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2016 Games Face Greater Challenges than Zika, Says Olympic Com...The mosquito-borne Zika virus "won't be a problem" in Rio de Janiero during the summer Olympic Games, according to Olympic Committee CEO Sidney Levy. "All the statistics show that in the winter months, the Zika mosquito doesn't proliferate," he ... VOA News, 2 days ago
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WARTON, Michael (1623-88), of Beverley, Yorks.
Available from Boydell and Brewer
Family and Education
bap. 27 Apr. 1623, 1st s. of Michael Warton (d.1645) of Beverley by Catherine, da. and coh. of Christopher Maltby of Maltby, Yorks.; bro. of Sir Ralph Warton. educ. Beverley g.s.; St. John's, Camb. 1640; G. Inn 1640. m. c.1646, Susan, da. of John Poulett†, 1st Baron Poulett of Hinton St. George, 4s. (1 d.v.p.) 3da. suc. gdfa. 1655.1
Offices Held
J.p. Yorks. (E. Riding) July 1660-87; commr. for assessment (E. Riding) Aug. 1660-80, Lincs. (Lindsey) 1663-9; dep. lt. (E. Riding) c. Aug. 1660-80, commr. for sewers Sept. 1660; col. of militia ft. 1661-?86; commr. for corporations, Yorks. 1662-3, oyer and terminer, Northern circuit 1665, recusants (E. Riding) 1675.2
Warton's ancestors had resided in Beverley since Tudor times and from 1586 regularly represented the borough, strengthening their hold under the Stuarts by leasing the manor from the crown. Three generations of the family appeared for the King in the Civil War. Warton's grandfather, who was at the general muster of the county in July 1642, compounded for estates valued at over £3,200 p.a. His father, who sat for Beverley in the Long Parliament until disabled for royalism, was killed at the siege of Scarborough Castle in 1645. Warton had also been in arms until Marston Moor and paid £1,600 on his father's estate for his delinquency. He married well, and was persuaded by his cousin, Sir Marmaduke Langdale, to apply for a pass overseas in 1656; but he is not definitely reported as visiting the exiled Court, and he took no part in royalist conspiracy.3
As a Cavalier Warton was ineligible at the general election of 1660; but he was returned for Beverley at a by-election after the Restoration when Hugh Bethell chose to sit for Hedon, and divided the borough with Sir John Hotham, 2nd Bt. until 1681. He took no ascertainable part in the Convention, apart from obtaining leave to attend the Lords as a witness on 19 July; but doubtless he voted with the Court. He undertook to serve at his own charge in 1661, and became an inactive Member of the Cavalier Parliament, in which he was appointed to no more than 22 committees, including those for preventing mischief from Quakers in the first session and repairing Bridlington pier in 1664. He obstructed the new charter for his constituency and may have been reckoned a 'country Cavalier' under Clarendon, for Sir Thomas Osborne included him among the Members who might be engaged for the Court by the Duke of Buckingham in 1669. From 1673 Warton cannot be distinguished in the Journals from the Hon. Thomas Wharton. He was probably added to the committee for the habeas corpus amendment bill in 1675, and, although he received the government whip, he seems to have acted as teller against going into committee on supply in the autumn. In 1677 he was appointed to the committee on the bill to prevent illegal exactions, and Shaftesbury marked him 'thrice worthy'; but in September Warton wrote to (Sir) Joseph Williamson:
I have had second thoughts how the merit has been digested, for the favour of being in your memory, and ascribe it as being one of your retinue when we meet at Westminster, and this I receive as a bribing monitor of my duty when you hold up your finger, (for as I take it that was the signal we agreed upon) and assuredly I shall not fail you if you please to remember the proviso in our articles that you must not wheedle to misguide me.
He may be regarded as loyal as late as Feburary 1678 when he again wrote to Williamson:
Having an odd gelding in the country, young, fresh and high mettle, ... I sent for him upon purpose to present to your stable. ... You are not to refuse him a quarter in your stable, he being intended as a bribe, which I ought to mention that you may know the extent of your management, which is that you are not to wheedle for a land tax when a poll will circumscribe the whole sum intended, for a northern farm may bear the first, when a regret will accompany the second. I intend to send him tomorrow morning and you are to order his reception, otherwise you forfeit the influence of being seconded by the gallery at Westminster when you hold up your finger.
But in the last month of the Cavalier Parliament, he was active in Opposition. He acted as teller for the address to warn the King of the danger arising from 'private advices'. He carried the impeachment of Lord Arundell of Wardour to the Lords, and supported the fourth article of the impeachment of Osborne (now Lord Treasurer Danby). On 28 Dec. he was sent to the Lords to desire a conference on disbanding the army.

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Ricoh GR first photos
Started Jun 29, 2013 | Discussions thread
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Re: Ricoh GR first photos
In reply to daciangroza • Jun 30, 2013
I'm already a fan of your work! Very nice images especially the one with the girl going down the stairs. Nice use of leading lines. I am waiting for mine to be shipped, should be in a week or two. Did you use snap focus in your photos?
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- Jeff Norland park, United States"biker review"ReviewedJune 24, 2016Trip type:Friends getawayhotel staff unnecessarily strict re beer in atrium ares. bike parking hassled by security over bike parking in a spot i have parked in for years on almost annual stays fathers day weekend. also was told I am not allowed to drink a beer I brought down from my room anywhere in the atrium/lobby area.StayedJune 2016Review collected in partnership with this hotelJune 28, 2016Dear Jeff N., Thank you for taking the time to offer feedback regarding to your recent stay at our hotel. We are very sorry to learn you did not find your visit enjoyable, and we apologize for any inconvenience you experienced. We would love for you to stay with us again, so that we can provide the service you expect from the Radisson Quad City Plaza. Best Regards, Christi
- soc5mom1Independence, Missouri"Convenient location"ReviewedJune 19, 2016Trip type:FamilyWe were in town to attend a baseball game at the Quad City River Bandits and they were just down the street. Our room was very comfortable other than the noise coming from the wedding reception going on down behind the bar. You might make sure your room does not open up on the Atrium. I would say a bit overpriced plus you have to pay $8 to park behind the hotel. Did not try the restaurant while there.StayedJune 2016Review collected in partnership with this hotelJune 21, 2016Dear soc5mom1, We appreciate your feedback in regard to your recent stay at the Radisson Quad City Plaza. As a Level 5 Contributor to the TripAdvisor community, we value your comments. We are happy to hear that you enjoyed your accommodations and our convenient downtown location. Thank you for your review, and we hope to welcome you back in the near future. Best Regards, Christi
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The scenario is strikingly familiar: A new owner of a storied but star-crossed franchise seeks an established general manager to serve as the architect who will end an incredible championship drought.
