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MANHATTAN, Kan.- The Texas A&M women's tennis team improved to 13-0 all-time against Kansas State as the Aggies cruised to a 7-0 victory against the injury-plagued Wildcats under blustery conditions today at the Washburn Tennis Facility.
KSU (5-14, 3-7 Big 12) was shorthanded from the start, having to compete with only four players due to injuries.
"The adversity we had to face first of all was the wind," A&M coach Bobby Kleinecke said. "When you are dealing with 20-plus miles-per-hour wind, it changes your game. And when you go up and they have only four healthy players, in your mind it is hard to get yourself set that this is a match. It's not just a practice. That is the adversity that we faced, and I thought we did a good job."
Because the Wildcats could only field two doubles teams, they were forced to default at the No. 3 line. The 28th-ranked doubles team of Tiffany Clifford and Sarah Foster quickly gave the Aggies a 1-0 lead and improved to 9-1 in Big 12 matches with an easy 8-0 victory against Katerina Kudlackova and Viviana Yrureta at the No. 1 line.
At the No. 2 court, 39th-ranked Anna Lubinsky and Nicki Mechem dropped their first three games to KSU's Tamar Kvaratskhelia and Natasha Vieira. The Aggie pair then regrouped and won eight of the next nine games to win the match and improve to 10-0 in conference play.
A&M immediately took a 3-0 lead at the start of doubles as KSU defaulted at the No. 5 and No. 6 lines. With A&M needing only one singles victory to seal the win, Mechem quickly clinched the winning point for the Aggies as she sailed to a 6-0, 6-0 victory against Vieira at the No. 4 line.
Foster followed with a 6-2, 6-2 win against Kvaratskhelia at No. 2 to put the Aggies ahead, 5-0.
In the only three-set match of the day, A&M's Anna Blagodarova won her first set against Kudlackova, 6-1, at the No. 3 position. Kudlackova rallied to even the match by taking the second set, 6-2. With the team victory already determined, a 10-point super tiebreaker was played for the third set. Blagodarova never trailed en route to a 10-5 win in the tiebreaker.
Lubinsky, ranked No. 24 in singles, completed the shutout with a 6-2, 6-3 victory over Yrureta at No. 1 singles.
"I thought we did a good job in the singles," Kleinecke said. "I felt like we were in control the whole time."
A&M improves to 18-5 overall and 8-2 in the Big 12. The Aggies, who remain tied with Colorado for third place in the league, conclude the regular season tomorrow at Kansas. The match begins at 11 a.m. at the First Serve Tennis Center in Lawrence, and live scoring will be available at AggieAthletics.com
No. 21 Texas A&M 7, Kansas State 0
1. (#24) Anna Lubinsky (A&M) def. Viviana Yrureta (KSU), 6-2, 6-3
2. Sarah Foster (A&M) def. Tamar Kvaratskhelia (KSU), 6-2, 6-2
3. Anna Blagodarova (A&M) def. Katerina Kudlackova (KSU), 6-1, 2-6, 1-0 (5)
4. Nicki Mechem (A&M) def. Natasha Vieira (KSU), 6-0, 6-0
5. Tiffany Clifford (A&M) wins by default
6. Marisa Druss (A&M) wins by default
Doubles (A&M wins the point)
1. (#28) Clifford/Foster (A&M) def. Kudlackova/Yrureta (KSU),
2. (#39) Lubinsky/Mechem (A&M) def. Kvaratskhelia/Vieira (KSU),
3. Blagodarova/Michelle Sammons (A&M) win by default
Order of finish (D3, D1, D2, S5, S6, S4*, S2, S3, S1)
*Clinched match
Washburn Tennis Courts (Attendance: n/a)
Team records: Texas A&M (18-5, 8-2 Big 12); KSU (5-14, 3-7)


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Rock and Mineral Exchange
Many geoscience educators have limited opportunities to collect good rock and mineral samples directly from the field. Others lack the financial resources to purchase collections. At the same time, many institutions have an abundance of specimens sitting unused in laboratory drawers. This service from NAGT and MSA aims to bring these educators together so that surplus resources can be moved between institutions as a way of improving educational opportunities for some and freeing up space for others.
Here you can browse through postings related to specimens that other users are making available as well as those related to specimens being sought. Click on the title for a posting below to get to the full information. If you would like to post an available or desired specimen to the exchange, use the links to the appropriate form in the navigation menu on the right.
Available Specimens
Desired Specimens
- Flute Casts and current ripples in Sandstone Wanted (12/20/11)
- Intermediate Volcanics (12/1/11)
- Gneiss needed (1/9/12)

subset: commoncrawl


text:
The BMW 3 Series remains the benchmark of the sporty medium category. On the training track, the light, agile and versatile sedan provides an intensive experience of BMW Driving Experience.
Technical specifications
- | Cars will vary according to availability | |||
- | Combined CO2 emissions: 109 to 205 g/km |


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Click here to
Next Scheduled Amber Alert Meeting: January 15, 2014 Agenda (pdf) Previous Meeting Agendas/Minutes Members
AMBER ALERT empowers law enforcement, the media and the public to combat abduction by sending out immediate, up-to-date information that aids in the child's safe recovery.
August 8, 2011 - iPhone App for children now released by FBI
AMBER ALERT may be activated only by law enforcement agencies
AMBER ALERT is intended only for the most serious, time-critical child abduction cases.
AMBER ALERT is not intended for cases involving runaways or parental abduction, except in life-threatening situations.
AMBER Advocate Spring 2013
Emergency Messages Go Mobile
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Copyright © 2007 State of Nevada. All rights reserved Site developed by DoIT Web Group - Nevada Internet Privacy Policy (PDF)


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Changes related to "2011-08-27 Moises Fuentes w pts 12 Raul Garcia, Benito Juarez Auditorium, Guadalajara, Jalisco, Mexico - WBO"
From Barry Hugman's History of World Championship Boxing
This is a list of changes made recently to pages linked from a specified page (or to members of a specified category). Pages on your watchlist are bold.
No changes on linked pages during the given period.


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Residents living in and around Mineral Wells can be screened to reduce their risk of having a stroke or bone fracture.
Mineral Wells Senior Center, 102 N.W. 6th Ave., will host Life Line Screening on Dec. 18.
Screenings identify potential cardiovascular conditions such as blocked arteries and irregular heart rhythm, abdominal aortic aneurysms, and hardening of the arteries in the legs, which is a strong predictor of heart disease. A bone density screening to assess osteoporosis risk is also offered and is appropriate for both men and women.
Four key points every person needs to know:
• Stroke is the third leading cause of death and a leading cause of permanent disability.
• Eighty percent of stroke victims had no apparent warning signs prior to their stroke.
• Preventive ultrasound screenings can help you avoid a stroke.
• Screenings are fast, noninvasive, painless, affordable and convenient.
Packages start at $149. All five screenings take 60-90 minutes to complete. For more information regarding the screenings or to schedule an appointment, call 1-877-237-1287 or visit our website at www.lifelinescreening.com. Pre-registration is required.
Local News
Stroke, osteoporosis screenings coming to Mineral Wells
- Local News
New regime
After over a month of speculation, the Ram faithful have a new leader in which to place their hope.
Friday night, the Mineral Wells ISD Board of Trustees announced their decision to hire Waco Midway's Co-Offensive Coordinator Gerald Perry as the new Athletic Director and head football coach of Mineral Wells High School.
ZONTA Status of Women Luncheon March 23
Women and their many contributions are about to be honored.
City Council passes 'hotdog' ordinance
Nobody likes being left inside a car during extreme temperatures, hot or cold – animals don't like it either.
CASA seeks 100 more child advocates
Casa means home, but for thousands of abused and neglected children, it's an acronym that has even more meaning – it's the hope of finally finding a safe, permanent home where they can thrive.
Paulsen honored for 25 years of volunteer work with DI
Recently the 2014 Greater Fort Worth Destination Imagination Tournament took place at Legacy High School in Mansfield, and 26 of the 256 teams that competed in the event are State competition bound.
Santo ISD student dies in house fire
ERATH COUNTY – A young student of Santo Junior High School is dead and his parents hospitalized after their rural home was consumed by a fire in the wee hours of Monday morning.
County incumbents hold firm in election
PALO PINTO – Candidates and their friends and families crowded into the Palo Pinto County Courthouse, Tuesday night, to watch as results from the primary elections came in. Ultimately, it was the Republican incumbents in contested county races that ruled the day, as County Judge David Nicklas, District Clerk Janie Glover and County Clerk Janette Green all maintained their titles for another four years.
MWISD:Henderson honored
During the February board meeting, Dr. Haterius presented the District's very first inductee into the newly organized Apple Corps program.
Clark Gardens gearing up for festival fun this month
Bringing the spring season in with a bang, Clark Gardens is hosting its Annual Spring Festival March 29, 10 a.m. to 5 p.m. and March 30, 11 a.m. to 4 p.m.
Symbolic old cross on hill will be restored
Amidst all the excitement going on with the Baker restoration, there is another project locals are getting excited about.
- More Local News Headlines
New regime


