Patent Publication Number: US-2023153623-A1

Title: Adaptively pruning neural network systems

Description:
INTRODUCTION 
     The present disclosure relates to neural networks, and more particularly to adaptively pruning a trained neural network. 
     Vehicles use sensors to collect data while operating, the sensors including radar, LIDAR, vision systems, infrared systems, and ultrasonic transducers. Vehicles can actuate the sensors to collect data while traveling along roadways. Based on the data, it is possible to determine parameters associated with the vehicle. For example, sensor data can be indicative of objects relative to the vehicle. 
     SUMMARY 
     A system can include a computer including a processor and a memory. The memory includes a trained neural network with instructions such that the processor is programmed to receive a pruning ratio and prune at least one node of the trained deep neural network based on a pruning ratio. 
     In other features, the processor is further programmed to actuate a vehicle component based on output generated by the trained deep neural network. 
     In other features, the processor is further programmed to select the at least one node for pruning based on a pruning threshold value. 
     In other features, the processor is further programmed to compare an output of an activation function of the at least one node to the pruning threshold value and select the at least one node for pruning when the activation function is less than the pruning threshold value. 
     In other features, the processor is further programmed to compare a derivative with respect to a weighted input of the at least one node to the pruning threshold value and select the at least one node for pruning when the derivative with respect to the weighted input is less than the pruning threshold value. 
     In other features, the processor is further programmed to receive the sensor data from a vehicle sensor of a vehicle and provide the sensor data to the trained deep neural network. 
     In other features, the processor is further programmed to periodically adjust which nodes in the neural network have been pruned. 
     In other features, the processor is further programmed to actuate an autonomous vehicle component based on sensor data received at a vehicle sensor. 
     A system includes a server and a vehicle including a vehicle system. The vehicle system includes a computer including a processor and a memory, the memory including a trained neural network along with instructions such that the processor is programmed to receive a pruning ratio and prune at least one node of the trained deep neural network based on the pruning ratio. 
     In other features, the processor is further programmed to actuate a vehicle component based on output generated by the trained deep neural network. 
     In other features, the processor is further programmed to select the at least one node for pruning based on a pruning threshold value. 
     In other features, the processor is further programmed to compare an output of an activation function of the at least one node to the pruning threshold value and select the at least one node for pruning when the activation function is less than the pruning threshold value. 
     In other features, the processor is further programmed to compare a derivative with respect to a weighted input of the at least one node to the pruning threshold value and select the at least one node for pruning when the derivative with respect to the weighted input is less than the pruning threshold value. 
     In other features, the processor is further programmed to receive the sensor data from a vehicle sensor of a vehicle and provide the sensor data to the trained deep neural network. 
     In other features, the processor is further programmed to actuate an autonomous vehicle component based on sensor data received at a vehicle sensor. 
     In other features, the processor is further programmed to periodically adjust which nodes in the neural network have been pruned. 
     A method comprises pruning, via a processor, at least one node of a trained deep neural network based on a pruning ratio and actuating a vehicle component based on an output generated by the trained deep neural network. 
     In other features, the method includes selecting the at least one node for pruning based on a pruning threshold value. 
     In other features, the method includes periodically adjusting which nodes of the neural network have been pruned. 
     In other features, the method includes comparing an output of an activation function of the at least one node to the pruning threshold value and selecting the at least one node for pruning when the activation function is less than the pruning threshold value. 
     In other features, comparing the derivative with respect to a weighted input of the at least one node to the pruning threshold value and selecting the at least one node for pruning when the derivative with respect to the weighted input is less than the pruning threshold value. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a diagram of an example system for adaptively pruning a neural network. 
         FIG.  2    is a diagram of an example server. 
         FIGS.  3 A through  3 C  are diagrams of an example deep neural network. 
         FIGS.  4 A through  4 C  illustrate an example process for training a deep neural network. 
         FIG.  5    is an example image frame of multiple objects detected by a vehicle sensor and the corresponding object classifications. 
         FIG.  6    is a flow diagram illustrating an example process for adaptively pruning a trained neural network. 
         FIG.  7    is a flow diagram illustrating an example process for determining whether to actuate a vehicle based on output from a pruned neural network. 
