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One entity we all have in widespread is our wish for to be winning.
The most primitive ask I frequently ask once I'm muttering to a supporters is: "who wishes to be successful?"
Naturally, all extremity in the legroom goes up lacking indecision.
People don't even have to estimate nearly it.
"Of programme I poverty to be successful, what a unintelligent inquiring."
I after narrate them that they have v records to write feathers what "success" is.
More regularly than not, the figure will sit nearby for the supreme (if not, all) of the allocated instance agaze indifferently at the page, not knowing where to start, or what to author.
"What do you privation us to write", they ask.
"You said you impoverishment to be successful, communicate low what that (success) is", I report to them.
Because, they have never really characterised glory (for their own life) I undertake a breadth of reactions. Some folks get defending and grumpy, several empire get stern ("this is stupid"), one get feeling shame and numerous have a revelation; they get it.
Yes, they poorness 'different' and 'better' and 'more' but they don't truly cognise what that is.
Here's a truth:
Very few grouping actually specify natural event for their own time....
and if you don't outline it, you probably won't get it.
We put our hand in the air and say, "yes I privation to be successful".. but we don't in actuality know what that technique for us.
We don't have limpidity in the order of our future; we have puzzlement and delay.
Sometimes we status to pace vertebrae from the busy-ness and law-breaking of our life, be unmoving for a minute....and get more than a few perspective, area and clearness.
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In the trajectory of my sweat I am perpetually conversation to population astir the realities of their life and it ne'er ceases to amaze me how some forty, cardinal and threescore yr olds have scorned their life, or surroundings of it for eld... but ne'er truly through with anything to renovate it, because they have always let beingness begin to them.
They are victims.
They're angry, they're bitter, they're resentful, they're sad, they're unsuccessful and they're abounding of acknowledgment.
Apparently their lousy beingness is all in the order of situations, circumstances, mislaid opportunities and bad luck.
When I component out that it's truly much in the order of how they think, behave, react and choose, than it is in the region of thing other... I get an assortment of responses.
Just resembling we pursue at building a habitation or a business, we need to toil at construction our unsurpassable duration.
We requirement to brand decisions.
We inevitability to traffic with our fears.
We stipulation to disconnect troubling active what others weighing.
We condition to break off procrastinating and making excuses.
We call for to standstill mendacious to ourself and others.
We call for to end sounding for the convenient, easy, warm street and watch for the rewarding, challenging, exciting, astonishing and fulfilling footsteps.
We obligation to hold back ready for the 'right' instance.
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The Torajii system was a system with several planets. The star, Torajii, was a living creature. (TV: 42) It was also the location of Torajii Alpha. (PROSE: 42 Prologue)
The Torajii system appeared to have five planets. The planet closest to the sun was ringed, and the planet farthest from the sun had at least two moons. (TV: 42)
An asteroid called Stanalan was inhabited here and temporarily played host to the Galactic Fair with the highly acclaimed 'Death Ride' inside. Inside a chamber, Drexxons were trapped. (PROSE: Death Riders)

subset: commoncrawl


text:
December 13, 2011 – Wearing a chapter name tag is a great way to introduce your members to guests and help new members get to know all the new faces. They also are nice to wear at chapter events to help identify yourself as a local chapter member.
Now is a great time to get name tags to help identify your new officers and leaders. EAA has appointed Chapter 1095 (Gaylord, Michigan) as the official EAA chapter name tag supplier for the chapter network. The name tags are available in two sizes, come engraved, are delivered to your door, and are offered
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For more information and to place your order, visit this website.


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One of the Devil's children. He and Sam are half-brothers, although they are nothing alike. He seemed to be the Devil's favorite, although the Devil starts to think otherwise. He is very greedy, as shown when he stole a soul's ring. The Devil says if he could combine Morgan and Sam he would have the perfect son.
In the episode, ' The Home Stretch', The Devil forces Morgan and Sam into a competition. Whoever catches the soul first will be the Devil's right hand man and the loser will be stripped of all earthly possessions. By accident, Sam wins the competition and Morgan becomes homeless.
In the episode, 'My Brother's Reaper, Morgan finds Sam and tries to kill him. He stops halfway stating that he "never had the instinct". He remorsefully apologizes and Sam pities him and lets him stay at his place. When Sock and Ben have had enough with him, Sam gives Morgan a pep talk on finding a goal in his life and go with it. Later on in the day, Nina brings him to Tony's place where he has members of a group that follow the "Path of Steve". Upon arrival and seeing Sam, Morgan quickly draws his gun believing that his goal is to get everything back and that the only way to do it is to kill Sam. Nina knocks him over and some of the group shift into their demon forms and kill Morgan. Later on, the Devil informs Sam that Morgan has arrived in Hell and is being tortured. He also claims that he keeps calling out Sam's name; which is eventually revealed that he is casting a vendetta on Sam. Had the show been renewed for a third season it's very likely that Morgan would become an escaped soul.


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2006 W silver eagle NGC PF70 UCAM JS
- Sold for: Start Free Trial or Sign In to see what it's worth.
- Item Category: Coins & Currency
- Source: eBay
- Sold Date: May 09, 2015
- Channel: Auction House Sell similar item on eBay
This coin is flawless and the slab is in good shape. This coin was slabbed in 06 and this is the first time since then that it has seen the light of day.


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GLAC Intrepid was a light attack craft of the Grayson Space Navy.
Commanded by Lieutenant Judson Hines, it was one of the ships escorting Grayson One and HMS Queen Adrienne when they were attacked by the Masadan Operation Hassan. Alfred Willis served as the ship's tactical officer, Allen was the helmsman and Bob was the communications officer. (HH9)