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Reebok Pants
(780 items)
Product ID: 982549
Main Content
Order today, receive no later than
Thursday, March 13th
Some limitations apply
$4.99 3-day shipping. Need it faster?
It's time to get wrapped up in your Buffalo Sabres style! The hockey season is about to start and you have to find a fabulous way to flaunt your Sabres pride. Now you can with these Wrapped fleece pants by adidas. These fleece pants feature "Sabres" screen printed down the left leg below a team logo. Nothing will have you cheering "Go Sabres!" more than these comfy and cozy pants!
- 60% Cotton/40% Polyester
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- Team colors and logo
- Imported
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Special event items are produced by manufacturers only after the outcome of a game
or event. These are advanced sale items and will ship immediately after they are
received in our warehouse.
Manufacturer direct items are shipped directly from the manufacturer. These items
are not available for international or expedited shipping.
Customized items can be personalized with options such as your name, your favorite
number, and/or designs. Some options may be limited by league rules.


text:
[ Source: scowl ]
Package: wamerican (7.1-1)
Links for wamerican
Ubuntu Resources:
Download Source Package scowl:
Please consider filing a bug or asking a question via Launchpad before contacting the maintainer directly.
Original Maintainer (usually from Debian):
- Don Armstrong
It should generally not be necessary for users to contact the original maintainer.
External Resources:
- Homepage [wordlist.sourceforge.net]
Similar packages:
American English dictionary words for /usr/share/dict
Other Packages Related to wamerican
- dep: debconf (>= 0.5)
- Debian configuration management system
- or debconf-2.0
- virtual package provided by cdebconf, cdebconf-udeb, debconf
Download wamerican
Architecture | Package Size | Installed Size | Files |
all | 262.7 kB | 990.0 kB | [list of files] |


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Please enter your travel dates.
Suite (Noyoltzi) - European plan
- FREE Internet
Internet is included in your stay.
Junior Suite - European plan
- FREE Internet
Internet is included in your stay.
Junior Suite, Jetted Tub - European plan
- FREE Internet
Internet is included in your stay.
Family Suite, 1 King Bed - European plan
- FREE Internet
Internet is included in your stay.
Beachfront, with full-service spa
This Zihuatanejo hotel is set on the beach a short distance from La Madera Beach, Principal Beach, and La Ropa Beach. Also nearby are Municipal Market and Fisherman's Walk.
Hotel Features
Spend the day relaxing on the beach or pampering yourself at Hotel Aura del Mar's full-service spa.
Room Amenities
Televisions come with cable channels. Guestrooms also feature air conditioning, safes, and minibars.
Information missing or incorrect? Tell us!Hotel Amenities
- General
Spend the day at the beach and work on your tan with sun loungers (chaise longues) or relax in the shade with umbrellas. Pamper yourself with a treatment at the full-service spa and enjoy amenities at Hotel Aura del Mar like an outdoor pool and a health club.
The hotel offers a restaurant and a snack bar/deli. A bar/lounge is on site where guests can unwind with a drink. A computer station is located on site and high-speed wireless Internet access is complimentary. This beach hotel also offers a steam room, a sauna, and a rooftop terrace. Onsite parking is complimentary.
- Beach umbrellas
- Beach sun loungers
- Beach towels
- Babysitting or childcare (surcharge)
- Wedding services
- Banquet facilities
- Total number of rooms - 30
- Number of floors - 8
- Number of buildings/towers - 1
- Airport transportation (surcharge)
- Room service (limited hours)
- Full-service health spa
- 24-hour front desk
- Business center
- Tour assistance
- Area shuttle (surcharge)
- Snack bar/deli
- One meeting/conference room
- Shopping on site
- Laundry facilities
- Pool table
- Health club
- Bar/lounge
- Restaurant
- Rooftop terrace
- Sauna
- Steam room
- Swimming pool - outdoor
- Breakfast available (surcharge)
- Free Wi-Fi
- Computer station
- Free parking
- Internet
Available in some public areas: Free Wi-Fi
- Parking
Free parking
- Room Amenities
- Rollaway/extra beds available
- Cable TV service
- Connecting / adjoining rooms available
- Balcony
- Air conditioning
- Minibar
- Coffee/tea maker
- Bathrobes
- Hair dryer
- In-room safe
- In-room massage available
- Dining
Continental breakfasts are available for a surcharge.
Bistro del Mar - This beachfront restaurant specializes in seafood and serves breakfast, lunch, and dinner. Guests can enjoy alfresco dining (weather permitting).
Room service (during limited hours) is available.
- Recreation
Recreational amenities at the hotel include an outdoor pool, a health club, a sauna, and a steam room.
The recreational activities listed below are available either on site or nearby; fees may apply.
- Sailing nearby
- Parasailing nearby
- Personal motorized watercraft nearby
- Golfing nearby
- Windsurfing nearby
- Kayaking nearby
- Water skiing nearby
- Tennis nearby
- Fishing nearby
- Snorkeling nearby
- Bicycle rentals nearby
- Scuba diving nearby
- Table tennis nearby
- Swimming on site
- Spa
Guests can indulge in a pampering treatment at the hotel's full-service spa.
Policies & Fees
- Check-in
Check-in time starts at 3 PM
- Check-out
Check-out time is noon
- Payment types
Accepted at this hotel: American Express, MasterCard, Visa
- Pets
Pets not allowed
- General
Extra-person charges may apply and vary depending on hotel policy.
Government-issued photo identification and a credit card or cash deposit are required at check-in for incidental charges.
Special requests are subject to availability upon check-in and may incur additional charges. Special requests cannot be guaranteed.
- Additional Details
The property has connecting/adjoining rooms, which are subject to availability and can be requested by contacting the property using the number on the booking confirmation. No pets and no service animals are allowed at this property.


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This property allows you to book online with a credit card. After submitting a reservation request, the owner or manager has 24 hours to accept, at which time your card will be charged and you will receive an email confirmation
Strawberry Travel Tips
Stay Near it All in Strawberry Cabins
When you take your next California vacation, you will want to consider Strawberry cabins to keep you close to the action in Gold Country. Strawberry lodging is not far from the many historical attractions that bring many people to this area. Whether you are coming with a group of friends, or as a family vacation, you will be amazed at what you find in Gold Country. With attractions for all age groups and interests, you can't go wrong in Gold Country.
History Comes Alive
There are many different options when you are trying to decide what to do on your Gold Country vacation. The California Military Museum will take you through many different historical military artifacts. This is a must see for anyone who is interested in the military or history, and close enough to your Gold Country High Sierra vacation rentals for a day trip. The Gold Country Museum will take you through the history of the gold rush. You can even take some time and pan for your own gold! If cars are your thing, you will want to stop by the California Automobile Museum. You will find the world's largest and most complete Ford collection in this museum dedicated to Henry Ford.
See the Animals
If you are in the mood to take in the wildlife, you will want to check out the zoos that are near your Strawberry cabins. The Folsom City Zoo may be small, but it has been home to unique, great animals since 1963. Also close by your Strawberry lodging is the Sacramento Zoo. This large zoo has a huge variety of exotic animals to gaze at. Whether you spend just a few hours at the zoo, or multiple days going back, you will see animals that you may never have a chance to see again.


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Making every event more successful.
Whether it's supporting credit unions by participating in industry events, or providing our credit unions with educational and networking opportunities through our own events, we actively help credit unions gain the knowledge they need to be successful in their marketplaces.
Check our calendar to find events that you can participate in from the convenience of your own desk, as well as local and national offsite events.
Industry Events
There are no events scheduled for the date you chose or the event date you chose is in the past. Please click on any future date with an underline to see upcoming scheduled events.