     
    
    
     DETAILED DESCRIPTION 
     Vehicle sensors can provide information about a vehicle&#39;s surrounding environment, and computers can use sensor data detected by the vehicle sensors to classify objects and/or estimate one or more physical parameters pertaining to the surrounding environment. Some vehicle computers may use machine learning techniques to assist in classifying objects and/or estimating physical parameters. 
     Existing deep learning models can be bulky in size and require expensive computational resources for training and inferencing. Due to the size and expensive computational resources, these models may be inefficient or impractical for deploying within a vehicle. In other words, these models may require hosting on cloud servers or data centers rather than vehicles. 
     The present disclosure discloses systems and methods for pruning a neural network, such as a deep neural network. A pruned neural network can result in a neural network that is relatively smaller in size, e.g., less storage footprint and less computational cost, and that can also generate accurate results, i.e., predictions, classification, etc. when deployed within a vehicle. 
       FIG.  1    is a block diagram of an example vehicle control system  100 . The system  100  includes a vehicle  105 , such as a car, a truck, a boat, an aircraft, etc. The vehicle  105  includes a computer  110 , vehicle sensors  115 , actuators  120  to actuate various vehicle components  125 , and a vehicle communications module  130 . Via a network  135 , the communications module  130  allows the computer  110  to communicate with a server  145 . 
     The computer  110  includes a processor and a memory. The memory includes one or more forms of computer-readable media, and stores instructions executable by the computer  110  for performing various operations, including as disclosed herein. 
     The computer  110  may operate a vehicle  105  in an autonomous mode, a semi-autonomous mode, or a non-autonomous (manual) mode. For purposes of this disclosure, an autonomous mode is defined as one in which each of vehicle  105  propulsion, braking, and steering are controlled by the computer  110 ; in a semi-autonomous mode the computer  110  controls one or two of vehicles  105  propulsion, braking, and steering; in a non-autonomous mode a human operator controls each of vehicle  105  propulsion, braking, and steering. 
     The computer  110  may include programming to operate one or more of vehicle  105  brakes, propulsion (e.g., control of acceleration in the vehicle by controlling one or more of an internal combustion engine, electric motor, hybrid engine, etc.), steering, climate control, interior and/or exterior lights, etc., as well as to determine whether and when the computer  110 , as opposed to a human operator, is to control such operations. Additionally, the computer  110  may be programmed to determine whether and when a human operator is to control such operations. 
     The computer  110  may include or be communicatively coupled to, e.g., via the vehicle  105  communications module  130  as described further below, more than one processor, e.g., included in electronic controller units (ECUs) or the like included in the vehicle  105  for monitoring and/or controlling various vehicle components  125 , e.g., a powertrain controller, a brake controller, a steering controller, etc. Further, the computer  110  may communicate, via the vehicle  105  communications module  130 , with a navigation system that uses the Global Positioning System (GPS). As an example, the computer  110  may request and receive location data of the vehicle  105 . The location data may be in a known form, e.g., geo-coordinates (latitudinal and longitudinal coordinates). 
     The computer  110  is generally arranged for communications on the vehicle  105  communications module  130  and also with a vehicle  105  internal wired and/or wireless network, e.g., a bus or the like in the vehicle  105  such as a controller area network (CAN) or the like, and/or other wired and/or wireless mechanisms. 
     Via the vehicle  105  communications network, the computer  110  may transmit messages to various devices in the vehicle  105  and/or receive messages from the various devices, e.g., vehicle sensors  115 , actuators  120 , vehicle components  125 , a human machine interface (HMI), etc. Alternatively or additionally, in cases where the computer  110  actually comprises a plurality of devices, the vehicle  105  communications network may be used for communications between devices represented as the computer  110  in this disclosure. Further, as mentioned below, various controllers and/or vehicle sensors  115  may provide data to the computer  110 . 
     Vehicle sensors  115  may include a variety of devices such as are known to provide data to the computer  110 . For example, the vehicle sensors  115  may include Light Detection and Ranging (lidar) sensor(s)  115 , etc., disposed on a top of the vehicle  105 , behind a vehicle  105  front windshield, around the vehicle  105 , etc., that provide relative locations, sizes, and shapes of objects and/or conditions surrounding the vehicle  105 . As another example, one or more radar sensors  115  fixed to vehicle  105  bumpers may provide data to provide and range velocity of objects (possibly including second vehicles  106 ), etc., relative to the location of the vehicle  105 . The vehicle sensors  115  may further include camera sensor(s)  115 , e.g., front view, side view, rear view, etc., providing images from a field of view inside and/or outside the vehicle  105 . 
