Abstract: This application relates to an image recognition model training method, an image recognition method, apparatus, and system. The method includes: obtaining a to-be-recognized image; extracting image feature information of the to-be-recognized image; and obtaining a lesion category recognition result of the to-be-recognized image by using the image feature information of the to-be-recognized image as an input parameter of a preset image recognition model, the image recognition model being trained by using a training image sample set comprising at least one strong-label training image sample, to determine the lesion category recognition result; and the strong-label training image sample representing an image sample having strong-label information, and the strong-label information comprising at least annotation information of a lesion category and a lesion position in the strong-label training image sample.

Abstract: Systems, devices, and methods of the present invention involve discourse trees. In some aspects, a system creates a discourse tree by identifying elementary discourse units in text. The discourse tree includes nodes, each nonterminal node representing a rhetorical relationship between two elementary discourse units and each terminal node associated with an elementary discourse unit. The system identifies, in a reference sentence of the discourse tree, a rhetorical relation of type elaboration or joint. The system selects a candidate sentence having a highest syntactic generalization score of a set of syntactic generalization scores. The system identifies a semantic relation corresponding to the candidate sentence. The semantic relation corresponds to a word in the candidate sentence and defines a role in the candidate sentence.

Abstract: The general problem of pattern change discovery between high-dimensional data sets is addressed by considering the notion of the principal angles between the subspaces is introduced to measure the subspace difference between two high-dimensional data sets. Current methods either mainly focus on magnitude change detection of low-dimensional data sets or are under supervised frameworks. Principal angles bear a property to isolate subspace change from the magnitude change. To address the challenge of directly computing the principal angles, matrix factorization is used to serve as a statistical framework and develop the principle of the dominant subspace mapping to transfer the principal angle based detection to a matrix factorization problem. Matrix factorization can be naturally embedded into the likelihood ratio test based on the linear models. The method may be unsupervised and addresses the statistical significance of the pattern changes between high-dimensional data sets.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by determining an anomalous event code subset using a pairwise interaction score determination machine learning model.

Abstract: The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.

Abstract: An image processing apparatus including a decoder configured to identify an area in an image frame at which object recognition is to be performed by using type information of a macroblock included in the image frame, and perform object recognition on the area of the image frame and skip the object recognition on areas of the image frame other than the area.

Abstract: Representations of sets of descriptors of reference objects are stored in a repository, with individual descriptors including information about entities identified in the reference objects. In response to a request to extract content from a particular data object, a reference object which satisfies a similarity criterion with respect to the particular data object is identified from the repository using the descriptors. A structural comparison of the particular data object and the reference object is performed to determine an entity related to another entity identified in the particular data object.

Abstract: Systems, devices, and methods of the present invention detect deceptive or fake content in text. In an example, a computer system generates, from text a discourse tree that represents rhetorical relationships between fragments of the text. The computer system generates a communicative discourse tree from the discourse tree. The computer system identifies a number of non-trivial rhetorical relations associated with the nonterminal nodes in the communicated discourse tree and, for each terminal edge having a communicative action, a level of nesting of the communicative action. The computer system derives, from the number of non-trivial rhetorical relations and the levels of nesting of the identified communicative actions, a complexity score that is indicative of a level of deception in the text.

Abstract: A system for patient positioning is provided. The system may acquire image data relating to a patient holding a posture and a plurality of patient models. Each patient model may represent a reference patient holding a reference posture, and include at least one reference interest point of the referent patient and a reference representation of the reference posture. The system may also identify at least one interest point of the patient from the image data using an interest point detection model. The system may further determine a representation of the posture of the patient based on a comparison between the at least one interest point of the patient and the at least one reference interest point in each of the plurality of patient models.

Abstract: This disclosure relates to a method and system for extracting information from images of one or more templatized documents. A knowledge graph with a fixed schema based on background knowledge is used to capture spatial and semantic relationships of entities present in scanned document and an adaptive lattice-based approach based on formal concepts analysis (FCA) is used to determine a similarity metric that utilizes both spatial and semantic information to determine if the structure of the scanned document image adheres to any of the known document templates. If a known document template whose structure is closely matching the structure of the scanned document is detected, then an inductive rule learning based approach is used to learn symbolic rules to extract information present in scanned document image and if a new document template is detected, then future scanned document images belonging to new document template are automatically processed using the learnt rules.

Abstract: The present invention discloses a system for linking first type of entities and second type of entities in a page. The system is configured to generate a feature vector for each of the first type of entities and each of the second type of entities. Further, the system is configured to receive into a neural network, a pair of the feature vectors, wherein one of the pair of the feature vector corresponds to a feature vector of a first type of entity and another feature vector corresponds to a feature vector of a second type of entity. The entities corresponding to the pair of feature vectors are neighboring each other. The neural network is configured to generate an output, indicating the likelihood of the pair of entities being linked.