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You are tying performance reviews to certain leadership behaviours, such as coaching. What are some others?
True leaders support company policies; if things are happening in the organization, they are communicating it down to people. They are mentoring.
Another thing: Managers tend to step down and do your job if you are not doing it well. They tend not to put a bright line between what you are doing and they are doing. But if you are not doing your job right, instead of compensating for your shortfall, they should be working with you to get it right.
So preventing people from overscoping their job downwards is extremely important. Ten to 20 per cent of the activities in an organization are just that. It can be that there isn't a clear definition of roles and responsibilities.
Are there role models for this kind of transition?
None have come to my attention. A lot of it is basic good management. You know what good leadership is. And if you get good leadership from 150 people, you're going to increase the productivity of the other 1,000 by 30 per cent. You get a massive payoff.
And you can't approach it by saying: "Let's fix processes and not fix culture," or "Let's fix structure and not fix processes." You have to do them all pretty much at the same time, or you're just not going to get there. You throw all the cards up in the air instead of just a few at a time.
Because of the IPO, everyone here is dislodged a little bit, and so you already have a little bit of a momentum for change. Our challenge would be significantly greater if we were just sitting here.
Are you a green company?
We are an environmentally responsible company. But the route to a lower-carbon world will not happen over a short period. Most of the world is driven by carbon and 70 per cent of the power in Alberta comes from carbon. It is going to be a very significant power-generating fuel source for a long time.
Our view is we should be putting money into technology, into things such as carbon capture and storage. But there are also smaller initiatives such as putting monitoring equipment the size of a bread box on the stack of our Genesee plant [a coal-fired facility in Alberta] which has dropped CO{-2} emissions by 2 per cent.
Green power generation is good and it has a role, but you're not going to see where 50 per cent of North American power is green. It's just not going to happen. We're a coal generator and we're not of a mind to try to wrap ourselves in green.
But we are going to be doing renewable projects and we are involved in calls for clean energy in Ontario and B.C. Out of those, we're hopeful to see big wind projects for us. Twenty per cent of our actual power generated last year was from green and renewable sources. It's not that we're just talking - we're doing a heck of a lot.
Are you going to make some growth moves soon?
We've got a lot of irons in the fire. You can never perfectly predict when some of these things might happen. But I'd be very disappointed if something didn't happen this year that would be newsworthy.
In acquisitions?
Or on the development side. The absolute stretch case is $30-billion [in assets] If we can move the organization from a leadership standpoint, and if the right deals and opportunities are there, we should be able to meet that. But we're not going to force that to happen. If valid opportunities aren't there, we are not going to be pushing it.
Brian Vaasjo
Title: President and CEO, Capital Power Corp., Edmonton
Born: August, 1955 in Edmonton
Education: Bachelor's degree and MBA, University of Alberta
Career highlights:
Spent 19 years with the Enbridge group of companies, where he rose to senior management.
Joined Epcor Utilities in 1998 as executive vice-president and chief financial officer. Led Epcor's initial public offering of debentures and preferred shares. Rose to chief operating officer of Epcor, as well as president of Epcor Power L.P.
2009: Became president of newly created Capital Power, which has 1,000 employees and 31 power facilities in Canada and the United States.Report Typo/Error
Follow us on Twitter: @Globe_Careers


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Staff and free snacks were great. Nothing
The staff was warm and welcoming from the moment I stepped through the door with my 12 year old. I detest the hold hotels put on your credit card and don't release for days and was told I could pay the $50 incidental in cash (which I received back at checkout before the housekeeper had even cleaned the room). The room was indeed small with only 1 bed which would have been a problem if I hadn't been traveling with family. However, it was clean, along with the bathroom. I did whack my elbow against the wall as I was showering as the stall was a bit cramped. We received great instructions on how to make our way to the White House and monuments - - all within walking distance. We finished our running around a bit early so we took advantage of the 2nd floor lounge for about a half hour before heading back to Union Station. There was an industrial coffee/espresso/cappuccino maker, tea bags, newspapers, a big screen tv set to CNN, a charging station, computers etc. The rooms are too small for friends to convene comfortably so the lounge is a nice spot. Convenience: A block away from the Circulator, a block away from the Farragut North and Farragut West station (it is actually on top of the Farragut West station, however, the entrance is closed so it's necessary to walk to the other side a block down). The complimentary bottled water saved us some money and we surely took advantage. The shower was uncomfortably small for a tall lanky person like me. Whacking my elbow against the wall isn't something I expect to happen in a hotel (usually hotel bathrooms are more comfy than my micro bathroom at home in NYC). Also, the room did not have a mini fridge which would have been nice for our leftovers. The hotel is downtown so the area at night is sparse (still feels safe though). You'll need to take a little walk to find some good breakfast if you set out early on a weekend because the nearby places are either never open on the weekends or only open closer to 9 am.
We went to DC to celebrate my husband's birthday for a long weekend. We looked for a long time online and a lot of the offering was overpriced for the amenities available. The Club Quarters was a great find!!!! The staff was very helpful throughout our stay. There is an espresso bar on the 2nd floor!! We were a few blocks from all the greatest attractions, walked to every site. Never had to take a cab or the Metro. Overall, we had a wonderful stay and will come back. We found a gem in D.C. and will be recommending it to our family and friends.
food and service was good My husband and i were given an end corner room 302 that was way to small and gave a confined effect. We spent as much time out of the room as possible. Parking was also difficult due to lack of easy access to our vehicle asit was in an underground garrage.
This is a great hotel, simple and convenient. The rooms are small, but quiet and the location is perfect. Right next to the metro and near the White House and mall. A little small... but otherwise great.
The location for downtown dc couldn't be better, and staff was excellent and very helpful Room was small but for the price value of everything else it didn't matter
Well maintained older hotel convenient to restaurants and tourist attractions. Nice linens and towels. Great cocktail lounge. Friendly and welcoming service. Street noise; will bring earplugs next time.
Location, convenience Rooms are dated, small and dark
The location 24 hr coffee station clean rooms Small room configured & supplied for the single business traveler. One bath towel 4 the family.
We were in DC to visit our son at GWH. Clean and efficient. Perfect location to the occasion. The bed could have been positioned 90° to the 8' room width.
The location was great as was the staff.
Nice lobby, courteous staff, comfortable bed. Tiny gym, mold in the bathroom grout, room amenities were average, surprising amount of wear and tear in our room for a hotel in this category.
Very clean, quiet and accommodating.
Location is ideal for seeing the museums and monuments. Lobby and restaurant staff are warm and welcoming. If you forget to tip housekeeping each morning, don't expect clean towels.
Location They offered to an upgraded room with a view for more $ Room had no view. AC broke , took 1 full day and multiple sells to get it repaired.
The Bellhops were amazing! So helpful and polite!!!
The staff at the hotel went out of their way to please us. In particular: Mr. Herb Wright greeted us daily with his million dollar smile and was always ready to be of assistance. Mekdes Pierce, Nathan and Limlin worked so hard each day in the lounge for breakfast and appetizers/dinner. They were so kind no matter how busy they were. Absolutely not one thing. It was an outstanding hotel.
I came down to ride my bike to all the museums and monuments. When I presented my claim ticket for my bike to the bellman my bike had been stolen. I spent 4 hours in the lobby filling out a police report
Location Maids Front desk staff on more than one occasion Very small rooms
Customer service The sofa sleepers
This is such a grand hotel. We spent 4 wonderful days here, great staff, exceptionally clean rooms and great views.
Excellent location. Beautiful view of the Washington Monument from our room. Staff was friendly, knowledgeable and helpful.
The staff was awesome Could've used a coffee maker in the room.
Everything about the hotel was fantastic, the staff were very courteous and helpful and all made a point of knowing my 7 year old son's name. Not applicable.
The room was lovely, and the staff was courteous, helpful and efficient.
Location was great. Wonderful room. Two good restaurants with great staff but a bit expensive. All staff members were great! Fitness room was available even in this boutique setting. My son lost his stuffed animal while we were there and they left a new one in the room for him - it made him very happy (they later found his but let him keep the new one, "Freddie". Restaurant was too expensive, especially for breakfast. Fitness room was a bit small.
Outstanding service and courtesy were the hallmarks of our stay here. The room was immaculate and well cared for. The location was perfect for everything we wanted to see and do. The conceirge was outstanding in his helpfulness. No coffee maker in the room, yikes, missed that first, fast heart- starting cup.
friendly staff,premium location.
Staff was excellent; great location The walls are paper thin and the person next door had a party until 3 am. Impossible to sleep.
Very comfortable and pleasant stay . Excellent service and perfect location.
Great location also amazing views ,fun staff and lobby had nice vibe turns into social setting with a DJ When I arrived was able to get a room right away as requested but it didn't have a view . And had ask the guy that was checking me in if I had a view he says yes I do , I walk in to my room it was a room facing the inside I of the building no view very disappointing so went back down and young lady was able to get us a much nicer room very happy . During the evening they have DJ in the lobby so when out all day and came back you requested your room key in order to get in the hotel not happy about that
Location was great. Checkout was advertised as 3:00 PM, We were not able to check into our room until 5:00. When we did check-in we had already pre-paid for a Fabulous Room, but the staff member that was checking us in recommended we switch to a Spectacular Room since we had a young child. He said the Fabulous Room would be too loud due to the location of being on the top floor under the bar. We agreed to change rooms, but were only offered breakfast vouchers which we knew we wouldn't use. After we walked away my husband said he felt like the guy was a used car salesman and that we got played. We went back to the desk and dealt with another employee (Harry) and he switched us back to our original room and said that the room was not that loud.
Great bed, nice, modern room. Amazing location. Our room had a view of a brick wall, very disappointing. The room service was very poor and very expensive. Travel channel fans, don't come here expecting an amazing sandwich, it is no longer available.
Very accomodating
Service was excellent They changed me an extra 200 dollars when I checked into the hotel, despite prepaying on Kayak
The hotel in a very nice location and the room very comfort. they give to me Room No 403 we can't sleep because of the noise of the air-condations from out side. and the hotel staff are not frindly
Location. Easy walk to many attractions as well as to my business destination.
upscale accomodations


