Abstract: Processing external code includes: parsing the external code to identify a first semantic entity, mapping the first semantic entity to a second semantic entity, the first semantic entity comprising a first set of one or more specified attributes and the second semantic entity comprising a second set of one or more attributes that are capable of being specified, determining that a first attribute of the second set of one or more attributes does not have a corresponding specified attribute within the first set of one or more specified attributes, determining available information for specifying the first attribute of the second set of one or more attributes, and storing the second semantic entity in association with the first attribute of the second set of one or more attributes specified based on user selection or specifying the first attribute in response to available information provided to a user interface system.

Abstract: Processing external code includes: parsing the external code to identify a first semantic entity, mapping the first semantic entity to a second semantic entity, the first semantic entity comprising a first set of one or more specified attributes and the second semantic entity comprising a second set of one or more attributes that are capable of being specified, determining that a first attribute of the second set of one or more attributes does not have a corresponding specified attribute within the first set of one or more specified attributes, determining available information for specifying the first attribute of the second set of one or more attributes, and storing the second semantic entity in association with the first attribute of the second set of one or more attributes specified based on user selection or specifying the first attribute in response to available information provided to a user interface system.

Abstract: Systems and methods validate the operation of a component of an executable model without inadvertently altering the behavior of the component. The model may be partitioned into a design space and a verification space. The component may be placed in the design space, while an observer for validating the component may be placed in the verification space, and linked to the component. During execution of the model, input or output values for the component may be computed and buffered. Execution of the observer may follow execution of the component. The input or output values may be read out of the buffer, and utilized during execution of validation functionality defined for the observer. Model compilation operations that may inadvertently alter the behavior of the component, such as back propagation of attributes, are blocked between the observer and the component.

Abstract: Systems and methods validate the operation of a component of an executable model without inadvertently altering the behavior of the component. The model may be partitioned into a design space and a verification space. The component may be placed in the design space, while an observer for validating the component may be placed in the verification space, and linked to the component. During execution of the model, input or output values for the component may be computed and buffered. Execution of the observer may follow execution of the component. The input or output values may be read out of the buffer, and utilized during execution of validation functionality defined for the observer. Model compilation operations that may inadvertently alter the behavior of the component, such as back propagation of attributes, are blocked between the observer and the component.

Abstract: Systems and methods validate the operation of a component of an executable model without inadvertently altering the behavior of the component. The model may be partitioned into a design space and a verification space. The component may be placed in the design space, while an observer for validating the component may be placed in the verification space, and linked to the component. During execution of the model, input or output values for the component may be computed and buffered. Execution of the observer may follow execution of the component. The input or output values may be read out of the buffer, and utilized during execution of validation functionality defined for the observer. Model compilation operations that may inadvertently alter the behavior of the component, such as back propagation of attributes, are blocked between the observer and the component.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a first transaction timestamp from a record that is replicated across a set of data sources. Next, the system includes at least a portion of the first transaction timestamp in a first key of a first mapping of the record to a first set of values for the record from the set of data sources. The system then audits the record by comparing the first set of values in the first mapping. Finally, the system outputs a result of the audited record based on the compared first set of values.

Abstract: A computing device may include a memory to store data that describes a state machine model that includes destination states and source states. The source states may be associated with conditions upon which the state machine model is to transition from a corresponding source state to one of the destination states. The device may also include a processor configured to generate data to describe a state diagram from the data that describes the state machine model. The state diagram may include the graphical symbols and lines. Each of the graphical symbols may represent one of the source states or one of the destination states. The lines may represent transitions and include one or more vertical lines to represent transitions to one of the destination states from more than one of the source states. The graphical symbol may represent the one of the destination states is not adjacent to the graphical symbols to represent the more than one of the source states.

Abstract: Systems and methods automatically construct a harness model having a selected component from source model. The systems and methods determine an execution context of the component in the source model, and construct and configure the harness model to provide the same or an equivalent execution context in the harness model. Model elements may be added to the harness model, and the model elements may be configured to replicate the execution context. The harness model may be executed, and the operation of the component evaluated. Changes to the component are synchronized between the source model and the harness model, as are changes to the component's execution context. Assessment operations may be included in the harness model, and these assessment operations may be evaluated when the harness model is executed. A verification report that includes the results of the assessments may be generated.

Abstract: Systems and methods may automatically generate code for deep learning networks. The systems methods may provide a code generation framework for generating target specific code. The code generation framework may include one or more predefined class hierarchies for constructing objects of the generated code. The objects of the class hierarchies may provide an interface to predefined libraries of deep learning functions optimized for use on a target platform. The systems and methods may perform one or more optimizations on the code being generated.

Abstract: A system includes a storage device configured to store a plurality of named entities collected from a plurality of sources, tokenized, and associated with a label. The system includes a tagging apparatus configured to receive textual communications, identify each of the textual communications, tokenize the textual communications, match the textual tokens with the named entities tokens stored in the storage device in order to assign the textual tokens to the labels associated with the named entity tokens, tag the textual communications based on the matching between the textual tokens and the named entity tokens in order to identify the intended meaning of each of the textual communications, and identify the intended meaning of the textual communications based on applying the tags to the textual communications. A method capable of disambiguating named entities using a common sense reasoning approach is also disclosed.