That is the scenario unfolding in Chicago, where Tom Ricketts -- who purchased the Cubs at the end of 2009 -- is looking for a new GM after dismissing Jim Hendry in August. And his target, by all accounts, is Red Sox GM Theo Epstein, the man who has helped bring two championships to Boston, in the process ending an 86-year run of epic futility.
The situation represents a through-the-looking-glass moment for Red Sox owners John Henry, Tom Werner and Larry Lucchino. After the group's first year in Boston, which saw the team dismiss former GM Dan Duquette and replace him with interim GM Mike Port, the Sox aimed high in their GM search.
At the time, A's GM Billy Beane -- shortly before the publication of Moneyball -- was regarded by many as the top executive in the game after taking the small-market, small-revenue, small-payroll Athletics to the postseason for two straight years. The Sox spent two months conducting their formal GM search, but the process always had one target in Beane.
The A's denied permission to the Sox, but Boston remained relentless, and Beane remained curious about exploring the idea of moving to a team with an enormous fan base and revenues. Finally, Oakland granted permission, Beane and the Sox reached an agreement on a five-year, $12.5 million deal for him to come to Boston, all parties popped champagne … and then Beane changed his mind, decided to stay with the A's to remain close to his family on the West Coast, the Sox hired Epstein and Moneyball is now out in a theater near you.
Now, that moment nine years ago is echoing on Yawkey Way, where the Sox are facing another team's efforts to acquire their GM in a fashion reminiscent of their attempt to pry Beane from Oakland. Given the similarities, here are some relevant lessons from l'Affaire Beane of 2002:
If Epstein wants to talk to the Cubs, it would be the height of hypocrisy for the Sox to deny him the opportunity to do so. After all, CEO Larry Lucchino essentially mounted a weeks-long sit-in – after the A's initially had denied the Sox permission to talk to the GM – until former A's owner Steve Schott finally relented and gave the Sox the chance to talk to Beane, at a time when he remained under contract in Oakland for six more seasons.
At a time when Epstein remains under contract with the Sox for just one more year, it would be difficult for the club to make the case that its deal represents a reason to deny its GM permission to talk to another club. That said, the Sox can cite the Beane precedent in order to demand compensation for Epstein's services.
In both Moneyball (the Michael Lewis bestseller about the A's under Beane) and Feeding the Monster (Seth Mnookin's tome about the Red Sox' construction of a champion), the Sox and A's -- with Beane negotiating for the Sox, and Oakland Assistant GM Paul DePodesta in charge of negotiations for the A's -- are said to have worked out a deal to send Beane to Boston for Kevin Youkilis.
In point of fact, noted one person familiar with the talks, while the A's and Sox discussed different trade possibilities, with Youkilis being in the conversation, there never was a final deal in place by the time that Beane's change of heart scuttled the deal.
Still, that connection of Beane to Youkilis (at the time, a promising yet undervalued third base prospect whose tremendous plate discipline had yielded high OBPs but no real power) serves as the closest thing to a precedent in determining the trade value of a GM.
If the Cubs end up making an offer to Epstein and he accepts, then the Sox could reference their own negotiations in seeking a meaningful prospect return for their general manager. However, that precedent would also likely work against Boston asking for a more outlandish haul such as budding star Starlin Castro or starter Matt Garza or even salary relief in the form of making the Cubs take on John Lackey.
At the start of the Red Sox' GM search in 2002, Epstein was not in consideration for the job in Boston. He was viewed as a GM-in-waiting and someone who, in time and with further grooming for the position, would eventually be a tremendous head of a baseball operations department. Beane was the target.
Yet when the dust settled, it was Epstein who emerged as the Red Sox GM. And then, in one remarkable offseason -- at a time when the baseball landscape created huge opportunities to exploit market inefficiencies -- Epstein positioned the Sox to begin their championship run, acquiring players such as Bill Mueller, Kevin Millar, Todd Walker and a certain David Ortiz to create a beast of a lineup.
Would Beane -- who has been back to the playoffs just twice since that offseason -- have been successful in Boston? Probably. Despite Oakland's struggles, he is still regarded as a creative, talented executive.
But there's no way to say whether he would have been able to match Epstein's track record of success, which has included a pair of championships. Maybe the Sox under Beane would have won more; perhaps they would have won less.
Ultimately, the speculation is irrelevant. The takeaway from that sequence of events is that no single GM represents a prerequisite for a championship team. While it is productive for a team to identify a top target for the position, no single person has a monopoly on the blueprint for building a successful baseball team.
In the current environment, that lesson would appear to hold for both the Cubs (should they fail to convince Epstein to move to Chicago) and the Red Sox (if Epstein leaves after nine years, thus positioning someone else to take charge of Boston's baseball operations).
The final lesson offered by Beane's Red Sox saga is that these sorts of negotiations have a significant element of unpredictability. Between the geographic pull of family and the loyalty built to an organization that has a GM's unquestionable mark, the final decision to leave one team for another features a number of complexities that make it almost pointless to try to forecast what will happen until it actually happens.
In the end, assuming that the Sox grant him permission to explore the opportunity in Chicago and that the Cubs want to hire him, it will be Epstein's decision and Epstein's alone as to whether he will leave the Red Sox after nine years. And perhaps not even he knows the answer to the question of whether he would take this opportunity -- at a time when he could leave the Sox feeling that the franchise was in better shape than when he started, but at a moment when the Sox are coming off a catastrophic conclusion to a season that once again didn't feature baseball in October -- to head elsewhere.
Dale, Michael and Jerry dig into the details of this draft for the Patriots with ESPN's Mike Reiss.