     The vehicle  105  actuators  120  are implemented via circuits, chips, motors, or other electronic and or mechanical components that can actuate various vehicle subsystems in accordance with appropriate control signals as is known. The actuators  120  may be used to control components  125 , including braking, acceleration, and steering of a vehicle  105 . 
     In the context of the present disclosure, a vehicle component  125  is one or more hardware components adapted to perform a mechanical or electro-mechanical function or operation—such as moving the vehicle  105 , slowing or stopping the vehicle  105 , steering the vehicle  105 , etc. Non-limiting examples of components  125  include a propulsion component (that includes, e.g., an internal combustion engine and/or an electric motor, etc.), a transmission component, a steering component (e.g., that may include one or more of a steering wheel, a steering rack, etc.), a brake component (as described below), a park assist component, an adaptive cruise control component, an adaptive steering component, a movable seat, etc. 
     In addition, the computer  110  may be configured for communicating via a vehicle-to-vehicle communication module or interface  130  with devices outside of the vehicle  105 , e.g., through a vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2X) wireless communications to another vehicle, to (typically via the network  135 ) a remote server  145 . The module  130  could include one or more mechanisms by which the computer  110  may communicate, including any desired combination of wireless (e.g., cellular, wireless, satellite, microwave and radio frequency) communication mechanisms and any desired network topology (or topologies when a plurality of communication mechanisms are utilized). Exemplary communications provided via the module  130  include cellular, Bluetooth®, IEEE 802.11, dedicated short range communications (DSRC), and/or wide area networks (WAN), including the Internet, providing data communication services. 
     The network  135  includes one or more mechanisms by which a computer  110  may communicate with a server  145 . Accordingly, the network  135  can be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and/or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies when multiple communication mechanisms are utilized). Exemplary communication networks include wireless communication networks (e.g., using Bluetooth, Bluetooth Low Energy (BLE), IEEE 802.11, vehicle-to-vehicle (V2V) such as Dedicated Short-Range Communications (DSRC), etc.), local area networks (LAN) and/or wide area networks (WAN), including the Internet, providing data communication services. 
     The server  145  can be a computing device, i.e., including one or more processors and one or more memories, programmed to provide operations such as disclosed herein. Further, the server  145  can be accessed via the network  135 , e.g., the Internet or some other wide area network. 
     A computer  110  can receive and analyze data from sensors  115  substantially continuously, periodically, and/or when instructed by a server  145 , etc. Further, object classification or identification techniques can be used, e.g., in a computer  110  based on lidar sensor  115 , camera sensor  115 , etc., data, to identify a type of object, e.g., vehicle, person, rock, pothole, bicycle, motorcycle, etc., as well as physical features of objects. 
     Various techniques such as are known may be used to interpret sensor  115  data. For example, camera and/or lidar image data can be provided to a classifier that comprises programming to utilize one or more image classification techniques. For example, the classifier can use a machine learning technique in which data known to represent various objects, is provided to a machine learning program for training the classifier. Once trained, the classifier can accept as input an image and then provide as output, for each of one or more respective regions of interest in the image, an indication of one or more objects or an indication that no object is present in the respective region of interest. Further, a coordinate system (e.g., polar or cartesian) applied to an area proximate to a vehicle  105  can be applied to specify locations and/or areas (e.g., according to the vehicle  105  coordinate system, translated to global latitude and longitude geo-coordinates, etc.) of objects identified from sensor  115  data. Yet further, a computer  110  could employ various techniques for fusing data from different sensors  115  and/or types of sensors  115 , e.g., lidar, radar, and/or optical camera data. 
       FIG.  2    is a block diagram of an example server  145 . The server  145  includes a computer  235  and a communications module  240 . The computer  235  includes a processor and a memory. The memory includes one or more forms of computer readable media, and stores instructions executable by the computer  235  for performing various operations, including as disclosed herein. The communications module  240  allows the computer  235  to communicate with other devices, such as the vehicle  105 . 
