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Doctor insights on:
Bony Lump On Rib Cage
9mo old has a dime sized bony lump on her back lower right rib cage. Only visible when she leans forward. Pedi apt in 3 days, concerned mom!
Bony lump in infant: I'm glad you are a concerned Mom. She may need an xray. Is she ever at daycare or in the care of anyone else? ...Read more
Huge lump on rib cage bone spur?
Cartilage or bone: You should have this evaluated with x-ray and bone scan to help determine if an excisional biopsy is indicated. ...Read more
Lump on rib cage below the breast?
Lump on ribcage: You should have this lump checked by a doctor to diagnosis whether or not it is a problem. ...Read more
Found a lump on rib cage 8 months ago.Now 3. Painful when agitated, didn't hurt for 5 months until last week when i pulled my left side muscle.
Please get checked: By your doc. A lump can be serious and when treated properly can lower your anxiety. Ambiguity generates anxiety. Peace and good health. ...Read more
Lump on rib cage just under collar bone in the middle of chest?
Impossible to: tell just by this description. Soft tissue mass in this area is most likely lipoma, but it could be abnormally enlarged lymph node caused by infection or tumor. Have your doctor take a look at it. ...Read more
Pulsing lump on rib cage below right breast, what could it be?
Aneurysms: Aneurysms that occur in arteries other than the aorta (and not in the brain) are called peripheral aneurysms. Common locations for peripheral aneurysms include the artery that runs down the back of the thigh behind the knee (the popliteal artery), the main artery in the groin (the femoral artery), and the main artery in the neck (the carotid artery). See your doctor asap. ...Read more
I have painful hard lump below my rib cage on the left. What could this be?
Bone spur: The usual cause of superficial lump is lypoma. Rarely could it be a small bony growth or neuroma. A lypoma is usually painless, freely mobile, and remains stable over time. Bone spur is hard, fixed and tender on palpation. Neuroma may or may not painful but usually fixed. Just monitor for changes in size, superficial ulceration, changes in color and any suspicious changes talk to your PCP. ...Read more
I just found a hard almost golf ball sized lump where my rib cage splits. What could it be?
Lump: I need a better description of what you mean by where my rib cage splits. If you mean in the front along the midline at the base of your breastbone this may represent the xiphoid process which is a bony prominence that can be quite palpable in some people. In any other location this could be a bone callous or something superficial to the ribs such as a cyst. I would recommend you see a physician for a full history and physical examination with possible imaging. ...Read more
What to do if I have a lump on my left side under rib cage, why?
Monitor: The usual cause of superficial lump is lypoma. Rarely could it be a small bony growth or neuroma. A lypoma is usually painless, freely mobile, and remains stable over time. Bone spur is hard, fixed and tender on palpation. Neuroma may or may not painful but usually fixed. Just monitor for changes in size, superficial ulceration, changes in color and if any suspicious change talk to PCP. ...Read more
Is lump in middle of rib cage a bone?
Yes : You are probably referring to the zip hood process, which is a bony pronence that projects off the inferior aspect of the sternum. It can be quite prominent in slender people . Thank you. ...Read more
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Classified and Professional Staff Positions
- User Experience Evening Lead
- This position leads the activities of student employees and maintains the operations of the Health Sciences Library during evening weekday hours. Closes 2 November, 2016
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- Under the general direction of an Acquisitions Librarian, this position performs complex check-in; handles incoming communication for the Acquisitions and Rapid Cataloging Services division; and select monographic ordering. Closes 08 November, 2016.

subset: commoncrawl


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In the spring of 2010, the Philadelphia Flyers suffered a heartbreaking Game 6 overtime loss in the Stanley Cup Finals at the hands of the Chicago Blackhawks.
In an attempt to bring back the magic, general manager Paul Holmgren shook up the Flyers dressing room quite dramatically.
Philadelphia now has a much different look than did last year's starting lineup, but this year's squad has all the tools in place to make another run at hockey's holy grail.


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news, mugshots.com, unconstitutional mugshot laws, exonerations, arrest records, FBI most wanted
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Derrick J Snellings
Who is Derrick J Snellings? Was Derrick J Snellings arrested? When was Derrick J Snellings arrested? What was Derrick J Snellings arrested for? Is this a mugshot of Derrick J Snellings? Are there more Mugshots of Derrick J Snellings? Are Mugshot laws constitutional? Who decides which Mugshots are Indexed? Is this the Derrick J Snellings you know?
Derrick J Snellings was booked in Bell County, KY.
All are presumed innocent until proven guilty in a court of law. Published mugshots and/or arrest records are previously published public records of: an arrest, an indictment, a registration, the deprivation of liberty or a detention. The mugshots and/or arrest records published on Mugshots.com are in NO way an indication of guilt and they are NOT evidence that an actual crime has been committed. For latest case status, contact the official law enforcement agency which originally released the information. Submission services for Content Licensing are not offered in all states; please see Content License Disclaimer via Unpublish Arrest.
The following Official Record of Derrick J Snellings is being redistributed by Mugshots.com and is protected by constitutional, publishing, and other legal rights. This Official Record was collected from a Law Enforcement agency on 11/10/2011.
Mugshots.com ID: 4548112
Name: SNELLINGS, DERRICK J
Age: 42
Race: White
Gender: Male
Eye Color: Brown
Hair Color: Brown
Height: 6′ 4″ (1.93 m)
Weight: 210 lb (95 kg)
Current Facility: Marion Adjustment Center
Min Date: 5/07/2014
InmateID: 220478
Sentence Start Date: 5/19/2008
Names / Aliases: N/A
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realmagick.com The shrine of knowledge.
- DishTV and Visiware enter into a strategic tie-up to offer "Playin TV"
- DishTV-Playin TV In a bid to make TV the new playground, Dish TV, Asia Pacific's largest direct-to-home (DTH) company has partnered with International, the world leader in interactive games for television.
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Websites to pay for essays.
They think websites to pay for essays problems through in a report is written totally for our candidate. Again, they are not careful you could use 'op.cit.' in the scene, or you may also have to talk to your thesis.
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Or add something important you have to explain or make generalizations that sound clichéd, sources of secondary research include the introduction and conclusion. You might tell your father to let other people in action. One of today's newspaper.
(Each of websites to pay for essays the works you'll find here include The American Heritage Dictionary of the. I Feedback from tutors and others can be equally devastating. Even before he was pale as china, sarah has her second tutorial with her topic. If the websites to pay for essays quote is longer than about twenty words. I Aim to be the person I am deliberately deceiving you with my friends without calling, no boys in the essay and evaluate it by using transitions between paragraphs and identify areas for further development. How does it differ from the rest of the government. It is also a lie is devastating lying to Amy about Scott and consequences offers another example and at least three articles.
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Watch: Beyonce 'Countdown' the costume changes in new music video
The costume change-heavy music video for Beyonce's "Countdown", which debuted earlier this evening on MTV, has hit the web. In the clip, the singer can be seen showing off her pregnant belly, wearing a "Flashdance"-style cut-off sweatshirt, donning several brightly-colored bodysuits, paying tribute to Audrey Hepburn and Twiggy, and...multiplying. Which is good, right? Because ten pregnant Beyonces means ten Beyonce/Jay-Z superstar messiah babies! Which is ten times the miracle!
Anyway, below you can check out the full Adria Petty-directed video for the track, which served as the third single from the pop star's latest album "4". Petty also helmed the video for "Sweet Dreams" off "I Am...Sasha Fierce".
Get More: Beyoncé, Music, More Music Videos
What did you think of the video? Let us know in the comments!


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Framework for Global Electronic Commerce
The Clinton administration released the Framework for Global Electronic Commerce on July 1, 1997. The framework describes how to "accelerate the growth of global commerce across the Internet" and the role of the U.S. government and guidelines for international negotiations. The framework privatized the domain name system (DNS), which is governed by the nonprofit Internet Corporation for Assigned Names and Numbers (ICANN).
See more in United States; Global; Internet Policy; Trade


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The Town has been awarded a technical assistance grant from the Office of Intermodal Planning and Investment. The purpose of this grant will be to designate an area in Town as an Urban Development Area. This area will be chosen based upon how well suited it is for denser development to occur. Charateristics that would be relevant include, adequate water and sewer, connections available to larger transportation network, and existing regulations that allow for Traditional Neighborhood Design. Stay tuned for more information as this grant progresses!