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A stunning structure of glass and steel, the beautiful city hotel towers over the heart of the Dubai. A plush, marbled lobby paves the way to the 106 large exquisitely-appointed guest rooms. The establishment is air-conditioned and the hotel offers 24-hour reception and check-out service. Facilities include a currency exchange and lift access. The hotel features a rooftop pool and the News Caf� as well as a restaurant. Guests can take advantage of the room and laundry services and those guests who are arriving by car may park their vehicle in the hotel's car park.
The hotel is conveniently situated close to some of the city's major attractions. The popular city centre shopping mall is right across the road and the Dubai Creek with its rolling lawns, yacht basin and PGA-rated golf course is just a short drive away. Dubai International Airport is also just minutes from the hotel (approximately 3 km).
The suites are exquisitely designed and luxuriously furnished and feature fully-equipped kitchenettes complete with a cooker and mini fridge. Original hand-painted artworks create an ambience of understated elegance. Each room has an en suite bathroom and rooms are furnished with king-size beds. Amenities include a TV, washing machine, hairdryer and an ironing set. Air conditioning is a standard feature in all accommodation.
The hotel offers a state-of-the-art health and fitness centre that includes a sauna, steam and massage rooms for men and women as well as an outdoor swimming pool, hot tub, a ladies' salon and beach access.
The extensive breakfast, lunch and dinner menu includes the finest international cuisine as well as a wide selection of desserts, coffees and teas for in-between. Guests are invited to experience Al Mabsam - the finest in Arabic hospitality. They can enjoy authentic Arabic food, coffees, teas, special fruits and of course, a traditional sheesha - the perfect way to spend an evening.
American Express, MasterCard and VISA are accepted as methods of payment at this hotel.
Room Facilities:
Mini fridge
Washing machine
Iron and board
King size bed
Central airconditioned


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Re: [SCA-Archery] Re: New Interest in Archery
Expand Messages
- Greetings
Ok, Ft. Smith, Arkansas. You should contact your Kingdom Live-Weapons marshal, Peregrine Fairchilde, for specific info on X-bow regs - he's fairly local to you, lives in the Little Rock area, a very close friend of mine, wrote the manual for Meridies/Gleann Abhainn and, unusual by Gleann Abhainn standards, owns a crossbow of his own.
In general, crossbows in the SCA must be reasonably period in appearance (i.e. no open-frame stocks, no forward sites, no pulleys and cams, etc.). Beyond that, you have fairly wide latitude -- you are allowed rear sites, two or three-fletch bolts, prods made of modern materials, rifle stocks, and a wide variety of cocking systems. The standard Mediaeval light-poundage crossbow was a wooden tiller of fair heft, with a prod-and-string cocked (usually) by a rolling nut locked in place with a long metal bar or rod underneath - it's a type you see quite frequently on SCA ranges. Obviously, the closer to period norms you can get, the better off you will be. Again, check your archery manual or ask marshals for particular details, since they can vary a bit from overall SCA norms.
As regards pistol grips, if by that term you mean the hand grip appearing behind triggers on rifle-style stocks, no problem in general. If, however, you are referring to pistol X-bows - i.e. small X-bows held in a single hand and fired like a pistol, you may have difficulties - I believe these are outlawed widely in the SCA (in fact, in certain areas, Canada is an example, they are outlawed mundanely as well).
Shire of Smythkepe is located in the Kingdom of Gleann AbhannT.
> My son is 17 and will be 18 in March.
> Is there a certain type of crossbow that is allowed? Are pistol grip
> cross bows allowed?
> Thank you,
> Aveline Frazer
Three things never heard from the mouth of a Celt:
"Do these colors match?"
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Vineyards Vineyard
Map Cool
Climate Grape Growing In
The Vineyard Russian
River Valley Green
VINEYARD PROFILES Angel Camp Azaya Ranch Cherry Ridge Devil's Gulch Emerald Ridge Fox Den Freestone Hill Green Valley JJJS Maurice Galante McDougall Mill Station Morelli Lane Rued Shop Block Silva Walker Hill
The Freestone HIll Vineyard is farmed by the Dutton family. Located on a south-facing hillside overlooking the tiny town of Freestone, the vineyard lies just inside the southwest corner of the Russian River Valley AVA. The first Dutton-Goldfield Freestone Hill Pinot Noir came from the 1999 vintage. The wine throws a complex set of aromas leading with blackberry, violet and a coriander-like aroma we refer to as "Freestone Spice." The vineyard is planted to Dijon clones and is the epitome of what we expect from a cold-climate Pinot Noir vineyard. In years when the harvest gives us fruit that stands out as truly unique and different, we will bottle this wine separately as a one of our single-vineyard designate wines; in other years, it will be blended into our Dutton Ranch Pinot Noir to add that exotic spice component to the wine.