Giardi and Price discuss the Tom Brady appeal getting overturned and Roger Goodell's victory tour. The guys can't believe the victory lap and some of his behavior. The more you listen to him, the more frustrating his behavior becomes.
Giardi and Price break down how the teams in the AFC East altered their futures after the NFL Draft weekend.
Isaiah Thomas talks to Glenn and Lou about why he loves Boston, the NBA Money Bump, oh, and some Durant guy
Danny Ainge joins Glenn and Christian to explain the Celtics draft process; and he also discusses the trade rumors that permeated the night.
Mut is flying solo the last hour, but he's giving reaction and analysis as the Celtics draft a power forward out of France by the name of Guerschon Yabusele and getting some early grades for the Celtics
We check in with the manager of the Boston Red Sox, John Farrell joins us live from Tampa for our weekly update on all things Sox.
Eduardo Rodriguez and the Sox stumbled in their 13-7 loss to the Rays. Mut recaps the game and takes your calls on all things Boston baseball.
Christian recaps the Red Sox 6-2 loss to the Rangers.
DJ and Pete continue to assess the season that was for the Bruins and look toward the future for the franchise and how they can improve.
DJ and Pete are together for the final Sunday Skate of the season. They get into their overall thoughts on the season, the Bruins keeping Claude Julien on as head coach and the end-of-season press conferences last week. They discuss the rebuild process, what the Bruins need and how they can obtain it. Plenty of callers have their own theories and opinions on all of these topics as well.
Butch is joined by two-thirds of the Sunday Skate show in DJ and Pete. The guys discuss the problems with Bruins including leadership and grit, who should stay and who should go and if Claude is to blame.
Kirk Minihane's Thursday Headlines welcome interns Tim and Conor to dicuss their trip to the Trump luncheon in downtown Boston.
A guest appearance by Chris Simms on WFAN with Boomer Esiason forces John, Gerry and Kirk back down the DeflateGate wormhole.
The Olympics in Brazil might have a few strange characters around, plus the guys discuss some changes in WEEI management and the sales staff bringing them breakfast
WEEI Bruins Reporter DJ Bean joins Glenn and Lou to talk about the B's waiving Dennis Seidenberg, and the Jacob Trouba rumors.
Glenn and Lou try to figure out what the Sox can do to right the ship
Isaiah Thomas talks to Glenn and Lou about why he loves Boston, the NBA Money Bump, oh, and some Durant guy
Ugh. Dale and Michael after another crap-fest of a Red Sox game break it all down and listen in to John Farrell and David Price's post game comments.
We close the show out with some of the best cuts of the day.
We check in with the manager of the Boston Red Sox, John Farrell joins us live from Tampa for our weekly update on all things Sox.
Kirk Minihane talks to Antoine Walker about his troubles with gambling, losing his $100m fortune, his relationship with Paul Pierce, and playing under Rick Pitino.
Former Celtics General Manager Jan Volk joins Kirk Minihane for a discussion that any Celtics fan will love. Kirk and Jan talk about the legendary 85/86 Celtics and how the team was constructed and of course the decision to draft Len Bias and his tragic death.
Kirk Minihane sits down with John Tomase to talk about the current political landscape, John's desire to write about social issues, and of course Spygate is discussed.
Ben, Russ and Alex start the show by discussing the breaking news that Zlatan Ibrahimovic is headed to Manchester United. They also continue to cover the EURO's and Iceland's improbable run through the tourney. And of course, inevitably, they address Lionel Messi's announcement that he is retiring from Argentina.
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Jerry Thornton and "Uncle Buck" Adam Cormack break down the greatest season finale of all-time, the season 6 finale of Game of Thrones. Jerry and Uncle Buck debate if this season was the greatest season in television history.
The guys finish the show bantering about Max Kellerman apparently getting the Skip Bayless job, Iceland's win over England in the Euro 2016, and Caitlyn Jenner on SIMore from this show
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text:
News Obama Makes Mom's Apple Pie Her Own
OCCOQUAN, Va. — Molly Glenn and her coworkers at Mom's Apple Pie in Occoquan got a call about 1 p.m. Thursday. A person on the other end of the line informed them "someone from D.C. was coming."
Hoping to make the bakery known for its homemade pies and breads even more presentable than usual, Glenn and coworkers hurried to clean. Later, another phone call – this time from the Secret Service – informed them that their D.C. visitor would be First Lady Michelle Obama.
Obama stopped by the Occoquan Mom's Apple Pie on her way to VFW Post 153 in Dale City, where she delivered a campaign speech for her husband, President Barack Obama, and greeted a crowd of about 450 people.
But the impromptu gathering in Occoquan was an intimate affair. The First Lady took time out to give everyone inside the bakery a hug, listen to a child play violin, and then – with her own money – bought an apple and cherry pie, said Glynn.
"I was so happy, and she was, honestly, so sweet. She was very, very kind," said Glynn, who admitted she was surprised to see the Obama in their bakery.
In an email, even Occoquan Mayor Earine Porta chimed in about the First Lady's visit.
"The First Lady could not have been more gracious, taking time to speak and take photographs with customers and staff–and to purchase some of Mom's fine offerings," stated Porta.
For Glynn, she's never been visited by a president or a first lady, and this is not something she'll soon forget.
"No, it was insane, it was really cool," she said.
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SEC Twitter account posts racy photos in apparent hack
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Baylor and Art Briles officially part ways
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North Dakota, Sam Houston set future games
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Merchandise shop at the Millennium stadium, Cardiff, South Glamorgan, Wales.
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NEW YORK (AP) – The Men's Wearhouse fight to own its one-time rival Jos. A. Bank is now complete.
The announcement Wednesday brings to a close a series of dueling bids, first from Jos. A. Bank to buy Men's Wearhouse, and then vice versa.
After a string of bids that grew quickly in value, the final price tag to combine the two retailers was $1.8 billion, or $65 per share.
Industry watchers say that intense competition in the retail space made the tie-up inevitable.
The first attempt to combine the two companies occurred just months after The Men's Wearhouse Inc. ousted its founder and executive chairman George Zimmer, who promised customers that, "You're going to like the way you look."
The new company will have about 1,700 stores and $3.5 billion in annual sales.