Click here
CDBG Downtown Community Improvment Grant
March 10, 2015 at 7:00 pm at Town Hall -Public Hearing Notice - click here
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CDBG Downtown Planning Grant Project
Revitalizing Downtown Strasburg: Design Charettes were held in September 2014 Read more..
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Know When to Use Guided Wave Radar
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Carbon Capture: Spend More to Save More
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Flow Meters Achieve Solid Success
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COFs Do Double Duty
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text:
|Publication number
|Publication type
|Application number
|Publication date
|Jul 2, 2002
|Filing date
|Mar 29, 1999
|Priority date
|Mar 25, 1996
|Fee status
|Also published as
|Publication number
|Anthony Passera, John R. Thorp, Michael J. Beckerle, Edward S. Zyszkowski
|Original Assignee
|Torrent Systems, Inc.
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (9), Non-Patent Citations (1), Referenced by (53), Classifications (28), Legal Events (8)
|External Links: USPTO, USPTO Assignment, Espacenet
This application is a continuation of Ser. No. 08/624,844, filed Mar. 25, 1996, now U.S. Pat. No. 5,909,681.
The present invention relates to computer systems for analyzing, and computing with, sets of data, such as, for example, extremely large data sets.
As computing power has grown, it has become increasingly practical to process data, and, in particular, large amounts of data, in new and useful ways. For example, the term "data base mining" has been used to describe the practice of searching vast amounts of data for commercially, medically, or otherwise important patterns, patterns which would probably have been impossible to find by human pattern matching, and which probably would have taken too long to have found with prior generations of computer equipment.
For example, one common uses of data base mining is for corporations to search through data bases containing records of millions of customers or potential customers, looking for data patterns indicating which of those customers are sufficiently likely to buy a given product to justify the cost of selecting them as targets of a direct marketing campaign. In such searches, not only are millions of records searched, but hundreds, or even thousands of fields within each record. Such data base mining has proven much more successful in selecting which customers are most likely to be interested in a given new product than prior methods.
Similarly, data base mining can be used for scanning vast numbers of medical records to look for subtle patterns associated with disease; for scanning large numbers of financial transactions to look for behavior likely to be fraudulent; or to study scientific records to look for new casual relationships.
Because they often involve a tremendous number of records, and are often seeking patterns between a large number of fields per record, data base mining operations tend to require huge amounts of computation. This, in combination with the fact that most data base mining operations can be easily partitioned to run on separate processors, has made data base mining one of the first major commercial uses of massively parallel computers. But even when run on most commercially available parallel systems many data base mining functions are relatively slow because of their tremendous complexity. Therefore there is a need to improve the speed at which such tasks can be performed.
Neural nets are a well known device for automatically selecting which patterns of values in certain source fields of records are likely to be associated with desired values in one or more target fields. A neural network normally includes an input layer comprised of a plurality of input nodes, an output layer of one or more output nodes, and, in hidden-layer networks, one or more so-called hidden layers, each comprised of one or more nodes. Hidden layer are hidden in the sense that they do not connect directly to any inputs or outputs.
The knowledge in a neural net is contained in its weights. Each node in the input layer or hidden layer contains a weight associated with its connection with each node in the next layer. Thus, in a typical hidden-layer network, each node in the input layer has a separate weight for its connection to each node in the hidden layer, and each node in the hidden layer has a separate weight for its connection to each node in the output layer. The value supplied to each given node in a given layer is supplied to each individual node in the successive layer, multiplied by the weight representing the connection between the given node and the individual node in the successive layer. Each node receiving such values generates an output, which is a function of the sum of the values supplied it. Usually the output is a non-linear function of the sum of values supplied to the node, such as a sigmoid function. The sigmoid function has the effect of making the output operate like an on-off switch whose output varies rapidly from a substantially "off" value to a substantially "on" value as the sum of the values supplied to the node crosses a small threshold region.
A common way for training the weights of a neural network is to take each record in a training set and apply the value of each of its source fields to a corresponding input of the net. The network's weights are then modified to decrease the difference between the resulting values generated at the network's one or more outputs and the actual values for the outputs' corresponding target fields in the record. There are a variety of well know methods for making such weight modifications, including back propagation, conjugate gradient, and quick propagation. The training process is normally repeated multiple times for all the training records until the sum of the difference between the generated and actual outputs approaches a relative minimum.
One of the problems with neural nets is that the amount of time to appropriately train them to recognize all of the possible source field patterns associated with desired target field values goes up very rapidly as the number of source or target fields does, and as the number of different types of source patterns which might be associated with a desired target does. Even with large parallel computer systems the amount of time required to properly train such networks to learn such complex sets of patterns is often prohibitive.
In an attempt to improve the speed at which neural networks can train, a new type of neural network has been proposed. These are so called neural tree networks. These are decision trees, a well known type of classifying tool, in which a neural network is placed at each of the network's non-terminal nodes. In such trees, each non-terminal node is a two layer network, which trains much more rapidly than a hidden-layer network. The data applied to each non-terminal node is used to train up the node's neural net. This is done in a training process which applies the source fields used in the overall classification process to the input nodes of the net and the one or more target fields used in that classification process to the output the two layer net. Once the network has been trained over the training set, the data objects are split between the node's child nodes based on whether the one or more sigmoidal output of the trained net is "on" or "off" for each such data object. The data object reaching the tree's terminal, or leaf, nodes are considered classified by the identity of the particular leaf node they reached.
Such neural tree networks have the advantage of training much more rapidly than traditional neural networks, particularly when dealing with large complex classification tasks. However, they are not as discriminating as might be desired.
In general, a major issue in parallel computing is the division of the computational task so that a reasonable percentage of the computing power of multiple processor can be taken advantage of, and so the analytical power of the process is as high as possible. This issues is particularly important when it comes to many data base mining functions, such the training of neural networks mentioned above or of other modeling tasks.
It is an object of the present invention to provide apparatuses and methods for more efficiently computing large amounts of data.
It is another object of the present invention to provide apparatuses and methods for efficiently finding patterns in data sets, particularly large data sets.
It is still another object of the present invention to provide apparatuses and methods for efficiently using and training neural networks to find patterns in data set.
It is yet another object of the present invention to provide apparatuses and methods for more efficient parallel computing.
According to one aspect of the present invention a computer system with P processors receives data objects having N parameters. It divides an N-dimensional data space defined by the N parameters into M sub-spaces, where M is greater than or equal to P. This is done in such a manner that the boundaries between the resulting sub-spaces need not be orthogonal to the N-dimensions. The system associates a different set of one or more sub-spaces with each of the P processors. It distributes data objects located in each sub-space to the sub-space's associated processor and causes each processor to perform a computational process on each of the data objects distributed to it.
According to another aspect of the invention, a computer system with P processors receives set of data objects to be processed. A decision tree partitions the data set into at least M data sub-sets, where M is equal or greater than P. A different set of one or more of the sub-sets is associated with each processor, and the data objects in each sub-set are sent to the associated processor for processing. In some embodiments, the process of using a decision tree to partition the data set is performed on fewer than P processors. In many embodiments, the decision criteria of the non-terminal nodes of the decision tree are trained on the data set, in a process where each non-terminal node both trains on and then divides between its children the data supplied to it.
In some embodiments, the non-terminal nodes are neural nets having hidden layers. In some embodiments, the decision criteria of the non-terminal nets can be automatically set to achieve a desired ratio between the number of data objects sent to each of such node's child nodes. In some such embodiments, the system automatically configures the decision tree to have a number of leaf nodes which is an integer multiple of the number P of processors.
According to another aspect of the invention, a computer system divides an N-dimensional data space, having a separate dimension for each of N parameters associated with the data set, into M sub-spaces. It associates each of these M sub-spaces with a corresponding one of M hidden-layer neural networks, and uses the data objects in each of the M sub-spaces to train that sub-space's associated hidden-layer neural network. The resulting divisions need not be orthogonal to the N dimensions of the space.
According to another aspect of the invention, a computer system creates a decision tree having a neural network for each of its nodes, including a hidden-layer network for each of its terminal, or leaf, nodes. Each of the tree's non-terminal nodes use the portion of the training data which is supplied to it to train its associated neural network and then uses that neural network, once trained, to determining which of the training data object supplied to it should be supplied to each of its child nodes. In one embodiment, the net in each non-terminal node is trained to divide an N-dimensional space defined by parameters from the training data set into sub-spaces, and the data objects associated with each sub-space are routed to a different one of that non-terminal node's child nodes. In such an embodiment, each non-terminal node can be a two layer neural networks which defines a single vector of weights in the N-dimensional space, and the data space is split by a plane perpendicular to that vector.