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Making Freedom: African Americans in U.S. History
Books 4 & 5 from this Primary Source series provide primary sources and classroom activities for teaching about Reconstruction & the Gilded Age.
Resources about the Civil War, Reconstruction, and the Gilded Age
This guide offers book, film, curriculum, and website resources for teaching and learning about the Civil War, Reconstruction, and the Gilded Age. Most books and films listed in this guide are available from the Primary Source Library. Please contact the librarian if you are interested in checking out or using sources from the library collection. Contact information for the Primary Source Library can be found in the right column of every page. Use the blue tabs above to navigate through this guide.
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Prince Resorts Hawaii was able to accommodate most Big Island travelers with holiday bookings at the temporarily shuttered Mauna Kea Beach Hotel, says Denise Anderson, Prince Resorts Hawaii's director of sales for luxury, leisure and corporate travel. Renovations are planned at the Mauna Kea to repair damage caused by the strong October 15 earthquake; Prince has moved all groups with 2007 bookings at Mauna Kea to neighboring sister property Hapuna Beach Prince. Retailers with clients holding forward Mauna Kea bookings can contact Keith Groves, director of revenue management, at email@example.com or Alvin Oshiro, front office manager, at email@example.com. Anderson says agent commissions have been protected.
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by Staff Writers
Luanda (AFP) May 6, 2012
A drought is threatening Angola's already modest food production, in a setback for efforts to revive once-vibrant farmlands abandoned during decades of war.
The dry season that normally lasts only about three weeks in December has stretched to three months in parts of the southern African country where most regions are used to abundant rainfall almost year-round.
The government has promised to help farmers, mostly smallholders producing for their own survival, but most of that aid has yet to materialise.
"Production has collapsed throughout the central and southern regions," said Belarmino Jelembi, national coordinator of the Association for Rural and Environmental Development.
That means a loss of revenue, but also "a threat of famine for families", he said.
During Portuguese colonial times, plantations and family farms made Angola a leading exporter of coffee, bananas and sisal, while growing enough food to meet most of its needs.
Most of the nation's farms were abandoned during decades of liberation battles and then a bloody civil war that ended only 10 years ago.
During the conflict, Angolans fled the countryside for the relative safety of the cities, leaving behind fields littered with landmines. In recent years, some areas have been cleared enough for farming to resume.
Government wants to increase domestic food production to ease Angola's dependence on expensive imports -- one of the main factors in the country's sky-high cost of living that makes life in Luanda more expensive than Tokyo.
Global risk consultancy Maplecroft last year ranked Angola as fifth in the world for unstable food supplies, placing it among troubled nations like Somalia.
The main opposition party Unita faults the government for failing to invest enough in Angola's farms. Since the end of the war, Africa's second-largest oil producer has emerged as one of the world's fastest-growing economies.
But that wealth hasn't reached the majority of the population, who still live in poverty.
Regional guidelines "recommend spending 10 percent of the national budget on the agricultural sector," said Unita secretary general Vitorino Nhany.
Government figures show the amount is actually five percent, and Nhany claims the true figure is only 1.2 percent.
The drought could make life even more difficult for Angola's poor, as production drops for staple crops like cereals, beans and sweet potatoes.
The most affected are Angola's two million small farmers, who depend on rain for their crops to survive, said Jelembi.
"Dependence on rain is the main problem for our farms," Jelembi said, warning of food shortages and rising prices later this year.
The hardest hit regions are provinces around the second city of Huambo -- Benguela, Huambo, Bie and Huila. The northern provinces of Zaire and Uige have also been affected, but to lesser degree.
The government unveiled a farm aid scheme in March, offering new seeds to try to recover lost production. Authorities also promised to launch irrigation projects, and to re-finance farm loans.
The plans called for "distribution of food and farm goods, as well as providing water by cisterns".
Over the long term, Angola wants to stop growing tubers, which consume lots of water, with plants less dependent on rains.
Jelembi's group supports these measures, but he said the government "systematically launches projects ill-suited to the needs and abilities of local communities.
"For example, on developing irrigation, the government wants to build a series of major dams, when all we're asking for is aid in building a network of small dams that farmers could manage themselves," he said.
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Ascend Combat Proven 03/16/13
Ascend Combat Proven 03/16/13 - Mixed Martial Arts (MMA) Event on March 16, 2013 in Shreveport, Louisiana USA
Join us for another exciting night of live cage fights in Shreveport, La on Saturday March 16th. Tickets are on sale now!! We have Catwalk ($100), Cageside ($75), VIP Tables ($800 or $1,000) and general admission $20 (advanced) $25 (day of show) still available. Contact Kim at (318) 402-6644 or by emailing us at email@example.com.


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- "A thousand years ago, there was another Rojixus tal Shiar that the Darukathians feared and continue to fear to this very day. That's why Rojixus is such a common name in my House, it's a taunt: a Rojixus tal Shiar can lay waste to their capital now as easily as one could a thousand years ago!"
- —Rojixus e Thraxus tal Shiar
Prince Rojixus e Fenrixus e Alarixus tal Shiar, known as the Great Scourge, was a Xaronese admiral during the Great Darukathian-Xaronese War. A fanatical believer in Xaronese racial and cultural superiority, Rojixus e Fenrixus tal Shiar was infamous for his cruelty and sadism. Shiar's most famous act was leading Xaronese forces in Operation Whirlwind which resulted in the capture of the Darukathian imperial capital at Darukath and the subsequent Sack of Darukath. Shiar's brutality during the Sack made him a reviled figure to Darukathians even into the 51st century.