(© Copyright 2014 The Associated Press. All Rights Reserved. This material may not be published, broadcast, rewritten or redistributed.)
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Star Tracks
The actress totes around her new pooch in Malibu. Plus: Kirsten Dunst, Uma Thurman, Prince William and more
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text:
|Publication number
|Publication type
|Application number
|Publication date
|Jun 19, 2007
|Filing date
|Nov 14, 2002
|Priority date
|Nov 14, 2002
|Fee status
|Also published as
|Publication number
|David L. Li, D. Reitz II Elliott, Dennis A. Tillotson
|Original Assignee
|Lockheed Martin Corporation
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (12), Non-Patent Citations (2), Referenced by (8), Classifications (6), Legal Events (7)
|External Links: USPTO, USPTO Assignment, Espacenet
1. Technical Field
The invention relates to a method of identifying and correcting for output classes with a multi-modal distribution across feature space in a pattern recognition system. Image processing systems often contain pattern recognition devices (classifiers).
2. Description of the Prior Art
Pattern recognition systems, loosely defined, are systems capable of distinguishing between various classes of real world stimuli according to their divergent characteristics. A number of applications require pattern recognition systems, which allow a system to deal with unrefined data without significant human intervention. By way of example, a pattern recognition system may attempt to classify individual letters to reduce a handwritten document to electronic text. Alternatively, the system may classify spoken utterances to allow verbal commands to be received at a computer console. In order to classify real-world stimuli, however, it is necessary to train the classifier to discriminate between classes by exposing it to a number of sample patterns.
A typical prior art classifier is trained over a plurality of output classes using a set of training samples. The training samples are processed, data relating to features of interest are extracted, and training parameters are derived from this feature data. As the system receives an input associated with one of a plurality of classes, it analyzes its relationship to each class via a classification technique based upon these training parameters. From this analysis, the system produces an output class and an associated confidence value.
The above assumes, however, that each class has a single set of average features to which it can be compared. Some output classes are not so easily categorized. For example, while the capital letter "A" may constitute a single class in an optical character recognition system, each individual printed font will likely differ on at least some of the selected features. In some cases, this variance will be sufficient to make it impossible to distinguish the variant fonts using the original class parameters. Accordingly, it would be desirable to identify situations where samples within a single class have multiple sets of varying characteristics and account for this multimodal distribution in the classification analysis.
In accordance with one aspect of the present invention, a method, operative within a pattern recognition system with a plurality of represented output classes, is disclosed for identifying output classes with multi-modal dispersion in feature space and incorporating multi-modal structure into a system architecture.
A plurality of input patterns, determined not to be associated with any of a set of at least one represented output class by a pattern recognition classifier, are rejected. The rejected pattern samples are grouped into clusters according to the similarities between the pattern samples. Clusters that contain samples associated with a represented output class are identified via independent review.
The classifier is then retrained to recognize the identified clusters as output pseudoclasses separate from the represented output class with which they are associated. The system architecture is reorganized to incorporate the output pseudoclasses. The output pseudoclasses are rejoined to their associated class after classification.
In accordance with another aspect of the present invention, a computer program product is disclosed for use in a pattern recognition system with a plurality of represented output classes for identifying output classes with multi-modal dispersion in feature space and incorporating multi-modal structure into a system architecture.
A classification portion rejects a plurality of input patterns determined not to be associated with any of a set of at least one represented output class by a pattern recognition classifier. A clustering portion groups the rejected pattern samples into clusters according to the similarities between the pattern samples. An identification portion identifies clusters that contain samples associated with a represented output class via independent review.
A training portion retrains the classifier to recognize the identified clusters as output pseudoclasses separate from the represented output class with which they are associated. A reorganization portion then reorganizes the system architecture to incorporate the output pseudoclasses. A post-processing portion rejoins the output pseudoclasses to their associated class after classification.
The foregoing and other features of the present invention will become apparent to one skilled in the art to which the present invention relates upon consideration of the following description of the invention with reference to the accompanying drawings, wherein:
In accordance with the present invention, a method for identifying output classes with multi-modal dispersion in feature space and incorporating multi-modal structure into a system architecture is described. The method may be applied to classifiers used in any traditional pattern recognition classifier task, including, for example, optical character recognition (OCR), speech translation, and image analysis in medical, military, and industrial applications.
It should be noted that a pattern recognition classifier to which the present invention may be applied will typically be implemented as a computer program, preferably a program simulating, at least in part, the functioning of a neural network. Accordingly, an understanding of the present invention will be facilitated by an understanding of the operation and structure of a neural network.
In the illustrated example, an input layer comprises five input nodes, 1–5. A node, generally speaking, is a processing unit of a neural network. A node may receive multiple inputs from prior layers which it processes according to an internal formula. The output of this processing may be provided to multiple other nodes in subsequent layers. The functioning of nodes within a neural network is designed to mimic the function of neurons within a human brain.
Each of the five input nodes 1–5 receives input signals with values relating to features of an input pattern. By way of example, the signal values could relate to the portion of an image within a particular range of grayscale brightness. Alternatively, the signal values could relate to the average frequency of an audio signal over a particular segment of a recording. Preferably, a large number of input nodes will be used, receiving signal values derived from a variety of pattern features.
Each input node sends a signal to each of three intermediate nodes 6–8 in the hidden layer. The value represented by each signal will be based upon the value of the signal received at the input node. It will be appreciated, of course, that in practice, a classification neural network may have a number of hidden layers, depending on the nature of the classification task.
Each connection between nodes of different layers is characterized by an individual weight. These weights are established during the training of the neural network. The value of the signal provided to the hidden layer by the input nodes is derived by multiplying the value of the original input signal at the input node by the weight of the connection between the input node and the intermediate node. Thus, each intermediate node receives a signal from each of the input nodes, but due to the individualized weight of each connection, each intermediate node receives a signal of different value from each input node. For example, assume that the input signal at node 1 is of a value of 5 and the weights of the connections between node 1 and nodes 6–8 are 0.6, 0.2, and 0.4 respectively. The signals passed from node 1 to the intermediate nodes 6–8 will have values of 3, 1, and 2.