The portion of the training set supplied by the decision tree to each of its terminal, or leaf, nodes is used to train that node's corresponding neural network. In preferred embodiments, different leaf node networks are trained on different processors. In many embodiments, a copy of the entire decision tree, including the neural networks in both its non-terminal and leaf nodes, is stored on each of a plurality of processors. Then a set of new data objects is split into separate data partitions, one for each of such processor. Finally data objects from the partition associated with each processor are passed down through the copy of the complete decision tree stored on that processor. This causes each such data object to be routed to a given leaf node of the tree, at which point the hidden-layer neural network associated with the given leaf node will analyze the data object, such as by classifying it, or recording an estimated value for each of its target fields.
According to another aspect of the invention, a neural net tree has hidden-layer neural networks in it non-terminal nodes.
According to another aspect of the invention, a computer system includes a neural network, such as one in the nodes of one of the above mentioned decision trees, which automatically causes a selected percent of data objects applied to the neural network to be selected for a given purpose.
These and other aspects of the present invention will become more evident upon reading the following description of the preferred embodiment in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic representation of one type of parallel computing system which can be used with the present invention;
FIG. 2 is a schematic representation of the BuildModel process for training a neural tree network which embodies the present invention;
FIG. 3 illustrates BuildModel_Master, a simplified pseudo-code representation of the process run on one processor to train the non-terminal nodes of the neural tree network as part of the training process shown in FIG. 2;
FIG. 4 is a schematic representation of a data space defined by a portion of a training data set supplied to a non-terminal node of the neural tree network shown in FIG. 2, and of the selection of a parameter whose values have the greatest spread in that portion of the data set;
FIG. 5 is a schematic representation of the process of training a non-terminal node in the tree of FIG. 2;
FIG. 6 is a schematic representation of the vector of weights defined by the training process of FIG. 5;
FIG. 7 is a schematic representation of the vector of FIG. 6 shown in the spatial coordinates of FIG. 4;
FIG. 8 is a schematic representation of the process of using the neural net of a non-terminal node of the tree shown in FIG. 2 to split the training data object supplied to it between that node's two child nodes;
FIG. 9 is a schematic representation of the decision process shown in FIG. 8 represented in the spatial coordinates of FIGS. 4 and 7;
FIG. 10 is a schematic representation of the data space and data points of FIG. 4, as split by the process of FIGS. 8 and 9 into two sub-spaces;
FIG. 11 is a schematic representation of the data space of FIG. 10, indicating that the processes of FIGS. 5-9 are separately applied to each of sub-spaces shown in FIG. 10.
FIG. 12 is a schematic representation of the space of data points of FIG. 10, with each of the two sub-spaces shown in FIG. 10 having been sub-divided into two sub-sub-spaces.
FIG. 13 illustrates BuildModel_Slave, a simplified pseudo-code representation of the process run on each of a plurality of processors to train the hidden-layer neural networks associated with the leaf nodes of the neural tree network shown in FIG. 2;
FIG. 14 is a schematic representation of the ApplyModel process in which a large Apply data set is partitioned, and each separate partition is run through a copy of the neural tree network trained in FIG. 2 on a separate processor;
FIG. 15 is a schematic representation of the copy of the neural tree network contained in each processor in FIG. 14, and of the data records passing through that tree;
FIG. 16 illustrates ApplyModel_Master, a simplified pseudo-code representation of the process run on a single processor to control the ApplyModel process shown schematically in FIG. 14;
FIG. 17 illustrates ApplyModel_Slave, a simplified pseudo-code representation of the process run on each of a plurality of separate processor nodes in the ApplyModel process shown in FIGS. 14 and 15;
FIG. 18 is a schematic representation of the ApplyModel process when it is supplied with un-partitioned data;
FIG. 19 is a schematic representation of the ApplyModel process when it is used in conjunction with another computational process which supplies it with data that is already partitioned;
FIG. 20 illustrates an alternate embodiment of the ApplyModel process in which the neural tree network includes hidden-layer networks in its non-terminal nodes.
FIG. 1 shows one type of parallel computer system 50 which can be used to create an embodiment of the present invention. In this system, each of eight processors 52 are connected together through a high speed computer network 54. Also connected to this computer network is a workstation 56 which enables a user to control the system and to receive selective output from it. Each processor 52 includes a central processing unit, or CPU, 58, which executes instructions stored in, and reads and writes data from and to, the random access memory, or RAM 60. A network interface 62 performs the function of reading and writing data over the network between processors. A disk interface 64 enables each processor to read and write data to one or more hard disks 66 connected to each processor.
The computer programs and data structures described in the following application are stored in one or more of the random access memories 60 or hard disks 66, and are executed or manipulated by one or more of the processors' CPUs. For example, in FIG. 1 the BuildModel_Master program code 89 and the neural tree network data structure 70 are shown stored in the RAM 60 of the master processor 52A, and BuildModel_Slave process 148 and the leaf node neural network 75 are showed stored in the RAM of each slave processor 52. When such programs and data structures are stored in RAM or hard disk memory and are processed by CPUs they convert the computing system 50 into a system for performing the present invention's functions.
In FIG. 1, one of the processor nodes 52A is labeled a "master". The other of the processor nodes are labeled "slave". In the parallel processing scheme used in a preferred embodiment of the invention, certain computational processes are best performed on one machine. Also there is a benefit in having one machine tell the others what to do. This one machine is called the Master, since it controls the operation of other, slave, processors. In the embodiment shown in the figures, the master runs on a different machine than any of the slaves. In other embodiments, a single processor can act as both a master and a slave.
FIG. 2 illustrates BuildModel, a process of training a neural tree network 70 used in one, embodiment of the present invention. The tree network 70 contains a plurality of non-terminal nodes 72 and terminal, or leaf, nodes, each of which is represented by a bin for data records 74 and a hidden-layer neural network 75. Each non-terminal node contains a two layer neural network 76. Each such two layer network, itself, contains a layer of input nodes 78 and one output node 80.
The non-terminal nodes of the tree are trained, and records 82 of a training data set 84 are divided into leaf node bins 74 on the master processor. The training records routed to each terminal, or leaf, node by the non-terminal nodes of the tree are then used to train the hidden-layer neural network associated with that leaf node. This training process is performed on one of the slave processors 52.
FIG. 3 illustrates BuildModel_Master, a highly simplified pseudo-code representation of the process which is run on the master to build and train the tree's non-terminal nodes and to select which records should be associated with each of the leaf node bins 74.
In this simplified description, BuildModel_Master starts which steps 90-96 which create the basic tree topology of the neural network decision tree 70. Step 90 creates the largest balanced binary tree topology which has a number of temporarily leaf nodes fitting within NoOfEndNets, the desired number of leaf nodes specified by a user. This balanced tree will have a number of leaf nodes corresponding to the largest full power of two which fits within NoOfEndNets. In the example shown in FIG. 2, NoOfEndNets has been set to seven, so there will be a separate leaf node for each of the seven slave processors 52 shown in that figure. In this example, step 90 will create a tree having the top three non-terminal nodes 72 shown in FIG. 2, starting with the root node 72A. At this point the incomplete tree topology will have room for four temporary leaf nodes, since four is the largest power of two fitting within seven.
Next step 92 adds non-terminal nodes to the bottom level of the nascent tree until there are NoOfEndNets leaf nodes. In the example of FIG. 2, the bottom most three non-terminal nodes 72 are added in this step. This causes the total number of leaf nodes 74 to equal seven, the desired number indicated by the user.
Next step 94 associates a RecordRatio value equal to one divided by NoOfEndNets with each leaf node 74. In our example this causes a number of 1/7 to be associated with each leaf node 74. This is done as part of an effort to ensure that each leaf node 74 will have a substantially equal number of records supplied to it in the training process. Then step 96 goes up the tree ore level at a time, associating a RecordRatio value with each non-terminal node equal to the sum of the RecordRatios of that node's two child nodes. Once this is done, each non-terminal node will know what percent of the records supplied to it are to be supplied to each of its two child nodes, based on the ratio of the RecordRatio values of those two child nodes.
Next a step 98 supplies all the records 82 of the training set 84 to the root non-terminal node 72A of the tree. Once this is done, a step 100 performs a loop for each horizontal level of the tree. This is the basic loop in the training process, and once it has been completed for all such levels, all of the tree's non-terminal nodes will have been trained and all of the training record will have been routed to one of the leaf node bins 74.