       FIGS.  3 A through  3 C  are diagrams of an example deep neural network (DNN)  300 . The DNN  300  can be a software program that can be loaded in memory and executed by a processor included in computer  110 , for example. In an example implementation, the DNN  300  can include, but is not limited to, a convolutional neural network (CNN), R-CNN (regions with CNN features), Fast R-CNN, and Faster R-CNN. The DNN  300  includes multiple nodes  305 , and the nodes  305  are arranged so that the DNN  300  includes an input layer, one or more hidden layers, and an output layer. Each layer of the DNN  300  can include a plurality of nodes  305 . While  FIGS.  3 A through  3 C  illustrate three (3) hidden layers, it is understood that the DNN  300  can include additional or fewer hidden layers. The input and output layers may also include more than one (1) node  305 . 
     The nodes  305  are sometimes referred to as artificial neurons  305 , because they are designed to emulate biological, e.g., human, neurons. A set of inputs (represented by the arrows) to each neuron  305  are each multiplied by respective weights. The weighted inputs can then be summed in an input function to provide, possibly adjusted by a bias, a net input. The net input can then be provided to an activation function, which in turn provides a connected neuron  305  an output. The activation function can be a variety of suitable functions, typically selected based on empirical analysis. As illustrated by the arrows in  FIGS.  3 A through  3 C , neuron  305  outputs can then be provided for inclusion in a set of inputs to one or more neurons  305  in a next layer. 
     The DNN  300  can be trained to accept sensor  115  data, e.g., from the vehicle  105  CAN bus or other network, as input and generate a distribution of possible outputs based on the input. The DNN  300  can be trained with ground truth data, i.e., data about a real-world condition or state. For example, the DNN  300  can be trained with ground truth data or updated with additional data by a processor of the server  145 . The DNN  300  can be transmitted to the vehicle  105  via the network  135 . Weights can be initialized by using a Gaussian distribution, for example, and a bias for each node  305  can be set to zero. Training the DNN  300  can include updating weights and biases via suitable techniques such as backpropagation with optimizations. Ground truth data can include, but is not limited to, data specifying objects within an image or data specifying a physical parameter, e.g., angle, speed, distance, or angle of object relative to another object. For example, the ground truth data may be data representing objects and object labels. In another example, the ground truth data may be data representing an object and a relative angle of the object with respect to another object. 
     After a training phase, the DNN  300  may be pruned to further compress the DNN  300  used during inference.  FIG.  3 A  illustrates an example DNN  300  after training and prior to pruning.  FIG.  3 B  illustrates an example DNN  300  in which various weighted inputs are pruned prior to operation of the DNN  300 .  FIG.  3 C  illustrates an example DNN  300  in which various nodes  305  are pruned. Pruning a weighted input and/or a node  305  may comprise deactivating the selected weighted input and/or the selected node  305 . 
     In some implementations, the DNN  300  may be pruned according to a target pruning ratio. The target pruning ratio may be fixed or dynamic. For example, the computer  110  and/or the server  145  may receive an input representing the target pruning ratio. The computer  110  and/or the server  145  may iteratively prune the DNN  300  according to the target pruning ratio. For instance, the computer  110  and/or the server  145  can prune one or more weighted inputs and/or nodes  305  during a first iteration, compare a current pruning ratio of the DNN  300  to the target pruning ratio, and prune one or more weighted inputs and/or nodes  305  during a second iteration when the current pruning ratio of the DNN  300  is less than the target pruning ratio. Once pruned, the computer  110  may implement that pruned DNN  300  for one or more tasks, such as object detection and/or object classification. 
     The computer  110  and/or the computer  245  can select weighted inputs and/or nodes  305  to prune by comparing a value of the weight of the weighted input or the value of the node  305  to a pruning threshold value or based on the gradient of the loss function with respect to the weight. The pruning threshold value can be selected based on empirical analysis. For example, once deployed to the vehicle  105 , the computer  110  can monitor one or more neurons of the DNN  300  during inference. 
       FIGS.  4 A and  4 B  illustrate an example process for training one or more DNNs  300  in accordance with one or more implementations of the present disclosure.  FIG.  4 A  illustrates an initial training phase in which the DNN  300  receives a set of labeled training data, e.g., in the form of training data  405  and training labels  410 . The training data  405  may include images that depict various objects of interest within a vehicle environment. The training labels  410  may comprise labels identifying the objects. After the initial training phase, at a supervised training phase, a set of N training data  415  are input to the DNN  300 . The DNN  300  generates outputs indicative of an object classification for each of the N training data  415 . The object classification is a probability indicative of what objects are present within the received training data. In an example implementation, the DNN  300  can generate a probability indicative of whether an object depicted within an image is a person, a vehicle, a sign, or the like. 