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Officials said the closure of the London office would cost up to 600 jobs, hit the ethnic breakdown of staff and lead to the loss of economics experts.
The Public and Commercial Services (PCS) union said staff were "livid" at the news, which they warned would increase the prospect of a national Civil Service strike.
Tens of thousands of civil servants are already being balloted for industrial action in a dispute over jobs and pay.
The union said it feared there could be up to 200 compulsory redundancies and pledged to fight the plan.
Officials claimed that £75 million had been spent on modernising the ONS but warned of computer problems and gaps in funding.
"The organisation is in freefall," said one official. "They plan to have just a token presence in London but we have major concerns about the effect on the credibility of the organisation."
Mark Serwotka, general secretary of the PCS, said: "These plans are unnecessary, ill thought-out and will undermine the quality of the statistics that the Government bases new initiatives and policies on.
"With compulsory redundancies likely on a far larger scale than ever before, today's announcement will anger staff, stiffen their resolve and increase the likelihood of a Civil Service-wide strike.
"If the Government is to avoid a conflict with its own workforce, then it needs to move swiftly in giving assurances over jobs and services."
ONS said it had recommended to ministers that all statistical work moves from London, reducing staff from 600 to between 50 and 100 by 2010, leaving Newport in South Wales as the corporate headquarters.
The proposal was part of moves to relocate Civil Service jobs away from London, said ONS, adding: "It would help meet relocation targets and reduce accommodation costs but it would also promote closer working relationships and reduce risks associated with working from split sites.
"With statistical staff on two sites only there is the potential for better career development."
The FDA, which represents senior managers at the ONS, said it was shocked by the announcement, which was "ill thought-out and inappropriate".
The union voiced concern that staffing decisions were being made without proper consultations.
National officer Ro Marsh said: "There is little doubt that this announcement will have an adverse impact on the ethnic breakdown of staff at the ONS.
"We believe that ONS could be in breach of its statutory duty to assess and consult on the likely impact of proposed policies on the promotion of race equality and its own Race Equality Scheme.
"Furthermore, communication with stakeholders in the Treasury, the Bank of England and the Department of Trade and Industry will be weakened through the loss of expertise in London, at a time when many people believe this needs strengthening."
- More about:
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Nexus will help you with super fast access to your most frequently used applications, with flexible options allowing it to dock or float anywhere on the screen, providing a myriad of "on demand" possibilities. From it's generous use of situational sensitive context menus to it's advanced Drag & Drop support, Nexus is not only easy to use – it's a ... Read More »
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Aiseesoft Screen Recorder is the easiest screen recorder with the best video/audio recording function. It will capture any video or audio on your computer and save them in high quality. You can use this app to record online movies and radio, make video tutorials, capture games, record webcam videos, make captures and save songs, etc. Record video with high quality ... Read More »
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Photo Slideshow Creator is a handy tool designed to create a music slideshow with beautiful transition effects. The program allows you to add unlimited number of photographs and captions, apply themes and much more. Once created, the slideshow can be saved as an executable file (EXE), screensaver or video, including full HD. Photo slideshow software has a convenient, multilingual interface ... Read More »
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Apowersoft Screen Recorder is an easy-to-use and professional desktop tool for recording screen and audio activity at the same time. It also allows you to save your videos as standard WMV files. By using its built-in converter, you can convert those WMVs into AVI, MP4, FLV, and SWF, and many other formats with no quality loss. – Record your PC ... Read More »
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VueScan, the world's most popular scanner software, is used extensively by photographers, home users, scanning services and corporations. VueScan is a scanning program that works with most high-quality flatbed and film scanners to produce scans that have excellent color fidelity and color balance. It is very easy to use, and also has advanced features for restoring faded colors, batch scanning ... Read More »


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Bryan Proult
- LocationGenappe, Belgium
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- WebsiteBryan Proult website
Available for work in:
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I am a passionate photographer. I'm constantly trying to improve my skills (shooting, editing, communication wise). I particularly love to take portraits of people as I always find it to be a different experience with each individuals. Whether it's being able to create still images that convey strong emotions or whether it's creating visually dazzling images that transcend the medium, I'm always looking for something new. Being a photographer implies working with a lot of different talented people including hairdressers, makeup artists, models, assistants,... And I can't wait to meet new ones ! I also diversify in all sorts of events photography and I'm constantly looking for new gigs so be sure to contact me if you're interested. I particularly love doing weddings as they always able me to capture strong emotions.


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Blower Whl, Dia 4 3/4 In, Width 2 15/16 In
REVCOR| Zoro #: G0697636| Mfr #: Q475-293S L
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|Wheel Width (In.): 2-15/16
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|Country of Origin (subject to change): United States
Product Description
Blower Wheel, Direct Drive, Single Inlet Forward Curve, Wheel Dia 4 3/4 In, Wheel Width 2 15/16 In, Bore Dia 1/2 In, Wheel Material Galvanized Steel, Rotation Counter Clockwise Viewing Closed End, 3600 Max RPM