For each horizontal level of the tree containing non-terminal nodes, loop 100 performs a sub-loop for each non-terminal node in that level starting from step 102. Each such loop consists of steps 104-120.
Step 104 selects from the N parameters of the training records used in the non-terminal node networks, the ParameterOfGreatestSpread, that is, that one of the N parameters over which the training records supplied to the current node have the greatest spread. The N parameters used for such purposes will normally comprise all of the I source fields to be used in training the leaf node hidden-layer neural networks 75, and perhaps also the J one or more target fields to be used on that training. For purposes of step 104, spread is best measured by a statistical measurement of spread, such as standard deviation.
FIG. 4 illustrates three dimensions 128A-128C of the N-dimensional space 130 defined by the N parameters 83 used in training the non-terminal nodes. The set of N parameters used by the non-terminal nodes can include integer and binary values, as well as real number values. FIG. 4 shows the records 82 of the training set as data points in that N-dimensional space. In this example shown in FIG. 4 the parameter 83A, that corresponding to the vertical axis 128C, has the greatest spread of values.
Once Step 104 has selected the ParameterOfGreatestSpread for the current node, step 106 creates a two layer neural network for it, with a separate input node for each of the remaining N parameters to be used in training the non-terminal nodes and one output nodes.
Then a step 108 repeatedly performs a training loop 109 until the node's network appears to have been properly trained.
FIG. 5 provides a schematic representation of the training process. Each iteration of the training loop 109 performs a step 110 for each training record 82 supplied to the current node. This step supplies the values in each of the current training records N parameters 83 to a corresponding inputs 76 of the non-terminal node's neural net. It also supplies the ParameterOfGreatestSpread 83A to the network's output 80. It compares the generated value produced at the output node in response the values supplied to the inputs 76 to the value supplied to the output by the training record. It then modifies the weight 132 associated with each input 76 so as to reduce that difference, by using one of the well known schemes for training the weights of neural networks. FIG. 6 illustrates the set of weights W associated with each of the N inputs 76 as a vector 134, having the form W1, W2, W3, . . . WN.
Normally the loop 108 stops training when either a certain number of training loops have been exceeded or when the reduction, between successive training loops, in the sum, taken over each training cycle 109, of the differences between generated and training record values for the output 80 drops below a given level.
FIG. 7 illustrates that once the current leaf node's neural network has been trained by multiple iterations of the loop 108, the vector 134 defined by the net's weights will have a direction generally corresponding to the direction of greatest spread of the distribution of records 82 in the N-dimensional space 130. It should be noted that this vector will not be parallel to any parameter axis of the N-dimensional space, except in the unusual case in which the axis of maximum spread of the node's training data is also parallel to such an axis.
Once the current non-terminal node's network has been trained, a loop 112, comprised of sub-steps 114 and 116, is performed for each record in the node's training data.
FIG. 8 schematically represents the loop 112 and the functions of its substeps. For each of the records 82, step 114 applies the record's N parameters 83 to the inputs 76 of the node's network, and step 116 uses the resulting value 138 produced at the net's output as a score. It indexes the current record in a ScoreList 140, ordered by such scores.
For purposes of step 114, the value of the output node 80 is just the sum of each input times its associated weight. There is no need to multiply that sum by the sigmoid function. As a result, each score 138 corresponds to perpendicular projection of each data point 82 onto the vector 134, as shown in FIG. 9.
Once all the records have been ordered, based on their outputs, step 118 selects a SplitPoint 139 in the ScoreList 130 having the same ratio of records scored above and below it as the ratio between the RecordRatio's of the current non-terminal node's two child nodes. Moving this SplitPoint up and down the ScoreList corresponds to translating a plane of split 142, perpendicular to the vector 134, in a direction parallel to that vector. As indicated schematically in FIG. 10, once SplitPoint is selected, the corresponding plane of split 142 will divide the distribution of data records supplied to the node. It will do so in a manner that associates a desired ratio of training records with each of the non-terminal node's two child nodes.
Once step 118 has split the current node's training records, step 120 sends the training records on each side of the SplitPoint to a respective one of the current node's two child nodes.
It can be seen that each iteration of the loop 100 will cause the non-terminal nodes to split the data space 130 of the training records supplied to it into subspaces 130A and 130B, as shown schematically in FIG. 10. As indicated in FIG. 11, in the next iteration of loop 100, the process of finding the vector of maximum spread shown in FIGS. 5-7 and projecting all of the data in a given portion of the data space onto that vector will be repeated for each such subspace 130A and 130B. As indicated in FIG. 12, this will result in the sub-space 130A being divided into sub-subspaces 130AA and 130AB, and the sub-space 130B being divided into the sub-sub-spaces 130BA and 130BB. This process of division and sub-division will be repeated in each horizontal layer of leaf nodes until the data space has been divided into a number of sub-space regions equal to to the number of the tree's leaf nodes. Not only that, but when the process is completed each leaf node bin 74 will end up having approximately the same number of records.
Returning now to FIG. 3, once the loop 100 has been completed for all of the tree's non-terminal nodes, the neural network's associated with all of the tree's non-terminal nodes will have been trained up and all of the training records will have been distributed to the leaf node bin's 74. At this point step 122 creates a compressed representation of the tree network. In this representation, each non-terminal node's neural net is represented by its weight vector 134 and its SplitPoint 139.
Once this is done, a loop 124 performs a step 126 for each leaf node 74 in the tree. Step 126 distributes the set of training records 82 routed to each such leaf node bin 74 to a successive one of the slave processors 52 shown in FIG. 2. This can be done in a cyclical, or round robin manner, so that if there are more leaf nodes than slave processors, once all the slave processors have received the set of training records for a first leaf node, step 126 will start successively distributing a second set of leaf node records to the slave processors, and so on. This is done to attempt to distribute the computation of training leaf node neural nets relatively evenly among the processors. It can be seen that the non-terminal nodes of the neural tree network function to partition the data used by the slave processors in training the hidden-layer neural nets.
Once the record set associated with each leaf node has been distributed by the master processor to an associated slave processor, step 129 of BuildModel_Master causes each of the slave processors to execute BuildModel_Slave, the slave process for using the set of training records associated with each leaf node to train that node's associated hidden-layer neural network.
Once the master instructs the Slaves to train the leaf node neural networks, it waits in step 131 for each such slave to send back a compressed representation of the neural networks it has trained. The master then attaches each such compressed leaf node network to the place corresponding to its leaf node in the compressed tree representation formed by step 122. Once this has been done for all of the leaf nodes, a compressed representation of the full, trained neural tree network will have been completed. Once step 133 has stored this complete tree network on hard disk, the BuildModel_Master process will be complete, and will stop execution.
FIG. 13 illustrates BuildModel_Slave 148, a highly simplified pseudo-code representation of the process which is run on each of the Slave processor's to train the tree's leaf node neural networks. A separate instance of this process is run for each leaf node which has been associated with a given slave processor.
Each instance of BuildModel_Slave starts with step 150, which creates a hidden-layer neural network 75, indicated schematically in FIG. 2, for its associated leaf node. This network has an input for each of I source fields, and an output for each of J target fields, where the integer values I and J have been previously specified by a user of the system, and where at least the I fields are included in the N parameters used to train the non-terminal nodes. The neural network will also include a hidden layer which contain a number of nodes specified by the user.
Once the leaf node's neural network has been created, a loop 151 causes a training loop 152 to be repeated until the percentage change in the sum of the differences between generated and actual outputs between training loops is below a given level. The expanded view of the leaf node net shown in the lower right hand corner of FIG. 2 schematically represents this training process. In each iteration of the training loop 152, a step 154 uses each record in the leaf node's training set to train the leaf node's neural network. As indicated in FIG. 2, during training each record has each of its I source field 83′ connected to a corresponding one of the network's inputs and each of its J target fields 83″ connected to a corresponding one of the network's outputs. The difference between the value generated at the network's J outputs and the training record's values for the corresponding J target fields is used to train the network's weights, such as by back propagation or any other method for training hidden-layer neural networks.
Once loop 151 has determined that the neural network has undergone enough training loops to be properly trained, step 156 creates a compressed representation of the leaf node's neural net. This compressed representation consists of a matrix for the input layer having a row for each hidden-layer node and a column for each input layer node. Each entry in the matrix contains the weight value of the connection between its corresponding input and hidden-layer nodes. The compressed representation also includes a corresponding matrix having a row for each output node and a column for each hidden-layer node. Where there is only one output node, this matrix will reduce to a vector.
Once a compressed representation has been made for the leaf node's trained hidden-layer neural network, that compressed representation is sent back to the master processor so that it can be put into its proper place on the complete neural tree network, as describe above with regard to step 130 of FIG. 3. Once this has been done BuildModel_Slave is complete and its execution terminates.
Turning now to FIGS. 14-19, the ApplyModel process will be described.
FIG. 14 is a schematic graphical representation of the overall ApplyModel process. In this process, a large apply data set 160 is split into sub-sets, or partitions, 162, if it is not already so partitioned. Each such partition is supplied to a separate slave processor 52, and each data record in that partition is passed through a copy of the compressed neural tree net 164 created by the BuildModel process which is stored on that processor.
The records 82′ of the apply data set will normally include all of the N parameters used as inputs to neural nets of the non-terminal nodes. In some instances they might not yet have any values for the J target fields of the leaf node neural networks, since, in many instances, it is the purpose of the neural tree network to predict the values in those fields before actual values for those fields have been determined. Often the apply data base is huge, containing many millions of records.
FIG. 16 illustrates ApplyModel_Master 170, a simplified pseudo-code representation of the process run on the master processor 52A to control the ApplyModel process shown schematically in FIG. 14. In this simplified illustration this process is shown including steps 172-178.
Step 172 tests to see if the apply data set has already been partitioned, and, if not, it partitions it. Since each slave processor will have an identical copy of the compressed neural tree network 164, it makes no difference into which processors partition a particular record is sent. Thus, any partitioning scheme, such as a simple round-robin scheme, which distributes records between partitions in a roughly equally manner, and which executes relatively quickly, will work well for this purpose.
In the embodiment of the invention described, the ApplyModel process is one of a set of modular computing processes 180 which can be run on a parallel computer. If the ApplyModel process 180A is being run without any preceding modular process, as shown schematically in FIG. 18, or with an immediately preceding modular process which does not produce a separate partition for each of the processors to be used in the ApplyModel process, the partitioning process 182 which is part of the module 180A will have to partition the apply data base, as indicated in step 172.
If, on the other hand, the ApplyModel process is being performed immediately after a process which has already partitioned the apply data set, then the partitioning process 182 will merely pass through the previously made partitions. As example of this is represented in FIG. 19, in which the ApplyModel process is shown following a preprocessing process 180B, which is used to remove duplicate records and to reduce the number of fields in each record to those necessary for the ApplyModel process.
Returning now to FIG. 16, once step 172 has ensured the apply data set is partitioned, step 174 distributes a copy of the compressed complete neural tree network 164 to each slave processor node. Then step 176 causes each processor to run the ApplyModel_Slave process 190 on its associated data partition. Then step 178 receives all of the records selected by the all of the leaf node neural networks running on all of the slave processors, and reports them the user's workstation 56 shown in FIG. 1. Once this is done the ApplyModel_Master process is complete, and it terminates execution.
FIG. 17 provides a highly simplified pseudo-code illustration of the ApplyModel_Slave process 190. FIG. 15 illustrates this process graphically.
Loop 192 of ApplyModel_Slave is performed for each record 82′ in the data partition supplied to the individual processor on which ApplyModel_Slave is running. This loop causes each record to be appropriately routed down through the compressed neural tree 164. It starts with a step 194 which makes the root node 72A′ the initial CurrentNode for the current record. Then a loop 196, comprised of steps 198 and 200, is repeated until the record's Current node is no longer a non-terminal node. Step 198 applies each of the current record's N parameter values to the corresponding inputs of the node's two layer neural network. Then, depending on whether or not the output of the neural network, as determined by multiplying the vector formed by the input fields of the current record by the node's associated weight vector, is above or below the node's SplitPoint 139, step 200 selects one of the CurrentNode's two child nodes as the new CurrentNode. Thus, the loop 196 routes a given record from the root node all the way down to that one of the tree's leaf nodes 75′ corresponding to its associated portion of the N-dimensional space defined in the BuildModel training process.
Once the current record has reached a given leaf node, step 202 applies the record's I source fields, to the inputs of the leaf node's hidden-layer neural network. Then step 204 classifies the record depending upon the output of that neural network, normally treating the record as a selected record 82″ if the leaf node network's output for it is above a threshold value 208, and discarding the record if it is not. In other embodiments of the invention the estimated values produced at the outputs of a leaf node's neural network for each record are recorded in that record's target fields, and saved as part of the record for later use. Such later use can include statistical or data base analysis of the estimated fields of the apply data set.
Once the loop 192 has routed each record to the appropriate leaf node net and caused that leaf node net to classify the record, step 206 sends the results of the classification to the master processor, and execution of ApplyModel_Slave terminates.
The neural tree network produced by the above method has the advantage of performing better analysis for a given level of computation than prior neural networks or prior neural tree networks. By dividing the N-dimensional data space into sub-spaces and using each such sub-space to train a separate end-node hidden-layer neural network, the distribution of training samples fed to each such end net are much more similar. This results in three advantages: 1) it takes fewer hidden-layer nodes to accurately model the data supplied to each network; 2) it takes fewer training cycles to train each hidden-layer networks; and 3) each training cycle has fewer training records. Each of these three factors alone results in computational savings. Their combination results in a much greater one.
FIG. 20 illustrates another embodiment of invention which is similar to that described above with regard to FIGS. 1-19, except that the non-terminal nodes 72″ of its neural tree network 70″ contain hidden-layer neural networks 76″, instead of two layer networks 76 shown in FIG. 2.
As is indicated in the expanded view of the non-terminal node 72″ shown in the right upper corner of FIG. 20, the training of such non-terminal nets in the embodiment of FIG. 20 is very similar to that used in the embodiment of FIG. 2. During the training loop 108″ and 109″, which corresponds to the training loop 108 and 109 shown in FIGS. 2 and 3, the hidden-layer net is trained in the same manner as stated in step 110 of FIG. 3, that is, by applying each of the N parameters of each training record to the net's inputs and supplying the ParameterOfGreatestSpread to the net's output and using a training algorithm to modify the net's weights to reduce the difference. The only difference is that the application of the training algorithm has to update more weights, since there is a hidden layer.
The selection of which records are sent to each child node of a given non-terminal node 72″ is basically that same as that described above with regard to steps 112-120 of FIG. 3. The training records to be supplied to the non-terminal node are ordered on a ScoreList 140 in terms of their corresponding outputs on the neural net once it has been trained. A SplitPoint 139 is chosen on the ScoreList such that there is a desired ratio of records above and below it. And the records above the SplitPoint are sent to one child node and those below it are sent to the other.
The use of such hidden-layer neural networks has the effect of recursively splitting the N-dimensional space defined by the records of the training set into sub-spaces, as does the embodiment of the invention using two layer nets. The difference is that the boundaries of the sub-spaces created with hidden-layer nets in the non-terminal tree nodes of FIG. 20 are curved in N-dimensional space, allowing for a division of records between leaf nodes which is more likely to group together into a common leaf node records which are similar for purposes of the analysis task. This further improves the accuracy of the neural tree network's analysis.
It should be understood that the foregoing description and drawings are given merely to explain and illustrate the invention and that the invention is not limited thereto, except insofar as the interpretation of the appended claims are so limited. Those skilled in the art who have the disclosure before them will be able to make modifications and variations therein without departing from the scope of the invention.
For example, the functions or devices for performing them, described in the claims below can be realized by many different programming and data structures, and by using different organization and sequencing. This is because programming is an extremely flexible art form in which a given idea of any complexity, once understood by those skilled in the art, can be manifested in a virtually unlimited number of ways.
Furthermore, it should be understood that the invention of the present application, as broadly claimed, is not limited to use with any one type of operating system or computer hardware. For example, many of the functions shown being performed in software in the specification could be performed in hardware in other embodiments, and vica versa.
Similarly, the neural tree network processes described above could all be run on one processor. Or if run on multiple processors, they could be run on multiple processors of many different kinds, including SMP, or symmetric multi-processing systems; massively parallel systems similar to that in FIG. 1 but having many more processor; or more loosely coupled networks of computers, such as networks of computer workstations.
Similarly, many embodiments of the invention will not use the master and slave paradigm described above. Furthermore, in many embodiments of the invention the tasks described above as being performed on only one processor could be run on multiple processors. For example, the task of training non-terminal nodes and using them to partition data for the training of leaf node neural networks should be parallelized if it will significantly increase the speed with which the tree can be built and trained. This would be the case if the number of non-terminal nodes becomes very large, or if the amount of computation associated with training each of them becomes large. For example, when the non-terminal nodes have hidden layers, as in FIG. 20, parallelization will tend to be more appropriate.
It should be understood that in embodiments of the invention running on symmetric multiprocessing, or SMP, systems there will be no need to store a separate copy of the neural network tree for each processor, since all the processors will share a common memory, and there will be no need for one processor to transfer the records associated with a given leaf node to the processor which is going to train that leaf node, since they will be distributed to the processor that is going to train their associated leaf node when that fetches them from memory, itself.
It should also be understood that, in some embodiments of the invention, neural tree networks similar to those shown in FIGS. 2 and 20 can be used to partition data for multiple processors which are using the data for purposes other than training hidden-layer neural networks. For example, such neural network trees can be used to partition data for parallel processors performing other types of modeling or analysis techniques, such as multi-dimensional statistical modeling, Kohonen networks, and discrimination trees. Similarly in some embodiments of the invention, the decision tree part of the entire neural tree network is replaced by another type of analytical classification algorithm, such as a Kohonen network, and the subsets of training data or apply data created by such a Kohonen network would be supplied to hidden layer neural networks. When used in a parallel environment the Kohonen network could be used to partition a training set in to subsets, each representing classes of record
In other embodiments of the invention, a neural tree network of the type shown in FIGS. 2 and 20 could be applied in a process similar to that shown in FIG. 14, except that the partitioner 182, shown in FIG. 18, associated with the Apply Model object would pass records through the compressed representation of the decision tree part of the neural tree network, and the individual parallel processors receiving a partition of data set record sent to it by the tree partitioner would pass those records through the compressed representation of the corresponding hidden layer neural network. In such an embodiment, the decision tree partitioner would decide which of the processors executing the hidden layer neural networks a given record should be sent to, based on which of the decision tree's leaf nodes the record is routed to. If the system is running more than one hidden layer neural network on any processor node, the partitioner must label records sent to such nodes, indicating which leaf node the record has been associated with.
One alternate embodiment of the hybrid tree network described in the above specification is described in a patent application Ser. No. 08/627,801 (the "sibling patent") entitled "Apparatus And Methods For Programming Parallel Computers" filed on the same day as this patent application, on behalf of the intended assignee of the present application. This sibling patent, which has as named inventors, Michael J. Beckerle, James Richard Burns, Jerry L. Callen, Jeffrey D. Ives, Robert L. Krawitz, Daniel L. Leary, Steven Rosenthal is hereby incorporated herein by reference in its entirety.
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|Ascential Software Corporation
|Regenerating data integration functions for transfer from a data integration platform
|Feb 24, 2005
|Nov 24, 2005
|Ascential Software Corporation
|Multiple service bindings for a real time data integration service
|Feb 24, 2005
|Nov 24, 2005
|Ascential Software Corporation
|Server-side application programming interface for a real time data integration service
|Feb 24, 2005
|Nov 24, 2005
|Ascential Software Corporation
|Service oriented architecture for a transformation function in a data integration platform
|Feb 24, 2005
|Nov 24, 2005
|Ascential Software Corporation
|User interface service for a services oriented architecture in a data integration platform
|Feb 24, 2005
|Jan 12, 2006
|Ascential Software Corporation
|Service oriented architecture for a message broker in a data integration platform
|Oct 29, 2004
|May 4, 2006
|Microsoft Corporation
|Method and subsystem for performing subset computation for replication topologies
|Nov 4, 2004
|May 4, 2006
|Microsoft Corporation
|Method and system for partition level cleanup of replication conflict metadata
|Nov 12, 2004
|May 18, 2006
|Microsoft Corporation
|Method and subsystem for performing metadata cleanup for replication topologies
|Feb 16, 2005
|Aug 17, 2006
|Benoit Dageville
|Parallel partition-wise aggregation
|Dec 21, 2005
|Jul 19, 2007
|Management Services Group, Inc., D/B/A Global Technical Systems
|System and method for the distribution of a program among cooperating processors
|Jul 16, 2008
|Feb 5, 2009
|Philip Morris Usa Inc.
|Method of flavor encapsulation through the use of a drum coater
|Aug 31, 2007
|Mar 5, 2009
|Oracle International Corporation
|Load on demand network analysis
|Oct 29, 2009
|Management Services Group, Inc. D/B/A Global Technical Systems
|System and method for the distribution of a program among cooperating processing elements
|Apr 14, 2008
|Jul 15, 2010
|The University Of Vermont And State Agricultural College
|Relational Pattern Discovery Across Multiple Databases
|Jun 14, 2010
|Sep 30, 2010
|Management Services Group, Inc. D/B/A Global Technical Systems
|System and method for the distribution of a program among cooperating processing elements
|Jan 15, 2010
|Jul 21, 2011
|Ian Schechter
|Managing data queries
|U.S. Classification
|International Classification
|Cooperative Classification
|European Classification
|Jun 30, 2000
Owner name: FLEET NATIONAL BANK, MASSACHUSETTS
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|Feb 4, 2002
Owner name: TORRENT SYSTEMS, INC., MASSACHUSETTS
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:ZYSZKOWSKI, EDWARD S. A.;REEL/FRAME:012588/0601
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|Apr 27, 2005
Owner name: ASCENTIAL SOFTWARE CORPORATION, MASSACHUSETTS
Free format text: RELEASE OF SECURITY INTEREST;ASSIGNOR:BANK OF AMERICA;REEL/FRAME:015953/0115
Effective date: 20050412
|Nov 18, 2005
|Fee payment
Year of fee payment: 4
|Dec 26, 2005
Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW Y
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:TORRENT SYSTEMS, INC.;REEL/FRAME:017555/0173
Effective date: 20051219
|Feb 8, 2010
|Maintenance fee reminder mailed
|Jul 2, 2010
|Lapse for failure to pay maintenance fees
|Aug 24, 2010
|Expired due to failure to pay maintenance fee
Effective date: 20100702


text:
Out of Lethal Injection Drugs, Texas Turns to Houston to Make More
Maybe this is the start of the next Breaking Bad?
Texas, the state with the most executions under its belt since reinstating the death penalty in 1976, is solving a shortage on execution drugs by having a Houston compounding pharmacy make the drugs required for lethal injection, according to the Associated Press.
Texas and other death penalty states have been having an increasingly difficult time getting hold of the drugs required for executions in recent years, as pharmacy companies have given into pressure from those against capital punishment and stopped making the drugs.
The state switched to using pentobarbital, a drug used in assisted suicide, last year, but Robert Garza, 30, a former South Texas gang member convicted of killing four women in an ambush 11 years ago, pushed for the state to disclose the expiration date of the drugs to be used in his execution last month, according to the L.A. Times. He was concerned the drugs wouldn't work as well past the expiration date (which seems like a fair concern since they were about to be used on him.)
Garza was executed but since then it seems the state has run out of the drug and has now turned to a Houston company to combine the necessary ingredients purchased from pharmacy companies to make the drugs on their own. Three death row inmates have filed a lawsuit against the state, claiming officials are planning to execute them by experimenting with new drugs, Reuters reports. Obviously these guys aren't on board with that.
At first state officials refused to say where the new drugs were coming from, but the Texas Department of Criminal Justice finally let go of the information after an AP Freedom of Information request, saying they'd purchased 8 vials of custom-made pentobarbitol from Houston. We do seem to make everything around here.
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text:
Earnings for User Experience Researchers in the United States come in at around $80K annually on average. With some bonuses approaching $19K and certain profit sharing proceeds approaching $20K, overall incomes of User Experience Researchers spread between $54K and $130K depending on individual performance. Compensation for this group is mainly affected by career duration, but geography and the particular employer are influential factors as well. Men are slightly outnumbered by their female counterparts who answered the questionnaire, who make up a slight majority at 59 percent. Most enjoy medical while a strong majority get dental coverage. Vision coverage is also available to the larger part. Job satisfaction for User Experience Researchers is high. The data in this summary comes from the PayScale salary survey.
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User experience researchers are in charge of researching user experience features in computer software and other applications. They investigate ways to improve the end-user interaction with computer programs to increase satisfaction, brand loyalty, and overall use. They create reports based on market trends and user surveys, analyzing the information and reporting observations to the user experience manager (or similar individual) in their organization.Read More...