       FIG.  4 B  illustrates an example of generating output for one training data  415 , such as a non-labeled training image, of the N training data  415 . Based on the initial training, the DNN  300  outputs a vector representation  420  of the object classification. The vector representation  420  can be defined as a fixed length representation of the probabilities for each of the N training data  415 . The vector representation  420  is compared to the ground-truth data  425 . The DNN  300  updates network parameters based on the comparison to the ground-truth boxes  425 . For example, the network parameters, e.g., weights associated with the neurons, may be updated via backpropagation. The DNN  300  may be trained at the server  145  and provided to the vehicle  105  via the communication network  135 . Backpropagation is a technique for propagating a derivative backwards through successive operations in a computation graph. The loss function determines how accurately the DNN  300  has processed the input data  415 . The DNN  300  can be executed a plurality of times on a single input  415  while varying parameters that control the processing of the DNN  300 , i.e., until the DNN  300  converges. Parameters that correspond to correct answers as confirmed by a loss function that compares the outputs to the ground truth are saved as candidate parameters. Following the training runs, the candidate parameters that produce the most correct results are saved as the parameters that can be used to program the DNN  300  during operation. 
     After training, the DNN  300  may be used by the vehicle computer  110  to detect and/or classify sensor data depicted within received images  430  as shown in  FIG.  4 C . For instance, the DNN  300  can receive sensor data  430  and generate output  435  indicative of an object classification, in one example. The output  435  can be used by the computer  110  to operate the vehicle  105  in some instances. For example, the computer  110  may send control data to one or more actuators  120  to control operation of the vehicle  105  based on the output  435 . 
       FIG.  5    illustrates an example image  500  captured by the sensors  115 . The output generated by the DNN  300  may be object classifications. As shown in  FIG.  5   , the DNN  300  can classify object  505  as a person and classify object  510  as a sign. 
       FIG.  6    is a flowchart of an example process  600  for pruning a DNN  300  during inference. Blocks of the process  600  can be executed by the computer  110  of the vehicle  105  and/or the computer  245  of the server  145 . The process  600  begins at block  605  in which a trained DNN  300  is received. For example, the DNN  300  can be trained as described above in reference to  FIGS.  4 A and  4 B . As discussed herein, various nodes  305  and/or weighted inputs of the DNN  300  are pruned during inference. 
     At block  610 , weighted inputs and/or nodes  305  are selected for pruning. For example, the computer  110  and/or the computer  245  can determine which nodes  305  comprise the largest activations relative to the pruning threshold value. In this example, the computer  110  and/or the computer  245  may select the nodes  305  having an activation value that is less than the pruning threshold value for pruning. In another example, the computer  110  and/or the computer  245  may compare the values of the weight of the weighted inputs to the pruning threshold value. In this example, if the values of the weight of the weighted inputs is less than the pruning threshold value, the corresponding weighted inputs and/or nodes are selected for pruning. In an example implementation, the selected weighted inputs and/or nodes  305  are deactivated. In some instances, the computer  110  and/or the computer  245  may select one layer of the DNN  300  at a time for pruning purposes. It is understood that the computer  110  and/or the computer  245  may prune the DNN  300  based on sensor data received at the DNN  300  during inference. In an example implementation, the computer  110  and/or the computer  245  may select the nodes  305  and/or weighted inputs after at least one batch of sensor data has been provided to the DNN  300  during inference. 
     At block  615 , a determination is made whether the current pruning ratio of the DNN  300  is less than the target pruning ratio. If the current pruning ratio is less than the target pruning ration, the process  600  returns to block  610 . If the current pruning ratio is greater than or equal to the target pruning ratio, the process  600  ends. 
       FIG.  7    is a flowchart of an example process  700  for controlling the vehicle  105  based on the determined output of a pruned DNN  300 . Blocks of the process  700  can be executed by the computer  110 . The process  700  begins at block  705 , in which the computer  110  determines whether to actuate the vehicle  105  based on the determined output. For example, the computer  110  can receive sensor data from one or more sensors  115 . The sensor data is provided to the pruned DNN  300 , and the pruned DNN  300  generates outputs based on the sensor data. For example, the DNN  300  may comprise a neural network that is configured to detect and/or identify objects based on the received sensor data. The computer  110  can include a lookup table that establishes a correspondence between a determined output and a vehicle actuation action. For example, based on data received at the pruned DNN  300 , the computer  110  may cause the vehicle  105  to perform a specified action, e.g., initiate a vehicle  105  turn, adjust vehicle  105  direction, adjust vehicle  105  speed, etc. In another example, based on the determined distance between the vehicle  105  and an object, the computer  110  may cause the vehicle  105  to perform a specified action, e.g., initiate a vehicle  105  turn, initiate an external alert, adjust vehicle  105  speed, etc. 
