text:
The diplomatic relationship between Japan and Korea has been a long rocky path and is not getting any better. In fact, the two countries' ties got even worse when South Korea compromised an agreement over the "comfort women"- South Korean women and girls forced during World War II to serve in Japanese military brothels.
It is without a doubt that the animosity between both countries is coming from difficult colonial history but contrary to popular belief, the young do not seem to care as much about colonialism.
In fact,polls suggest that young Japanese and South Koreans feel closer to each other than the older generation. The highest-selling foreign act in Japan last year was from a South Korean boyband, BTS. In South Korea, noir-ish detective novels by Keigo Higashino, a Japanese crime writer, was part of the three of the ten best-selling works of fiction. Furthermore, more South Koreans are visiting Japan every year while South Korea is becoming a prime destination for Japanese.


text:
Living Room Wall Decorating Ideas
Living Room Wall Decorating Ideas has a variety pictures that related to Living Room. Find out the most recent pictures of Living Room Wall Decorating Ideas here, and also you can get the pictures through our best Living Room collection. Living Room Wall Decorating Ideas pictures in here are posted and uploaded by the Author on December, 23 2016 for your Kitchen Ideas images collection. The images that existed in Living Room Wall Decorating Ideas are consisting of best images and high quality pictures.
These many pictures of Living Room Wall Decorating Ideas list may become your inspiration and informational purpose. We hope you enjoy and satisfied with our best picture of Living Room Wall Decorating Ideas from our Living Room collection that posted here and also you can use it for suitable needs for personal use only. The Home Design Ideas team also provides the other pictures of Living Room in High Definition and Best Quality that can be downloaded by click on the gallery below the Living Room Wall Decorating Ideas picture.
If you are interested in similar pictures relating to Living Room Wall Decorating Ideas, you are free to browse through Living Room category, search feature that located on top this page or related post section at below of this post.
Homegrowndecor.com can help you to get the latest information about the Living Room Remodel Ideas. We supply a top quality high picture with reputable certificate as well as everything if you're chatting concerning the house design as it's formally called. This web is made to turn your incomplete area into a pleasantly functional area in just a short amount of time. So allow's take a more detailed look at just what the living room wall decorating ideas is about as well as just what it can potentially do for you. When developing an enhancement to an existing house it is difficult to create a well-resolved remodelling if the current kind as well as design have actually not been taken into account.

subset: commoncrawl


text:
Tom Brady has talked of playing until he's 45, but the quarterback of the New England Patriots understands the "R" word is not that far off.
"I think about it more now than I used to," Brady said of retirement in an interview with Oprah Winfrey, per the Boston Herald. "I think I'm seeing there's definitely an end coming sooner, rather than later."
Brady, who will turn 41 in August and is entering his 19th season with the Patriots, was interviewed by Winfrey for a special that will air this weekend on the OWN Network.
The notion of retirement is an obvious one for Brady, but a reported rift with head coach Bill Belichick last season and the fact that he skipped voluntary team workouts for the first time in years has fueled the idea that he may not be long for New England.
Brady cited "family reasons" for bypassing organized team activities, insisted his relationship with Belichick is fine and declined to address his contract when meeting with the media earlier this month.
Asked by Winfrey if he had an idea how long he would continue playing, the five-time Super Bowl champion offered no specifics, although he cited family concerns.
"As long as I'm still loving it," said Brady. "As long as I'm loving the training and the preparation and willing to make the commitment. But it's also, I think what I alluded to a lot in the docu-series ("Tom vs. Time"), there's other things happening in my life, too.
"I do have kids that I love, and I don't want to be a dad that's not there, driving my kids to their games ... my kids have brought a great perspective in my life. Kids just want the attention. You better be there. And be available to them."
Brady has led the Patriots to eight Super Bowls in 17 seasons since taking over as the team's starter early in the 2001 season.
A three-time NFL Most Valuable Player and four-time Super Bowl MVP, Brady is always in the conversation of "Greatest of All Time" -- a topic he said makes him uncomfortable.
"I don't like it. I don't like when people say it. ... I don't feel that way," Brady told Winfrey. "I don't care whether people think that or not. I want to be the best that I can be. I know when I go out there, it's not to compare myself to this guy or that guy. Everyone plays good."
Brady showed he can still compete at the highest level last season, throwing for a league-best 4,577 yards along with 32 touchdowns and only eight interceptions to lead New England to another Super Bowl appearance.
So what drives the future Hall of Famer at this point in his career?
"There's still more to be accomplished," Brady told Winfrey. "I was practicing the last two days, working on my technique, on my fundamentals, all the things with my training. I still feel like I can be better, be a percentage better. I've played a long time. It's not like you go, 'Hey man, I'm going to become something different.'
"I am what I am. I know my strengths. I've improved on some of the weaknesses and I still think I want to go out there and compete and play with a bunch of 22-year-olds."


text:
Sanctuary Rededication!
Finally! The Day we've all been waiting for and working towards! This Sunday, June 5, we'll be moving back into the sanctuary! And what a glorious day this will be. In addition to rededicating the worship space to the glory of God, we'll also be doing a baptism, recognizing our graduates, and honoring our newest Elder Emeritus!
We'll also have a special guest preacher, the Rev. Dr. LaTaunya Bynum, our regional minister. She will have a challenging word for us as we consider what comes next as we continue to Widen Our Welcome.
We do hope you'll join us on this incredible day. You'll be glad you did.


text:
NYU medical school stops studies after ethical violations
Eight psychiatric studies were quietly shut down at New York University's medical school after investigators discovered a series of violations, including falsified records. A top researcher was also dismissed.
According to the New York Times, violations included lax oversight of study participants and failure to keep accurate case histories.
The violations "jeopardize subject safety and welfare, and raise concerns about the validity and integrity of the data collected at your site," the Food and Drug Administration (FDA) said in a letter, obtained by The Times, to Dr. Alexander Neumeister, the studies' lead investigator.
Neumeister was put on leave after complaints about the lab's compliance with research standards were confirmed. He later resigned.
One of the studies included individuals who had a diagnosis of post-traumatic stress disorder caused by childhood abuse. Participants took a relatively untested drug intended to mimic the effects of marijuana so researchers could observe if their symptoms improved, The Times reported.
Individuals with mental disorders are more suseptible to adverse reactions, experts told The Times.
"These are people who are more vulnerable to being exploited in the research process, and more vulnerable to things going wrong during the research, so you want extra vigilance," Elisa Hurley, the executive director of Public Responsibility in Medicine and Research, a national nonprofit devoted to promoting high research standards, told The Times. "If someone in my family were in a situation like this, I would want to be sure that the institution was crossing its t's and dotting its i's."
According to the FDA investigation, the research team failed to assess at least three of the 50 subjects 24 hours after they were administered the experimental drug, failing study protocol. Neumeister falsified documents by signing another investigator's name on reports and at least one study participant was part of another trial 16 days earlier, violating the 30-day window required by protocol.
Study participant Diane Ruffcorn, 40, said she was sexually abused as a child. For the study, she was given the drug and the tests, but researchers failed to follow up with her. To prepare for the study, she told The Times, she had to stop taking all of her medications, but the study's false starts forced her to repeatedly stop and restart her medicatons.
"It was horrible," Ruffcorn told The Times. "I had flashbacks, returning nightmares, every symptom coming on full force, not to mention the withdrawal. After going off and back on four or five times, I told them, 'I can't do this anymore.'"
Ruffcorn, who lives in Seattle, was flown to New York to enter the trial. After the trial period, she said she experienced odd symptoms but was unable to find out whether they were related to the drug because the study was halted and the data belonged to Pfizer, the drug manufacturer. In June, she learned she'd taken the placebo.
According to both NYU and Neumeister's lawyer, there is no evidence any study participant had been harmed, The Times reported.
The news comes at critical time in two controversial areas of psychiatry research, The Times reported: the search for a blood test or biological sign of post-traumatic stress disorder and use of recreational drugs to treat the diagnosis.
In January, one man died and five others were hospitalized in France during an experimental drug trial for a painkiller. The drug in question had similar marijuana-like effects.
NYU has deemed all study data unreliable and followed up with study participants to monitor their health, The Times reported.


text:
A Harlem hoodlum who was ensnared in the largest gang takedown in city history has been sentenced to at least 60 years behind bars, prosecutors said Thursday.
Davon Pinkston, 23, was linked to a stunning array of crimes — shootings, stabbings, assaults and robberies — as a member of the "Money Avenue" gang.
Pinkston was convicted in August of multiple conspiracy counts, attempted murder and gang assault.
He was responsible for at least three shootings, acting as either the triggerman or accomplice.
Pinkston was "quite proud of the gun violence he engaged in," prosecutor Andrew Warshawer said at the sentencing, citing the gang member's emails and Facebook messages.
The case stemmed from a 2014 indictment that charged a total of 103 gang members from three separate crews — "Money Avenue," their affiliated gang "Make it Happen Boys" and their rivals "3 Staccs."
"Money Avenue" and the "Make it Happen Boys" were operating out of the Manhattanville Houses while the Grant Houses acted as the base of operation for "3 Staccs."
Beef between the gangs spawned a brutal and bloody turf war that terrorized residents of the housing complexes for years.
Between Jan. 2010 and May 2014, the gang members were responsible for two homicides, 19 non-fatal shootings and about 50 others without hits, prosecutors say.
To date, 84 suspects have been convicted for their roles in the conspiracy. The case was brought by the Manhattan District Attorney's Violent Criminal Enterprises Unit.
Residents of the Harlem housing complexes said the massive bust helped transform the neighborhood from a blazing battlefield to a blissful community.
"The war zone is over," said Agnes Colston, a 65-year-old resident of the Grant Houses on Amsterdam Ave.
"We feel 100 percent safer...What's different now is we can come out and our children and grandchildren can come out here and don't have to be ducking bullets."
Pinkston was sentenced Wednesday to 60 1/2 years to life in prison after being convicted of 16 out of 26 counts..
Defense lawyer Gary Cutler pleaded for leniency — saying Pinkston, who was shot three times at age 16, had been a solid student at Booker T. Washington Middle School despite growing up in a crime-riddled neighborhood.
But Judge Edward McClaughlin wasn't hearing it.
"To actually carry out some of the intended conduct oneself or to be present at the scene of the shootings where, even if you were not the shooter, you knew damn well what was about to happen," McClaughlin said.
"This is inexcusable, unimaginable, unconscionable."
McClaughlin threw the book at Pinkston weeks after the Manhattan jurist came under scrutiny for sending cop killer Tyrone Howard to drug diversion screening last December. Howard, 30, a repeat felon, now faces murder charges for the slaying of Police Officer Randolph Holder, 33, in East Harlem last month.
Two other gangbangers - Keith Goodman, 30; and Alejandro Rivera, 23 - were also convicted along with Pinkston.
Javann Garnes, 23, pleaded guilty and was sentenced to 5 1/2 to 16 1/2 years in prison.
Goodman and Rivera are awaiting sentencing.