Abstract: Systems and methods generate code from a source program where the generated code may be compiled and executed on a Graphics Processing Unit (GPU). A parallel loop analysis check may be performed on regions of the source program identified for parallelization. One or more optimizations also may be applied to the source program that convert mathematical operations into a parallel form. The source program may be partitioned into segments for execution on a host and a device. Kernels may be created for the segments to be executed on the device. The size of the kernels may be determined, and memory transfers between the host and device may be optimized.

Abstract: Systems and methods may automatically generate code for deep learning networks. The systems methods may provide a code generation framework for generating target specific code. The code generation framework may include one or more predefined class hierarchies for constructing objects of the generated code. The objects of the class hierarchies may provide an interface to predefined libraries of deep learning functions optimized for use on a target platform. The systems and methods may perform one or more optimizations on the code being generated.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains input data containing a set of replicated records from a set of data sources. Next, the system generates, in a data store, a first mapping of a first key to a first set of values for a first replicated record in the set of replicated records. The system then audits the input data by comparing the first set of values in the first mapping. Finally, the system outputs a result of the audited input data based on the compared first set of values.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a first transaction timestamp from a record that is replicated across a set of data sources. Next, the system includes at least a portion of the first transaction timestamp in a first key of a first mapping of the record to a first set of values for the record from the set of data sources. The system then audits the record by comparing the first set of values in the first mapping. Finally, the system outputs a result of the audited record based on the compared first set of values.

Abstract: A computer-readable memory device may include instructions to store data describing a state machine model including source states and destination states. The device may also include instructions to store, for each of the source states, a condition field identifying a condition upon which, when satisfied, the state machine model transitions from the source state to one of the destination states. The device may also include instructions to store, for each of source states, a destination field identifying the one of the destination states. Each of at least two of the source states may identify an identical destination state in the corresponding destination field. Each of at least two of the source states may identify an identical condition in the corresponding condition field.

Abstract: A system and method may generate executable models having message sending objects and message receiving objects. A message may include a fixed data payload, and the message may persist for only a determined time interval of a total execution or simulation time of model. Message queues may be established for the messages, and the queues may have attributes. The model may include a state-based portion having states and transitions. States may be configured to generate and send messages, and to receive and process messages. In addition, transitions may be guarded by particular messages. The system and method also may generate standalone code, such as source code, for the model. The standalone code may include code that establishes a message passing service to support the sending and receiving of messages.

Abstract: A device receives information that identifies a model, of a finite state machine, that includes information that identifies a parent state and sub-states of the parent state. The device generates a data structure that designates an enumerated data type associated with the parent state, where the enumerated data type includes values corresponding to the sub-states. The device executes the model, based on the data structure, and monitors operation of the finite state machine and state transitions among some of the sub-states. The device detects, based on the state transitions, a change in an active sub-state that represents a particular sub-state, of the sub-states, that is active at a particular time during execution of the model. The device provides state transition information that identifies changes in the active sub-state over time.

Abstract: A method for processing non-lignocellulosic waste comprises pre-treating the non-lignocellulosic waste by enzymatic hydrolysis to yield a pre-treated non-lignocellulosic waste subjecting the pre-treated non-lignocellulosic waste to microwave hydrothermal carbonization to yield at least one of a hydrochar and a biocrude liquor. A system for processing non-lignocellulosic waste includes an enzymatic hydrolysis station for receiving non-lignocellulosic waste and generating a treated non-lignocellulosic waste, and a microwave hydrothermal carbonization station downstream of the enzymatic hydrolysis station for receiving the treated non-lignocellulosic waste from the enzymatic hydrolysis station and generating at least one of a hydrochar and a biocrude liquor.

Abstract: A system and method provides top-down programming in a graphical programming environment. A developer may utilize a component constructor to create a graphical skeleton component that represents a template for a procedure. The graphical skeleton component may include one or more hole elements that mark locations in the component at which functions may be specified. An instance of the component may be included in a graphical model, and one or more functions specified for the hole elements, thereby completing the component. The one or more functions may refer to model parameters, and bindings may be captured among the parameters. Upon execution or simulation of the model, the one or more functions specified for the component are executed, and the parameters evaluated. The functionality of the completed may depend on the one or more functions specified for the hole elements.

Abstract: A system and method may generate executable block diagrams in which at least some of the blocks run in accordance with message-based execution semantics. A message may include an input data payload that does not change over time, and the message may persist for only a determined time interval during execution of block diagram. A message-based execution engine may control execution of message-based blocks in which a source block may generate a message at a particular point in time, the message may be sent to one or more destination blocks triggering execution of those blocks, and the message may be destroyed on or after a determined time interval. Other execution domains, such as a time-based or state-based execution domain, may be provided, and the system may implement a hybrid execution model.