     If the computer determines that no actuation is to occur, the process  700  returns to block  705 . Otherwise, at block  710 , the computer  110  causes the vehicle  105  to actuate according to the specified action. For example, the computer  110  transmits the appropriate control signals to the corresponding vehicle  105  actuators  120 . The process  700  then ends. 
     In general, the computing systems and/or devices described may employ any of a number of computer operating systems, including, but by no means limited to, versions and/or varieties of the Microsoft Automotive® operating system, the Microsoft Windows® operating system, the Unix operating system (e.g., the Solaris® operating system distributed by Oracle Corporation of Redwood Shores, Calif.), the AIX UNIX operating system distributed by International Business Machines of Armonk, N.Y., the Linux operating system, the Mac OSX and iOS operating systems distributed by Apple Inc. of Cupertino, Calif., the BlackBerry OS distributed by Blackberry, Ltd. of Waterloo, Canada, and the Android operating system developed by Google, Inc. and the Open Handset Alliance, or the QNX® CAR Platform for Infotainment offered by QNX Software Systems. Examples of computing devices include, without limitation, an on-board vehicle computer, a computer workstation, a server, a desktop, notebook, laptop, or handheld computer, or some other computing system and/or device. 
     Computers and computing devices generally include computer-executable instructions, where the instructions may be executable by one or more computing devices such as those listed above. Computer executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, Java™, C, C++, Matlab, Simulink, Stateflow, Visual Basic, Java Script, Perl, HTML, etc. Some of these applications may be compiled and executed on a virtual machine, such as the Java Virtual Machine, the Dalvik virtual machine, or the like. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer readable media. A file in a computing device is generally a collection of data stored on a computer readable medium, such as a storage medium, a random-access memory, etc. 
     Memory may include a computer-readable medium (also referred to as a processor-readable medium) that includes any non-transitory (e.g., tangible) medium that participates in providing data (e.g., instructions) that may be read by a computer (e.g., by a processor of a computer). Such a medium may take many forms, including, but not limited to, non-volatile media and volatile media. Non-volatile media may include, for example, optical or magnetic disks and other persistent memory. Volatile media may include, for example, dynamic random-access memory (DRAM), which typically constitutes a main memory. Such instructions may be transmitted by one or more transmission media, including coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to a processor of an ECU. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read. 
     Databases, data repositories or other data stores described herein may include various kinds of mechanisms for storing, accessing, and retrieving various kinds of data, including a hierarchical database, a set of files in a file system, an application database in a proprietary format, a relational database management system (RDBMS), etc. Each such data store is generally included within a computing device employing a computer operating system such as one of those mentioned above, and are accessed via a network in any one or more of a variety of manners. A file system may be accessible from a computer operating system, and may include files stored in various formats. An RDBMS generally employs the Structured Query Language (SQL) in addition to a language for creating, storing, editing, and executing stored procedures, such as the PL/SQL language mentioned above. 
     In some examples, system elements may be implemented as computer-readable instructions (e.g., software) on one or more computing devices (e.g., servers, personal computers, etc.), stored on computer readable media associated therewith (e.g., disks, memories, etc.). A computer program product may comprise such instructions stored on computer readable media for carrying out the functions described herein. 
     With regard to the media, processes, systems, methods, heuristics, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes may be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps may be performed simultaneously, that other steps may be added, or that certain steps described herein may be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating certain embodiments, and should in no way be construed so as to limit the claims. 
     Accordingly, it is to be understood that the above description is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the arts discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the invention is capable of modification and variation and is limited only by the following claims. 
     All terms used in the claims are intended to be given their plain and ordinary meanings as understood by those skilled in the art unless an explicit indication to the contrary in made herein. In particular, use of the singular articles such as “a,” “the,” “said,” etc. should be read to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary.