity
to be a known constant: it’s likely we would need to learn this. We could also think about learning at two levels: learning global variables for population trends as well as local variables for individual level effects. The current model is an individual only model. The EIG could still be used as a
means to select the optimal design in such scenarios.
References¶
[1] Miller, G.A., 1956. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological review, 63(2), p.81.
[2] Chaloner, K. and Verdinelli, I., 1995. Bayesian experimental design: A review. Statistical Science, pp.273-304.
[3] Foster, A., Jankowiak, M., Bingham, E., Horsfall, P., Teh, Y.W., Rainforth, T. and Goodman, N., 2019. Variational Bayesian Optimal Experimental Design. Advances in Neural Information Processing Systems 2019 (to appear).
[4] van der Linden, W.J., 2006. A lognormal model for response times on test items. Journal of Educational and Behavioral Statistics, 31(2), pp.181-204.


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To start off, I am 15 years old and have had A LOT of exp. with diphenydramine (benadryl). I have been tripping on it for a little over 2 years. I started off lightly, I would take 4-10 25mg benadryls about once every other week. I discovered that I liked it, a lot. It's something different, undescribable, it just literally takes me to another world.
In the past year I have been taking benadryl like a heroin addict. I have probably not went more than 2 weeks without tripping in the past year. I usually now take 12-20 dryls every other night or so.
I remember my doctor telling me about tumors from benadryl over long-term use. I recently (past 2 or 3 months) have been experiencing weird side effects. Like messed up vision, hearing, random muscle spasms, EXTREME tiredness and EXTREME boredom/paranoia. Earlier today I was researching signs of tumors and I have almost (if not every) sign. I am extremely scared.
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.


Plugin Information |
|---|
View Pipeline: Milestone Step on the plugin site for more information. |
Provides the milestone step in Pipeline scripts.
By default, Pipeline builds can run concurrently. The milestone step ensures an older build will not override a newer build, so the older build will never be allowed to pass a milestone
(it is aborted) if a newer build already passed it.
This step is specially useful in Continuous Delivery pipelines, where builds ordering is very important as the order defines what is going to be delivered, so it provides a control point that aborts any build trying to deliver an old commit when a newer one is already being delivered.
In general this step grants:
- Builds pass milestones in order (taking the build number as sorter field).
- Older builds will not proceed (they are aborted) if a newer one already passed the milestone.
- When a build passes a milestone, any older build that passed the previous milestone but not this one is aborted (see TIP 2 below).
There are two optional parameters:
ordinal
: sequential number which makes milestones comparable. For any given two consecutive milestones, ordinal for the second milestone must be a greater integer than the ordinal for the first one. If not specified an auto-incremented ordinal is internally generated.label
: just for displaying purposes in Pipeline visual representations (provided by other plugins).
This examples are both valid:
milestone 1 ... pipeline code here milestone 2 ... more pipeline code milestone 3 ... and more
milestone 100 ... pipeline code here milestone 200 ... more pipeline code milestone 300 ... and more
TIP 1: A combination of this step and the lock
step can be used to configure a reliable Continuous Delivery pipeline as both delivering latest code status and unique deployments are granted. There is an example of this pattern in the Pipeline CD demo Jenkinsfile.
TIP 2: retroactive builds abort, which is older builds being cancelled once a newer one passes a milestone that the older builds has not passed yet. For example, given this script:
milestone 1 input message: 'Continue?' milestone 2
If three builds (#1, #2 and #3) start concurrently they are all going to stop on input
step waiting for user interaction. If the user allows build #3 to proceed (so it passes milestone 2) then builds #2 and #1 will be automatically cancelled.
Changelog
1.3.1 (Mar 28, 2017)
- NPE in CleanupJobsOnDelete (JENKINS-41311)
1.3 (Dec 28, 2016)
- Clean up milestone build counter on job delete (JENKINS-38641)
1.2 (Nov 17, 2016)
- Fix a small logging quirk (logging the wrong ordinal)
1.1 (Oct 5, 2016)
- Fix milestone step being incorrectly detected as inside a parallel (JENKINS-38464)
1.0 (Sep 20, 2016)
- First release


Dynamically properties via computed properities
add
remove
validation: {{ validation }}
added dynamic property: prop:{{key}}, value:{{value}}


Just a note, I've found that I can't test my markdown parser vs others. For example, both markdown.js and showdown code blocks in lists wrong. They're also completely inconsistent with regards to paragraphs in list items.
A link. Not anymore.
List Item 1
List Item 2
Code goes here.
Lots of it...
List Item 3 The final item.
List Item 4 The real final item.
Paragraph.
- bq Item 1
- bq Item 2
- New bq Item 1
- New bq Item 2 Text here
Another blockquote! I really need to get more creative with mockup text.. markdown.js breaks here again
Hello world. Here is a link. And an image .
Code goes here.
Lots of it...


I am a 35 year old male.
I am currently unemployed.My most recent Job was working for a start-up company called Gametech as an operations manager.
It is at Gametech that i became exposed first hand to the power of technology, which sparked my interest in learning programming.
I enjoy reading a good book.
I love watching movies, series and documentaries.
I have always been interested in music hence i picked up playing the piano.
I enjoy good conversation with friends and family.


You create and share, they then simply use or customize and even re-share themselves.
In fact, there are many benefits to using checklists.
These benefits allow people to engage with you (as a brand, as an organization, as a person) in a positive context that brings them value.
It is always better to... "check yo self, before you wreck yourself."
Registration is simple. Usage is free. We respect and value privacy.


Carbon footprint tracker for purchases Enfuce raises €10m
by Ruby HinchliffeFinnish fintech Enfuce, a start-up showing eight million end users how large the carbon footprint is on their purchases, has just secured €10 million in new funding to scale its newly-launched ‘My Carbon Action’ service.
Led by Finland-based early stage venture capital firm Maki.vc, the financial services partner showing banking customers their impact on the environment also secured venture debt from Nordea, insurance company LähiTapiola, and Finnish state-owned financing company Finnvera.
Read more: Tickr plans to raise capital in 2020 to aid its European expansion
Dubbed the biggest fintech start-up in Finland by Helsinki Fintech Farm – a resource for Finnish fintech activity, the 2016-founded company is now working with the likes of MasterCard, Visa and Amazon Web Services (AWS) to develop its new service.
“Our goal is to help consumers become more carbon conscious, which is why we wanted to create a product that gives individuals accurate feedback based on their actual purchase history and lifestyle,” says Enfuce co-founder Monika Liikamaa.
“Only by bringing individualised and systematic insights can we help consumers make sustainable choices a habit.”
Enfuce’s new service helps banks to track and calculate the carbon dioxide equivalent (CO2e) emissions of every purchase made by their consumers, which then in turn provides insights into each person’s climate change impact.
Read next: Three key steps that can put banks ahead in the sustainability revolution


This is Words and Buttons Online — a collection of interactive #tutorials, #demos, and #quizzes about #mathematics, #algorithms and #programming.
TL&DR: Yes, but you get what you pay for.
I stole this idea from an old Russian book named Notable Curves by Alekséi Markushévich. The book was published in 1952 and, as you can imagine, it has nothing to do with computer graphics at all.
In that book, however, was a casual mention of a mathematical proof that the multifocal lemniscate can approximate any possible curve with some given error. There were also some promising but frankly unrealistic pictures of a man's face and a bird, both produced by lemniscates with a handful of foci.
Which got me thinking. SVG is relatively cheap memory-wise but this looks even cheaper. For instance, to approximate just a piece of a curve with the Bézier cubic, you already have to store 4 points somewhere. With the lemniscate, you can approximate a whole face or a bird with only 6 or 7 points. Isn't it wonderful?! Almost too good to be true, right?
A lemniscate is an isoline of the product of distances to the foci. First, you set some n points xi you call foci, then you build a function that for any point x, computes all the distances to the foci, and multiply them together. Where your function meets some number — there is your curve.
|x - x1| × |x - x2| × ... × |x - xn| = ρ
As it turns out, this is not too handy to play with since you have to adjust the ρ every time you add or remove a point. It is a little irritating so I changed the formula just a bit to make it more practical.
|x - x1| × |x - x2| × ... × |x - xn| = r n
Now you can add and remove points without readjusting the r.
I've made a mini-editor for lemniscate graphics with a few examples. Feel free to explore!
You can export your image into a set of points, and reimport it back.
Ok, so it's not that easy to make it draw exactly what you want it to. It is mathematically possible to draw everything, sure, but pragmatically, it only makes sense to draw the simplest shapes with this tool. With a limited set of points, it's not very powerful, and with adding more and more points, it gets less and less user-friendly.
But what about image tracing? Shouldn't it automagically turn poorly pixelated pictures into nice and smooth vector images?
Well, yes and no. With lemniscate approximating a pixel image, the error function is not very friendly to the gradient descent method. There are some seemingly random platoes and points masquerading as local minimums that make the minimizer stall every now and then. I tried a few approaches but, apparently, a simple random crawler works the best.
And this “the best” is still not very good.
Here's my tracer. It minimizes the error function one small step at a time, making the vector image closer to the pixelated original. If you're not afraid to put your browser into a coma, you can ask for drastically more iterations or raise the step size.
Of course, you're more than welcome to explore it further! You can start from the code for this very page available on GitHub. Besides, multifocal lemniscate is a very simple concept, it all revolves around one simple formula. You can easily reimplement it in your exploration environment of choice.
The practicality of this approach is limited. For real-world vector graphics, its application is doubtful. It's way easier to approximate curves with splines of all kinds both automatically and manually.
But when you're really constrained by memory, and a badly drawn cat of 19 bytes is a wanted alternative for a nice cat of a few kilobytes, then yes — multifocal lemniscate is your friend. For instance, it might give you an advantage in the js13kGames or the js1024 competition.
| Index #mathematics #demos | ← there's more. |
+
So You Think You Know C (free book) + Geometry for Programmers (paid book) + Github & RSS |


Front End Developer
with a demonstrated history of working in the Computer Software industry. Strong engineering professional skilled in HTML, CSS, Javascript, JQuery and Angular(2+).
Worked on projects with latest technologies which include Angular 4, Node.js, Backbone.js, Marionette.js.
Learnt technologies like JSP, Servlets, Javascript, MySql, Apache
The project is a conversion of flex built application into HTML5 based web application.
It's a Canada organization banking product which maintains an admin and console screens for their members.Project allow the students of a college to select their course which they want to study in their upcoming semester from a set of optional courses from which students can opt.
The software deals with the interaction between a faculty and his/her student. It allows faculty to create lecture plans, test, assignments, marks entry etc basic functionalities of a classroom for his subjects which is visible to his students.
The software is for the organizations to manage and analyze customer interactions and data, throughout the customer lifecycle with the organization. Along with maintaining customer data it provides multiple functionalities like graphical analytics, reminder and notification services etc. .
A simple example demonstrating image drop on a particular container using html drop event. Application shows a container where any image from the file system can be dropped. API takes in a array of elements for binding drop event, and returns a callback method for executing your code for the drop event handle.
A simple example demonstrating Mustache.js templating engine, rendering html content on the server and send the pdf content in response. And on client side, the response is just displayed in the next browser tab which shows the rendered pdf into the browser.
To store your any text message, image, or a video into a specified Video (say, Stego Video) such that there is no difference in Video after embedding the data. Same data hiding can be done in your specified images without altering Stego Image. Algorithms used :
Aggregate : 79.8%
Aggregate : 79.8%
Aggregate : 87.6%
Aggregate : 89.8%
When i am not working, i try my hands on sketching, drawing. Other measures for time cut includes movies and playing cricket.


PMR
DataMining
Welcome to PMR DataMining Dashboard
Monthly
/ Level-2
Loading...
Header
Welcome to PMR Data Mining


|
EMDB
Queryable Embedded Database for the Tiniest of Devices
|
Header for Query Functions. More...
Go to the source code of this file.
Classes | |
| struct | Where |
| struct | QueryResults |
| struct | SimpleQueryContext |
Typedefs | |
| typedef enum WhereType | WhereType |
| Type of Equality. More... | |
| typedef enum ValueType | ValueType |
| typedef struct Where | Where |
| typedef struct QueryResults | QueryResults |
| typedef struct SimpleQueryContext | SimpleQueryContext |
Enumerations | |
| enum | WhereType { query_and = 0, query_or = 1, query_equals = 2, query_gt = 3, query_lt = 4, query_gte = 5, query_lte = 6, query_between = 7, query_in = 8 } |
| Type of Equality. More... | |
| enum | ValueType { string, integer, floatingpoint } |
Functions | |
| void | emdb_query_db (EMDB *, Where *, void(*)(QueryResults *)) |
| void | emdb_free_results (QueryResults *) |
| uint8_t | compare_float (float, float, WhereType) |
| Compares two floats. More... | |
| uint8_t | compare_int (uint16_t, uint16_t, WhereType) |
| Compares two integers. More... | |
| uint8_t | compare_string (uint8_t *, uint8_t *, WhereType) |
| Compares two strings. More... | |
| uint8_t | compare_float_between (float, float, float) |
| Checks to see if a float is between two other floats, inclusively. More... | |
| uint8_t | compare_int_between (uint16_t, uint16_t, uint16_t) |
| Checks to see if an integer is between two other integers, inclusively. More... | |
| uint8_t | compare_string_between (uint8_t *, uint8_t *, uint8_t *) |
| Checks to see if a string is between two other strings, inclusively. More... | |
| uint8_t ** | and_list (uint8_t **, uint8_t **, uint16_t, uint16_t) |
| Compares two lists and returns all entries that are in both lists. More... | |
| uint8_t ** | or_list (uint8_t **, uint8_t **, uint16_t, uint16_t) |
| Compares two lists and returns all entries of the combined lists. More... | |
| void | destroy_simple_query_context (SimpleQueryContext *) |
Header for Query Functions.
| enum WhereType |
Type of Equality.
Type of equality to check in comparisons.
| uint8_t** and_list | ( | uint8_t ** | , |
| uint8_t ** | , | ||
| uint16_t | , | ||
| uint16_t | |||
| ) |
Compares two lists and returns all entries that are in both lists.
Iterates through two lists, and returns every entry that is in both lists, NULL terminated. The results must be cleaned up by calling free(), as this will allocate memory.
| list1 | - uint8_t ** first list |
| list2 | - uint8_t ** second list |
| len1 | - length of list 1 |
| len2 | - length of list 2 |
| uint8_t compare_float | ( | float | , |
| float | , | ||
| WhereType | |||
| ) |
Compares two floats.
Compares the value of two floats for equality, less than, greater than, less than or equal, or greater than or equal.
| float1 | - float |
| float2 | - float |
| type | - type of match to do |
| uint8_t compare_float_between | ( | float | , |
| float | , | ||
| float | |||
| ) |
Checks to see if a float is between two other floats, inclusively.
Compares the value of a float to two other floats, to see if it is between them, or equals them
| float | - float to check |
| minimum | - float marking the minimum |
| maximum | - float marking the maximum |
| uint8_t compare_int | ( | uint16_t | , |
| uint16_t | , | ||
| WhereType | |||
| ) |
Compares two integers.
Compares the value of two integers for equality, less than, greater than, less than or equal, or greater than or equal.
| int1 | - uint16_t |
| int2 | - uint16_t |
| type | - type of match to do |
| uint8_t compare_int_between | ( | uint16_t | , |
| uint16_t | , | ||
| uint16_t | |||
| ) |
Checks to see if an integer is between two other integers, inclusively.
Compares the value of an integer to two other integers, to see if it is between them, or equals them
| integer | - uint16_t to check |
| minimum | - uint16_t marking the minimum |
| maximum | - uint16_t marking the maximum |
| uint8_t compare_string | ( | uint8_t * | , |
| uint8_t * | , | ||
| WhereType | |||
| ) |
Compares two strings.
Compares the value of two strings for equality, less than, greater than, less than or equal, or greater than or equal.
| string1 | - uint8_t * |
| string2 | - uint8_t * |
| type | - type of match to do |
| uint8_t compare_string_between | ( | uint8_t * | , |
| uint8_t * | , | ||
| uint8_t * | |||
| ) |
Checks to see if a string is between two other strings, inclusively.
Compares the value of a string to two other strings, to see if it is between them, or equals them
| string | - string to check |
| minimum | - string marking the minimum |
| maximum | - string marking the maximum |
| uint8_t** or_list | ( | uint8_t ** | , |
| uint8_t ** | , | ||
| uint16_t | , | ||
| uint16_t | |||
| ) |
Compares two lists and returns all entries of the combined lists.
Iterates through two lists, and returns every entry in each list, NULL terminated. The results must be cleaned up by calling free(), as this will allocate memory.
| list1 | - uint8_t ** first list |
| list2 | - uint8_t ** second list |
| len1 | - length of list 1 |
| len2 | - length of list 2 |


List of Figures
-
Figure 1-1: The thief T starts where indicated. He is nine blocks from the northern, eastern, southern, and western borders, though only the first two are shown.
-
Figure 1-2: How can Indy use his whip to navigate from the bottom of the corridor to the top without hitting the barriers or the sides? Assume he can approach the bottom from any angle. The dot represents a pole.
-
Figure 1-3: Indy connects briefly to the pole (the dark circle) to rotate to a line parallel to the walls of the corridor.
-
Figure 1-4: Indy wants to go from the bottom to the top without hitting the barriers or the walls. Indy may enter the bottom at any angle. Can Indy do it with the two poles indicated as black circles?
-
Figure 1-5: What is the minimum number of poles Indy will need to skate over all the Xs?
-
Figure 1-6: A four-node network in which there is a one- or two-link path from any node to any other even if a node is deleted.
-
Figure 1-7: A six-node network in which there is a one- or two-link path from any node to any other even if a node is deleted.
-
Figure 1-8: Two possible ways of cutting the original pie (above) into three pieces of equal size. The bottom left shows two parallel cuts. The bottom right shows two perpendicular cuts.
-
Figure 1-9: The goal is to go from the S to the E, always staying on black squares. Don’t fall off the board.
-
Figure 1-10: You are to travel this maze from entrance to exit by keeping your left hand on the wall and walking forward. Will you make it?
-
Figure 1-11: Somewhere between Start and End in this road network is an eight-block tunnel some bad guys have built.
-
Figure 1-12: Bob’s preferences in life. Note that he has no preferences for certain qualities, e.g., he has not said whether he prefers Alice to be cultured or to be a windsurfer.
-
Figure 1-13: Alice’s preferences in life. Note that she has no preference about whether Bob is rich or a biker.
-
Figure 1-14: The northern half of the city T wants to escape from. The police are represented by dashed arrows and the thief by solid arrows. T leaves after 22 block steps.
-
Figure 1-15: Indy uses the first pole both to straighten out and then eventually to curve in.
-
Figure 1-16: This is the route Indy should take starting from the bottom to win at Treasure Skater. The numbers indicate the order in which Indy visits the Xs. Notice that he does a sharp turn using the lower left pole and then uses that pole again to curve up to X1. He then uses the lower left pole and the lower right pole twice to swing to 2 and 3.
-
Figure 1-17: The difficult part of the solution is to change directions as Indy descends between X2 and X4. He has to use a distant pole.
-
Figure 1-18: An eight-node network in which there is a two-link path from any node to any other even if a node is deleted. Within one foursome, there is a single link from any node to any other. For a trip from a node n in one foursome to a node m in the other foursome, there are two paths of length at most two. These go through different intermediate nodes.
-
Figure 1-19: A twelve node, wounded distance two network using only 21 links.
-
Figure 1-20: Switchboard nodes are clear circles. Supervertices, each consisting of 24 nodes, are big dark circles. The basic design represents 576 links. In addition, we have shown two links (the dashed curves) between non-neighboring switchboard nodes. Altogether there are 12 such links.
-
Figure 1-21: Recursive perpendicular cuts satisfying the child cut rule.
-
Figure 1-22: Without the child cut rule, one can obtain a smaller total perimeter for five pieces.
-
Figure 1-23: Without the child cut rule, here is the best design I know of for nine pieces.
-
Figure 1-24: Without the child cut rule or the requirement that all cuts must be parallel to an original side, one can reduce the perimeter quite noticeably.
-
Figure 1-25: Because you know the direction of the roads as they leave an intersection, each pair on each row determines the fate of the tunnel on that row and the next.
-
Figure 1-26: The center column’s point probes tell whether the tunnel goes up the center line. If it misses the center, it must go either east or west. Because of the length limitation, it cannot go in both directions. Without reading the explanation, you may be able to figure out which column is taken between every pair of rows.
-
Figure 1-27: The groups have been ordered in ascending order based on their medians. Thus, M1 ≤ M2 ≤ M3 ≤ M4 ≤ M5. Within each group imagine low blood pressures to be at the bottom of the line segment corresponding to that group and high blood pressures near the top.
-
Figure 1-28: The dark rectangles represent values that are less than or equal to M3.
-
Figure 1-29: The dark rectangles represent values greater than or equal to M1.
 The industrial zone must be far from the housing.
-
Figure 2-2: Because the industry is clean, the industrial zone can be folded among the other uses, yielding a 4 by 4 square.
-
Figure 2-3: The larger circle has a radius of 450 meters and the smaller has a radius of 350 meters. Their centers at (0,0) and (300,400) are 500 meters apart by the Pythagorean theorem.
-
Figure 2-4: The larger circle has a bigger uncertainty, reflecting the 10% accuracy.
-
Figure 2-5: For each approximate rectangle surrounding an intersection point, put the sensor 30 meters to the right of the center of the rectangle. Thus, not only will one know which rectangle contains the treasure, but also more or less where in the rectangle. For example, if the sensor reading is greater than 42.3 meters, then the treasure trunk is in the subrectangle to the right of the portion completely covered by the circle.
-
Figure 2-6: Given a sensor reading of d, the sketch shows the nominal circle, the smaller error circle (dashed), and the larger error circle (dashed). The treasure must be inside the dotted rectangle.
-
Figure 2-7: The beginning of the dynamic programming matrix. The top row represents the number of inserts to go from an empty string to, respectively, an empty string, a string consisting of ‘T’ alone, a string consisting of ‘TG’, and so on. The left column represents the number of deletes to go to an empty string from, respectively, an empty string, a string consisting of ‘A’, a string consisting of ‘AG’, and a string consisting of ‘AGA’.
-
Figure 2-8: Filling in the second row corresponds to the problem of transforming ‘A’ to ‘TGGAG’. As you can see from the arrows, the best thing to do is to insert ‘TGG’ and then ‘G’ along with a zero-cost replacement of ‘A’ by ‘A’.
-
Figure 2-9: Filling in the third row of the dynamic programming matrix plus indications of some possible best paths, though not all of them.
-
Figure 2-10: The entire matrix for the warm-up. In the end, we match ‘GA’ with ‘GA’ and insert or relabel for the rest.
-
Figure 2-11: Fill in this matrix to solve the problem.
-
Figure 2-12: An optimal edit sequence to go from “TAGATGGTCT” to “TGGAGACAGTCT”. Starting from the first letter, there are many replacements. Arrows always point to the right, downward, or to the right and downward.
-
Figure 2-13: The salesman starts at C and ends at C. Each time he goes to the closest unvisited city. How well will Bob do?
-
Figure 2-14: What happens when Bob visits the nearest previously unvisited city first?
-
Figure 2-15: A better route for Bob.
-
Figure 2-16: A set of cities. Assume that cost is proportional to distance. The goal is to construct a minimum spanning tree.
-
Figure 2-17: The minimum spanning tree in this case. Note that we do not need to know which is the starting city.
-
Figure 2-18: Take the minimum spanning tree and then go up and down each edge of the tree. That gives a route no matter which city one starts from. It’s true that this route revisits cities, so can be improved. Already, though, its cost is no more than twice that of the optimal one.
-
Figure 2-19: Some simple heuristic improvements to the minimum spanning tree-based route that will either leave the cost the same or reduce it (assuming the triangle inequality holds).
-
Figure 2-20: All unmarked edges have a cost of 5. The marked edge Z to C has a cost of 60 (violating symmetry and the triangle inequality). The marked edge Z to Y has a cost of 6, as does the edge from Y to Z.
-
Figure 2-21: The spanning tree starting at C contains the directed edges C to Z, C to X, and C to Y. Back edges play no role. Unfortunately, the travel route using that tree must traverse the back edge having cost 60. Altogether we get a cost of 85.
-
Figure 2-22: A much cheaper route moves clockwise through the nodes and has a cost of only 21.
-
Figure 2-23: The horizontal axis represents the search space. The vertical represents the cost. A greedy approach, given a location in the search space, will perform moves that decrease cost. So the arrows always start from a higher cost and go to a lower one.
-
Figure 2-24: In simulated annealing and other heuristic search techniques, some moves increase the cost. You can see that with the rightmost arrow which goes from a lower cost valley to a higher cost hill. The idea is to escape local cost minima.
-
Figure 2-25: A search space for which simulated annealing might have a difficult time finding a solution. The optimum is at a very specific part of the search space, so a move is unlikely to find it.
-
Figure 2-26: Each line segment (dashed or solid) represents a road. The solid line represents an alliance. Only C4 and C5 are matched. No other city has an ally.
-
Figure 2-27: In this case, we have two matchings: C3 with C4, and C5 with C7. Other pairs of cities could be matched, but there cannot be more than two matching pairs in all.


| Routine | Description |
|---|---|
| {{r.name}} | {{r.description}} |
These handle events like entering the room (M-ENTER), looking in the room (M-LOOK), beginning of each turn (M-BEG), end of each turn (M-END).
| Action Function | For Rooms |
|---|---|
| {{o.Name}} |
0">, {{objName}} |
These are called early during the turn (after Room M-BEG), often just to sanity check whether a verb/action can even be done. Example: PRE-READ handles "It's impossible to read in the dark."
| Pre-Action Function | For Syntax |
|---|---|
| {{o.Name}} |
{{s.Verb.toLowerCase()}} {{s.Preposition1?.toLowerCase()}} {{s.Object1?.toLowerCase()}} {{s.Preposition2?.toLowerCase()}} {{s.Object2?.toLowerCase()}}
|
If the command passed the Room Action and Verb Pre-Action, these action functions get a chance to handle the command. The indirect object gets a chance before the direct object.
| Action Function | For Object |
|---|---|
| {{o.Name}} | 0">, {{objName}} |
These are called only if a command was not already handled by a room, verb pre-action, direct object, or indirect object. So they usually handle generic scenarios with little consequence. Ex: V-SMELL just says "It smells like a _____."
| Action Function | For Syntax |
|---|---|
| {{o.Name}} |
{{s.Verb.toLowerCase()}} {{s.Preposition1?.toLowerCase()}} {{s.Object1?.toLowerCase()}} {{s.Preposition2?.toLowerCase()}} {{s.Object2?.toLowerCase()}}
|
Coming Soon!
All other functions not listed above.
| Source | Name | ArgSpec | First Line |
|---|---|---|---|
| {{o.Source}} | {{o.Name}} |


Myanmar judge extends sentences for poets jailed for mocking military
by ReutersA Myanmar judge on Monday extended the sentences of a troupe of satirical performers jailed for mocking the military, in a case that has raised new concern about freedom of expression.
Seven members of the Peacock Generation troupe were arrested in April after taking part in Thangyat, a centuries-old form of poetry and performance that pokes fun at the flaws and foibles of the social and political system.
During live shows and a Facebook broadcast, they criticised the army’s continued role in politics and shared pictures of a dog in a military jacket.
Five members of the troupe were sentenced last month to a year in jail with hard labour for undermining the armed forces.
A court in the commercial capital of Yangon on Monday added another year under the same charge. The performers are being sued in multiple townships.
The sentences will run concurrently, meaning the poets will serve an additional 18 days in prison, lawyer San San Myint told Reuters.
A sixth performer was sentenced to a year with hard labour, while another was acquitted.
Some of them are facing additional charges under the Telecommunications Act which covers online defamation.
The performers all denied any wrongdoing.
Two military spokesmen did not answer phone calls seeking comment.
The military ruled with an iron fist for nearly 50 years until a gradual transition to democracy began in 2011 under a constitution drafted with the supervision of the generals, which gives them significant powers over a civilian government.
Human rights group Amnesty International condemned the judge's ruling as "a relentless assault on freedom of expression".
"These new convictions against the Peacock Generation are signs of a relentless assault on freedom of expression in Myanmar," Nicholas Bequelin of Amnesty International said in a statement.
"It is ludicrous that these performers have yet to face even more charges and jail terms in the future. The authorities must end this madness and release them all immediately and without conditions."
Zeyar Lwin, one of the poets, addressed reporters outside the court in a blue prison uniform, his feet shackled.
"The military, which sues and imprisons people, is an institution with no dignity," he said.
"Such an institution with no dignity is a villain for our country."


|
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ABOUT CAMERON
I was born in El Paso, Texas 1990.
I moved 6 times before graduating high school.
I prefer vegan, but I cheat from time to time.
My morning routines:
- Meditation
- Green Tea
- Work Out
My inspirations include:
- Haas and Hahn : Artist, Architects
- Jay-Z : Music artist, businessman
- Steve Jobs : Innovator, Entrepreneur