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21-23 Nguyen Thi Minh Khai Street, District 1, District 1 - Consulate / Notre-Dame Cathedral, Ho Chi Minh City, Vietnam (Show on map)
Book now!
based on 386 reviews
Cleanliness, Excellent Kitchen area, Great Bed...
Nearest transport:
Saigon Railway Station (2.5 Km / 30 min walk)
Area recommended for:
Distance to airport:
Tan Son Nhat International Airport (5.2 Km)
Long Thanh International Airport (24.4 Km)
Customer comments:
"Comfortable beds", "Great swimming pool", "Very friendly staff"
Top attractions in the area:
Diamond Plaza (0.4 Km / 5 min walk)
Nha Tho Duc Ba (Notre Dame Cathedral) (0.5 Km / 6 min walk)
Notre Dame Cathedral (0.5 Km / 6 min walk)
Main Post Office (0.5 Km / 6 min walk)
Note: Distances are measured as a straight line, calculated from a starting point to a destination point.
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outdoor pool
Free WiFi access
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  Hotel Description
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Guests over 7 years old are considered as adults.
Extra beds are dependent on the room you choose, please check the individual room policy for more details.
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from 386 reviews
Somerset Vista Ho Chi Minh City Serviced Residence
Fantastic 8.1
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Somerset Ho Chi Minh City Serviced Residence
Very Good 7.9
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Graphic Design / Film / Video Intern
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West Oaks Urgent Care
Our urgent care center is seeking an undergraduate student to create high quality logos, photographs, and videos to promote the clinic's doctors and services online and in print. This is a part-time, unpaid position for about 2 hours each week for the next 2-6 months. College credit is available. The intern will have a chance to shadow the doctors and learn about medical procedures including x-rays, stitches, and illnesses. This would be ideal for a student to improve their professional portfolio.Responsibilities
- Assists in the design and development of layouts for marketing materials, using appealing graphics that are well organized- Responsible to incorporate current design trends into each unique layout to produce an up-to-date style for custom ads
- Ability to exercise good judgment when interpreting instructions and to identify the strengths and weaknesses of alternative solutions
- Utilize strong written skills and bring effective visual ideas from concept through to completion
- Working towards a BA or certificate from a Fine Arts program/Graphic Design/Film/Video school- Experience with Adobe InDesign, and proficiency in Adobe Photoshop and Adobe Illustrator
- Knowledge of Mac OS and software upgrades and proficiency in Microsoft Office programs
- Must be a team player and flourish in a fast-paced, deadline-oriented environment
- Open to constructive criticism and customer feedback


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38 wooded acres North of Gray in Jones County. Driveway and good road system, power, well, pole barn and multiple food plots. Peaceful getaway. Lots of deer and turkey. Surrounded by large acreage. Perfect for homesite or small hunting tract. Evenings... More info Email a friend |
Secluded 4.7 acre lot with 55' wide and 185' long private entrance road. Originally proposed as a portion of Fulton Hills Subdivision off Michael Dr. Within 1/2 mile of Skyview Elementary School and Bibb County Fire Station. May be subdivided into four (... More info Email a friend |
7 acres for sale 2 miles from Perry, Ga. Beautiful home site. Covenant protected. Pasture and Hardwoods. Also has a nice pond site. Livestock welcome. More info Email a friend |


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Sikhism is one of the world`s most vibrant religious orders, whose followers characteristically tie their long hair in a turban, sport flowing beards, pray at the gurudwara (literally, at the feet of the guru) wear a steel bangle on the right wrist, and occasionally, carry a small dagger, called a kirpanand, most importantly, believe in the age-old adage of `work is worship`.
Related Links
Essence of Sikhism
This is a visually pleasing and comprehensive site on various aspect of the Sikh holy book, the Guru Granth Sahib. It also provides links to audio clips of Sikh devotional songs, videos of preachings, and biographies of Sikh gurus. More....
History of the Sikhs
A mini-encyclopedia on the Sikh faith, this site provides a detailed history of the Sikhs, along with brief bios of Sikh martyrs. More....


text:
Honey Snack Mix Recipe
Little cubs can't resist gobbling up the crackers, cereal, raisins and candy in this sweet snack mix created in our Test Kitchen.
- 1 package (10 ounces) honey bear-shaped crackers
- 3 cups Honey-comb cereal
- 1-1/2 cups Reese's pieces
- 1 cup chocolate-covered raisins
- In a large bowl, combine all ingredients. Store in an airtight container. Yield: 9 cups.
Originally published as Honey Snack Mix in Taste of Home's Holiday & Celebrations Cookbook
Annual 2004, p199
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Posted on May 20th, 2010 by Nick Catchdubs
Just another RATatouille: samples, nerdery, Christmas music. Go! Read More
Rappin Ass Thursdays #32
Posted on Apr 22nd, 2010 by Nick Catchdubs
Was the last one of these really TWO MONTHS ago? Thats a lot of missed Thursdays, a lot of rappin ass raps left behind… Make up for lost time and hit the jump for a bunch of stuff that has caught my ear since then (and hasn't been talked to death elsewhere). Underground and under-appreciated, poppy-as-hell but still awesome, lessgooo… Read More
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Baldur's Gate
Beta-testers Wanted: Keldorn romance mod
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Joined: 03 Aug 2010
Posts: 840
Location: Hither and yon
Hmm. There is a CombatCounter() on all LTs, but it's impossible to prevent dialogues from starting unless Keldorn and the PC are actually participants in the combat. If one non-party member is fighting another non-party member, there is no way to prevent the talk from firing. Bad timing, but unavoidable.
"Imagination was given man to compensate for what he is not, and a sense of humor to console him for what he is." - Francis Bacon
Kindred Spirits
] - Dragon Age II fanfic
Sun Jan 29, 2012 12:12 am
Joined: 27 Jan 2012
Posts: 2
Alright, I ran into a few problems (which I tweaked around, but you should know about):
Keldorn has no respawns set to initiate the romance process. After his initial personal quest and getting back together with Maria, there is no timer to spawn the messenger. Even after creating the messenger using the console, there is no timer set to respawn Keldorn. I had to run a global to make him appear back at his home, boot him out of my party, manually set his KelRomReturn Global to 7, and go from there. This is from a completely clean install (updated ToB) with no other mods. I checked the d files to make sure, but there wasn't anything to set the KelRomReturn variable after his vanilla quest. I'm new to modding myself, so I wouldn't know how to set the spawn timers, but I know they aren't there.
You mentioned in the readme to play Watcher's Keep in SoA. I did, but I wasn't sure what the "romance" dialogue initiated from it was (I couldn't find it in Near Infinity, either). Or was it those night-time dialogues and I totally didn't get that?
Other than that, everything ran smoothly. I should note that using the CreateCreature command to make Keldorn will cause him to talk to you literally every five seconds, no matter how you tweak the globals, locals, boot him out, or kill him. The best way to "overcome" this is to simply use Bioware's global to spawn him in his home, recruit him, set the KelRomReturn global to 7, boot him out, and let things play out naturally.
I should say that, technical issues aside, I'm a big fan. This is fantastic work!
My beta version: keldorn_rom-b20120118
Tue Feb 14, 2012 12:35 am
Joined: 23 Feb 2012
Posts: 3
Are you still looking for beta testers? I'm interested if you are.
Thu Feb 23, 2012 9:29 pm
Joined: 03 Aug 2010
Posts: 840
Location: Hither and yon
Beta testing is pretty much over, but if you want to play the mod because you want to play the mod, the link above will work. Just make sure you get the one that ends in 20120118.
"Imagination was given man to compensate for what he is not, and a sense of humor to console him for what he is." - Francis Bacon
Kindred Spirits
] - Dragon Age II fanfic
Fri Feb 24, 2012 4:33 pm
Joined: 31 Mar 2011
Posts: 42
Location: On the Front Lines
Uhh... I don't know if it was a mistake somewhere on my part, but I just made it to ToB with Keldorn and I've run into a problem.
I don't know what usually triggers the first ToB talk, but after restoring Sarevok, I tried resting in the Pocketplane. Keldorn's romance music began to play, so I THINK a talk was trying to fire, but all that popped up was the PID menu with the "Can you tell me about this place, I feel uneasy, etc" dialogue options.
I tried resting again when nothing else happened, but couldn't get the dialogue to fire. Did I jack this all up? I'll admit I played through SoA with Haer Dalis, thinking the two mods wouldn't affect each other since Haer Dalis never really commits to the PC, and doesn't have any content yet for ToB.
Keldorn seemed fine up until ToB.
Sat Feb 25, 2012 6:10 am
Joined: 03 Aug 2010
Posts: 840
Location: Hither and yon
What version do you have? The first talk is actually supposed to happen once complete the first challenge, not after giving your soul to Sarevok.
The global for the first ToB dialogue is B!KR_ToBc1. It fires on 1.
"Imagination was given man to compensate for what he is not, and a sense of humor to console him for what he is." - Francis Bacon
Kindred Spirits
] - Dragon Age II fanfic
Sat Feb 25, 2012 2:58 pm
Joined: 31 Mar 2011
Posts: 42
Location: On the Front Lines
Oh I made sure to get the version that ends in 20120118.
Sorry! False alarm. The talk triggered normally after the first challenge. I guess I was a little paranoid, since I kind of cheated by romancing two guys at once. That'll teach me to flirt with other men...
FWIW, I'm really enjoying the romance so far. I'm sure you hear the words 'mature and realistic' used to describe it a lot, but they really are the most appropriate ones I can come up with. The naughtier flirts that have cropped up have been an unexpected delight as well.
Overall, this mod is a breath of fresh air, I think. Thank you so much for your hard work, Berelinde.
Sat Feb 25, 2012 8:07 pm
Joined: 23 Feb 2012
Posts: 3
berelinde wrote:
Beta testing is pretty much over, but if you want to play the mod because you want to play the mod, the link above will work. Just make sure you get the one that ends in 20120118.
Oh, excellent! I am looking forward to it.
Sat Feb 25, 2012 8:50 pm
Joined: 27 Dec 2011
Posts: 9
Location: UK
Finally got round to finishing ToB. I just carried on from my save post SoA and everything ran smoothly with the mod.
I just wanted to say that I enjoyed playing this through so much. I love writing and look forward to more.
Also, why hasn't anyone written any Keldorn/PC fanfiction yet?! Surely this is an issue that must be addressed? If only I could write...
Mon Feb 27, 2012 2:46 am
Joined: 28 Dec 2011
Posts: 26
Mostly out of curiosity, what is the status on the final release version? I played a little bit of the beta test, but not enough to really count. As soon as I got my shiny new laptop, I installed BGT and am currently working my way through BG1. I've got a little ways to go yet, before I meet up with Keldorn. So, yes, mostly curiosity.
Tue Mar 06, 2012 1:48 pm
Joined: 28 Dec 2011
Posts: 26
silverleaf wrote:
Also, why hasn't anyone written any Keldorn/PC fanfiction yet?! Surely this is an issue that must be addressed? If only I could write...
LOL I had been wondering the same thing. I wish I had the time to pursue it myself. Le sigh.
Tue Mar 06, 2012 1:53 pm
Joined: 03 Aug 2010
Posts: 840
Location: Hither and yon
The big thing that is holding up the non-beta version is that I am moving house. It won't take much to get a non-beta out, but finding time is challenging.
"Imagination was given man to compensate for what he is not, and a sense of humor to console him for what he is." - Francis Bacon
Kindred Spirits
] - Dragon Age II fanfic
Tue Mar 06, 2012 2:15 pm
Joined: 28 Dec 2011
Posts: 26
That's certainly understandable. Quite difficult to work on a mod when pieces and parts are packed in boxes and moving around.
Good luck, though, I hate moving. So. Much.
Tue Mar 06, 2012 7:51 pm
Joined: 22 Jan 2012
Posts: 4
Just wanted to say that I loved this and didn't run into any problems. Don't have anything more constructive than thanks sorry
Mon Mar 19, 2012 6:37 pm
Joined: 19 Aug 2011
Posts: 35
Location: USA
I did mail this little review but was encouraged to post it here
I hope it doesn't spoil for anyone but I wanted to give an honest opinion :3
This only applies for the ToB portion mind you.
I will begin by saying I enjoyed this mod a lot. It was very well written, an amazing change, and a well thought out mod which thickened up the character
of Keldorn and ToB.
You bought an entirely new demension to the game which I enjoyed.
I shall first begin with the complaints, and then end with the happy stuff so you end the message on a happy note, please do not be offended or kill me for this, but I want to be honest as i can be
My first complaint in the language, is the use of the word "Love" For example "It's alright, love" - It just reminds me of a british sitcom "ELLO LUV! WHAT CANNA I GET YOU FROM THE BAR" - I think for a man of Keldorn's standard, (even my own toons standard)
saying "My love" Is much more poetic and respectful "It's alright, my love". And being a person who is portrayed as one with much wisdom, and from being from a rich background he should have had higher (maybe more poetic?) standards in his speech. I don't think he should have been talking to me like he was, I needed more wisdom. I think the romance should have been continued to be a little awkward, he's an older, wiser man, out of his comfort zone, and I'm a hellspawn of bhaal who is young enough to be his daughter..
My second complaint, is I felt like a virgin school girl who just got dumped by the popular guy in school and when he was done with his next chick and ran back to me I fell head over heels (see, John Tucker must die).
I feel the character shouldn't have taken Keldorn back so easily if she didn't want to, I felt he had to earn his way back into my panties, (I understand ToB is short) But it just dissapointed me that basically what happened was
"You broke my heart? tore me up inside! WANNA FUCK!? SURE !"- I felt my character lost all self respect and should have put up more resistance, but again I understand ToB is short.
I was dissapointed he had NOOO interest in letting me mother his kids, like bitch what is wrong with MY uterus, but then I can also understand his veiw, I just wish there could have been two paths on this. I also wanted to suddenly have
my character get pregnant JUST to see his reaction (I'm one for teen drama, I apologize)
Every now and then Keldorn was getting a little too mushy for me, I wish he kept his head about him, I liked his more rough exterior, then "omg pookie i wubs you WUBWUBWUBWUB" (But this is YOUR keldorn, I respect that, please do not be offended by anything I say
I just thought you'd like opinions!)
But otherwise, the mod was very good, it did thicken up ToB and Keldorn considerably, I really enjoyed playing it and don't regret playing it at all! And I hope perhaps there will be more content for it in the future!
Sat Mar 24, 2012 10:09 am
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Warner Robins City Leaders Discuss Transit System, Switch to Natural Gas for City Vehicles
WARNER ROBINS, Georgia (41NBC/WMGT) - Warner Robins city leaders devoted an entire day to talks about the city's future.
Those talks ranged from how SPLOST money will be used to the possibility of a switch to natural gas for the city's vehicles.
One of the most pressing topics of discussion to Mayor Chuck Shaheen is the city's transit system--or lack thereof.
"I went to a presentation about transit and bringing transit to Warner Robins," says Shaheen. "We're one of the largest cities in that study that doesn't have transit service."
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"They're (Thomasville) recognizing roughly a $300,000 a year savings," says councilman Mike Brashear. "That's just on their garbage fleets. So we'll take a look at that, and we could translate that into a greater savings for the city of Warner Robins. It'd be cleaner air, plus we'd save the citizens a whole lot of money."
Deeper looks and extra time to discuss items like these is exactly why this worksession took place.
"I think it's always good for them to get in a less formal and time-constrained situation," says city attorney, Jim Elliott. "They can sort of go off on a tangent, if necessary, to discuss things--they always do. I think, ultimately, at the end of the day, better decisions are made when that happens."
No official decisions were made during Friday's worksession as it was mostly informational.
Even with an entire day devoted to hashing out some of the city's biggest priorities, some council members say some of the ideas they talked about still need more time for discussion.


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Height: | 5-6 |
Year: | Senior |
Hometown: | Corpus Christi, Texas |
High School: | Calallen HS |
Position: | SS |
Bats/Throws: | R/R |
2013 (JUNIOR): Played in 46 games while starting in 44 games at shortstop … was third on the team with 18 RBIs and 142 at-bats … tied the team lead for doubles with nine … recorded 37 hits and 14 runs … led the team with 107 assists … went 2-for-3 against North Texas (2/9/13) … scored twice against Southern (2/24/13) … went 2-for-4 against Sam Houston including a walk off grand slam to help lift the Islanders to a 6-5 conference victory at Chapman Field … earned the Islanders Outstanding Leadership award.
2012 (SOPHOMORE): Played in 20 games while starting in 11 … split time between a pinch hitter, designated hitter and shortstop … recorded six hits and two runs … in her second career at-bat, she ripped her first career two-run homer against Stephen F. Austin (3/3) at the Ragin' Cajuns Invitational … tallied her first double of the season at Nicholls State … collected 13 put outs and 22 assists at shortstop.
HIGH SCHOOL: Four-year letterwinner at Calallen High School under head coach D.J. Kilgore … recorded a 3.42 batting average while leading her team to a third-place finish during her senior campaign … helped her team to a runner-up finish at districts her freshman and junior year … registered a .412 batting average her junior year while earning First Team All-South Texas honors as a designated hitter and First Team All-State shortstop honors … named First Team All-District her last three years … led her team to the regional finals her freshman year and the regional semifinals her senior year … four-year letterwinner in basketball and two-year letterwinner in cross country … earned First Team All-District honors her sophomore, junior and senior years … named First Team All-South Texas her senior year … regional finalist her junior year in cross country … named the 2007 Academic Athlete of the Year.
PERSONAL: Born Valerie Marie Perez on Aug. 12, 1991, in Corpus Christi, Texas … parents are Rene and Maria Perez … has three brothers, Chris, Andy and Jerry … major is kinesiology.