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Stores »
I love fresh rosemary. And its aroma is divine! I put it in my pizza dough, pasta sauces and more.
Another great herb to have around and use fresh.
If you can, fresh herbs are a must. If you have to, I recommend growing fresh thyme, rosemary, and basil in your apartment. Its simple, easy, fun and the pay off is tremendous. It also will save you money at the grocery.
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The article presents information on how professional associations can enhance career development of its affiliates. Chapters can play a key role in members' career development and thus improve satisfaction and increase recruitment. A career development committee composed of chapter members,...
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In the article, the author discusses about career mentoring and the things it offers with the parties involved. She cites the pivotal role played by membership associations in the mentoring process. She claims that such associations see mentoring as a tool to enhance the quality of practitioners...
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A graph-search framework for associating gene identifiers with documents
One step in the model organism database curation process is to find, for each article, the identifier of every gene discussed in the article. We consider a relaxation of this problem suitable for semi-automated systems, in which each article is associated with a ranked list of possible gene identifiers, and experimentally compare methods for solving this
We show that named entity recognition (NER) systems with similar F-measure performance can have significantly different performance when used with a soft dictionary for geneId-ranking. The graph-based approach can outperform any of its component NER systems, even without learning, and learning can further improve the performance of the graph-based ranking approach.
The utility of a named entity recognition (NER) system for geneId-finding may not be accurately predicted by its entity-level F1 performance, the most common performance measure. GeneId-ranking systems are best implemented by combining several NER systems. With appropriate combination methods, usefully accurate geneId-ranking systems can be constructed based on easily-available resources, without resorting to problem-specific, engineered components.
The GeneId curation task
Curators of biological databases, such as MGI
To accomplish the final stage of curation, a curator must find, for each article being curated, the unique database identifier of every gene that will be annotated. Because the criteria for annotation is different for every model-organism database, it is useful to consider a slightly more general version of this task, namely
GeneId finding is an important step in the curation process of many biological databases. It is also often a challenging task, because the nomenclature used for genes is inconsistent for many model organisms. In particular, authors often use novel variations of existing gene names in their papers; hence, even a large dictionary of genes and their synonyms will not contain all names appearing in a newly-published article. In addition, many existing gene names are ambiguous, corresponding to multiple unique gene identifiers.
GeneId finding is often broken down into two substeps: recognizing gene names in text, which is a a type of
Automatic GeneId finding
Much prior work on biological text-mining is relevant to geneId finding.
NER systems are usually evaluated by their
In some domains, the output of a NER system can be immediately used to solve the "id finding" problem for that domain, by simply looking up each extracted entity name in an appropriate database. For gene or protein entities, however, the "database lookup" step is non-trivial, for the reasons detailed earlier. Therefore, even a very accurate NER system for gene-protein entities may not be accurate at geneId finding; likewise, NER performance, as usually measured, is an imperfect proxy for performance on the geneId-finding task.
An important prior experimental study of geneId-finding was the BioCreAtIvE challenge, task 1B
Performance on the BioCreAtIvE datasets probably somewhat overstates performance in a realistic curation setting, as some simplifying assumptions were made in creating the challenge problem sets. Documents were abstracts, rather than full papers. It was assumed that a document was relevant to only one model organism, which was known to the geneId-finding system: thus, different versions of a system could be used for documents about different organisms, and no system needed to deal with the complications arising from a non-homogenous corpus, including documents relevant to multiple organisms, e.g., identically-named homologs. (This difficulty would not typically be relevant for a model-organism database, but could be relevant for other curation tasks, e.g., developing a BIND-like database of interactions.) Finally, the BioCreAtIvE task considers finding all genes
In spite of these simplifications, the BioCreAtIvE geneId-finding systems performed only moderately well for two of the three model organisms. (Many systems performed well for yeast, where the nomenclature for genes is fairly consistent.) For fly, the median and top F-measure of the systems fielded were 0.66 and 0.82, and for mouse, the median and top F-measure were 0.74 and 0.79, respectively.
GeneId ranking
The results on the BioCreAtIvE challenge suggest to us that completely automatic systems are unlikely to be useful for geneId finding. A more realistic short-term goal would be a system that, given a document, provides a
In this paper we experimentally compare a number of methods for geneId ranking. To evaluate such systems, we primarily use
Results and discussion
To evaluate our approach, we use the data from the BioCreAtIvE challenge. Our primary focus is the mouse dataset, which proved to be hard for the BioCreAtIvE participants. This dataset consists of several parts.
• The
• The
• The
Table 4
GeneId-Ranking Methods on Blind Test data
Avg Max F1
likely-prot + softTFIDF
possible-prot + softTFIDF
graph-based ranking
+ extra links & learning
Mean average precision and average maximal F1, for several geneId-ranking methods, on the 250 abstracts from the mouse test dataset.
For those familiar with BioCreAtIvE, our training set and development set are subsets of the BioCreAtIvE "devtest" set, and the historical data corresponds to the BioCreAtIvE "training" set. The terminology we use here more accurately reflects our use of the data. The test data is the same as the blind test set used in BioCreAtIvE. We did not manually examine the test set or use it for error analysis, program development, or debugging – its only purpose was as a final prospective test.
NER systems
We used two closely related NER systems in our experiments. Both were trained using an off-the-shelf machine learning system for NER called Minorthird
To train the first NER system, we used Minorthird's default tokenizer and feature set, and Minorthird's implementation of a voted-perceptron based training scheme for HMMs due to Collins
This method learns a NER system with precision of 0.87 and recall of 0.62 on the YAPEX test set, which compares favorably with the performance of the original YAPEX system on the same data. We call this first system the
Precision and recall are very sensitive to slight errors in entity boundaries. An alternative pair of measures are
A second NER system was constructed by modifying the likely-protein extractor to improve its recall, at the expense of precision, by tuning it to optimize F3 (favoring recall – see Methods section for the complete
Figure 1
Sample abstract
Sample abstract. A sample abstract from the mouse training dataset, together with the entries from the gene synonym list for the associated identifiers. Terms produced by the possible-protein extractor and likely-protein extractor (see text) are highlighted in the abstract. (The missing open parenthesis after the word "Niban" is a typographical error.)
Table 1
Performance of NER Systems
YAPEX system
Performance of the NER systems on the mouse development corpus and the YAPEX test corpus. The row labeled "dictionary" is for simple dictionary lookup using the mouse synonym list. The row labeled "YAPEX system" is from [15].
Likely-protein and possible-protein still have high token precision and high token recall on the mouse data; they also have comparable F measures on this dataset. However, performance is much worse for both than on the YAPEX data; in fact, the F-measure performance of the two systems is only slightly better than merely looking for exact matches to an entry in the mouse synonym list (recorded as "dictionary" in the table).
Presumably most of the performance degradation is due to systematic differences between the data on which the NER system was tested and the data on which it was trained; similar degradation has been documented elsewhere in transferring learned gene-protein NER systems from one distribution of documents to another
GeneId ranking by soft matching
The row labeled "dictionary" in Table
Specifically, we used a similarity metric for strings called
A simple baseline approach to implementing geneId finding, given a NER system, is to soft-match extracted gene names against the synonym list. Specifically, given a document
We implemented and evaluated this approach for the likely-protein and possible-protein extractors. The performance of these systems on the mouse development data is shown in Table
Table 2
GeneId-Ranking Methods on Development Data
Mean Average Precision (MAP)
likely-prot + softTFIDF
possible-prot + softTFIDF
graph-based ranking
+ extra links
+ extra links & learning
Mean average precision of several geneId-ranking methods on the 50 abstracts from the mouse development dataset. The first two lines are NER systems, coupled with using softTFIDF to matching extracted names with gene synonyms. The graph-based ranking method (see text) ranks geneIds by proximity to a query abstract in a graph that includes the results of both NER systems.
Here and elsewhere, we evaluate the ranked lists by
As an example, consider a ranked list of items (gene identifiers), where the items at ranks 1,2,5 are correct and those at ranks 3,4 are not. The precision at ranks 1 and 2 equals 1.0, and the precision at the next correct item is 0.6 (since there are 3 correct answers before rank 5). The non-interpolated average precision on this ranked list is thus (1 + 1 + 0.6)/3 = 0.87.
Elsewhere, it has been noted that
In our ranking systems, it may happen that some correct answers do not appear in the ranking at all: in this case, we follow standard practice and define
For this dataset, the geneId ranker based on possible-protein performs statistically significantly better (with
We thus observe that for this dataset, the widely-used F1 score does not predict the relative performance of these two NER systems, when they are used as components of a geneId-ranking using soft matching. This may be because in a geneId finding pipeline, NER systems that have higher recall are preferable, as the subsequent process of normalization to unique ids may downweight gene names that are incorrect. More generally, F1 score may be an imperfect measure of performance for any NER system that is used in the context of a larger problem – an observation which has implications on the proper evaluation of such systems.
Graph search for GeneId ranking
We will now describe a scheme for combining multiple NER systems into a single unified geneId-ranking system. The core of the idea is to first represent all information as a labeled directed graph which includes the test abstracts, the extracted names, the synonym list, and the historical data; and then to use
Figure 2
Graph used for geneId-ranking
Graph used for geneId-ranking. Part of a simplified version of the graph used for geneId ranking. In the ranking system, geneIds are ranked by a certain notion of proximity in the graph (see text). The graph is simplified by not showing inverse links, and by showing only a single NER-related link type.
The nodes and edges listed above are all produced automatically by our system as it reads in NER-annotated files. (The graph structure produced is slightly more complex than suggested by Figure
Given this graph, gene identifiers for an abstract are generated by traversing the graph away from the abstract, and looking for
As an example, consider the two shaded nodes in Figure
Paths through the historical data also contribute to the final ranking. For instance, the proximity of
More formally, similarity between two nodes is defined by a
Conceptually, the edge weights above define the probability of moving from a node
The similarity metric
We note that this graph framework is general, and can be used for various other types of queries. For example, starting the random walk from a particular extracted protein name
Engineering the graph for better performance
As shown in Table
Each baseline method also algorithmically incorporates the knowledge that a group of paths through the graph are important – namely, paths from an abstract to a NER-extracted string to a synonym (via a
As Table
Learning to rank
We next consider the topic of improving the results of Table
Intuitively, it would be desirable for this feature vector to summarize the lazy walk process which led to the ranking. We assume a vector f of primitive feature functions that describe the individual edges in a graph. For instance, one such feature function might be
In our experiments, we constructed one such feature function for each possible combination of a source node type
We then recursively define another vector function F which aggregates these primitive feature functions by computing the expected value of the primitive features appearing in any edge of the graph (see the Methods section for details). This vector F(
Once feature vectors are constructed, any of several existing methods for learning-to-rank can be employed. The learning algorithm that we use is based on
After learning, the weight vector W can be used to re-rank the output of the graph-based ranker as follows. Given a node
As Table
As a final prospective test, we applied the two baseline methods to the 250-abstract mouse test data. We compared their performance to the graph-based search method combined with a reranking postpass learned from the 100-abstract mouse training data. The performance of these methods is summarized in Table
All of the methods perform somewhat less well than on the 50-abstract mouse development set. This is probably due to variation in the two samples – for instance, the test-set abstracts contain somewhat more proteins on average (2.2 proteins/abstract) than the development-set abstracts (1.7 proteins/abstract).
Figure 3
Average recall vs rank for geneId-ranking
Average recall vs rank for geneId-ranking. Average recall for lists truncated at rank
As shown in the graph, an ideal re-ranking would find 70% of the correct proteins by rank one, rather than rank two, and could achieve almost 97% recall by rank five, rather than only 84%. The graph walk results are encouraging, compared to this optimal curve. In particular, the graph walk reaches nearly maximal recall within the top 15–20 ranks.
This performance might be further improved by better learning methods or (more readily) by extending the graph to include more information sources. (It should be recalled that this result was obtained requiring as resources only a synonym list and a collection of previously-curated documents.) In addition, it might be beneficial to improve on the quality of the information sources used – for example, to use NER components trained on a more appropriate set of documents, to use domain-specific string similarity measures, or to use less noisy tagged historical data. It is also quite possible that performance would improve with larger collections of previously-curated documents; in general, one would expect the performance of this system to improve over time, as the corpus of labeled history data grows.
Our performance measures are
As a rough measure of relative performance, we computed the maximal F-measure (over any threshold) of each ranked list produced, and then averaged these measures over all queries. This "Average Maximum F-measure" computation supports the claim that the graph-based method outperforms both of the NER systems on which it is based. The average maximum Fl-measure of the graph-based system with learning is 0.755.
Three of the eight BioCreAtIvE participants achieved aggregate F1 scores above this. Two of these participants
We have proposed a method for solving the "geneId finding" problem using easily-available resources, and no problem-specific engineered components. Specifically, the suggested method requires only a dictionary of protein synonyms; one or more inexact NER systems, trained on rather different corpora; generic, problem-independent soft-matching methods for strings; and a set of previously-curated documents that have been associated with protein identifiers mentioned in the documents.
In our proposed method, a graph is generated that combines the historical data and the outputs of multiple NER systems, and produces a ranked list of gene identifiers by traversing this graph with a stochastic approximation to a 10-step
We tested our method on the BioCreAtIvE datasets for mouse proteins – mouse being one model organism that was found to be difficult for previous researchers. The graph-based approach outperformed any of its component NER systems. On a 50-problem development set, the graph-based approach with learning outperforms the best-performing single NER-system by nearly 30%, as measured by mean average precision; also, the graph-based approach
Our system cannot be easily adapted to aggregate predictions from multiple abstracts; however, if the
A central claim we make in this paper is that a ranked list of protein identifiers is more useful than a fixed set, as it is more suitable for semi-automatic curation. In absolute terms, the performance is probably satisfactory to aid in curation: for instance, on average, more than 84% of the protein identifiers that should be associated with an abstract are ranked in the top five. In practical terms, the usefulness of a real curator's assistant based on this sort of ranking scheme might well be dominated not by minor improvements in the ranking, but by user-interface issues (e.g., how to explain to a curator why a protein was suggested).
The training data and development data are subsets of the BioCreAtIvE "devtest" set (abstract numbers 0–99 and 100–149 respectively). The historical data was called "training data" in the BioCreAtIvE publications, but we use the term "historical data": we did not use them as training data
To evaluate NER systems, the development-data abstracts were hand-annotated (by the first author) by marking up all gene-protein entity names.
NER systems
We used two closely related NER systems in our experiments. Both were trained using an off-the-shelf machine learning system for NER called Minorthird
The YAPEX corpus consists of a training corpus of 99 MEDLINE abstracts and a testing corpus of 101 MEDLINE abstracts. These documents deal primarily with protein-protein interactions, and are annotated for gene-protein entities. They contain 1745 and 1966 entities, respectively.
To train the first NER system, we used version 220.127.116.11 of Minorthird. We used the default to-kenizer, the "Recommended.TokenFE" feature extractor, and Minorthird's implementation of a voted-perceptron based training scheme for HMMs due to Collins
Defining F(x, y) = and letting W be a weight vector over the components of F, we can now succinctly describe the learning method, the goal of which is to find a W that leads to the globally best overall performance. This "best" W is found by repeatedly updating W to improve the quality of the Viterbi decoding on a particular example (x
After training, one takes as the final learned weight vector W the average value of W
We configured the algorithm to make 20 passes over the training data.
Unlike the case for simple classifiers, it is non-trivial to modify a sequential classifier to improve recall. We use the following scheme to trade recall for precision
As it turns out, voted-perceptron NER systems are quite sensitive to the value of
It is common in information extraction research to use the F1-measure, a specialization of the more general
Building the graph
Minorthird's NER system store their output in a particular data structure, and in incorporating the NER system's output, we found it easiest to construct an analog of that data structure in the graph. Rather than linking a document directly to entities found by the NER system, we constructed a
Approximate name matching
We now define the distance between the two words sets
Two advantages of TFIDF over edit-distance based similarity metrics are: first, token order is not important, so "B-chain of lactate dehydrogenase", "lactate dehydrogenase-B" are considered similar; and second, common but uninformative words like "chain" do not greatly affect similarity.
The secondary similarity metric we used was the
and finally, letting
Combining NER and soft matching
To reduce the cost of repeatedly applying the softTFIDF algorithm in matching all possible entity pairs, we constructed an inverted index for each token that occured in the gene synonym list, and only computed similarities between a string
Graph search for ranking
Formal details
Formally, a graph
Similarity between two nodes is defined by a
For most node and edge types, after ℓ is picked,
then we can also easily define this default weighting scheme as Pr(
Conceptually, the edge weights above define the probability of moving from a node
Given probability
This can be approximated by limiting the summation to some maximal value
The graph created from the mouse data is moderately large – over a million nodes – so we store it in secondary memory. We use the open-source search engine Lucene
We use
Table 3
GeneId-Ranking With a Graph
(1) let
(2) for
• let
• for
- sample
- for each edge label ℓ ∈
* for each node
· let
· increment
(3) return (
An efficient approximation algorithm for computing
Learning to rank
Recall the definition of
Table 5
Features Used for Ranking
The final set of features used by learning system, in the format , where
We then recursively define another vector function F which aggregates these feature primitive functions over a walk that starts at node
Finally, we can define
The algorithm of Table
In our learning experiments, we iterated over all of the examples 200 times.
Availability and requirements
The source code for the software discussed in this paper is available as an additional file with the manuscript (see
Additional File 1
The code used for graph-search and learning-to-rank in this paper has been submitted as an additional file: a gzip-compressed tar file, with all code under an open source license. The code requires installation of Minorthird, another open-source package. The README.txt file in the top-level directory details how to compile and use the code. The code for combining NER and soft matching is implemented a special case of the graph search that uses a restricted set of paths through the graph, realized using the class SoftDictEntitySearcher.
export CVS_RSH=ssh
export cvsroot=:pserver:email@example.com:/usr1/cvsroot
% cvs login
(anything as a password)
% cvs checkout ghirl
Click here for file
Authors' contributions
EM implemented some of the graph-search and learning framework, contributed to the recall-precision adjustment of the NER methods. WC performed the remainder of the implementation and experimentation. All authors read and approved the final manuscript.
The authors wish to thank the organizers of BioCreAtIvE for assembling and sharing their data, Bob Murphy and Tom Mitchell for many helpful discussions, and the anonymous reviewers for numerous helpful comments. The work described here is supported by NIH K25 grant DA017357-01, for Cohen, and grants from the Information Processing Technology Office (IPTO) of the Defense Advanced Research Projects Agency (DARPA), for Minkov.
MGI: Mouse Genome Informatics
FlyBase: A Database of the Drosophila Genome
The TREC 2004 genomics track categorization task: classifying full text biomedical documents
Gene Ontology: tool for the unification of biology
Overview of BioCreAtIvE: critical assessment of information extraction for biology
Overview of BioCreAtIvE task 1B: normalized gene lists
BioCreAtIvE Task 1A: gene mention finding evaluation
What makes a gene name? Named entity recognition in the biomedical literature
A survey of current work in biomedical text mining
Comparative Experiments on Learning Information Extractors for Proteins and their Interactions
Two Applications of Information Extraction to Biological Science Journal Articles: Enzyme Interactions and Protein Structures
EDGAR: Extraction of Drugs, Genes and Relations from the Biomedical Literature
Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Minorthird: Methods for Identifying Names and Ontological Relations in Text using Heuristics for Inducing Regularities from Data
Protein names and how to find them
Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
Hidden Markov Support Vector Machines
Shallow parsing with conditional random fields
NER Systems that Suit User's Preferences: Adjusting the Recall-Precision Trade-off for Entity Extraction
High-recall protein entity recognition using a dictionary
A Comparison of String Distance Metrics for Name-Matching Tasks
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Diffusion Kernels on Graphs and Other Discrete Structures
Contextual Search and Name Disambiguation in Email Using Graphs
Large Margin Classification Using the Perceptron Algorithm
ProMiner: rule-based protein and gene entity recognition
A simple approach for protein name identification: prospects and limits
Automatically An- noting Documents with h
Unsupervised gene/protein entity normalization using automatically extracted dictionaries
SecondString: An Open-source Java Toolkit of Approximate String-Matching Techniques
Understanding Captions in Biomedical Publications
Sleepycat Software