text:
Pink Robin (sub adult male)
Pink Robins occur in the wet gullies and rainforests of the southern mainland of Australia, but Tasmania is the species' stronghold. At Black Sugarloaf they breed in the swamp and gullies and build their nests in the upper fork of a small tree such as a dogwood, musk or paperbark.
Their nests are well constructed and well hidden and are unlikely to be seen unless a bird is building the nest or feeding young. Bark strips are bound with spiders' web and the outside of the nest is decorated with mosses, liverworts and flakes of lichen.
I was fortunate one late January afternoon to watch and photograph a family of several young birds being fed by the female. The male watched but seemed to take no part in the feeding. While the young were still in the nest, however, he did do his share of food gathering.


text:
Juvadent Beauty Event with LDN Meet-Up*
Who doesn't love a good old-fashioned blogger event?
Attending events has always been something I have been wary of. If you've seen my overcoming shyness video you'll probably get the gist of why. Anyway last year, I started making a more concerted effort to attend events and meet new people (internal screaming). Anyway, it's actually starting to become something I really enjoy.
So, one windy weekend in January I was lucky enough to attend a beauty blogger event run by the LDN meet-up which was founded by a lovely blogger named Lauren. The event was sponsored by Juvadent, a aesthetic clinic based in Chelsea, London who specialise in botox. I've been to a aesthetic clinic event before, so I was pretty excited. The event was hosted by the Chelsea All in One Spa, a beautiful boutique salon on the Kings Road.
Upon arriving we were met with a nice glass of something bubbly, perfect for combating the bitter weather outside. A pleasant surprise after a rather chilly walk. The first brand I met at the event was EDGE Nails, originally only supplied to salons and beauty industry professionals, the brand has now expanding to the everyday consumer. Their newest product, the Annika nail polish range, consists of 30 gorgeous shades, fit for any occasion. I was lucky enough to receive a bottle in the shade Bella Ballerina, a dainty pink shade, which I know I will wear to death.
After having a lovely chat with EDGE, I then made my way over to the ARK Skincare stand, which if you know me you'll know I was super excited about. I am an absolute skincare fiend and am a sucker for the latest skincare trends. So ARK are a skincare brand who focus around intelligent skincare. All of their products are put into three age categories and/or categories by skin concern. This makes shopping for skincare a lot easier and so much more convenient. I have noticed in the skincare market that many brands are now going down this route, putting the customer first, which is great especially if you have a very specific skincare problem. All of ARK's products contain active natural ingredients and are cruelty free! So you don't need to worry about any nasties being in there which is great if you have sensitive skin like me.
The next brand on my list to visit was False Eyelashes. Funnily enough I had actually met one of the lovely False Eyelashes girls at the KISS X Jess Wright Party in the summer of last year (small world!). So False Eyelashes are a huge online false lashes store who sell an absolutely massive range of lashes and lash accessories like glue, mascaras and more recently eyebrow products. The lovely ladies on the stall even allowed me to try out some of their Red Cherry lashes. I opted for some more wispy, natural looking ones. Personally, I prefer more subtle false eyelashes, mainly because I hate how heavy some of the more fuller lashes feel. Anyway I was really pleased with the finished result, which I am sure you've seen spammed all over my Snapchat (which by the way, if you haven't added is poppymayy).
Finally after meeting all of the visiting brands I met the main sponsor Juvadent. As I said above, Juvadent are a aesthetic clinic who provide treatments such as botox and lip fillers. Being someone who has never had any cosmetic surgery before I was intrigued to know more. The clinic do pride themselves on achieving more natural looking results, which I was relieved to know. As I am sure, like myself, many of you are aware of those awful plastic surgery stories which are plastered over the internet. However, I am happy to say that I saw no signs of these at Juvadent. They even showed a vlog posted by Lauren herself, taking us through her lip filler journey. Even though watching the procedure did make me squirm a little, the results looks so natural and positively flawless.
After a few speeches and competition winner announcements by some of the brands, it was time to leave. Upon exiting I was lucky enough to receive a goodie bag, full of beauty products to try out (stay tuned for reviews coming soon). Before I sign this post off I'd just like to thank Lauren from the LDN meet-up for organising such a brilliant event and for giving me the oppurtunity to meet some other lovely brands and bloggers too!


text:
This Week at State: March 30, 2018
By DipNote Bloggers on March 30, 2018
Did you miss key foreign policy developments this week? We've got you covered. Each week, DipNote recaps the latest U.S. Department of State highlights spanning a wide range of global issues, events, and initiatives in one blog post.
Here are key moments from This Week at State:
U.S. Takes Action To Hold Russia Accountable for Its Destabilizing Behavior
On March 26, the Department of State announced its decision to expel 60 Russian officials from the United States, including 48 serving at Russia's bilateral mission to the United States and 12 intelligence operatives from the Russian Mission to the United Nations. The United States will also require the Russian government to close its Consulate General in Seattle by April 2, 2018.
Today, President Trump ordered the expulsion of dozens of Russian intelligence officers from the U.S. and the closure of the Russian consulate in Seattle. With these steps, the U.S. and our allies and partners make clear to Russia that its actions have consequences. pic.twitter.com/rfGFXXrUlH— The White House (@WhiteHouse) March 27, 2018
Today, @StateDept announced its decision to expel 60 Russian officials from the United States, including 48 serving at #Russia's bilateral mission to the United States and 12 intelligence operatives from the Russian Mission to the @UN. https://t.co/hTFIDbyz4k pic.twitter.com/fr3D4oiYTx— Department of State (@StateDept) March 26, 2018
Ambassador Jon Huntsman of @USEmbRu on the Expulsion of Russian Intelligence Officers: With these actions, the United States along with its allies and partners make clear to #Russia that its actions have consequences. pic.twitter.com/f61v832U4q— Department of State (@StateDept) March 26, 2018
In a statement by the White House Press Secretary, the United States responded to Russia's March 29 decision to expel U.S. diplomats and close the U.S. Consulate in St. Petersburg.
.@PressSec: #Russia's decision to expel American diplomats only further deteriorates the US-Russia relationship. The US responded appropriately in the expulsion of undeclared Russia Intel officers after the deadly attack in the U.K. pic.twitter.com/28T6eDwbvy— Heather Nauert (@statedeptspox) March 30, 2018
Deputy Secretary Sullivan Hosts Bilateral Meetings with Turkish, Indonesian, and Malaysian Officials
On March 30, Deputy Secretary of State John Sullivan met with Turkish Under Secretary of Foreign Affairs Ümit Yalçın, at the Department of State.
On March 26, Deputy Secretary Sullivan met with Indonesian Foreign Minister Retno Marsudi in Washington, D.C.
.@statedeptspox: Deputy Secretary Sullivan met with Indonesian FM Marsudi today to discuss U.S.-#Indonesia partnership & mutual commitment to deepening cooperation in the Indo-Pacific region for the benefit of security, prosperity, & international order. https://t.co/RgDuwTUNrq pic.twitter.com/mgWZCNTlGG— Department of State (@StateDept) March 26, 2018
On March 26, Deputy Secretary Sullivan met with Malaysian Foreign Minister Anifah Aman in Washington, D.C.
.@statedeptspox: During their meeting today, Deputy Secretary Sullivan & Malaysian FM Anifah Aman discussed strengthening diplomatic, economic, & security ties & pledged to strengthen cooperation to counter the growing threat of ISIS. https://t.co/YoCEsMMspT pic.twitter.com/YyfHxSeDJV— Department of State (@StateDept) March 26, 2018
U.S. Condemns Houthi Missile Attacks in Saudi Arabia
In a press statement, Department Spokesperson Heather Nauert condemned the Houthi missile attacks aimed at several cities in Saudi Arabia on March 25.
"Our condolences go out to the families of any who were killed or injured. We support the right of our Saudi partners to defend their borders against these threats. We continue to call on all parties, including the Houthis, to return to political negotiations and move toward ending the war in Yemen."
-Spokesperson Heather Nauert
United Nations Security Council Holds Briefing on Syria
U.S. Ambassador to the United Nations Nikki Haley delivered remarks at a UN Security Council (UNSC) briefing on Syria on March 27. In those remarks, Ambassador Haley called out members of the Security Council -- specifically Russia -- for failing to take action to protect the at least 1,700 civilians who were killed during a military campaign in violation of UNSC resolution 2401, which called for a ceasefire in Syria.
.@USUN Ambassador Nikki Haley: A responsible #UNSC would condemn Syrian authorities, along with #Russia and #Iran, for launching a military offensive to seize #EasternGhouta the same day that we called for a ceasefire. #Syria pic.twitter.com/ZwzPP2CiSR— Department of State (@StateDept) March 27, 2018
Under Secretary Shannon Delivers Remarks at the Tashkent Conference on Afghanistan
Under Secretary for Political Affairs Thomas Shannon delivered remarks at the Tashkent Conference on Afghanistan in Uzbekistan, where he thanked Uzbek and Afghan leaders for bringing the group together "to reaffirm our support for peace and stability in Afghanistan, as well as security and prosperity across the region." In his remarks, Under Secretary Shannon underscored that the only way to achieve our shared objectives is through a negotiated political settlement between the Afghan government and the Taliban.
United States and India Host Working Group on Counterterrorism
The 15th meeting of the India-U.S. Counterterrorism Joint Working Group was held in New Delhi on March 27, 2018. Led by Ministry of External Affairs Joint Secretary Shri Mahaveer Singhvi and the State Department's Principal Deputy Coordinator for Counterterrorism, Alina L. Romanowski, the Indian and the U.S. delegations achieved important progress on counterterrorism cooperation between the two countries, including strengthening information sharing and support for international and bilateral cooperation on justice, law enforcement, and capacity building efforts to counter terrorism, and United Nations Security Council Resolution 2396.
State Department Comments on the Arrest of Serbian Official in Kosovo
State Department Spokesperson Heather Nauert condemned the arrest of a Serbian official in Kosovo in a press statement on March 28.
"The United States strongly condemns yesterday's events in Mitrovica, which unnecessarily heighten tensions and threaten regional stability. We urge all parties to avoid further escalation of the situation, and to act calmly and resolve disputes peacefully. To create lasting peace and stability in the Balkans, both Kosovo and Serbia need to focus on normalizing relations through the EU-led Dialogue. The United States stands ready to support all parties in Dialogue negotiations."
-Spokesperson Heather Nauert
United States and Poland Sign Patriot Deal
Spokesperson Nauert released a press statement following the signing in Warsaw of an agreement, between the United States and Poland, concluding Phase I of Poland's purchase of the Patriot missile defense system, worth $4.6 billion and including a brand new, state-of-the-art command and control system for Poland's air and missile defense.
"The signing of this agreement will strengthen the security and capabilities of the United States, Poland, and NATO along the Alliance's Eastern Flank. It reflects Poland's investment in sharing the burden of collective defense... It also creates jobs in both the United States and Poland."
-Spokesperson Heather Nauert
Follow the State Department on Twitter and Facebook for additional information and updates.
Editor's Note: This entry is also published in the U.S. Department of State's publication on Medium.


text:
Bible Concordance: "render"
Word "render" found in 27 verse(s), 21 chapter(s) and 16 book(s).
What the Bible says about "render":
Numbers 18:9
This shall be thine of the most holy things, reserved from the fire: every oblation of theirs, every meat offering of theirs, and every sin offering of theirs, and every trespass offering of theirs which they shall render unto me, shall be most holy for thee and for thy sons.
Deuteronomy 32:41
If I whet my glittering sword, and mine hand take hold on judgment; I will render vengeance to mine enemies, and will reward them that hate me.
Deuteronomy 32:43
Rejoice, O ye nations, with his people: for he will avenge the blood of his servants, and will render vengeance to his adversaries, and will be merciful unto his land, and to his people.
Judges 9:57
And all the evil of the men of Shechem did God render upon their heads: and upon them came the curse of Jotham the son of Jerubbaal.
1st Samuel 26:23
The LORD render to every man his righteousness and his faithfulness; for the LORD delivered thee into my hand to day, but I would not stretch forth mine hand against the LORD's anointed.
2nd Chronicles 6:30
Then hear thou from heaven thy dwelling place, and forgive, and render unto every man according unto all his ways, whose heart thou knowest; (for thou only knowest the hearts of the children of men:)
Job 33:26
He shall pray unto God, and he will be favourable unto him: and he shall see his face with joy: for he will render unto man his righteousness.
Job 34:11
For the work of a man shall he render unto him, and cause every man to find according to his ways.
Psalms 28:4
Give them according to their deeds, and according to the wickedness of their endeavours: give them after the work of their hands; render to them their desert.
Psalms 38:20
They also that render evil for good are mine adversaries; because I follow the thing that good is.
Psalms 56:12
Thy vows are upon me, O God: I will render praises unto thee.
Psalms 79:12
And render unto our neighbours sevenfold into their bosom their reproach, wherewith they have reproached thee, O Lord.
Psalms 94:2
Lift up thyself, thou judge of the earth: render a reward to the proud.
Psalms 116:12
What shall I render unto the LORD for all his benefits toward me?
Proverbs 24:12
If thou sayest, Behold, we knew it not; doth not he that pondereth the heart consider it? and he that keepeth thy soul, doth not he know it? and shall not he render to every man according to his works?
Proverbs 24:29
Say not, I will do so to him as he hath done to me: I will render to the man according to his work.
Proverbs 26:16
The sluggard is wiser in his own conceit than seven men that can render a reason.
Isaiah 66:15
For, behold, the LORD will come with fire, and with his chariots like a whirlwind, to render his anger with fury, and his rebuke with flames of fire.
Jeremiah 51:6
Flee out of the midst of Babylon, and deliver every man his soul: be not cut off in her iniquity; for this is the time of the LORD's vengeance; he will render unto her a recompence.
Jeremiah 51:24
And I will render unto Babylon and to all the inhabitants of Chaldea all their evil that they have done in Zion in your sight, saith the LORD.
Hosea 14:2
Take with you words, and turn to the LORD: say unto him, Take away all iniquity, and receive us graciously: so will we render the calves of our lips.
Joel 3:4
Yea, and what have ye to do with me, O Tyre, and Zidon, and all the coasts of Palestine? will ye render me a recompence? and if ye recompense me, swiftly and speedily will I return your recompence upon your own head;
Zechariah 9:12
Turn you to the strong hold, ye prisoners of hope: even to day do I declare that I will render double unto thee;
Matthew 21:41
They say unto him, He will miserably destroy those wicked men, and will let out his vineyard unto other husbandmen, which shall render him the fruits in their seasons.
1st Corinthians 7:3
Let the husband render unto the wife due benevolence: and likewise also the wife unto the husband.
Proverbs 20:7
The just man walketh in his integrity: his children are blessed after him.


text:
New mentor program for AAM members only
Members can now apply to be part of its exclusive mentor program that matches experienced managers the emerging and aspiring and across each level of their current tiers in a 9 month one on one relationship. Successful applicants will be matched with the most relevant mentor from the AAM's Executive and General Member base, and the AAM Board of Directors.
"With years of experience as a collective force the AAM is in a position to assist to develop the skills of younger managers," AAM Chairperson Cath Haridy explains, "it is the aim of the AAM to support the growth of the contemporary music industry and build the capacity for artist management."
Applications are open now and will close on October 17, 2012, and are currently only available through the AAM membership, of which there are a number of tiers designed to suit ever career level. To find out how to get involved, email firstname.lastname@example.com.
The AAM mentor program is made possible through the assistance and continued support from APRA.
Posted by


text:
Moses did not immediately go to Pharaoh after his God-encounter at the burning bush. He listened to God's Word, yes. But he didn't like what he heard – especially the part God was calling Moses to do. So, he talked it through with God in prayer. The discussion didn't go where Moses wanted it to go. It ended with The LORD still sticking with the plan. Moses needed to go to Pharaoh.
But Moses again pleaded, "Lord, please! Send anyone else." Then the LORD became angry with Moses. "All right," He said. "What about your brother, Aaron the Levite? I know he speaks well. And look! He is on his way to meet you now. He will be delighted to see you. Talk to him, and put the words in his mouth. I will be with both of you as you speak, and I will instruct you both in what to do. Aaron will be your spokesman to the people. He will be your mouthpiece, and you will stand in the place of God for him, telling him what to say. And take your shepherd's staff with you, and use it to perform the miraculous signs I have shown you." (Exodus 4:13-17 NLT)
At this point, you may think that Moses would go do what The LORD had called him to do. But, he did not. Now, he began the next part of his faith-journey process: discussion.
So Moses went back home to Jethro, his father-in-law. "Please let me return to my relatives in Egypt," Moses said. "I don't even know if they are still alive." "Go in peace," Jethro replied. (Exodus 4:18 NLT)
Moses didn't need to talk to his father-in-law about his God-encounter, but he did. He even asked permission to leave. With God commanding Moses' next steps, this wasn't really necessary either. But Moses' process took him through relationship after relationship, preparing him for the calling that The LORD had placed on his life. Discussions build our faith so we will do what God calls us to do.
Before Moses left Midian, the LORD said to him, "Return to Egypt, for all those who wanted to kill you have died." So Moses took his wife and sons, put them on a donkey, and headed back to the land of Egypt. In his hand he carried the staff of God. (Exodus 4:19-20 NLT)
Moses would talk things through with his wife and children as well. On the journey, his boys would need to be circumcised in obedience to The LORD. Moses' wife wasn't too happy about the idea, but they obeyed God as a family anyway. Discussions influence those around us to do what God calls them to do.
Now the LORD had said to Aaron, "Go out into the wilderness to meet Moses." So Aaron went and met Moses at the mountain of God, and he embraced him. Moses then told Aaron everything the LORD had commanded him to say. And he told him about the miraculous signs the LORD had commanded him to perform. (Exodus 4:27-28 NLT)
God told Moses that he could do this ministry with his brother, Aaron. God then spoke with Aaron to let him know what was coming. We don't have any record of Aaron's early stages of the process. How did The LORD appear to Aaron? We don't know. Did Aaron have a conversation with God in prayer like Moses did? We don't know. We do know, however, that Aaron did what The LORD told him to do. He went to meet Moses. Then they were ready to take the next step together as a team. Discussions build teams of people committed to God's Will.
Then Moses and Aaron returned to Egypt and called all the elders of Israel together. Aaron told them everything the LORD had told Moses, and Moses performed the miraculous signs as they watched. (Ex 4:29-30 NLT)
Finally, Moses and Aaron had a discussion with the elders of Israel. God did tell Moses to have this discussion as a part of Moses' "making it real" prayer. All of this, though, was a part of Moses' process. Moses clearly needed to talk things through with people so that he would do what God had called him to do.
How about you? Do discussions with other people strengthen your faith in what God is calling you to do? Do your discussions help others around you to do what God is calling them to do? Do discussions in your life build teams committed to God's Will? Do you make your faith real through discussions?


text:
Stylish for inside and outside of the home, this modern dining table and bench made of Western Red Cedar will be an instant entertaining hit. View our simple Do-It-Yourself video below now!
Visit our Retailer Locator page to view a list of Authorized Western Red Cedar retailers in an area near you!
Visit our Western Red Cedar Outdoor Project Plans page, and choose from a wide variety of free downloadable plans and videos for your DIY pleasure!
Visit our Ready-Made-Kits page to find a list of our partners who provide easy-to-assemble "pre-cut" cedar projects.


text:
Related Keywords: exports two-thirds of yarn output, fabric determines cost and quality, facing many problems, finished garment products, imports fabric every year, no specific policy designed, spinning and cotton industry., Textile production, textiles cannot meet garment requirements, Vietnam textile industry
Vietnam textile industry is still facing many problems in textile production, as fabric determines the cost and quality of finished garment products hence textiles still cannot meet garment requirements, said Vu Huy Dong, general director of Damsan JSC.
Vietnam has to import 65-70 percent of fabric every year, it exports two-thirds of yarn output. This means that Vietnam has yarn in excess but not enough fabric.


text:
Holistic Veterinary Care is a philosophy which tries to grasp the entire picture and come up with a program that is preventative in nature and is right for the health and welfare of your pet. Our clinic has many unique aspects that you'll not find in a traditional, medicine-oriented veterinary practice.
Contact Details
|Seattle , WA
|Zip Code
|5303 Rainier Avenue South
|Phone Number
|(206) 723-3809


text:
When we travel or are just starting a new practice, we often encounter situations which make it difficult to commit to our yoga lifestyle—especially if we feel our practice might seem out of place in a certain context, or that our friends and family won't understand. But as we learn to be honest with ourselves about what we need, and open with others, we begin to find valuable solutions. So the question then becomes, how do we find confidence in our practice with or without the support of a sangha?
Do you feel the same? Share your stories with us in the comment section below!
Audio and video downloads are available for Digital Members. Digital Members.


text:
MADRID, Feb 26 (Reuters) - Former Real Madrid and Wales coach John Toshack has described Gareth Bale as an "extraordinary talent" and said injuries have prevented the winger being seen as a resounding success in Spain.
The 29-year-old Bale has been named as a substitute in Real's past two La Liga games and refused to celebrate after scoring the decisive goal in his side's 2-1 victory over Levante on Sunday.
Toshack, who twice coached Real and handed Bale his international debut while in charge of Wales, said that it is not easy for players to deal with setbacks but Bale should show his frustrations in better ways.
"Injuries have really taken their toll (on Bale)," Toshack told Spanish radio station SER.
"He gets strange injuries from strange situations, things that aren't normal. He's going well and then all of a sudden he's injured. It's really difficult when you stop and start over again and more so in Real Madrid.
"What I am sure of is that Gareth is an extraordinary talent, and it's a real shame that his injuries have meant he's missed so many games. Nobody likes to be on the bench and sometimes you have to hide your anger out of respect for your team mates and what he did wasn't nice."
Bale has also faced criticism from fans and local media for a perceived failure to assimilate into Spanish culture and Toshack said his former player should have made more effort to learn the language.
"You have to say that if you go to a club like Real Madrid, or even a Malaga or Oviedo type team, and they've shown confidence in you and you work for them, you should at least try to learn the language," he said.
"I've never had any classes and it's difficult for me, but people can understand me when I talk. Bale could've tried harder and spoken more of the language in the country where he's working."
The emergence of Brazilian 18-year-old Vinicius Jr has led to Bale starting on the bench in recent weeks and prompted media reports that the Welshman could be set for a close-season exit.
Toshack said that the club will possess more power than the player when it comes to making the final decision. "This is a question of what Real Madrid want to do more than what Bale wants or needs to do," he said. (Reporting by Joseph Cassinelli Editing by Toby Davis)


text:
School District No. 91's Board of Education held their annual Trustee Elections and Committee selections at the December 11th, 2017 regular Board Meeting. The newly elected chair … [Read more...] about Dec. 11, 2017 – Trustee Elections and Committee Selections
News from Board Meetings
Strategic Plan 2017 – 2020
The Board of Education for School District No. 91 began the process of engaging with their local communities in late 2015 and throughout 2016 with the intention of creating a … [Read more...] about Strategic Plan 2017 – 2020
September 2016 Board News
Monday, September 19, 2016 91 NEWS – from our most recent Board Meeting… On Monday, September 19, 2016, the Board of Education of School District No. 91 (Nechako Lakes) held its … [Read more...] about September 2016 Board News


text:
Olay Regenerist Luminous Overnight Facial Mask Gel Moisturizer, 4.5 oz Packaging may Vary
Olay Regenerist Luminous Overnight Facial Mask Gel Moisturizer, 4.5 oz Packaging may Vary
As of 2019-05-17 22:37:32 GMT (more info)
Not Available - stock arriving soon
Product Information (more info)
Manufacturer Description
Awaken to radiance with Olay regenerist luminescent over night mask. Unlike a common peel-off mask this gel-based mask takes in into the surface of the skin throughout the night exposing bright pearlescent-looking skin. Formulated with vitamin b3 mulberry extract and humectants it helps to brighten as well as complexion in time. Hydrate your skin while you sleep and attempt Olay regenerist luminescent over night mask.
Product Features
From the very first use, this Overnight Mask immediately reveals pearlescent skin that glows. Hydrates skin overnight for bright, radiant-looking skin Non-peel, Gel-Moisturizer mask absorbs into the surface of the skin while you sleep Formula works overnight at the surface cell level for brighter-looking skin Formulated with vitamin B3, mulberry extract, and humectants to hydrate and brighten your skin's appearance


text:
Welcome to Podcast Evolved Spartans! Your new favorite Halo podcast!
This episode the crew eases back into the swing of things with the various Terminals located on certain levels in Halo 3. This also gives Krysta a great opportunity to delve a bit into who Mendicant Bias really is. Enjoy!!!
Drew - Drewcifer R8DM
David - CanineCerberus
Krysta - KonanXD
Would you kindly subscribe to us on iTunes and leave us a review, or grab our RSS Feed! It is greatly appreciated! Evolved!


text:
Welcome to Bayesian Methods for Hackers. The full Github repository, and additional chapters, is available at github/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers. We hope you enjoy the book, and we encourage any contributions!
You are a skilled programmer, but bugs still slip into your code. After a particularly difficult implementation of an algorithm, you decide to test your code on a trivial example. It passes. You test the code on a harder problem. It passes once again. And it passes the next, even more difficult, test too! You are starting to believe that there may be no bugs in this code...
If you think this way, then congratulations, you already are a Bayesian practitioner! Bayesian inference is simply updating your beliefs after considering new evidence. A Bayesian can rarely be certain about a result, but he or she can be very confident. Just like in the example above, we can never be 100% sure that our code is bug-free unless we test it on every possible problem; something rarely possible in practice. Instead, we can test it on a large number of problems, and if it succeeds we can feel more confident about our code. Bayesian inference works identically: we update our beliefs about an outcome; rarely can we be absolutely sure unless we rule out all other alternatives.
Bayesian inference differs from more traditional statistical inference by preserving uncertainty about our beliefs. At first, this sounds like a bad statistical technique. Isn't statistics all about deriving certainty from randomness? To reconcile this, we need to start thinking like Bayesians.
The Bayesian world-view interprets probability as measure of believability in an event, that is, how confident we are in an event occurring. In fact, we will see in a moment that this is the natural interpretation of probability.
For this to be clearer, we consider an alternative interpretation of probability: Frequentist methods assume that probability is the long-run frequency of events (hence the bestowed title). For example, the probability of plane accidents under a frequentist philosophy is interpreted as the long-term frequency of plane accidents. This makes logical sense for many probabilities of events, but becomes more difficult to understand when events have no long-term frequency of occurrences. Consider: we often assign probabilities to outcomes of presidential elections, but the election itself only happens once! Frequentists get around this by invoking alternative realities and saying across all these universes, the frequency of occurrences defines the probability.
Bayesians, on the other hand, have a more intuitive approach. Bayesians interpret a probability as measure of belief, or confidence, of an event occurring. Simpley, a probability is a summary of an opinion. An individual who assigns a belief of 0 to an event has no confidence that the event will occur; conversely, assigning a belief of 1 implies that the individual is absolutely certain of an event occurring. Beliefs between 0 and 1 allow for weightings of other outcomes. This definition agrees with the probability of a plane accident example, for having observed the frequency of plane accidents, an individual's belief should be equal to that frequency, excluding any outside information. Similarly, under this definition of probability being equal to beliefs, it is clear how we can speak about probabilities (beliefs) of presidential election outcomes: how confident are you candidate A will win?
Notice in the paragraph above, I assigned the belief (probability) measure to an individual, not to Nature. This is very interesting, as this definition leaves room for conflicting beliefs between individuals. Again, this is appropriate for what naturally occurs: different individuals have different beliefs of events occurring, because they possess different information about the world. The existence of different beliefs does not imply that anyone is wrong. Consider the following examples demonstrating the relationship between individual beliefs and probabilities:
I flip a coin, and we both guess the result. We would both agree, assuming the coin is fair, that the probability of heads is 1/2. Assume, then, that I peek at the coin. Now I know for certain what the result is: I assign probability 1.0 to either heads or tails. Now what is your belief that the coin is heads? My knowledge of the outcome has not changed the coin's results. Thus we assign different probabilities to the result.
Your code either has a bug in it or not, but we do not know for certain which is true, though we have a belief about the presence or absence of a bug.
A medical patient is exhibiting symptoms $x$, $y$ and $z$. There are a number of diseases that could be causing all of them, but only a single disease is present. A doctor has beliefs about which disease.
This philosophy of treating beliefs as probability is natural to humans. We employ it constantly as we interact with the world and only see partial evidence. Alternatively, you have to be trained to think like a frequentist.
To align ourselves with traditional probability notation, we denote our belief about event $A$ as $P(A)$. We call this quantity the prior probability.
John Maynard Keynes, a great economist and thinker, said "When the facts change, I change my mind. What do you do, sir?" This quote reflects the way a Bayesian updates his or her beliefs after seeing evidence. Even — especially — if the evidence is counter to what was initially believed, the evidence cannot be ignored. We denote our updated belief as $P(A |X )$, interpreted as the probability of $A$ given the evidence $X$. We call the updated belief the posterior probability so as to contrast it with the prior probability. For example, consider the posterior probabilities (read: posterior beliefs) of the above examples, after observing some evidence $X$.:
1. $P(A): \;\;$ the coin has a 50 percent chance of being heads. $P(A | X):\;\;$ You look at the coin, observe a heads has landed, denote this information $X$, and trivially assign probability 1.0 to heads and 0.0 to tails.
2. $P(A): \;\;$ This big, complex code likely has a bug in it. $P(A | X): \;\;$ The code passed all $X$ tests; there still might be a bug, but its presence is less likely now.
3. $P(A):\;\;$ The patient could have any number of diseases. $P(A | X):\;\;$ Performing a blood test generated evidence $X$, ruling out some of the possible diseases from consideration.
It's clear that in each example we did not completely discard the prior belief after seeing new evidence $X$, but we re-weighted the prior to incorporate the new evidence (i.e. we put more weight, or confidence, on some beliefs versus others).
By introducing prior uncertainty about events, we are already admitting that any guess we make is potentially very wrong. After observing data, evidence, or other information, we update our beliefs, and our guess becomes less wrong. This is the alternative side of the prediction coin, where typically we try to be more right.
If frequentist and Bayesian inference were programming functions, with inputs being statistical problems, then the two would be different in what they return to the user. The frequentist inference function would return a number, whereas the Bayesian function would return probabilities.
For example, in our debugging problem above, calling the frequentist function with the argument "My code passed all $X$ tests; is my code bug-free?" would return a YES. On the other hand, asking our Bayesian function "Often my code has bugs. My code passed all $X$ tests; is my code bug-free?" would return something very different: a probabilities of YES and NO. The function might return:
YES, with probability 0.8; NO, with probability 0.2
This is very different from the answer the frequentist function returned. Notice that the Bayesian function accepted an additional argument: "Often my code has bugs". This parameter is the prior. By including the prior parameter, we are telling the Bayesian function to include our belief about the situation. Technically this parameter in the Bayesian function is optional, but we will see excluding it has its own consequences.
As we acquire more and more instances of evidence, our prior belief is washed out by the new evidence. This is to be expected. For example, if your prior belief is something ridiculous, like "I expect the sun to explode today", and each day you are proved wrong, you would hope that any inference would correct you, or at least align your beliefs better. Bayesian inference will correct this belief.
Denote $N$ as the number of instances of evidence we possess. As we gather an infinite amount of evidence, say as $N \rightarrow \infty$, our Bayesian results align with frequentist results. Hence for large $N$, statistical inference is more or less objective. On the other hand, for small $N$, inference is much more unstable: frequentist estimates have more variance and larger confidence intervals. This is where Bayesian analysis excels. By introducing a prior, and returning probabilities (instead of a scalar estimate), we preserve the uncertainty that reflects the instability of statistical inference of a small $N$ dataset.
One may think that for large $N$, one can be indifferent between the two techniques since they offer similar inference, and might lean towards the computational-simpler, frequentist methods. An individual in this position should consider the following quote by Andrew Gelman (2005)[1], before making such a decision:
Sample sizes are never large. If $N$ is too small to get a sufficiently-precise estimate, you need to get more data (or make more assumptions). But once $N$ is "large enough," you can start subdividing the data to learn more (for example, in a public opinion poll, once you have a good estimate for the entire country, you can estimate among men and women, northerners and southerners, different age groups, etc.). $N$ is never enough because if it were "enough" you'd already be on to the next problem for which you need more data.
Frequentist methods are still useful or state-of-the-art in many areas. Tools like Least Squares linear regression, LASSO regression, EM algorithm etc. are all very powerful and incredibly fast. Bayesian methods are a compliment to solve the problems these solutions cannot or to gain further insight into the underlying system by offering more flexibility in modeling.
Paradoxically, big data's predictive analytic problems are actually solved by relatively simple algorithms [2][4]. Thus we can argue that big data's prediction difficulty does not lie in the algorithm used, but instead on the computational difficulties of storage and execution on big data. (One should also consider Gelman's quote from above and ask "Do I really have big data?" )
The much more difficult analytic problems involve medium data and, especially troublesome, really small data. Using a similar argument as Gelman's above, if big data problems are big enough to be readily solved, then we should be more interested in the not-quite-big enough datasets.
We are interested in beliefs, which can be interpreted as probabilities by thinking Bayesian. We have a prior belief in event $A$, beliefs formed by previous information, e.g., our prior belief about bugs being in our code before performing tests.
Secondly, we observe our evidence. To continue our buggy-code example: if our code passes $X$ tests, we want to update our belief to incorporate this. We call this new belief the posterior probability. Updating our belief is done via the following equation, known as Bayes' Theorem, after its discoverer Thomas Bayes:\begin{align} P( A | X ) = & \frac{ P(X | A) P(A) } {P(X) } \\\\[5pt] & \propto P(X | A) P(A)\;\; (\propto \text{is proportional to } ) \end{align}
The above formula is not unique to Bayesian inference: it is a mathematical fact with uses outside Bayesian inference. Bayesian inference merely uses it to connect prior probabilities $P(A)$ with an updated posterior probabilities $P(A | X )$.
Every statistics text must contain a coin-flipping example, I'll use it here to get it out of the way. Suppose, naively, that you are unsure about the probability of heads in a coin flip (spoiler alert: it's 50%). You believe there is some true underlying ratio, call it $p$, but have no prior opinion on what $p$ might be.
We begin to flip a coin, and record the observations: either $H$ or $T$. This is our observed data. An interesting question to ask is how our inference changes as we observe more and more data? More specifically, what do our posterior probabilities look like when we have little data, versus when we have lots of data.
Below we plot a sequence of updating posterior probabilities as we observe increasing amounts of data (coin flips).
The book uses a custom matplotlibrc file, which provides the unique styles for matplotlib plots.
If executing this book, and you wish to use the book's styling, provided are two options:
1. Overwrite your own matplotlibrc file with the rc-file provided in the book's styles/ dir.
See http://matplotlib.org/users/customizing.html
2. Also in the styles is bmh_matplotlibrc.json file. This can be used to update the styles
in only this notebook. Try running the following code:
import json
s = json.load( open("../styles/bmh_matplotlibrc.json") )
#the code below can be passed over, as it is currently not important.
%pylab inline
figsize( 11, 9)
import scipy.stats as stats
dist = stats.beta
n_trials = [0,1,2,3,4,5,8,15, 50, 500]
data = stats.bernoulli.rvs(0.5, size = n_trials[-1] )
x = np.linspace(0,1,100)
for k, N in enumerate(n_trials):
sx = subplot( len(n_trials)/2, 2, k+1)
plt.xlabel("$p$, probability of heads") if k in [0,len(n_trials)-1] else None
plt.setp(sx.get_yticklabels(), visible=False)
heads = data[:N].sum()
y = dist.pdf(x, 1 + heads, 1 + N - heads )
plt.plot( x, y, label= "observe %d tosses,\n %d heads"%(N,heads) )
plt.fill_between( x, 0, y, color="#348ABD", alpha = 0.4 )
plt.vlines( 0.5, 0, 4, color = "k", linestyles = "--", lw=1 )
leg = plt.legend()
plt.autoscale(tight = True)
plt.suptitle( "Bayesian updating of posterior probabilities",
y = 1.02,
fontsize = 14);
Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline]. For more information, type 'help(pylab)'.
The posterior probabilities are represented by the curves, and our confidence is proportional to the height of the curve. As the plot above shows, as we start to observe data our posterior probabilities start to shift and move around. Eventually, as we observe more and more data (coin-flips), our probabilities will lump closer and closer around the true value of $p=0.5$ (marked by a dashed line).
Notice that the plots are not always peaked at 0.5. There is no reason it should be: recall we assumed we did not have a prior opinion of what $p$ is. In fact, if we observe quite extreme data, say 8 flips and only 1 observed heads, our distribution would look very biased away from lumping around 0.5. As more data accumulates, we would see more and more probability being assigned at $p=0.5$.
The next example is a simple demonstration of the mathematics of Bayesian inference.
Let $A$ denote the event that our code has no bugs in it. Let $X$ denote the event that the code passes all debugging tests. For now, we will leave the prior probability of no bugs as a variable, i.e. $P(A) = p$.
We are interested in $P(A|X)$, i.e. the probability of no bugs, given our debugging tests $X$. To use the formula above, we need to compute some quantities.
What is $P(X | A)$, i.e., the probability that the code passes $X$ tests given there are no bugs? Well, it is equal to 1, for a code with no bugs will pass all tests.
$P(X)$ is a little bit trickier: The event $X$ can be divided into two possibilities, event $X$ occurring even though our code indeed has bugs (denoted $\sim A\;$, spoken not $A$), or event $X$ without bugs ($A$). $P(X)$ can be represented as:
We have already computed $P(X|A)$ above. On the other hand, $P(X | \sim A)$ is subjective: our code can pass tests but still have a bug in it, though the probability there is a bug present is reduced. Note this is dependent on the number of tests performed, the degree of complication in the tests, etc. Let's be conservative and assign $P(X|\sim A) = 0.5$. Then\begin{align} P(A | X) & = \frac{1\cdot p}{ 1\cdot p +0.5 (1-p) } \\\\ & = \frac{ 2 p}{1+p} \end{align}
This is the posterior probability. What does it look like as a function of our prior, $p \in [0,1]$?
p = np.linspace( 0,1, 50)
plt.plot( p, 2*p/(1+p), color = "#348ABD", lw = 3 )
#plt.fill_between( p, 2*p/(1+p), alpha = .5, facecolor = ["#A60628"])
plt.scatter( 0.2, 2*(0.2)/1.2, s = 140, c ="#348ABD" )
plt.xlim( 0, 1)
plt.ylim( 0, 1)
plt.xlabel( "Prior, $P(A) = p$")
plt.ylabel("Posterior, $P(A|X)$, with $P(A) = p$")
plt.title( "Are there bugs in my code?");
We can see the biggest gains if we observe the $X$ tests passed when the prior probability, $p$, is low. Let's settle on a specific value for the prior. I'm a strong programmer (I think), so I'm going to give myself a realistic prior of 0.20, that is, there is a 20% chance that I write code bug-free. To be more realistic, this prior should be a function of how complicated and large the code is, but let's pin it at 0.20. Then my updated belief that my code is bug-free is 0.33.
Recall that the prior is a probability: $p$ is the prior probability that there are no bugs, so $1-p$ is the prior probability that there are bugs.
Similarly, our posterior is also a probability, with $P(A | X)$ the probability there is no bug given we saw all tests pass, hence $1-P(A|X)$ is the probability there is a bug given all tests passed. What does our posterior probability look like? Below is a graph of both the prior and the posterior probabilities.
figsize( 12.5, 4 )
colours = ["#348ABD", "#A60628"]
prior = [0.20, 0.80]
posterior = [1./3, 2./3]
plt.bar( [0,.7], prior ,alpha = 0.70, width = 0.25, \
color = colours[0], label = "prior distribution",
lw = "3", edgecolor = colours[0])
plt.bar( [0+0.25,.7+0.25], posterior ,alpha = 0.7, \
width = 0.25, color = colours[1],
label = "posterior distribution",
lw = "3", edgecolor = colours[1])
plt.xticks( [0.20,.95], ["Bugs Absent", "Bugs Present"] )
plt.title("Prior and Posterior probability of bugs present, prior = 0.2")
plt.legend(loc="upper left");
Notice that after we observed $X$ occur, the probability of bugs being absent increased. By increasing the number of tests, we can approach confidence (probability 1) that there are no bugs present.
This was a very simple example of Bayesian inference and Bayes rule. Unfortunately, the mathematics necessary to perform more complicated Bayesian inference only becomes more difficult, except for artificially constructed cases. We will later see that this type of mathematical analysis is actually unnecessary. First we must broaden our modeling tools. The next section deals with probability distributions. If you are already familiar, feel free to skip (or at least skim), but for the less familiar the next section is essential.
Let's quickly recall what a probability distribution is: Let $Z$ be some random variable. Then associated with $Z$ is a probability distribution function that assigns probabilities to the different outcomes $Z$ can take. Graphically, a probability distribution is a curve where the probability of an outcome is proportional to the height of the curve. You can see examples in the first figure of this chapter.
We can divide random variables into three classifications:
$Z$ is discrete: Discrete random variables may only assume values on a specified list. Things like populations, movie ratings, and number of votes are all discrete random variables. Discrete random variables become more clear when we contrast them with...
$Z$ is continuous: Continuous random variable can take on arbitrarily exact values. For example, temperature, speed, time, color are all modeled as continuous variables because you can progressively make the values more and more precise.
$Z$ is mixed: Mixed random variables assign probabilities to both discrete and continuous random variables, i.e. it is a combination of the above two categories.
If $Z$ is discrete, then its distribution is called a probability mass function, which measures the probability $Z$ takes on the value $k$, denoted $P(Z=k)$. Note that the probability mass function completely describes the random variable $Z$, that is, if we know the mass function, we know how $Z$ should behave. There are popular probability mass functions that consistently appear: we will introduce them as needed, but let's introduce the first very useful probability mass function. We say $Z$ is Poisson-distributed if:$$P(Z = k) =\frac{ \lambda^k e^{-\lambda} }{k!}, \; \; k=0,1,2, \dots $$
What is $\lambda$? It is called the parameter, and it describes the shape of the distribution. For the Poisson random variable, $\lambda$ can be any positive number. By increasing $\lambda$, we add more probability to larger values, and conversely by decreasing $\lambda$ we add more probability to smaller values. One can describe $\lambda$ as the intensity of the Poisson distribution.
Unlike $\lambda$, which can be any positive number, the value $k$ in the above formula must be a non-negative integer, i.e., $k$ must take on values 0,1,2, and so on. This is very important, because if you wanted to model a population you could not make sense of populations with 4.25 or 5.612 members.
If a random variable $Z$ has a Poisson mass distribution, we denote this by writing$$Z \sim \text{Poi}(\lambda) $$
One very useful property of the Poisson random variable, given we know $\lambda$, is that its expected value is equal to the parameter, ie.:$$E\large[ \;Z\; | \; \lambda \;\large] = \lambda $$
We will use this property often, so it's something useful to remember. Below we plot the probability mass distribution for different $\lambda$ values. The first thing to notice is that by increasing $\lambda$ we add more probability to larger values occurring. Secondly, notice that although the graph ends at 15, the distributions do not. They assign positive probability to every non-negative integer.
figsize( 12.5, 4)
import scipy.stats as stats
a = np.arange( 16 )
poi = stats.poisson
lambda_ = [1.5, 4.25 ]
plt.bar( a, poi.pmf( a, lambda_[0]), color=colours[0],
label = "$\lambda = %.1f$"%lambda_[0], alpha = 0.60,
edgecolor = colours[0], lw = "3")
plt.bar( a, poi.pmf( a, lambda_[1]), color=colours[1],
label = "$\lambda = %.1f$"%lambda_[1], alpha = 0.60,
edgecolor = colours[1], lw = "3")
plt.xticks( a + 0.4, a )
plt.ylabel("probability of $k$")
plt.title("Probability mass function of a Poisson random variable; differing \
$\lambda$ values");
Instead of a probability mass function, a continuous random variable has a probability density function. This might seem like unnecessary nomenclature, but the density function and the mass function are very different creatures. An example of continuous random variable is a random variable with a exponential density. The density function for an exponential random variable looks like:$$f_Z(z | \lambda) = \lambda e^{-\lambda z }, \;\; z\ge 0$$
Like the Poisson random variable, an exponential random variable can only take on non-negative values. But unlike a Poisson random variable, the exponential can take on any non-negative values, like 4.25 or 5.612401. This makes it a poor choice for count data, which must be integers, but a great choice for time data, or temperature data (measured in Kelvins, of course), or any other precise and positive variable. Below are two probability density functions with different $\lambda$ value.
When a random variable $Z$ has an exponential distribution with parameter $\lambda$, we say $Z$ is exponential and write$$Z \sim \text{Exp}(\lambda)$$
Given a specific $\lambda$, the expected value of an exponential random variable is equal to the inverse of $\lambda$, that is:$$E[\; Z \;|\; \lambda \;] = \frac{1}{\lambda}$$
a = np.linspace(0,4, 100)
expo = stats.expon
lambda_ = [0.5, 1]
for l,c in zip(lambda_,colours):
plt.plot( a, expo.pdf( a, scale=1./l), lw=3,
color=c, label = "$\lambda = %.1f$"%l)
plt.fill_between( a, expo.pdf( a, scale=1./l), color=c, alpha = .33)
plt.ylabel("PDF at $z$")
plt.title("Probability density function of an Exponential random variable;\
differing $\lambda$");
This question is what motivates statistics. In the real world, $\lambda$ is hidden from us. We only see $Z$, and must go backwards to try and determine $\lambda$. The problem is so difficult because there is not a one-to-one mapping from $Z$ to $\lambda$. Many different methods have been created to solve the problem of estimating $\lambda$, but since $\lambda$ is never actually observed, no one can say for certain which method is better!
Bayesian inference is concerned with beliefs about what $\lambda$ is. Rather than try to guess $\lambda$ exactly, we can only talk about what $\lambda$ is likely to be by assigning a probability distribution to $\lambda$.
This might seem odd at first: after all, $\lambda$ is fixed, it is not (necessarily) random! How can we assign probabilities to a non-random event. Ah, we have fallen for the frequentist interpretation. Recall, under our Bayesian philosophy, we can assign probabilities if we interpret them as beliefs. And it is entirely acceptable to have beliefs about the parameter $\lambda$.
Let's try to model a more interesting example, concerning text-message rates:
You are given a series of text-message counts from a user of your system. The data, plotted over time, appears in the graph below. You are curious if the user's text-messaging habits changed over time, either gradually or suddenly. How can you model this? (This is in fact my own text-message data. Judge my popularity as you wish.)
figsize( 12.5, 3.5 )
count_data = np.loadtxt("data/txtdata.csv")
n_count_data = len(count_data)
plt.bar( np.arange( n_count_data ), count_data, color ="#348ABD" )
plt.xlabel( "Time (days)")
plt.ylabel("count of text-msgs received")
plt.title("Did the user's texting habits change over time?")
plt.xlim( 0, n_count_data );
Before we begin, with respect to the plot above, would you say there was a change in behaviour during the time period?
How can we start to model this? Well, as I conveniently already introduced, a Poisson random variable would be a very appropriate model for this count data. Denoting day $i$'s text-message count by $C_i$,$$ C_i \sim \text{Poisson}(\lambda) $$
We are not sure about what the $\lambda$ parameter is though. Looking at the chart above, it appears that the rate might become higher at some later date, which is equivalently saying the parameter $\lambda$ increases at some later date (recall a higher $\lambda$ means more probability on larger outcomes, that is, higher probability of many texts.).
How can we mathematically represent this? We can think, that at some later date (call it $\tau$), the parameter $\lambda$ suddenly jumps to a higher value. So we create two $\lambda$ parameters, one for behaviour before the $\tau$, and one for behaviour after. In literature, a sudden transition like this would be called a switchpoint:$$ \lambda = \begin{cases} \lambda_1 & \text{if } t \lt \tau \cr \lambda_2 & \text{if } t \ge \tau \end{cases} $$
If, in reality, no sudden change occurred and indeed $\lambda_1 = \lambda_2$, the $\lambda$'s posterior distributions should look about equal.
We are interested in inferring the unknown $\lambda$s. To use Bayesian inference, we need to assign prior probabilities to the different possible values of $\lambda$. What would be good prior probability distributions for $\lambda_1$ and $\lambda_2$? Recall that $\lambda_i, \; i=1,2,$ can be any positive number. The exponential random variable has a density function for any positive number. This would be a good choice to model $\lambda_i$. But, we need a parameter for this exponential distribution: call it $\alpha$.\begin{align} &\lambda_1 \sim \text{Exp}( \alpha ) \\\ &\lambda_2 \sim \text{Exp}( \alpha ) \end{align}
$\alpha$ is called a hyper-parameter, or a parent-variable, literally a parameter that influences other parameters. The influence is not too strong, so we can choose $\alpha$ liberally. A good rule of thumb is to set the exponential parameter equal to the inverse of the average of the count data, since we're modeling $\\lambda$ using an Exponential distribution we can use the expected value identity shown earlier to get:$$\frac{1}{N}\sum_{i=0}^N \;C_i \approx E[\; \lambda \; |\; \alpha ] = \frac{1}{\alpha}$$
Alternatively, and something I encourage the reader to try, is to have two priors: one for each $\lambda_i$; creating two exponential distributions with different $\alpha$ values reflects a prior belief that the rate changed after some period.
What about $\tau$? Well, due to the randomness, it is too difficult to pick out when $\tau$ might have occurred. Instead, we can assign an uniform prior belief to every possible day. This is equivalent to saying\begin{align} & \tau \sim \text{DiscreteUniform(1,70) }\\\\ & \Rightarrow P( \tau = k ) = \frac{1}{70} \end{align}
So after all this, what does our overall prior for the unknown variables look like? Frankly, it doesn't matter. What we should understand is that it would be an ugly, complicated, mess involving symbols only a mathematician would love. And things would only get uglier the more complicated our models become. Regardless, all we really care about is the posterior distribution. We next turn to PyMC, a Python library for performing Bayesian analysis, that is agnostic to the mathematical monster we have created.
PyMC is a Python library for programming Bayesian analysis [3]. It is a fast, well-maintained library. The only unfortunate part is that documentation can be lacking in areas, especially the bridge between beginner to hacker. One of this book's main goals is to solve that problem, and also to demonstrate why PyMC is so cool.
We will model the above problem using the PyMC library. This type of programming is called probabilistic programming, an unfortunate misnomer that invokes ideas of randomly-generated code and has likely confused and frightened users away from this field. The code is not random. The title is given because we create probability models using programming variables as the model's components, that is, model components are first-class primitives in this framework.
B. Cronin [5] has a very motivating description of probabilistic programming:
Another way of thinking about this: unlike a traditional program, which only runs in the forward directions, a probabilistic program is run in both the forward and backward direction. It runs forward to compute the consequences of the assumptions it contains about the world (i.e., the model space it represents), but it also runs backward from the data to constrain the possible explanations. In practice, many probabilistic programming systems will cleverly interleave these forward and backward operations to efficiently home in on the best explanations.
Due to its poorly understood title, I'll refrain from using the name probabilistic programming. Instead, I'll simply use programming, as that is what it really is.
The PyMC code is easy to follow along: the only novel thing should be the syntax, and I will interrupt the code to explain sections. Simply remember we are representing the model's components ($\tau, \lambda_1, \lambda_2$ ) as variables:
import pymc as mc
alpha = 1.0/count_data.mean() #recall count_data is
#the variable that holds our txt counts
lambda_1 = mc.Exponential( "lambda_1", alpha )
lambda_2 = mc.Exponential( "lambda_2", alpha )
tau = mc.DiscreteUniform( "tau", lower = 0, upper = n_count_data )
In the above code, we create the PyMC variables corresponding to $\lambda_1, \; \lambda_2$. We assign them to PyMC's stochastic variables, called stochastic variables because they are treated by the backend as random number generators. We can test this by calling their built-in
random() method.
print "Random output:", tau.random(),tau.random(), tau.random()
Random output: 58 31 17
def lambda_( tau = tau, lambda_1 = lambda_1, lambda_2 = lambda_2 ):
out = np.zeros( n_count_data )
out[:tau] = lambda_1 #lambda before tau is lambda1
out[tau:] = lambda_2 #lambda after tau is lambda2
return out
This code is creating a new function
lambda_, but really we think of it as a random variable: the random variable $\lambda$ from above. Note that because
lambda_2 and
tau are random,
lambda_ will be random. We are not fixing any variables yet. The
@mc.deterministic is a decorator to tell PyMC that this is a deterministic function, i.e., if the arguments were deterministic (which they are not), the output would be deterministic as well.
observation = mc.Poisson( "obs", lambda_, value = count_data, observed = True)
model = mc.Model( [observation, lambda_1, lambda_2, tau] )
The variable
observation combines our data,
count_data, with our proposed data-generation scheme, given by the variable
lambda_, through the
value keyword. We also set
observed = True to tell PyMC that this should stay fixed in our analysis. Finally, PyMC wants us to collect all the variables of interest and create a
Model instance out of them. This makes our life easier when we try to retrieve the results.
The below code will be explained in the Chapter 3, but this is where our results come from. One can think of it as a learning step. The machinery being employed is called Markov Chain Monte Carlo (which I delay explaining until Chapter 3). It returns thousands of random variables from the posterior distributions of $\lambda_1, \lambda_2$ and $\tau$. We can plot a histogram of the random variables to see what the posterior distribution looks like. Below, we collect the samples (called traces in MCMC literature) in histograms.
### Mysterious code to be explained in Chapter 3.
mcmc = mc.MCMC(model)
mcmc.sample( 40000, 10000, 1 )
[****************100%******************] 40000 of 40000 complete
lambda_1_samples = mcmc.trace( 'lambda_1' )[:]
lambda_2_samples = mcmc.trace( 'lambda_2' )[:]
tau_samples = mcmc.trace( 'tau' )[:]
figsize(12.5, 10)
#histogram of the samples:
ax = plt.subplot(311)
plt.hist( lambda_1_samples, histtype='stepfilled', bins = 30, alpha = 0.85,
label = "posterior of $\lambda_1$", color = "#A60628",normed = True )
plt.legend(loc = "upper left")
plt.title(r"Posterior distributions of the variables $\lambda_1,\;\lambda_2,\;\tau$")
plt.xlabel("$\lambda_2$ value")
ax = plt.subplot(312)
plt.hist( lambda_2_samples,histtype='stepfilled', bins = 30, alpha = 0.85,
label = "posterior of $\lambda_2$",color="#7A68A6", normed = True )
plt.legend(loc = "upper left")
plt.xlabel("$\lambda_2$ value")
w = 1.0/ tau_samples.shape[0] * np.ones_like( tau_samples )
plt.hist( tau_samples, bins = n_count_data, alpha = 1,
label = r"posterior of $\tau$",
color="#467821", weights=w, rwidth =2. )
plt.xticks( np.arange( n_count_data ) )
plt.legend(loc = "upper left");
plt.xlim([35, len(count_data)-20])
plt.xlabel("$\tau$ (in days)")
Recall that the Bayesian methodology returns a distribution, hence we now have distributions to describe the unknown $\lambda$'s and $\tau$. What have we gained? Immediately we can see the uncertainty in our estimates: the more variance in the distribution, the less certain our posterior belief should be. We can also say what a plausible value for the parameters might be: $\lambda_1$ is around 18 and $\lambda_2$ is around 23. What other observations can you make? Look at the data again, do these seem reasonable? The distributions of the two $\\lambda$s are positioned very differently, indicating that it's likely there was a change in the user's text-message behaviour.
Also notice that the posterior distributions for the $\lambda$'s do not look like any exponential distributions, though we originally started modeling with exponential random variables. They are really not anything we recognize. But this is OK. This is one of the benefits of taking a computational point-of-view. If we had instead done this mathematically, we would have been stuck with a very analytically intractable (and messy) distribution. Via computations, we are agnostic to the tractability.
Our analysis also returned a distribution for what $\tau$ might be. Its posterior distribution looks a little different from the other two because it is a discrete random variable, hence it doesn't assign probabilities to intervals. We can see that near day 45, there was a 50% chance the users behaviour changed. Had no change occurred, or the change been gradual over time, the posterior distribution of $\tau$ would have been more spread out, reflecting that many values are likely candidates for $\tau$. On the contrary, it is very peaked.
We will deal with this question for the remainder of the book, and it is an understatement to say we can perform amazingly useful things. For now, let's end this chapter with one more example. We'll use the posterior samples to answer the following question: what is the expected number of texts at day $t, \; 0 \le t \le70$? Recall that the expected value of a Poisson is equal to its parameter $\lambda$, then the question is equivalent to what is the expected value of $\lambda$ at time $t$?
In the code below, we are calculating the following: Let $i$ index samples from the posterior distributions. Given a day $t$, we average over all possible $\lambda_i$ for that day $t$, using $\lambda_i = \lambda_{1,i}$ if $t \lt \tau_i$ (that is, if the behaviour change hadn't occurred yet), else we use $\lambda_i = \lambda_{2,i}$.
figsize( 12.5, 5)
# tau_samples, lambda_1_samples, lambda_2_samples contain
# N samples from the corresponding posterior distribution
N = tau_samples.shape[0]
expected_texts_per_day = np.zeros(n_count_data)
for day in range(0, n_count_data):
# ix is a bool index of all tau samples corresponding to
# the switchpoint occurring prior to value of 'day'
ix = day < tau_samples
# Each posterior sample corresponds to a value for tau.
# for each day, that value of tau indicates whether we're "before"
# (in the lambda1 "regime") or
# "after" (in the lambda2 "regime") the switchpoint.
# by taking the posterior sample of lambda1/2 accordingly, we can average
# over all samples to get an expected value for lambda on that day.
# As explained, the "message count" random variable is Poisson distributed,
# and therefore lambda (the poisson parameter) is the expected value of "message count"
expected_texts_per_day[day] = (lambda_1_samples[ix].sum()
+ lambda_2_samples[~ix].sum() ) /N
plt.plot( range( n_count_data), expected_texts_per_day, lw =4, color = "#E24A33",
label = "expected number of text-messages recieved")
plt.xlim( 0, n_count_data )
plt.xlabel( "Day" )
plt.ylabel( "Expected # text-messages" )
plt.title( "Expected number of text-messages received")
plt.ylim( 0, 50 )
plt.bar( np.arange( len(count_data) ), count_data, color ="#348ABD", alpha = 0.65,
label="observed texts per day")
plt.legend(loc="upper left");
Our analysis shows strong support for believing the user's behavior did change ($\lambda_1$ would have been close in value to $\lambda_2$ had this not been true), and the change was sudden rather then gradual (demonstrated by $\tau$'s strongly peaked posterior distribution). We can speculate what might have caused this: a cheaper text-message rate, a recent weather-2-text subscription, or a new relationship. (The 45th day corresponds to Christmas, and I moved away to Toronto the next month leaving a girlfriend behind.)
1. Using
lambda_1_samples and
lambda_2_samples, what is the mean of the posterior distributions of $\lambda_1$ and $\lambda_2$?
#type your code here.
2. What is the expected percentage increase in text-message rates?
hint: compute the mean of
lambda_1_samples/lambda_2_samples. Note that this quantity is very different from
#type your code here.
3. What is the mean of $\lambda_1$ given we know $\tau$ is less than 45. That is, suppose we have new information as we know for certain that the change in behaviour occurred before day 45. What is the expected value of $\lambda_1$ now? (You do not need to redo the PyMC part, just consider all instances where
tau_samples<45. )
#type your code here.
from IPython.core.display import HTML
def css_styling():
styles = open("../styles/custom.css", "r").read()
return HTML(styles)


text:
Introducing Blacki Di Danca
This young man is doing big things in the dancehall scene, on a global scale.
I had the privlege of getting to know Blacka when we worked together on my Pop bella hall project several years ago. Watching him grow into this fearless trendsetter has been an inspirational pleasure.
Blacka had an undeniable passion for dance since the first day I met him, he is a natural dancer that brings a spiritual quality to his style of dancehall. He has recently returned to New York fresh off a solo European tour where he taught dancehall workshops in countries like Russia, England, Italy, France, Germany amongst many others. Keeping up with his instagram is like following a travel channel, the boy gets around.
What is so impressive and inspiring about Blacka is his fearless determination and commitment to his vision. He wants to share the beauty and culture of dancehall with the world, and with a positive and humble spirit he went out and did just that. Can't wait to bring him to Toronto to teach a workshop at the new DSBCD studios***
check out this video of one of Blacka's workshops in the UK:
Madeleine's mind is pleased to present Blacka Di Dancer as our 'Mind Influencer'.
Check out more info and stay connected with him here:
Facebook: Blacka Fan page


text:
I know it's a cliche thing to say, but I really can't believe this year has gone by so fast! I feel like last Christmas was just a few months ago! I am the type of person who loves to look back and reminisce, so I thought "What would be a better way to wrap up the 2015 season on this blog than to look back at my favorite shoots of the year?" This was an amazing year for Emily Rose Photography, and I couldn't be more thankful to everyone who worked with me, supported me, and helped me along through my many endeavors this year. Thank you especially to my clients, to my husband, and to my family. I'm so excited about what the 2016 season holds! Now I hope you enjoy this look back at 2015!
The year started with me finally investing in a camera upgrade that I had been wishing and saving for since I started my business. What a way to kick off an amazing year for Emily Rose Photography! The second the camera arrived, I went out for a practice shoot with my little one. This is him playing in the back of his grandpa's truck. :)
One of my first official shoots of the year was with my sister, Kate. I still love the wonderful headshots that we got on this brisk day in March.
My session with the W family followed soon after. It was almost spring, and we had lots of fun playing in the gardens in the late afternoon sun.
Early April brought Chris and Crystal's engagement session. This is one of my favorite ring shots of the year!
In mid-April I had the unique opportunity to shoot a prom, which was a first for me. It ended up being one of my favorite experiences of the year! All of the kids were amazing to work with, and the photos came out beautifully.
May brought Brian's senior session at Railroad Park. I loved hanging out and catching up with Brian while we shot his portraits!
I was also able to squeeze in a mini-session with Chalice and the adorable littles she was babysitting over the year. Look how cute!
Early Summer started with James and Brooke's 1-Year-Anniversary session at their alma mater where they met! Their puppy Dottie even joined in on the fun!
One of this year's favorites was this sweetheart session with my good friends and small group leaders! They were so much fun to work with!
On a hot July evening I had the pleasure of working with my little brothers and sisters-in-law. They all matched in little yellow outfits, which went perfectly with the yellow roses at The Botanical Gardens.
Another highlight this year was my headshot exchange with my good friend, Ashlyn. Her photos turned out beautifully, and I am thrilled with the headshots she took of me!
David and Emma's sweetheart session in August was another favorite, especially because David was completely surprised! Also can I mention that I have the most photogenic friends ever?!
It was hard to choose a favorite from the G family's session, but I love this one of just mom and dad together. So romantic and the perfect kick-off to the Fall photo season.
Early November brought my family's annual Christmas card shoot. Always a chaotic production, but I couldn't be happier with the result! Thanks to my dad for gracefully taking instruction and getting this shot for me. :)
Next up in my packed Fall season was Laura's graduation shoot. We shot on campus, which is one of the most beautiful locations in our area, and the light at golden hour couldn't have been better!
I had the chance to do another mini-session with a family friend who just received her Ph.D and needed some new headshots. She looked stunning!
Late November brought one of the biggest and most ambitious photoshoots of my career. Mitch and Ren's styled wedding shoot at The Barn at Shady Lane was a production that came from an idea, months of planning, plenty of trial and error, and lots of help from willing friends and family. I could not have pulled it off without my amazing vendors! Thank you again The Barn at Shady Lane, Stems and Styles, Somer Beauty, and Angie's Cakes. <3
On an unseasonably warm day after Thanksgiving, my sisters and I went to the park for a small fantasy-inspired shoot in the woods.
Next up was Josh and Katharine's engagement session at The Botanical Gardens. I love this shot highlighting both of their engagement rings!
Samuel and Hosanna's Christmasy sweetheart session was also a favorite of the year. I love when I get to work with my good friends! These two are absolutely adorable together.
And finally, I wrapped up the year with a wintery 1st birthday party for this darling little cousin of mine. Spending time with family was the perfect way to end the photography season, and begin the holiday season. <3
I hope you enjoyed this little look at the year past. I wish you all a Merry Christmas and a Happy New Year! I look forward to working with you and sharing with you in 2016!
Cheers, Emily


text:
If you're new to Art Journal Every Day, there is a short introduction here. All of the previous posts can be found archived here. Remember, it's just ten minutes of nourishing your creative self every day! No need to finish anything or even like it. If you've done some art journaling this week, use the hashtag #artjournaleveryday so that we can all take a peek. Even if you're not on social media, you can see everything (from twitter, pinterest, instagram, and facebook) that uses that hashtag here.
I must have had a lot of brown paint hanging around on my palette. Or maybe I was going through a phase where I was trying to use colors I don't like? Perhaps I was trying to get away from always use bright colors? Whatever the reason, these are some pretty somber-colored pages (for me):
You can see that this is a short page.
The stamp in her face is an Art Foamie (free shipping right now if you use the code "Summer") and the stamp along the edge is a hand carved stamp.
Here's a look at those short pages again:
The brown floral is an Art Foamie and the black patterns above it are hand carved stamps.
The purple stamping is an Art Foamie. And then there are a ton of hand carved stamps on this page:
This next page features just a touch of hand carved stamping and a lot of doodling:
The green tag was a leftover art part that I built the face around.
I hope you manage to get some art journaling time in this weekend!
Thanks for stopping by!


text:
Riverdale Point wants to change your view of Toronto's skyline/
All east-enders know about Riverdale Park and its commanding view of the Toronto skyline. The top of the hill located on the east side of the park, next to Broadview Avenue, offers an unobstructed vantage point to take in views of the downtown core and surrounding skyline.
This view is part of what makes the park one of the most active outdoor areas in the city yet, despite its popularity, it lacks any kind of infrastructure to promote and support sightseeing as one of its defining characteristics. There is a single lone bench on the crest of the hill.
Now a group of urban designers want to enhance that view with an observation deck and tobogganing platform. Billed by the group as Riverdale Point, plans call for a sweeping wooden deck upon the eastern embankment. The structure will be constructed of locally sourced wood to complement the natural landscape. A lower deck built at ground level will allow users space to stand. An upper deck will provide ample space for seating. In winter, the deck will become a tobogganing launch pad. The design would even bring back the lanes the park once featured to keep sledders safe from collisions with others.
"The park provides a unique opportunity to escape the bustle of the city," says Stas Ukhanov of the Riverdale Point group. "Our design strengthens and supports the existing use of the space by drawing people to the park, the ravine system and the nearby retail areas.
The Riverdale Point project was developed as part of the NXT City Prize 2014, a competition that invites urban enthusiasts under 30 to submit create and innovative ideas to improve public spaces across Toronto. It was shortlisted out of 140 entries and, since then, has received the support of local councillor Paula Fletcher and chief city planner Jennifer Keesmaat.
The group is now currently working to build local support for the project. Ukhanov says next steps include officially registering the project with the Parks Department Partnership Program and beginning to apply for grants and funding.
"We currently don't have a price tag for the project and hope to have a better idea of costs in the near future as we get an engineer to look at the proposal," Ukhanov says.
For more info, visit riverdalepoint.tumblr.com.


text:
By Mike Jurkovic
By way of an intro, I want to let you know what you won't find here. You won't find reviews of music by American idles, Survivor rejects, or artists who demand more time in rehab or on Hollywood gossip shows than CD players. You will find reviews of regional talent as well as new releases by some of our heroes who have, because of age or fad, fallen off the radar. With that said, here we go:
The Trapps – Cheap Seats
Driven by the fully formed and finely honed songwriting of guitarist/vocalist Sean Schenker and the glorious old school roots rock of his brothers-in-arms – lead guitarist Warren Gold, bassist Jason Sarubi, and drummer Seth Moutal – 'Cheap Seats' is not only a defining follow-up to their '06 debut 'Good Luck or Goodbye', it is a in spades confirmation of all that first disc offered.
You won't find pandering homage to any particular musical brand here. The Trapps are too intelligent for that and too determined to mark their own territory, which is why the Middle Eastern flavor of Chasing Bees feels as normal to the heart and soul of this disc as any of the brilliant Band-like songs that precede or follow it. In a better world where song once ruled out over celebrity, the jammy, anthemic Hope, and the soaring Everything Good Is Gone would be playing far and wide, loud and clear over the majestic Shawangunk Mountain Range that the band calls home.
As my closing argument to how natural The Trapps sound next to the classics, I offer this. The other day while driving, I had 'Blood On The Tracks' cued up to play after 'Cheap Seats' and it felt like I had been listening to these two grand discs all my life. www.thetrapps.com
Pearl Jam – Live On Ten Legs (Sony Legacy)
Fierce, ferocious, and more resilient than all their grunge peers rolled into one, PJ stormed North American stages last year and this is what it sounded like. I know this absolutely 'cos I was at the tour finale at Madison Square: a three hour wrecking ball tremblor of faith affirming rock n roll. And speaking of after shocks, check out the recently released, expanded and remastered 3 CD deluxe set of their second & third blistering discs, Vs. and Vitalogy, which includes a roaring 1994 show from the Orpheum Theater in Boston.
Bridge Over Troubled Water – Simon and Garfunkel (Sony Legacy)
If some recorded version of this landmark disc isn't already in your collection, then nothing I say here will convince you of its must-have-ness. This 40th anniversary release includes a sonic upgrade and a great DVD on the making of this generational, indelible music (The Boxer, So Long Frank Lloyd Wright, Cecilia, the epic title track). The DVD also includes the once broadcast and then controversial 'Songs of America' – virtually S & G's take on the chaotic end of the 60's, which sadly reflects the beginnings of the very troubled decade we now inhabit. www.simonandgarfunkel.com
Paul Simon – So Beautiful or So What (Concord Music Group)
With the release of 'So Beautiful or So What' we have our first serious contender for Album of The Year and easily Simon's finest and most rewarding disc since 1990's 'The Rhythm of The Saints'. Effortless, funky, jaunty, ebullient, and full of the lyrical images only Simon can conjure ("A pilgrim on a pilgramage/Walked across the Brooklyn Bridge"); ("Oh the music may be merry/but it's only temporary/I know Santa Claus is coming to town"), 'So Beautiful or So What' features three songs The Afterlife, Dazzling Blue, and Rewrite that arguably stand alongside many of his classics, and how many of his peers can say that or even dare to surpass their glory days? www.paulsimon.com
Brad Mehldau – Live In Marciac (Nonesuch)
Recorded in 2006, this 2 CD/1 DVD set highlights his deft touch and creative depth riding high in southwest France. Playing triumphantly between his two major influences – the brilliantly improvisational Keith Jarrett and the whispery melodicism of Bill Evans – Mehldau's twists and turns on Lennon/McCartney's Martha My Dear,Kurt Cobain's Lithium, and Roger/Hammerstein's quintessential My Favorite Things are joyous re-inventions,while his own compositions – Storm, Trailer Park Ghost, and others, reveal a musical range few dabble in. If you're not familiar with his work, may I also recommend 'Largo', 'Live In Tokyo', 'Brad Mehldau Trio','Live at The Village Vanguard', and last year's expansive 'Highway Rider'. www.bradmehldau.com
Sumi Tonooka – Initiation (Artists Recording Collective)
'Initiation' finds Beacon resident and Hudson Valley educator Sumi Tonooka in high creative and collaborative drive, turning what she has learned rhythmically from piano masters Kenny Barron, Thelonious Monk, and McCoy Tyner, into her own spirited, luxurious, and melodically complex, but never un-nerving, textures.
Saxophonist, composer, and fellow educator Erica Lindsay may be the second voice, but it as clear, resonant, and challenging as Tonooka's. Veteran bassist and Sumi stalwart Rufus Reid is, as always, a rock and a revelation as is the propulsion and swing of late drummer Bob Braye. www.sumitonooka.com
The Grand Slambovians
Though the rousing and uplifting The Trans-Slambovian Bipolar Express kicks things off 'The Grand Slambovians' unlike a trio of unforgettable, DIY predecessors - 98's 'A Good Thief Tips His Hat', 04's supreme double-set 'Flapjacks From the Sky' and 08's 'The Great Unravel – takes a while to gather steam and its emotional core.
But when it does, 'The Grand Slambovians' takes that full gale and sets its rootsy psychedelica for a truly grand, and certainly spiritual sail, with several of the songs rivaling any in their rich, intensely original, and defiantly quirky canon. With all its Slambovian variations on musical history and the human heart, Lost Highway; the elegiac The Invisible, the deep blue, stark hollow stomp of the reverberating title track; the impish pop of Who Is This Girl and The Quiet Beatle homage Ravenous Ways all, on first listen mind you, become vivid watermarks of why these Hudson Valley based myth-makers are loved and heralded around the world. www.slambovia.com