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Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing, pages 33– 41, Los Angeles, California, June 2010. c 2010 Association for Computational Linguistics Parallel Active Learning: EliminatingWait Time with Minimal Staleness Robbie Haertel, Paul Felt, Eric Ringger, Kevin Seppi Department of Computer Science Brigham Young University Provo, Utah 84602, USA rah67@ cs. byu. edu, pablofelt@ gmail. com, ringger@ cs. byu. edu, kseppi@ cs. byu. edu http:// nlp. cs. byu. edu/ Abstract A practical concern for Active Learning ( AL) is the amount of time human experts must wait for the next instance to label. We propose a method for eliminating this wait time inde-pendent of specific learning and scoring al-gorithms by making scores always available for all instances, using old ( stale) scores when necessary. The time during which the ex-pert is annotating is used to train models and score instances– in parallel– to maximize the recency of the scores. Our method can be seen as a parameterless, dynamic batch AL algo-rithm. We analyze the amount of staleness introduced by various AL schemes and then examine the effect of the staleness on perfor-mance on a part- of- speech tagging task on the Wall Street Journal. Empirically, the parallel AL algorithm effectively has a batch size of one and a large candidate set size but elimi-nates the time an annotator would have to wait for a similarly parameterized batch scheme to select instances. The exact performance of our method on other tasks will depend on the rel-ative ratios of time spent annotating, training, and scoring, but in general we expect our pa-rameterless method to perform favorably com-pared to batch when accounting for wait time. 1 Introduction Recent emphasis has been placed on evaluating the effectiveness of active learning ( AL) based on re-alistic cost estimates ( Haertel et al., 2008; Settles et al., 2008; Arora et al., 2009). However, to our knowledge, no previous work has included in the cost measure the amount of time that an expert an-notator must wait for the active learner to provide in-stances. In fact, according to the standard approach to cost measurement, there is no reason not to use the theoretically optimal ( w. r. t. a model, training proce-dure, and utility function) ( but intractable) approach ( see Haertel et al., 2008). In order to more fairly compare complex and time- consuming ( but presumably superior) selec-tion algorithms with simpler ( but presumably in-ferior) algorithms, we describe " best- case" ( mini-mum, from the standpoint of the payer) and " worst-case" ( maximum) cost scenarios for each algorithm. In the best- case cost scenario, annotators are paid only for the time they spend actively annotating. The worst- case cost scenario additionally assumes that annotators are always on- the- clock, either annotat-ing or waiting for the AL framework to provide them with instances. In reality, human annotators work on a schedule and are not always annotating or waiting, but in general they expect to be paid for the time they spend waiting for the next instance. In some cases, the annotator is not paid directly for wait-ing, but there are always opportunity costs associ-ated with time- consuming algorithms, such as time to complete a project. In reality, the true cost usually lies between the two extremes. However, simply analyzing only the best- case cost, as is the current practice, can be misleading, as illustrated in Figure 1. When excluding waiting time for a particular selection algorithm1 (" AL An-notation Cost Only"), the performance is much bet- 1We use the ROI- based scoring algorithm ( Haertel et al., 2008) and the zero- staleness technique, both described below. 33 0.78 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 100000 200000 300000 400000 500000 600000 700000 800000 900000 1e+ 06 Accuracy Time in Seconds AL Annotation Cost Only Random Total Cost AL Total Cost Figure 1: Accuracy as a function of cost ( time). Side- by- side comparison of best- case and worst-case cost measurement scenarios reveals that not ac-counting for the time required by AL to select in-stances affects the evaluation of an AL algorithm. ter than the cost of random selection (" Random Total Cost"), but once waiting time is accounted for (" AL Total cost"), the AL approach can be worse than ran-dom. Given only the best- case cost, this algorithm would appear to be very desirable. Yet, practition-ers would be much less inclined to adopt this al-gorithm knowing that the worst- case cost is poten-tially no better than random. In a sense, waiting time serves as a natural penalty for expensive selection algorithms. Therefore, conclusions about the use-fulness of AL selection algorithms should take both best- case and worst- case costs into consideration. Although it is current practice to measure only best- case costs, Tomanek et al. ( 2007) mention as a desideratum for practical AL algorithms the need for what they call fast selection time cycles, i. e., algo-rithms that minimize the amount of time annotators wait for instances. They address this by employing the batch selection technique of Engleson and Da-gan ( 1996). In fact, most AL practitioners and re-searchers implicitly acknowledge the importance of wait time by employing batch selection. However, batch selection is not a perfect solution. First, using the tradtional implementation, a " good" batch size must be specified beforehand. In research, it is easy to try multiple batch sizes, but in practice where there is only one chance with live annotators, specifying a batch size is a much more difficult prob-lem; ideally, the batch size would be set during the process of AL. Second, traditional methods use the same batch size throughout the entire learning pro-cess. However, in the beginning stages of AL, mod-els have access to very little training data and re-training is often much less costly ( in terms of time) than in the latter stages of AL in which models are trained on large amounts of data. Intuitively, small batch sizes are acceptable in the beginning stages, whereas large batch sizes are desirable in the latter stages in order to mitigate the time cost of training. In fact, Haertel et al. ( 2008) mention the use of an increasing batch size to speed up their simulations, but details are scant and the choice of parameters for their approach is task- and dataset- dependent. Also, the use of batch AL causes instances to be chosen without the benefit of all of the most recently anno-tated instances, a phenomenon we call staleness and formally define in Section 2. Finally, in batch AL, the computer is left idle while the annotator is work-ing and vice- verse. We present a parallel, parameterless solution that can eliminate wait time irrespective of the scoring alogrithm and training method. Our approach is based on the observation that instances can always be available for annotation if we are willing to serve instances that may have been selected without the benefit of the most recent annotations. By having the computer learner do work while the annotator is busy annotating, we are able to mitigate the effects of using these older annotations. The rest of this paper will proceed as follows: Section 2 defines staleness and presents a progres-sion of four AL algorithms that strike different bal-ances between staleness and wait time, culminat-ing in our parallelized algorithm. We explain our methodology and experimental parameters in Sec-tion 3 and then present experimental results and compare the four AL algorithms in Section 4. Con-clusions and future work are presented in Section 5. 2 From Zero Staleness to Zero Wait We work within a pool- and score- based AL setting in which the active learner selects the next instance from an unlabeled pool of data U. A scoring func-tion ( aka scorer) assigns instances a score using a model trained on the labeled data A; the scores serve to rank the instances. Lastly, we assume that 34 Input: A seed set of annotated instances A, a set of pairs of unannotated instances and their initial scores S, scoring function , the candidate set size N, and the batch size B Result: A is updated with the instances chosen by the AL process as annotated by the oracle 1 while S = 6 ; do 2 TrainModel( A) 3 stamp | A| 4 C ChooseCandidates( S, N) 5 K {( c[ inst], ( c[ inst], )) | c 2 C} 6 S S − C [ K 7 T pairs from K with c[ score] in the top B scores 8 for t 2 T do 9 S S − t 10 staleness | A| − stamp ; // unused 11 A A [ Annotate( t) 12 end 13 end Algorithm 1: Pool- and score- based active learner. an unerring oracle provides the annotations. These concepts are demonstrated in Algorithm 1. In this section, we explore the trade- off between staleness and wait time. In order to do so, it is bene-ficial to quantitatively define staleness, which we do in the context of Algorithm 1. After each model is trained, a stamp is associated with that that indi-cates the number of annotated instances used to train it ( see line 3). The staleness of an item is defined to be the difference between the current number of items in the annotated set and the stamp of the scorer that assigned the instance a score. This concept can be applied to any instance, but it is particularly in-formative to speak of the staleness of instances at the time they are actually annotated ( we will simply refer to this as staleness, disambiguating when nec-essary; see line 10). Intuitively, an AL scheme that chooses instances having less stale scores will tend to produce a more accurate ranking of instances. 2.1 Zero Staleness There is a natural trade- off between staleness and the amount of time an annotator must wait for an instance. Consider Algorithm 1 when B = 1 and N = 1 ( we refer to this parameterization as ze-rostale). In line 8, a single instance is selected for annotation (| T | = B = 1); the staleness of this in-stance is zero since no other annotations were pro-vided between the time it was scored and the time it was removed. Therefore, this algorithm will never select stale instances and is the only way to guaran-tee that no selected instances are stale. However, the zero staleness property comes with a price. Between every instance served to the an-notator, a new model must be trained and every in-stance scored using this model, inducing potentially large waiting periods. Therefore, the following op-tions exist for reducing the wait time: 1. Optimize the learner and scoring function ( in-cluding possible parallelization) 2. Use a different learner or scoring function 3. Parallelize the scoring process 4. Allow for staleness The first two options are specific to the learning and scoring algorithms, whereas we are interested in re-ducing wait time independent of these in the general AL framework. We describe option 3 in section 2.4; however, it is important to note that when train-ing time dominates scoring, the reduction in waiting time will be minimal with this option. This is typi-cally the case in the latter stages of AL when models are trained on larger amounts of data. We therefore turn our attention to option 4: in this context, there are at least three ways to decrease the wait time: ( A) train less often, ( B) score fewer items, or ( C) allow old scores to be used when newer ones are unavailable. Strategies A and B are the batch se-lection scheme of Engelson and Dagan ( 1996); an algorithm that allows for these is presented as Al-gorithm 1, which we refer to as " traditional" batch, or simply batch. We address the traditional batch strategy first and then address strategy C. 2.2 Traditional Batch In order to train fewer models, Algorithm 1 can pro-vide the annotator with several instances scored us-ing the same scorer ( controlled by parameter B); consequently, staleness is introduced. The first item annotated on line 11 has zero staleness, having been scored using a scorer trained on all available anno-tated instances. However, since a model is not re-trained before the next item is sent to the annotator, 35 the next items have staleness 1, 2, · · · , B− 1. By in-troducing this staleness, the time the annotator must wait is amortized across all B instances in the batch, reducing the wait time by approximately a factor of B. The exact effect of staleness on the quality of instances selected is scorer- and data- dependent. The parameter N, which we call the candidate set size, specifies the number of instances to score. Typ-ically, candidates are chosen in round- robin fash-ion or with uniform probability ( without replace-ment) from U. If scoring is expensive ( e. g., if it involves parsing, translating, summarizing, or some other time- consuming task), then reducing the can-didate set size will reduce the amount of time spent scoring by the same factor. Interestingly, this param-eter does not affect staleness; instead, it affects the probability of choosing the same B items to include in the batch when compared to scoring all items. Intuitively, it affects the probability of choosing B " good" items. As N approaches B, this probabil-ity approaches uniform random and performance ap-proaches that of random selection. 2.3 Allowing Old Scores One interesting property of Algorithm 1 is that line 7 guarantees that the only items included in a batch are those that have been scored in line 5. However, if the candidate set size is small ( because scoring is expen-sive), we could compensate by reusing scores from previous iterations when choosing the best items. Specifically, we change line 7 to instead be: T pairs from S with c[ score] in the top B scores We call this allowold, and to our knowledge, it is a novel approach. Because selected items may have been scored many " batches" ago, the expected stale-ness will never be less than in batch. However, if scores do not change much from iteration to itera-tion, then old scores will be good approximations of the actual score and therefore not all items nec-essarily need to be rescored every iteration. Con-sequently, we would expect the quality of instances selected to approach that of zerostale with less wait-ing time. It is important to note that, unlike batch, the candidate set size does directly affect staleness; smaller N will increase the likelihood of selecting an instance scored with an old model. 2.4 Eliminating Wait Time There are portions of Algorithm 1 that are trivially parallelizable. For instance, we could easily split the candidate set into equal- sized portions across P pro-cessors to be scored ( see line 5). Furthermore, it is not necessary to wait for the scorer to finish training before selecting the candidates. And, as previously mentioned, it is possible to use parallelized training and/ or scoring algorithms. Clearly, wait time will decrease as the speed and number of processors in-crease. However, we are interested in parallelization that can guarantee zero wait time independent of the training and scoring algorithms without precluding these other forms of parallelization. All other major operations of Algorithm 1 have serial dependencies, namely, we cannot score until we have trained the model and chosen the candi-dates, we cannot select the instances for the batch until the candidate set is scored, and we cannot start annotating until the batch is prepared. These depen-dencies ultimately lead to waiting. The key to eliminating this wait time is to ensure that all instances have scores at all times, as in al-lowold. In this way, the instance that currently has the highest score can be served to the annotator with-out having to wait for any training or scoring. If the scored instances are stored in a priority queue with a constant time extract- max operation ( e. g., a sorted list), then the wait time will be negligible. Even a heap ( e. g., binary or Fibonacci) will often provide negligible overhead. Of course, eliminating wait time comes at the expense of added staleness as explained in the context of allowold. This additional staleness can be reduced by allow-ing the computer to do work while the oracle is busy annotating. If models can retrain and score most in-stances in the amount of time it takes the oracle to annotate an item, then there will be little staleness. 2 Rather than waiting for training to complete be-fore beginning to score instances, the old scorer can be used until a new one is available. This allows us to train models and score instances in parallel. Fast training and scoring procedures result in more instances having up- to- date scores. Hence, the stale- 2Since the annotator requests the next instance immediately after annotating the current instance, the next instance is virtu-ally guaranteed to have a staleness factor of at least 1. 36 ness ( and therefore quality) of selected instances de-pends on the relative time required to train and score models, thereby encouraging efficient training and scoring algorithms. In fact, the other forms of par-allelization previously mentioned can be leveraged to reduce staleness rather than attempting to directly reduce wait time. These principles lead to Algorithm 2, which we call parallel ( for clarity, we have omitted steps re-lated to concurrency). AnnotateLoop represents the tireless oracle who constantly requests instances. The call to Annotate is a surrogate for the actual annotation process and most importantly, the time spent in this method is the time required to provide annotations. Once an annotation is obtained, it is placed on a shared buffer B where it becomes avail-able for training. While the annotator is, in effect, a producer of annotations, TrainLoop is the con-sumer which simply retrains models as annotated in-stances become available on the buffer. This buffer is analagous to the batch used for training in Algo-rithm 1. However, the size of the buffer changes dynamically based on the relative amounts of time spent annotating and training. Finally, ScoreLoop endlessly scores instances, using new models as soon as they are trained. The set of instances scored with a given model is analagous to the candidate set in Algorithm 1. 3 Experimental Design Because the performance of the parallel algorithm and the " worst- case" cost analysis depend on wait time, we hold computing resources constant, run-ning all experiments on a cluster of Dell PowerEdge M610 servers equipped with two 2.8 GHz quad- core Intel Nehalem processors and 24 GB of memory. All experiments were on English part of speech ( POS) tagging on the POS- tagged Wall Street Jour-nal text in the Penn Treebank ( PTB) version 3 ( Mar-cus et al., 1994). We use sections 2- 21 as initially unannotated data and randomly select 100 sentences to seed the models. We employ section 24 as the set on which tag accuracy is computed, but do not count evaluation as part of the wait time. We simulate an-notation costs using the cost model from Ringger et al. ( 2008): cost( s) = ( 3.80 · l + 5.39 · c + 12.57), where l is the number of tokens in the sentence, and Input: A seed set of annotated instances A, a set of pairs of unannotated instances and their initial scores S, and a scoring function Result: A is updated with the instances chosen by the AL process as annotated by the oracle B ; , null Start( AnnotateLoop) Start( TrainLoop) Start( ScoreLoop) procedure AnnotateLoop() while S 6 = ; do t c from S having max c[ score] S S − t B B [ Annotate( t) end end procedure TrainLoop() while S 6 = ; do TrainModel( A) A A [ B endB ; end procedure ScoreLoop() while S 6 = ; do c ChooseCandidate( S) S S − { c} [ {( c[ inst], ( c[ inst], ))| c 2 S} end end Algorithm 2: parallel c is the number of pre- annotated tags that need cor-rection, which can be estimated using the current model. We use the same model for pre- annotation as for scoring. We employ the return on investment ( ROI) AL framework introduced by Haertel et. al ( 2008). This framework requires that one define both a cost and benefit estimate and selects instances that max-imize benefit( x)− cost( x) cost( x) . For simplicity, we esti-mate cost as the length of a sentence. Our bene-fit model estimates the utility of each sentence as follows: benefit( s) = − log ( maxt p( t| s)) where p( t| s) is the probability of a tagging given a sen-tence. Thus, sentences having low average ( in the geometric mean sense) per- tag probability are fa-vored. We use a maximum entropy Markov model to estimate these probabilities, to pre- annotate in-stances, and to evaluate accuracy. 37 Figure 2: Staleness of the allowold algorithm over time for different candidate set sizes 4 Results Two questions are pertinent regarding staleness: how much staleness does an algorithm introduce? and how detrimental is that staleness? For zerostale and batch, the first question was answered analyti-cally in a previous section. We proceed by address-ing the answer empirically for allowold and parallel after which we examine the second question. Figure 2 shows the observed staleness of instances selected for annotation over time and for varying candidate set sizes for allowold. As expected, small candidate sets induce more staleness, in this case in very high amounts. Also, for any given candidate set size, staleness decreases over time ( after the be-ginning stages), since the effective candidate set in-cludes an increasingly larger percentage of the data. Since parallel is based on the same allow- old-scores principle, it too could potentially see highly stale instances. However, we found the average per-instance staleness of parallel to be very low: 1.10; it was never greater than 4 in the range of data that we were able to collect. This means that for our task and hardware, the amount of time that the oracle takes to annotate an instance is high enough to allow new models to retrain quickly and score a high percent-age of the data before the next instance is requested. We now examine effect that staleness has on AL performance, starting with batch. As we have shown, higher batch sizes guarantee more staleness so we compare the performance of several batch sizes ( with a candidate set size of the full data) to ze-rostale and random. In order to tease out the effects that the staleness has on performance from the ef-fects that the batches have on wait time ( an element of performance), we purposely ignore wait time. The results are shown in Figure 3. Not surprisingly, zerostale is slightly superior to the batch methods, and all are superior to random selection. Further-more, batch is not affected much by the amount of staleness introduced by reasonable batch sizes: for B < 100 the increase in cost of attaining 95% accu-racy compared to zerostale is 3% or less. Recall that allowold introduces more staleness than batch by maintaining old scores for each in-stance. Figure 4 shows the effect of different candidate set sizes on this approach while fixing batch size at 1 ( wait time is excluded as before). Larger candidate set sizes have less staleness, so not surprisingly performance approaches zerostale. Smaller candidate set sizes, having more staleness, perform similarly to random during the early stages when the model is changing more drastically each instance. In these circumstances, scores produced from earlier models are not good approximations to the actual scores so allowing old scores is detrimen-tal. However, once models stabilize and old scores become better approximations, performance begins to approach that of zerostale. Figure 6 compares the performance of allowold for varying batch sizes for a fixed candidate set size ( 5000; results are similiar for other settings). As before, performance suffers primarily in the early stages and for the same reasons. However, a batch excerbates the problem since multiple instances with poor scores are selected simultaneously. Neverthe-less, the performance appears to mostly recover once the scorers become more accurate. We note that batch sizes of 5 and 10 increase the cost of acheiving 95% accuracy by 3% and 10%, respectively, com-pared to zerostale. The implications for parallel are that stalness may not be detrimental, especially if batch sizes are small and candidate set sizes are large in the beginning stages of AL. Figure 5 compares the effect of staleness on all four algorithms when excluding wait time ( B = 20, N = 5000 for the batch algorithms). After achiev-ing around 85% accuracy, batch and parallel are virtually indistinguishable from zerostale, implying that the staleness in these algorithms is mostly ignor-able. Interestingly, allowold costs around 5% more than zerostale to acheive an accuracy of 95%. We attribute this to increased levels of staleness which parallel combats by avoiding idle time. 38 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 100000 200000 300000 400000 500000 600000 700000 800000 900000 1e+ 06 Accuracy Time in Seconds Zero Staleness Batch size 5 Batch size 50 Batch size 500 Batch size 5000 Random Figure 3: Effect of staleness due to batch size for batch, N = 1 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 100000 200000 300000 400000 500000 600000 700000 800000 900000 1e+ 06 Accuracy Time in Seconds Zero Staleness Candidate Set Size 5000 Candidate Set Size 500 Candidate Set Size 100 Candidate Set Size 50 Random Figure 4: Effect of staleness due to candidate set size for allowold, B = 1 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 100000 200000 300000 400000 500000 600000 700000 800000 900000 1e+ 06 Accuracy Time in Seconds Zero Staleness Parallel Traditional Batch Allow Old Scores Random Figure 5: Comparison of algorithms ( not including wait time) 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 100000 200000 300000 400000 500000 600000 700000 800000 900000 1e+ 06 Accuracy Time in Seconds Zero Staleness Batch Size 5 Batch Size 10 Batch Size 50 Batch Size 100 Batch Size 500 Random Figure 6: Effect of staleness due to batch size for allowold, N = 5000 0 50000 100000 150000 200000 250000 0 2000 4000 6000 8000 10000 12000 Instances Scored Model Number Figure 7: Effective candidate set size of parallel over time 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 100000 200000 300000 400000 500000 600000 700000 800000 900000 1e+ 06 Accuracy Time in Seconds Zero Staleness Parallel Traditional Batch Allow Old Scores Random Figure 8: Comparison of algorithms ( including wait time) 39 Since the amount of data parallel uses to train models and score instances depends on the amount of time instances take to annotate, the " effective" candidate set sizes and batch sizes over time is of in-terest. We found that the models were always trained after receiving exactly one instance, within the data we were able to collect. Figure 7 shows the number of instances scored by each successive scorer, which appears to be very large on average: over 75% of the time the scorer was able to score the entire dataset. For this task, the human annotation time is much greater than the amount of time it takes to train new models ( at least, for the first 13,000 instances). The net effect is that under these conditions, parallel is parameterized similar to batch with B = 1 and N very high, i. e., approaching zerostale, and therefore has very low staleness, yet does so without incurring the waiting cost. Finally, we compare the performance of the four algorithms using the same settings as before, but in-clude wait time as part of the cost. The results are in Figure 8. Importantly, parallel readily outper-forms zerostale, costing 40% less to reach 95% ac-curacy. parallel also appears to have a slight edge over batch, reducing the cost to acheive 95% accu-racy by a modest 2%; however, had the simulation continued, we we may have seen greater gains given the increasing training time that occurs later on. It is important to recognize in this comparison that the purpose of parallel is not necessarily to significantly outperform a well- tuned batch algorithm. Instead, we aim to eliminate wait time without requiring pa-rameters, while hopefully maintaining performance. These results suggest that our approach successfully meets these criteria. Taken as a whole, our results appear to indicate that the net effect of staleness is to make selection more random. Models trained on little data tend to produce scores that are not reflective of the actual utility of instances and essentially produce a ran-dom ranking of instances. As more data is collected, scores become more accurate and performance be-gins to improve relative to random selection. How-ever, stale scores are by definition produced using models trained with less data than is currently avail-able, hence more staleness leads to more random-like behavior. This explains why batch selection tends to perform well in practice for " reasonable" batch sizes: the amount of staleness introduced by batch ( B− 1 2 on average for a batch of size B) intro-duces relatively little randomness, yet cuts the wait time by approximately a factor of B. This also has implications for our parallel method of AL. If a given learning algorithm and scoring function outperform random selection when using zerostale and excluding wait time, then any added staleness should cause performance to more closely resemble random selection. However, once wait-ing time is accounted for, performance could ac-tually degrade below that of random. In parallel, more expensive training and scoring algorithms are likely to introduce larger amounts of staleness, and would cause performance to approach random selec-tion. However, parallel has no wait time, and hence our approach should always perform at least as well as random in these circumstances. In contrast, poor choices of parameters in batch could perform worse than random selection. 5 Conclusions and Future Work Minimizing the amount of time an annotator must wait for the active learner to provide instances is an important concern for practical AL. We presented a method that can eliminate wait time by allowing in-stances to be selected on the basis of the most re-cently assigned score. We reduce the amount of staleness this introduces by allowing training and scoring to occur in parallel while the annotator is busy annotating. We found that on PTB data us-ing a MEMM and a ROI- based scorer that our pa-rameterless method performed slightly better than a hand- tuned traditional batch algorithm, without re-quiring any parameters. Our approach's parallel na-ture, elimination of wait time, ability to dynamically adapt the batch size, lack of parameters, and avoid-ance of worse- than- random behavior, make it an at-tractive alternative to batch for practical AL. Since the performance of our approach depends on the relative time spent annotating, training, and scoring, we wish to apply our technique in future work to more complex problems and models that have differing ratios of time spent in these areas. Fu-ture work could also draw on the continual compu-tation framework ( Horvitz, 2001) to utilize idle time in other ways, e. g., to predict annotators' responses. 40 References S. Arora, E. Nyberg, and C. P. Ros ´ e. 2009. Estimating annotation cost for active learning in a multi- annotator environment. In Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing, pages 18– 26. S. P. Engelson and I. Dagan. 1996. Minimizing manual annotation cost in supervised training from corpora. In Proceedings of the 34th annual meeting on Associa-tion for Computational Linguistics, pages 319– 326. R. A. Haertel, K. D. Seppi, E. K. Ringger, and J. L. Car-roll. 2008. Return on investment for active learning. In NIPS Workshop on Cost Sensitive Learning. E. Horvitz. 2001. Principles and applications of con-tinual computation. Artificial Intelligence Journal, 126: 159– 96. M. P. Marcus, B. Santorini, and M. A. Marcinkiewicz. 1994. Building a large annotated corpus of en-glish: The penn treebank. Computational Linguistics, 19: 313– 330. E. Ringger, M. Carmen, R. Haertel, K. Seppi, D. Lond-sale, P. McClanahan, J. Carroll, and N. Ellison. 2008. Assessing the costs of machine- assisted corpus anno-tation through a user study. In Proc. of LREC. B. Settles, M. Craven, and L. Friedland. 2008. Active learning with real annotation costs. In Proceedings of the NIPS Workshop on Cost- Sensitive Learning, pages 1069– 1078. K. Tomanek, J. Wermter, and U. Hahn. 2007. An ap-proach to text corpus construction which cuts annota-tion costs and maintains reusability of annotated data. Proc. of EMNLP- CoNLL, pages 486– 495. 41
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Search results for : | mguk welcome's |
MGUK For enthusiasts of modern and future MG cars.
For enthusiasts of modern and future MG cars. MGUK For enthusiasts of modern and future MG cars.
mguk, chinese, news, thread, roewe, saic, nanjing, longbridge, cars
Topics linked your research : | mguk welcome's |
MGUK's Target Markets
I note that Mr. & Mrs. Tyler, Apple MG's customers and perhaps the first people in Britain to drive their own MG3 on the road, are a young married couple and therefore perhaps not the frenetic FUN-seeking ravers apparently targeted by the teaser
MG's adverts
Regarding the new adverts for MG that are appearing on telly and on their web site, they have made a big deal about trying to get to people who fell in love with the brand many many years ago through the likes of MGA's and B's and even before that, but no


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Murrle Bennett Peridot & Pearl Pendant
Delicate and fine, this 9k yellow gold pendant is pure detail. Tiny seed pearls dot every surface of this wreath and drop pendant. From the famous maker in England, Murrle Bennett, it is constructed in four movable parts, the center drop and bottom drop are graced with vivid green natural peridot gemstones. The original bale (top) is even dotted with tiny pearls. Eternally feminine and wearable for every day or every special occasion.
Date: Circa 1900.
Chain not included. Please inquire if you wish to purchase a matching chain.
Measurements: 2 inches (5.0 cm) in length including bale by 7/8 of an inch (2.2 cm) in width. Weight of 3.4 grams (2.2 dwt).
Hallmarks: 9ct, MB, and another hallmark.
Condition: Excellent.
Origin: English.
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You may apply online using the Common Application, an undergraduate college admission application that applicants may use to apply to any of 488 member universities in the United States and other countries. Member institutions like Salve Regina often require a Common Application supplement in which we ask additional questions that help us in the application decision.
The Common Application website has an excellent support center for students who have questions about the online application process. Please also review our frequently asked questions page before beginning your application process.
In recognition of the varying academic calendars around the world, Salve Regina has a flexible admissions deadline for international students. However, students are advised to use the following dates as a guideline for their application. Students hoping to be awarded merit scholarships should apply as early as possible to obtain the best opportunities.
A complete application is required before application review begins. A complete application contains:
If you have questions about how to submit your application requirements, or other questions, please review our Frequently Asked Questions page, e-mail firstname.lastname@example.com or join a Skype question and answer session.
All accepted international students will need to submit a financial certification form showing their ability to support themselves financially while in the United States. Once the financial certification form is received, we will issue a form I-20 to be used in obtaining your F-1 student visa to study in the United States. For more information, instructions and forms, visit our section on I-20s and visas.
All accepted applications for the fall term are required to submit an enrollment deposit to reserve space in the entering class and in on-campus housing at Salve Regina. The enrollment deposit of $500 is non-refundable.


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- Glossary updated with some new terms: Spirit Guide, Death Eater and Dragon Lines.
- Map added to Trees page
- New poem 'Floods of Tears' added to Poetry page.
- Blood Moon information added to the Moon page.
- 18 new books added to the Books page.
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This is an updated version of a 'tip' that I originally posted in 2012. It reflects recent changes to the Kiva Web site.
Many Kiva loans are repaid in monthly installments, but there may be times when you cannot check your Kiva credit on repayment day (for example, if you are travelling, ill or just very busy). To avoid leaving money sitting idle when it could be used for another loan, or to control what happens to your Kiva credit when you depart this life, you can, as a precaution, set up Auto-Lending.
This means that if you have $25 in credit but don't log in within a certain time (which you specify), Kiva can act for you to allocate that credit to a new loan. You can (if you wish) control this by setting criteria such as countries, sectors, risk rating, repayment term, gender, etc, as shown in the example on the right.
It's easy to do. Just log in to Kiva, select 'Settings', then (in the drop-down list) 'Credit'; scroll down to 'Auto-Lending Settings', set your preferences, and click 'Save'.
Check your account settings now. Do they suit the way you use Kiva?
~ ~ ~
Join Genealogists for Families. Together we can make a difference!
"There are people trying to change this world that seems to be going down. Some people think there is nothing that can be done... But then there is you, who really do believe that it is still possible." -- Join genealogists worldwide (and our relatives and friends) who have found a simple way to make a big difference.
In 2011 we won an award for Best New Community Project. We enable people without access to traditional banks (including many women) to expand their businesses, educate their children, save for the future and raise themselves out of poverty. Through Kiva, a non-profit organisation working with microfinance institutions, you choose a borrower to support with a loan of just $5 via Kiva Zip or $25 via Kiva. Similar loans by other lenders are combined until the required total is reached. As your money is repaid (often in monthly instalments), you can withdraw it or lend it to someone else. Money that is loaned over and over again does more good than a one-time donation. Join 'Genealogists for Families' - together we are making a difference!
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The Ohio Academy
Styles For The Aisle
Shannon Goode - Wedding Hair Specialist
Change up your look with the addition of hair extensions from our exciting hair stylists! Prep for your big day in advance by creating beautiful up-do's or adding length and thickness to your mane for a flowy, romantic look! Search here for Hair Extensions & Hair Stylists in Columbus.


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Filter Media
Aquarium Filter Media
We carry many types of filter media, that perform important roles within the aquarium. Fortunately, we sell all the media you'll ever need for your aquarium, from mechanical (sponges and pads) and chemical (carbon, phosphate removers) to biological (bio balls) media.-
Lifegard Aquatics 1-1/2 inch Bio-mate Bulk
Lifegard Aquatics 1 1/2" Bio-Mate Bioballs Provides the maximum surface area and the greatest possible mix of air and water! Promotes gas exchange and increases oxygen level. Will not clog with organic matter. Totally inert and n...ONLY$429.87


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Four Tet compiles his ventures into techno, UK garage and dubstep
Over the last two/three years, Four Tet's productions have aimed more and more at the dancefloor. This shift was nowhere as audible as on the 12"es he has put out on his very own Text Records label. "Pink" compiles these ventures into techno and dubstep idiosyncrasies. Four Tet injects those tried and true templates with his own unique sense of musicality and attention to detail, managing to combine rawness and obvious production wits to form rather beautiful club tracks with a lot of personality.
Read more
Artist: Four Tet
Online Release Date: 08/20/12
Label: Text Records
Release Type: Album
Total: 8 Tracks (61:40 Min.)
Quality: MP3/AAC 320kbps
MP3AAC Available Download Formats
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Admiral Ale's Book Depot
Henry Cabot Lodge was the Editor-In-Chief of Funk & Wagnalls Company in 1909. This set of literary classics are from Greece 484 B.C. - 200 A.D. (Book I) thru Rome (Book II), Great Britain and Ireland (Books III thru VI), Continental Europe (Books VII & VIII) to America (Books IX & X). Included are authors like Aristotle, Cicero, and Milton, the list goes on. Some of the corners show wear, but the books are in great condition. There is 10 books measuring 6x4 inches and weigh about 4 pounds...
Admiral Ale's Book Depot
aunties. This is an 'Art Supplement to Brooklyn Daily Eagle, September 15, 1901'. This large photo has chips along the edges. It was copied from an 1898 photo copyrighted by G. G. Rockwood. And is in color. 16in tall, 12" across
Admiral Ale's Book Depot
aunties. This is an 'Art Supplement to Brooklyn Daily Eagle, September 22, 1901'. This large photo has chips along the edges, better condition than Mckinley photo. Pitcher looks as if it is cock-eyed, that is my photo taking. It was copied from an 1898 photo copyrighted by G. G. Rockwood. And is in color. 17" x 12"
Admiral Ale's Book Depot
Printed by the United States Navel Institute in Annapolis, Maryland in 1940. This is the last issue prior to World War II. The Seaman Recruits' initials were painted on the reverse. His name is on the side. Good to Very good condition. Has a couple of foldouts. Media Mail
Admiral Ale's Book Depot
This is the 1955 POPEYE #667 Wonder Book with washable cover. The spine is split and chipped. PO name in inside cover, otherwise, clean and complete.
All Items : : Pre 1960
item #301020
(stock #PB00092)
Admiral Ale's Book Depot
Second TAB Book (Scholastic) printing in March 1959. Written by Helen Dore Boylston, Sue Barton, Student Nurse was 1st published in 1936. There is no FFEP (unsure if there was one), Small bends and creases at corners. Wear at spine. Media mail with del confirmation required. Priority mail available.
Admiral Ale's Book Depot
HC2081. The Baseball Encyclopedia, The complete and official record of major league baseball. 1969, author/editor unknown. Tan Cloth boards, Green title area on spine with gilt lettering, Part of this label is faded. This book contains over 2300 pages of baseball history. Has pictorical slipcase.
All Items : Theatre and Movies : Pre 1990
item #260075
(stock #PB00018)
Admiral Ale's Book Depot
Liberace at the RADIO CITY MUSIC HALL!!!!!!
This 1985 (date determined by Disney Advert) Program has Liberace with the Rockettes (see 2nd photo) in the center. and the program for the night was to feature the Rockettes and the Dancing Waters. Priority mail recommended.
Admiral Ale's Book Depot
$2.25 for each title
$2.25 for each title
These books are from the Harlequin Romance series numbered 2300 thru 2399. Only those titles that are available will be listed. Postage begins @ $3.00 in the US, but additional books will reflect close to actual postage cost. I HATE INFLATED POSTAGE.
I have a scan of each book, ask for it before you order. I stress!!!! I will attempt to make note of any problems with the book, BUT these are used books waiting for you to give it a new home.
The current available titles are:
All Items : Books and Publications : Historical : Civil War : Pre 1980
item #372876
(stock #PB00164)
Admiral Ale's Book Depot
Two copies of THE SKIRMISH LINE, published bi-weekly by the North-South Skirmish Association. The May 1973 edition has 39 pages, whereas the July edition has 43. Both are in very good condition with a little wear (over 30 years old). Many ads on Civil war items.
1989 Second Edition. Beverly Rae Kimes and Henry Austin Clark, Jr. Paperback with over 1550 pages and is about 11 inches tall. Appears to be clean. Extra postage for weight. Media mail.
Admiral Ale's Book Depot
Three Archie books from the Digest Library. 1st is "Archie Digest Magazine #48; 2nd is the "Jughead Jones Comic Digest Magazine #25" and finally, the "Joke book Comics Digest Annual" (comic Book) #12, 1983. All three books are in Fair to Good condition. Media Mail is recommended.