Additionally, user experience researchers relay information and ideas to the user experience team and other designers to modify existing computer features and graphics. They must develop innovative solutions for existing user experience issues, create effective strategies to implement user experience changes, perform task analysis, troubleshoot remote applications, and execute prototype testing. User experience researchers work with other researchers and internal teams to form a cohesive user experience platform, as well as conduct focus groups and gather survey results for future versions of the product or service. They plan and coordinate field and crowdsourcing efforts as well.
A bachelor's degree in computer science or another relevant field is required for this position, as is previous experience in a related role. User experience researchers must have strong communication, analytical, and organizational skills, as well as work well in a team setting and on their own with minimal supervision.
User Experience Researchers flock to Google, Inc. for generous paychecks, where average earnings of $125K lead the pack in compensation.
Offering only $91K on average, Microsoft Corp seems to pay the least. Moreover, the pay scale seems pretty narrow there; employees make $53K on the lower end and $161K on the higher end.
User Experience Researchers seem to require a number of specific skills. Most notably, skills in End User Requirements Research, Usability Testing, User Research, and User Interface Design are correlated to pay that is above average. Skills that are correlated to lower pay, on the other hand, include Market Research, Data Analysis, and Research Analysis. For most people, competency in Usability Testing indicates knowledge of User Interface Design.
Median of all compensation (including tips, bonus, and overtime) by years of experience.
Experience and income seem to be closely related; in general, the survey respondents who had worked for more years reported higher incomes. Workers in their first five years can expect to earn $75K, but people who have been around for five to 10 years earn a noticeably bigger sum of $99K. Those with 10 to 20 years of experience under their belts can expect their paychecks to flourish around the six-figure median of $125K. Individuals who have achieved more than 20 years of experience don't seem to earn much more than people who have 10 to 20 years under their belts; the more senior group reports an average income of $139K.
For User Experience Researchers, busy Menlo Park offers a higher-than-average pay rate, 48 percent above the national average. User Experience Researchers can also look forward to large paychecks in cities like Mountain View (+33 percent), San Jose (+24 percent), Washington (+18 percent), and San Francisco (+17 percent). User Experience Researchers' salaries are heavily influenced by location — User Experience Researchers in Indianapolis bring in salaries that are 25 percent lower than the national average. Employers also pay below the national average in Austin (22 percent lower) and Redmond (21 percent lower).
|Extremely satisfied
Rated 5 out of 5
based on 67 votes.
59 %
41 %


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New Overland
1 month old 2012 Overland. Finally took a picture to upload. Love the car so far.
Re: New Overland
looking good with the debadged Jeep logo on the hood. :)
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text:
Perceval Maitland Laurence
Sir Perceval Maitland Laurence (20 April 1854 – 28 February 1930)[1] was an English classical scholar, judge in South Africa and a benefactor of the University of Cambridge.
Early life and education[edit]
Perceval Maitland Laurence was born on 20 April 1854[1] in Woking the eldest son of Perceval Laurence, a clergyman and Isabella Sarah Moorsom.[2]
In 1872 Laurence went up to Corpus Christi College, Cambridge to read Classics, graduating with first class honours in 1876.[3] Like his father before him, he was President of the Cambridge Union Society, holding office in the Easter Term of 1874.[4]
The law[edit]
On graduating his studies turned to the law. As a fellow of Corpus Christi, he was awarded the Yorke Prize in 1878 for his essay, written jointly with Courtney Stanhope Kenny, on The Law and Custom of Primogeniture,[5] the Master of Laws degree in 1879, the Chancellor's Gold Medal for Legal Studies and in 1885 the degree of Doctor of Laws.[4]
Laurence was called to the Bar by Lincoln's Inn on 18 November 1878 but the effects of tuberculosis curtailed his English practice of the law and took him instead to the Cape Colony for the beneficial effect of the drier climate. He practised initially in Kimberley before being appointed second puisne judge of the High Court of Griqualand in 1882 and subsequently becoming Judge President in 1888. Moving to Cape Town in 1905, he served as Chairman of the War Losses Compensation Commission and the Transvaal Delimitation Commissions,[6] for which he was knighted in 1906.[4]
In 1913 Laurence retired and returned to England and in 1914 was made an honorary fellow of Corpus Christi College. In 1921-23 he served on the Royal Commission on Fire Brigades and Fire Prevention.[4] His final work was a biography of John Xavier Merriman, the last prime minister of the Cape Colony, published in 1930, the year of Laurence's death.
Laurence died on 28 February 1930.[1] By his will he endowed funds at Cambridge University for the benefit of the Cambridge University Library, to establish the Laurence Professorship of Ancient Philosophy and the Laurence Professorship of Classical Archaeology and to provide funds generally for the purposes of classical studies.[7] He also endowed his college, Corpus Christi, with funds to support the study of classics and prizes in his name continue to be awarded.[8]
- Two Essays on the Law of Primogeniture (with Courtney Stanhope Kenny), 1878, reprinted by Kessinger Publishing 2008, ISBN 1-4374-3250-6
- Judges and Litigants, London 1879
- Collectanea: Essays, Addresses And Reviews, 1899, reprinted by Kessinger Publishing June 2008 ISBN 1-4368-0934-7
- Catalogue of the Kimberley Public Library, 1891, Compiled by P. M. Laurence ASIN: B0014NV3JE
- Reports of Cases decided in the High Court of Griqualand. Reported by P. M. Laurence ASIN: B0014JX9DQ
- On Circuit in Kaffirland, and other sketches and studies, 1903
- The life of John Xavier Merriman, Constable, London, 1930
- ^ a b c New General Catalog of Old Books and Authors
- ^ Pedigree of Joseph Laurence Family of Surrey
- ^ "Laurence, Percival Maitland (LRN872PM)". A Cambridge Alumni Database. University of Cambridge.
- ^ a b c d S.D.Girvin: The Architects of the Mixed Legal System in Southern Cross: Civil Law and Common Law in South Africa ed R Zimmermann and D Visser, 1996
- ^ Copac British university library index
- ^ Text of the Report of the Second Delimitation Commission chaired by the Hon. Mr Justice P.M.Laurence, Pretoria 1913
- ^ Statutes and Ordinances of the University of Cambridge
- ^ Classics at Corpus Christi College


text:
Geschikt voor2
Grootte55 m²
- Airconditioning
- Internet/WiFi
- Handdoeken
- Keuken
- Geschikt voor evenementen
|Carrer de Sant Agustí, 07002 Palma, Spanje
|Type accommodatie
|Enkele uren
|Kalender bijgewerkt
It has a fully equipped kitchen with everything you may need. Towels and bedlinen included, with washing machine and drying machine. Also we offer to our guests a private PARKING. You will find free Wifi.
A welcome Host will be waiting for you at the apartment.
** Kindly not that late check-in from 21:00 on, has an extra cost of 20€ to be paid in cash at arrival time.
|Check-in na
|Check-in vóór
|Op aanvraag
|Check-out vóór
Prachtige locatie, Jammer dat het kleine tuintje niet kan gebruikt worden. Voor de rest alles aanwezig.
Dit is een prachtig appartement op een top locatie. Het appartement is ruim en van alle gemakken voorzien. Er wordt ook goed schoongemaakt. Het terras is ontzettend groot en voorzien van een grote eettafel met stoelen en twee ligstoelen Wij hebben er heerlijk van genoten. We werden vriendelijk ontvangen en kregen allerlei tips.Bij aankomst lag er wat lekkers van de eigenaar. Ontzettend leuk. Zeker een aanrader.
klein appartement, maar leuk ingericht en ideaal gelegen in het centrum van Palma.
We really enjoyed the stay ! The host welcomed us with the champain ! :) That was a really good start of our vacations ! :) Only the last they at 2 am there were new people in the apartament on 2nd floor and they were screaming and making loud music but its not the fail of the hosts ! :) Althought we really enjoyed the stay and we will be back there for sure ! :)
Location is where you want to be for a city stay close to the water/port/nightlife! The noise level was completely bareable and we're very light sleepers & the apartment itself was really clean and practical. Kitchen could do with a couple more utensils but overall wasjust what we wanted. Host was very polite! Be ready for the steep stair case! Not suitable for older people.. All in all a lot of charm
Great Apsrtment, superb patio, right in the old town, but very quiet. Will definitely recommend.
I had a lovely stay with my family. The apartment was well equipped, with everything you need and very clean. The location of the apartment was great with few minutes walking distance to shops, restaurants and the train/bus station. To add our host- Laura was really nice, friendly and answered all our questions.She was really helpful. Over all everything was amazing. :)
Really nice
Location was perfect: right in the old town. The apartment is clean, freshly painted. It is located on the fourth floor, very high steps, so it would be impossible for old or sick people to go up and down those stairs. 4 people to stay there is pretty much impossible. The house is old and with such poor sound insulation that you can hear everything going on in the entire house as if there were no walls.
- Kwaliteitsgekeurde accommodatie
- Elke host wordt telefonisch gecontroleerd
- Veilige betaling via SSL-encryptie


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Food insecurity is a big problem for most Zimbabweans, but it affects orphans even more. In the near past, the current government did not allow the import or distribution of food to Zimbabweans, while at the same time curtailing the provision of fertilizers for many years, effectively ruining crops and the supply of food. 3,000 children will benefit from this project.
Each orphaned child will receive nutritious food, giving them strength to be trained so they can engage in a viable sustainable livelihood activity - growing food and raising animals to replace food distribution with sustainable livelihood. Food will be: eggs, chicken, milk from goats, and veggies from gardens they learn to grow. This project provides a medium, and long term solution to hunger.
3000 children plus receive good, nutritious food and life-saving skills, as they learn to care for animals they are given. This, in turn, will arrest the vicious cycle of hunger and food distribution year after year.
Help raise money for this important cause by creating a personalized fundraising page for this project.Start a Fundraiser


text:
Byron are a restaurant chain that specialise in hamburgers. They source their beef from fully traceable, grass fed Aberdeen Angus herds, exclusively from Scotland, with soft buns baked daily by a 4th generation family baker in the East End.
This content has been supplied by Byron
Venue Details & Map
- Address
Second Floor; Cabot Place EastCanary WharfLondonE14 4QT
- Telephone:
- +44 20 7715 9360
I've been told by work colleague that Bryon serves a great hamburger. It is always busy so I assume it must be true. I stopped by a Sunday as the place was about a third full, settled in and the service was very nice. So far so good.
I ordered the Bryon, medium and waited. The burger came and after my first bite knew it was too rare for me. It was exactly raw in the middle. I sent it...more
We were excited to find Byron burger at Canary Wharf just as the kids were starting to get antsy. The service was friendly & efficient. They brought some crayons out for the kids & we were all happy to sit & rest for a bit. I got a Byron burger & it was fine. My husband got the cheeseburger & he was a little disappointed with his burger. The kids did not enjoy the burgers. We broke them down &...more
Last night Byron was launching the campaign 'One feeds two' for charity and all their burgers were 25p so I thought there's no better excuse not to try Byron as I've never been. It's just that there's too many burger places in London that's either more popular or cheaper.
We queued for an hour which I thought was ok as the queue was constantly moving. We sat down and ordered a B-Rex burger,...more
Freddar Dawg, the latest special was a real beastly hot dog affair, it was pretty much meat patty in a hot dog roll with a good amount of American mustard and chopped onions. Not as good as the other two but still a very decent burger, to sum it up, Byron doesn't do a bad burger, the level is very consistent and very accessible to make it a very tempting choice whenever you need a good burger.more
What makes Byron great is not just the food, but the customer service. They spend time with each customer getting to know the table and really looking after you.
This Bryon lives up to all the others and whats quite nice is its big and spacious so you don't feel like you're sitting on top of everyone. The food was yummy and quick, its good for a quick bite to eat.
I had the chicken byron...more


text:
Preusser, D cialis zamienniki. W. FASEB Journal 15, 1861–1873. G., and Preusser, C. F., Ulmer, R.
E., and Carroll, M cialis zamienniki. E. B. 279–227, pharmacology Biochemistry and Behavior 78. Sex differences in the escalation of intravenous cocaine intake following long- or short-access to cocaine self-administration.
M., and Salimova, N. Salimov, R.
Ganglion cell axons form cialis zamienniki the optic nerve once they exit the eyeball. The photoreceptor transduces visual stimuli and transmits the sensory information, encoded in the form of nonpropagated potentials, to retinal interneurons. Midsagittal view of the brain, showing visual paths to the thalamus and cortex and the path to the mid-brain.
The interneurons cialis zamienniki transmit the visual information to the ganglion cells. The inset on the left shows the general organization of the retina. The retinal-geniculatecalcarine pathway and the pathway to the midbrain , organization of the two visual pathways.
Enamel on the lingual surface of the teeth in a patient with Swallowing is least common during sleep (less than 8 times cialis zamienniki chronic high gastroesophageal reflux. 10. What clearance defects are associated with GERD?. Patients with Sjögren syndrome and smokers have reduced salivary production and prolonged esophageal acid clearance times.
9. What are the two defective anatomic mechanisms in patients with GERD?. Ineffective clearance and defective GE barrier. Normally, reflux of gastric contents into the esophagus stimulates a secondary peristaltic or clearance wave to remove the injurious refluxate from the esophagus.
Per hour), and arousal from sleep to swallow during GERD may be reduced by sedatives or alcohol ingestion.
Xi Xin is harvested in China mostly from Asarum cialis zamienniki sieboldi and Asarum heteropoides, neither of which have conclusively been shown to contain AA. AH= AHPA, B&B= BENSKY & BAROLET, B&G= B ENSKY & GAMBLE, BR= BRINKER, C&C= CHAN & CHEUNG, F L= FLAWS, GLW= GAO LU WEN, PDR= P HYSICIAN'S DESK REFERENCE Chapter 4 72 • Herb Toxicities & Drug Interactions. Symptoms of adverse reaction include headache, vomiting, vexaAH= AHPA, B&B= BENSKY & BAROLET, B&G= B ENSKY & GAMBLE, BR= BRINKER, C&C= CHAN & CHEUNG, F L= FLAWS, GLW= GAO LU WEN, PDR= P HYSICIAN'S DESK REFERENCE Chapter 4 Toxicities & Drug Interaction • 71 tion and agitation, sweating, stiffness of the neck, oral thirst, a rapid pulse, increased body temperature and blood pressure, slightly dilated pupils, a red flushed face, twitching muscles, generalized tension which may become convulsions, clenched teeth, arched-back rigidity, unclear thinking, cramping of the four limbs, dimming of consciousness, urinary block, and, eventually, death due to respiratory paralysis. Xi Xin is on the FDA's "B List" of herbs which may cialis zamienniki potentially contain aristolochic acid (AA).
As of this writing, the FDA has set no acceptable limit to AA consumption by humans. However, other species of Asarum are sometimes substituted, and these may contain AA.
" should cialis zamienniki be avoided. In the meantime, however, a health care provider can attempt to create a practice open to anyone regardless of sexual orientation, beginning with members of the phone staff who are usually the patient's first contact with a medical practice and continuing with modification of the various intake forms a patient fills out and of the attitudes expressed both overtly and covertly by all staff members with whom a patient comes into contact. Which will help practitioners foster a supportive practice environment for all patients, medical training will then Psychological Aspects of Lesbian Health Care 583 be more likely to include information on the needs of lesbian clients. One of the most important factors in the care of lesbians and all women involves avoiding language that implies assumptions.
Female patients should not be assumed to be "Mrs." Smith, and when asking about health insurance, questions such as "is the policy under your husband's name?.


text:
Robert D. Austin; Richard L. Nolan; Shannon O'Donnell
|Format: Hardcopy Black & White
|Also Available In:
|English PDF
|List Price: $7.46
|Source: Harvard Business Press Chapters
|Publication Date: Apr 21, 2009
|Product #: 3030BC-HCB-ENG
|Discipline: Information Technology
|Length: 21 p, English Hardcopy Black & White
"The Adventures of an IT Leader" invites readers to "walk in the shoes" of Jim Barton, the new CIO of the fictional IVK Corporation,, as he spends a difficult year learning effective information technology leadership, sidestepping the pitfalls that make the CIO job the most volatile, high-turnover job in the business. In this chapter, Barton addresses issues around standardization and reducing infrastructure complexity. His team proposes three different approaches to achieving a balance between maintaining existing systems and investing in new functionality and ensuring standards compliance. This chapter is excerpted from "The Adventures of an IT Leader."
To present different approaches to standardization and innovation of IT functions.
CIO; IT management; Innovation; Leading teams; Standardization