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|"Tim Smith (JIRA)" <j...@apache.org>
|[jira] Commented: (LUCENE-1826) All Tokenizer implementations should have constructors that take AttributeSource and AttributeFactory
|Thu, 20 Aug 2009 23:28:14 GMT
[ https://issues.apache.org/jira/browse/LUCENE-1826?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12745724#action_12745724 ] Tim Smith commented on LUCENE-1826: ----------------------------------- without the Tokenizer.reset(Reader, AttributeSource), i won't be able to reuse Tokenizer instances (will have to create a fresh one each time) this can get costly if each Tokenizer is layered with a bunch of TokenFilters obviously, adding this method would be nasty (and impose additional requirements on TokenFilters/Streams) but it would allow reusing the token streams to the utmost Question: Is the reflection penalty on the new TokenStream stuff incurred per root AttributeSource?, or per TokenFilter/TokenStream? that is, if i pass the same AttributeSource to 10 TokenStreams, is the reflection cost the same as if i passed it to just one? > All Tokenizer implementations should have constructors that take AttributeSource and AttributeFactory > ----------------------------------------------------------------------------------------------------- > > Key: LUCENE-1826 > URL: https://issues.apache.org/jira/browse/LUCENE-1826 > Project: Lucene - Java > Issue Type: Improvement > Components: Analysis > Affects Versions: 2.9 > Reporter: Tim Smith > Fix For: 2.9 > > > I have a TokenStream implementation that joins together multiple sub TokenStreams (i then do additional filtering on top of this, so i can't just have the indexer do the merging) > in 2.4, this worked fine. > once one sub stream was exhausted, i just started using the next stream > however, in 2.9, this is very difficult, and requires copying Term buffers for every token being aggregated > however, if all the sub TokenStreams share the same AttributeSource, and my "concat" TokenStream shares the same AttributeSource, this goes back to being very simple (and very efficient) > So for example, i would like to see the following constructor added to StandardTokenizer: > {code} > public StandardTokenizer(AttributeSource source, Reader input, boolean replaceInvalidAcronym) { > super(source); > ... > } > {code} > would likewise want similar constructors added to all Tokenizer sub classes provided by lucene -- This message is automatically generated by JIRA. - You can reply to this email to add a comment to the issue online. --------------------------------------------------------------------- To unsubscribe, e-mail: firstname.lastname@example.com For additional commands, e-mail: firstname.lastname@example.com
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|Marc Richards <mjr...@yahoo.com>
|Running multiple instances of 5.5x as Win services
|Mon, 10 Jul 2006 14:57:33 GMT
Hi all, I would like to run multiple instances of Tomcat on a single Windows box for the purposes of instantiating multiple jvm's (to keep the heap small and use more of the system's available memory and cpu) and then balance between them. I currently have one instance setup as a service - this was put in at install time by the 5.5x installer, but I would like to add an additional service that will run the same web apps and use the same binaries, while using alternate configuration files. Any tips? Thanks, -marc __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com --------------------------------------------------------------------- To start a new topic, e-mail: firstname.lastname@example.com To unsubscribe, e-mail: email@example.com For additional commands, e-mail: email@example.com
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