Each intermediate node 6–8 sums the weighted input signals it receives. This input sum may include a constant bias input at each node. The sum of the inputs is provided into a transfer function within the node to compute an output. A number of transfer functions can be used within a neural network of this type. By way of example, a threshold function may be used, where the node outputs a constant value when the summed inputs exceed a predetermined threshold. Alternatively, a linear or sigmoidal function may be used, passing the summed input signals or a sigmoidal transform of the value of the input sum to the nodes of the next layer.
Regardless of the transfer function used, the intermediate nodes 6–8 pass a signal with the computed output value to each of the nodes 9–13 of the output layer. An individual intermediate node (i.e. 7) will send the same output signal to each of the output nodes 9–13, but like the input values described above, the output signal value will be weighted differently at each individual connection. The weighted output signals from the intermediate nodes are summed to produce an output signal. Again, this sum may include a constant bias input.
Each output node represents an output class of the classifier. The value of the output signal produced at each output node represents the probability that a given input sample belongs to the associated class. In the example system, the class with the highest associated probability is selected, so long as the probability exceeds a predetermined threshold value. The value represented by the output signal is retained as a confidence value of the classification.
Focusing on the function of a classification system 20 compatible with the present invention, the classification process begins at a pattern acquisition stage 22 with the acquisition of an input pattern. The pattern 24 is then sent to a preprocessing stage 26, where the pattern 24 is preprocessed to enhance the image, locate portions of interest, eliminate obvious noise, and otherwise prepare the pattern for further processing.
The selected portions of the pattern 28 are then sent to a feature extraction stage 30. Feature extraction converts the pattern 28 into a vector 32 of numerical measurements, referred to as feature variables. Thus, the feature vector 32 represents the pattern 28 in a compact form. The vector 32 is formed from a sequence of measurements performed on the pattern. Many feature types exist and are selected based on the characteristics of the recognition problem.
The extracted feature vector 32 is then provided to a classification stage 34. The classification stage 34 relates the feature vector 32 to the most likely output class, and determines a confidence value 36 that the pattern is a member of the selected class. This is accomplished by a statistical or neural network classifier. Mathematical classification techniques convert the feature vector input to a recognition result 38 and an associated confidence value 36. The confidence value 36 provides an external ability to assess the correctness of the classification. For example, a classifier output may have a value between zero and one, with one representing maximum certainty.
The actual training process begins at step 54 and proceeds to step 56. At step 56, the program retrieves a pattern sample from memory. The process then proceeds to step 58, where the pattern sample is converted into a feature vector input similar to those a classifier would see in normal run-time operation. After each sample feature vector is extracted, the results are stored in memory, and the process returns to step 56. After all of the samples are analyzed, the process proceeds to step 60, where the feature vectors are saved to memory as a set.
The actual computation of the training data begins in step 62, where the saved feature vector set is loaded from memory. After retrieving the feature vector set, the process progresses to step 64. At step 64, the program calculates statistics, such as the mean and standard deviation of the feature variables for each class. Intervariable statistics may also be calculated, including a covariance matrix of the sample set for each class. The process then advances to step 66 where it uses the set of feature vectors to compute the training data. At this step in an example embodiment, an inverse covariance matrix is calculated, as well as any fixed value terms needed for the classification process. After these calculations are performed, the process proceeds to step 68 where the training parameters are stored in memory and the training process ends.
The process then proceeds to step 106, where feature data is extracted from the pattern sample. Feature extraction converts the pattern into a vector of numerical measurements, referred to as feature variables. Thus, the feature vector represents the pattern in a compact form. The vector is formed from a sequence of measurements performed on the pattern. Many feature types exist and are selected based on the characteristics of the recognition problem.
The process then advances to step 108, where the system determines if the input pattern sample is associated with an output class represented by the system. This determination will vary depending on the classification technique used. If the system determines that the input pattern sample is associated with a represented output class, the process proceeds to step 110. At step 110, the system associates any pattern samples identified as part of a pseudoclass with its proper class. Thus, the classification result associated with the pattern is changed from the pseudoclass to the appropriate class. The process then progresses to step 112, where the classification result is outputted. The process then returns to step 104 to process another input pattern sample.
If the input pattern sample is rejected (i.e. not determined to be associated with a particular class), the process advances to step 114, where the pattern sample is stored in memory. The process then advances to step 116, where the system determines if a sufficient number of pattern samples have been collected to conduct a clustering analysis. The necessary number will vary, depending upon the type of analysis used. If an insufficient number of pattern samples have been collected, the system returns to step 104 to process another pattern.
If a sufficient number of pattern samples have been collected, the process proceeds to step 118, where the collected pattern samples are grouped into clusters according to their proximity in feature space. In other words, pattern samples possessing similar features are grouped together, according to a clustering algorithm. A number of clustering algorithms exist for use in this application, including k-means algorithms, gradient search techniques, and similar optimization techniques. In the example embodiment, a type of genetic clustering algorithm is used to group the pattern samples. A number of genetic clustering algorithms are known in the art.
The process then advances to step 120, where the clusters are subjected to independent review to identify clusters of pattern samples that are associated with a represented output class. The identified clusters will represent secondary distributions of existing output classes, rejected by the classifier because they did not match the dominant feature model for the class within the classification process. In a preferred embodiment, the clustered pattern data is outputted to a human operator for review. Alternatively, the clustering may be verified by a computerized process, such as an alternate clustering algorithm.
The process then proceeds to step 122, where the clustered pattern samples are used to retrain the classifier to recognize the distributions represented by the clusters as independent classes. Since these classes are not actually independent, but are instead associated with an existing output class, they will be referred to hereinafter as pseudoclasses. This allows the classifier to identify pattern samples falling within the secondary distributions of a multi-modal output class.
The process continues at step 124, where the system reorganizes the classification architecture to maintain its efficiency with the addition of the new pseudoclasses. A clustering analysis is performed on a set of vectors consisting of the mean feature vector for each class and pseudoclass to identify clusters of classes that are poorly separated in feature space. A number of clustering algorithms are available for this purpose, including Ward's method, k-means analysis, and iterative optimization methods.
After the clustering analysis, the system establishes the identified clusters as a classification level. A level of classification discriminates between the identified clusters using a selected set of features. The system then determines if any of the clusters contain multiple output classes. If one or more clusters with multiple output classes are found, the classes within these clusters are poorly separated in feature space, and it is necessary to arrange the output classes within the clusters into at least one additional sublevel. The clustering analysis will be repeated, using a different set of features, within each cluster containing multiple classes until every class is separated at some level of the architecture. At this point, the generated classification architecture is accepted by the system.
The process continues at step 126, where the system rejoins the pseudoclass pattern sample sets to the associated original class. After discovery of a new pseudoclass and reorganization of the classifier, the system alters the post-classification processing to associate the new pseudoclass with its original class. Consequently, patterns classified as members of a pseudoclass during run-time operation are associated with the original class during post-processing.
The preprocessed image segment is then passed to a feature extraction portion 154. The feature extraction portion 154 analyzes preselected features of the image. The selected features can be literally any values derived from the image that vary sufficiently among the various output classes to serve as a basis for discriminating among them. Numerical data extracted from the features can be conceived for computational purposes as a feature vector, with each element of the vector representing a value derived from one feature within the image. Features can be selected by any reasonable method, but typically, appropriate features will be selected by experimentation. Generally, the features are selected at the time a classification architecture is established. In the preferred embodiment of a postal indicia recognition system, example features include a histogram variable set containing sixteen histogram feature values, and a downscaled feature set, containing sixteen "Scaled 16" feature values.
A scanned grayscale image consists of a number of individual pixels, each possessing an individual level of brightness, or grayscale value. The histogram feature variables focus on the grayscale value of the individual pixels within the image. Each of the sixteen histogram variables represents a range of grayscale values. The values for the histogram feature variables are derived from a count of the number of pixels within the image having a grayscale value within each range. By way of example, the first histogram feature variable might represent the number of pixels falling within the lightest sixteenth of the range all possible grayscale values.
The "Scaled 16" variables represent the average grayscale values of the pixels within sixteen preselected areas of the image. By way of example, the sixteen areas may be defined by a four by four equally spaced grid superimposed across the image. Thus, the first variable would represent the average or summed value of the pixels within the upper left region of the grid.
The extracted feature vector is then inputted into a classification portion 156. At the classification portion, the feature vector is compared to training data from a number of output classes. A variety of classifier architectures and techniques may be selected for making this determination. In the example embodiment, the classification portion 156 makes use of a Bayesian Mahalanobis distance classification system.
The classification portion attempts to match the input feature vector to one of a plurality of represented classes using the selected classification technique. In this process, a class will be selected and a corresponding confidence measurement will be calculated. If the confidence measurement is above a predetermined threshold, the feature vector is classified as the corresponding class and stored in a classified sample memory 158 as a member of that class. If the confidence measurement does not exceed the threshold, the feature vectors are stored in a rejected sample memory 160 for later analysis.
Once a sufficient number of image samples have been accumulated, the image samples are passed to a clustering portion 162. The clustering portion 162 groups the image samples into clusters based upon similarities in their features. More precisely, the clustering portion 162 groups together those images whose feature vectors are relatively close to one another within an N-dimensional space, where each of the N dimensions represents a feature variable. A number of clustering algorithms exist for this purpose, including, for example, nearest neighbor clustering, gradient trace techniques, and k-means clustering. In the example embodiment, a type of genetic clustering algorithm is applied to group the rejected samples. After the clusters are identified, the newly classified image samples are passed to a pseudoclass identification portion 164.
At the pseudoclass identification portion 164, the clusters are subjected to independent review to determine if any clusters of samples that are associated with a represented output class. Samples that represent a secondary grouping of established classes are identified as pseudoclasses. Identification of pseudoclasses can be accomplished by a number of methods, ranging from computerized review, such as review by a customized clustering algorithm, to identification of the new classes by a human operator. In the preferred embodiment, a human operator reviews the clustered samples to determine if any clusters warrant treatment as a pseudoclass. If such clusters are found, the identified pseudoclasses are passed to a training portion 166.
The training portion 166 retrains the classifier to accept the pseudoclasses as output classes. In essence, the training portion 166 uses the image samples identified as part of each new pseudoclass, and uses them to train the classifier to accept the pseudoclasses as output classes. Training data for each of the various pseudoclasses is derived at this point, such as the mean, variance, and range of each feature over the pseudoclass. After the classifier is retrained to accept the pseudoclasses, the training data for the original classes and the psuedoclasses are passed to a reorganization portion 168.
The reorganization portion 168 reorganizes the classification architecture of the system to incorporate the new pseudoclasses. In the reorganization portion 168, the extracted data is analyzed to determine if the output classes and pseudoclasses are well-separated in feature space. The clustering analysis can take place via any number of methods, depending on the number of levels of classification expected or desired, the time necessary for classification at each iteration, and the number of output classes represented in the analysis.
In the example embodiment, a Kohonen algorithm is applied to group the classes. Each of the output classes, including the new pseudoclasses, is represented by a vector containing as its elements the mean feature value for that class for each of the features used by the classifier. The clustering analysis begins with a distance determination among all of these class representative vectors in a training set.
In the Kohonen algorithm, a map is formed with a number of discrete units. Associated with each unit is a weight vector, initially consisting of random values. Each of the class representative vectors is inputted into the Kohonen map as a training vector. Units respond to the input vector according to the correlation between the input vector and the unit's weight vector. The unit with the highest response to the input is allowed to learn by changing its weight vector in accordance with the input, as are some other clusters in the neighborhood of the clusters. The neighborhood decreases in size during the training period.
The result of the training is that a pattern of organization emerges among the units. Different units learn to respond to different vectors in the input set, and units closer together will tend to respond to input vectors that resemble each other. When the training is finished, the set of class representative vectors is applied to the map once more, marking for each class the unit that responds the strongest (is most similar) to that input vector. Thus, each class becomes associated with a particular unit on the map, creating natural clusters of classes.
These natural clusters may be further grouped by combining map units that represent similar output classes. In an example embodiment, this is accomplished by a genetic clustering algorithm. Once the Kohonen clustering is established, it can be altered slightly, by combining or separating map units. For each clustering state, a metric is calculated to determine the utility of the clustering. This allows the system to select which clustering state is optimal for the selected application. Often, this metric is a function of the within groups variance of the clusters, such as the Fisher Discriminant Ratio. Such metrics are well known in the art.
In the example embodiment, the clustering portion 162 includes of a number of single class classification portions, each representing one of the output classes or pseudoclasses of interest. Each of these classifiers receives a number of known image samples to classify. Each classifier is assigned a cost function based upon the accuracy of its classification of the samples, and the time necessary to classify the samples. The cluster arrangement that produces the minimum value for this cost function is selected as the clustering state for the analysis.
The architecture reorganization portion 168 then arranges the system architecture in accordance with the results of the clustering analysis. The various clusters of classes are arranged into a first level of classification. A number of classifiers are available for use at each level, and different classifiers may be used in different sublevels of classification. In the example embodiment, a technique based on radial basis function networks is used for the various classification stages. Classification techniques based on radial basis functions are well known in the art.
For clusters found to contain more than one class, a sublevel of processing is created to aid the classification process. The reorganization process is repeated for each new sublevel, each of which can utilize different features and classification techniques.
The post-processing portion 170 applies the relationships between classes and pseudoclasses determined during the pseudoclass identification portion 164 to rejoin the image sets from the original class and the pseudoclass. The post-processing portion 170 is updated with the discovery of a new pseudoclass to allow images identified as part of a pseudoclass to be rejoined to their proper class. During post-processing, image samples identified as members of the various pseudoclasses are recognized as members of their associated class. Accordingly, upon receiving image identified as a member of the pseudoclass during run-time operation, the post-processing portion associates the class with the original class and outputs the proper class association.
It will be understood that the above description of the present invention is susceptible to various modifications, changes and adaptations, and the same are intended to be comprehended within the meaning and range of equivalents of the appended claims. The presently disclosed embodiments are considered in all respects to be illustrative, and not restrictive. The scope of the invention is indicated by the appended claims, rather than the foregoing description, and all changes that come within the meaning and range of equivalence thereof are intended to be embraced therein.
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|Teuvo Kohonen, The Self-Organizing Map, 1990, Proceedings of the IEEE, vol. 78, No. 9, p. 1464-1480.
|U.S. Cecala et al, U.S. Appl. No. 10/208,447, filed Jul. 30, 2002 for Method and Computer Program Product for Identifying and Incorporating New Output Classes in a Pattern Recognition System During System Operation.
|Citing Patent
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|Dec 30, 2008
|Trw Automotive U.S. Llc
|Method and apparatus for arbitrating outputs from multiple pattern recognition classifiers
|Oct 6, 2009
|Sep 10, 2013
|International Business Machines Corporation
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|May 17, 2016
|Kabushiki Kaisha Toshiba
|Pattern recognition apparatus, method thereof, and program product therefor
|Feb 24, 2004
|Aug 25, 2005
|Trw Automotive U.S. Llc
|Method and apparatus for arbitrating outputs from multiple pattern recognition classifiers
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|Xerox Corporation
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|U.S. Classification
|382/159, 382/225
|International Classification
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Owner name: LOCKHEED MARTIN CORPORATION, NEW YORK
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:II, DAVID L.;REITZ, II, ELLIOTT D.;TILLOTSON, DENNIS A.;REEL/FRAME:013502/0443;SIGNING DATES FROM 20021105 TO 20021112
|Nov 13, 2007
|Certificate of correction
|Nov 27, 2007
|Certificate of correction
|Dec 20, 2010
|Fee payment
Year of fee payment: 4
|Jan 30, 2015
|Maintenance fee reminder mailed
|Jun 19, 2015
|Lapse for failure to pay maintenance fees
|Aug 11, 2015
|Expired due to failure to pay maintenance fee
Effective date: 20150619


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Top class Irish crime comedy thriller with Brendan Gleeson and Don Cheadle - class!
looking forward to seeing it and to meet up afterwards
0 · February 15, 2013
Sure you've got the right title, Julian? It is after midnight so perhaps I've misread it?
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text:
Well, it's happened. My distributors are completely out of all XD's, all Smith and Wessons and a bunch of others. Also received an email from NICS apologizing for the long hold times. I wouldn't be surprised if it shuts down (it's happened before).
Hawaii becomes first state to register gun owners in FBI database - NRA: "Exercising a constitutional right is not inherently suspicious"


text:
Relevant offers
New York City and much of the US Northeast are digging out from a snowstorm that hammered a region still struggling to recover from the devastation of Superstorm Sandy.
The unseasonably early winter storm dumped more than 30 centimetres of snow on parts of Connecticut and slapped the region with 80 km/h winds, plunging hundreds of thousands of homes and businesses back into darkness and creating a new commuting nightmare for a region whose transportation system was already under repair.
Bitter cold, rain, snow and powerful winds added to the misery of disaster victims whose homes were destroyed or power was knocked out by Sandy, which smashed ashore on October 29 and caused widespread flooding.
"God hates us!" the New York Post said in a front-page headline. Some 3 to 6 inches (8 to 15 cm) of snow fell on the city, which awoke to dry weather.
New York Governor Andrew Cuomo fired the state's emergency management chief, Steven Kuhr, for sending government workers to Kuhr's Long Island home to clear a tree toppled by Sandy, according to media reports.
Some 715,000 homes and businesses in the region were without power, an increase of nearly 43,000 from Wednesday night after the nor'easter knocked power out to more customers following those who had lost power due to Sandy, the US Department of Energy said.
"I thought I was lucky when power was restored last Thursday, but last night it went out again," said Michael Platt, 49, an electrician from Toms River, New Jersey, who estimated a foot of snow fell in his area. "The kids have been home for nearly two weeks and I'm not working, and when I'm not working I'm not making any money. This hasn't been easy."
The storms battered New Jersey's shore, a summer tourist haven where hundreds of beachfront homes were destroyed by Sandy's record storm surge.
Sandy's death toll in the United States and Canada reached 121 after New York authorities reported another death linked to the storm in the hard-hit coastal neighbourhood of the Rockaways, a barrier island facing the Atlantic Ocean.
"Can you believe this? Enough is enough," said Cindy Casey, whose Belle Harbor home one block from the beach in the Rockaways was swamped by Sandy, as she looked out at the snow blanketing the neighbourhood devastated by flooding and fire.
Sandy surrounded Casey's home with six feet (two meters) of water and sparked a fire that destroyed at least 20 houses in the neighbourhood before stopping short of her own.
"I said, 'I'm going to die,'" said Casey, who does not know how to swim but vows to learn. "It was non-stop. I just felt hopeless. There was nothing I could do. ... At least I still have a house."
New York and New Jersey evacuated the most vulnerable coastal areas ahead of the nor'easter storm.
New York City officials urged people whose homes have been flooded by Sandy to relocate to the homes of friends or family members or to go to city shelters. The city also distributed space heaters and blankets to residents without heat or power and opened shelters to those in need of a warm place to sleep.
Commuter bus and train services had been disrupted by the storm, with the Long Island Rail Road briefly shutting down all operations to the city's eastern suburbs on Wednesday night.
All of the region's major airports experienced cancelled flights and delays on Wednesday due to the storm, and gasoline remained in short supply. Four companies told the United States they intended to take advantage of a rare waiver allowing them to use foreign-flagged ships to transport oil products to the storm-hit region.
- Reuters
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text:
Kirch vows low-cost digital set-top boxes
Munich media mogul Leo Kirch hopes to revive flagging interest in digital pay TV by offering lower-priced digital set-top boxes to German consumers.
Munich media mogul Leo Kirch hopes to revive flagging interest in digital pay TV by offering lower-priced digital set-top boxes to German consumers.
As the charity season kicks into high gear Friday with the Carousel Ball, Hollywood's generosity is being tested by piranha-like election-year fundraising, a rising tide of charities and a hiccuping…
PBS' fledgling underwriting sales force lined up $7.7 million in program sponsorships during the third quarter, up from $4 million during the same frame last year, the pubcaster said Wednesday.
An innovative program to develop plays as well as new playwrights is under way Off Broadway at the Cherry Lane.
With competition looming for its lock on TV ratings research, Nielsen Media Research signed ABC and CBS to new four-year contracts, Nielsen said Wednesday.
As organizers prepare to open the 65th Mifed film market in Milan Nov. 1-6, the advance data suggests a slight downturn in attendance figures, with U.S. exhibs once again the dominant presence.
Fox profits were swept away along with the San Diego Padres Wednesday night, as the New York Yankees completed their World Series romp with a 3-0 victory.
London-based ITN is teaming up with three major PBS outlets --including Gotham's WNET -- to launch a nightly newscast targeted at American audiences.
Ending a summer of speculation in which every top-gun publishing player was rumored up for the job, Broadway editor-in-chief John Sterling has been appointed the new president and publisher of Henry…
Stevie Wonder will be honored as the MusiCares Person of the Year during the annual fundraising dinner gala held by the National Academy of Recording Arts & Sciences.
Rupert Murdoch has decided to up Star TV's stake in the music channel venture Channel V to 60% from its current 50%.


text:
GCC Sovereigns: A Little Better off
this post is by Rachel Ziemba
Thanks again to Brad for letting me fill in while he's on vacation. Sorry I'm late catching up with you all but I'll try to chime in on some of the key releases especially on China in the next week. But let me start out with something from the world of oil wealth.
Timothy Geithner, the U.S. Treasury Secretary, travels to the GCC (Saudi Arabia and the UAE) in a few days to commune with some of the more significant creditors of the U.S. and possibly urge these savings-rich countries to contribute to the IMF, as several emerging market economies have pledged. As a result it seems an apt time to re-estimate how much these governments and their neighbors in Qatar and Kuwait have accumulated.
While the Gulf's holdings of U.S. assets pale in comparison to China's, the GCC possesses the largest trove of US stocks among foreign governments. With most of its assets managed by the central bank, Saudi Arabia likely holds the most US treasury bonds. The other GCC countries, most of whom entrusted their oil windfall (and gas in Qatar's case) to an array of investment funds, tend to have a more diversified portfolio. However, the U.S. dollar still dominates the Gulf's foreign asset position.
With the rise in the price of oil in Q2, some analysts have again been talking again about the global role of sovereign funds. While some, such as the China Investment Corporation (CIC), for one, seem to have become more active investors again, armed with new advisers, the Gulf funds still seem to be homeward looking for now. The latest –and forthcoming – RGE Monitor Global Outlook suggests that growth in the GCC will be flat in real terms, with a slight contraction possible in 2009. The significant assets of the region have allowed GCC countries to steer their economies to a softer, if still, harsh landing. Much of the region's output, investment and sentiment remain linked to oil despite various attempts to diversify its economy. Steffen Hertog has a nice new piece on the lessons learned from the 80s by Arab oil producers.
Many GCC sovereign funds have boosted their holdings in domestic banks, domestic equity markets other savings been drawn on to meet fiscal deficits. Meanwhile, some of the few identifiable foreign investments in H1 2009 were made by hybrid funds like the UAE's Mubadala, which tend to invest in sectors that could help domestic economic development or in sectors they already dominate (such as oil and petrochemicals).
The foreign assets of GCC central banks and sovereign funds are estimated to have fallen to just over $1.1 trillion at the end of June 2009 from about $1.2 trillion at the end of 2008. This estimate draws on a methodology Brad and I created last year, which estimates the inflows to the funds, and assumes similar performance to benchmark indices for each asset class. This estimate though, is somewhat lower than some other prevailing estimates. Recently released analysis by McKinsey Global Institutes suggests that Sovereign investors of the GCC, one of their 'new power brokers' managed closer to $2 trillion at the end of 2008.
The national breakdown suggests that Saudi Arabia accounts for over $400 billion of the assets (including the non-reserve assets of the Saudi Arabian monetary Agency and the foreign assets it manages for other parts of the government). The UAE, accounts for the next largest amount, around $350 billion, not including Mubadala and some Dubai funds. Kuwait's fund managed just under $240 billion and Qatar, over $60 billion. The remainder is managed by the central banks of the region.
The slightly more fiscally-conservative (and richer) countries with more oil reserves per capita, like Abu Dhabi and Kuwait, or gas reserves (Qatar) and their more risk-tolerant funds should have seen the value of their assets reflate along with risky assets. Despite the increase in the oil price, the correction at the end of June 2009, limited the paper gains.