Abstract: A learning assistance device acquires a plurality of learned discriminators obtained by causing learning discriminators provided in a plurality of respective terminal devices to perform learning using image correct answer data, acquires a plurality of discrimination results obtained by causing a plurality of learned discriminators to discriminate the same input image, determines the correct answer data of the input image on the basis of the plurality of discrimination results, causes the discriminator to perform learning the input image and the correct answer data, and outputs a result thereof as a new learning discriminator to each terminal device.

Abstract: A method for using an object key to deprioritize processing of relative regions of images of an object includes capturing an image of an object to be tracked and selecting an object key of the object. The object key includes a portion of the object, the object key is attached to the object and is recognizable by an image capture device. The method includes calculating a relative size of the object key with respect to a size of the object and a location of the object key with respect to the object, and tracking one or more locations of the object from the relative size and location of the object with respect to the object key and by capturing a plurality of images of the object key at a resolution sufficient for tracking movement of the object key.

Abstract: Systems, devices, and methods of the present invention detect affective argumentation in text. In an example, an application executing on a computing device accesses text comprising fragments. The application creates a discourse tree from the text. The discourse tree includes nodes, each nonterminal node representing a rhetorical relationship between two of the fragments and each terminal node of the nodes of the discourse tree is associated with one of the fragments. The application matches each fragment that has a verb to a verb signature, thereby creating a communicative discourse tree. The application determines whether the communicative discourse tree represents text that includes affective argumentation by applying a classification model trained to detect affective argumentation to the communicative discourse tree.

Abstract: A system includes an image capture device including a plurality of image sensors that are each configured to capture a mutually distinct visual of a scene. Further, a spatial data device is included that is configured to generate and transmit a signal, and to receive reflections of the signal from a plurality of objects within the scene, and to convey information corresponding to respective positions of the plurality of objects. At least one processor is configured to generate an image using at least one captured visual of the scene by at least one of the plurality of image sensors and using a measurement associated with the information conveyed by the spatial data device.

Abstract: The present application discloses a system, readable storage medium, and method for analyzing a cause of training-task queuing, wherein the method includes the steps of: obtaining a required resource that is required by a training task inputted by a user and a remaining resource of a cluster; in responding to that the remaining resource does not satisfy the required resource, obtaining a plurality of cluster center data that are pre-generated in a cluster model; regarding the required resource and the remaining resource as sample data, and calculating distances between the sample data and each of the cluster center data; and feeding back a cause corresponding to the cluster center datum that has a minimum distance with the sample data.

Abstract: Systems, devices, and methods of the present invention calculate a rhetorical relationship between one or more sentences. In an example, a computer-implemented method accesses a sentence comprising a plurality of fragments. At least one fragment includes a verb and a words. Each word includes a role of the words within the fragment. Each fragment is an elementary discourse unit. The method generates a discourse tree that represents rhetorical relationships between the sentence fragments. The discourse tree includes nodes including nonterminal and terminal nodes, each nonterminal node representing a rhetorical relationship between two of the sentence fragments, and each terminal node of the nodes of the discourse tree is associated with one of the sentence fragments. The method matches each fragment that has a verb to a verb signature, thereby creating communicative discourse tree.

Abstract: Systems, devices, and methods of the present invention validate argumentation in text. In an example, an application forms a communicative discourse tree from a subset of text by matching each fragment of the subset that has a verb to a verb signature. The application identifies that the subset includes an argument by applying a classification model trained to detect argumentation to the communicative discourse tree. The application further creates a logic system based on a claim of the argument, a domain definition clause associated with a domain of the text, a set of defeasible rules based on the communicative discourse tree, and a set of facts from one or more of the communicative actions of the communicative discourse tree. The application evaluates a consistency of the argument with respect to itself and with respect to the domain definition clauses by solving the logic system, thereby validating a claim associated with the argument.

Abstract: A system for generating a sequential visual narrative is provided. The system includes a media analyzer to obtain media and descriptive data pertaining to a subject and to categorize the media into narrative categories. The system includes a description generator to process the descriptive data and the narrative categories to generate text descriptions describing the subject in relation to the media. The system includes a visual display generator to sequence the media in a narrative sequence, map the narrative sequence to a display layout, and to generate and output a visual display including the text descriptions in visual association with the media.

Abstract: The disclosure relates to method and system for improving performance of an artificial neural network (ANN) model. The method includes receiving the ANN model and input dataset. The ANN model includes neurons arranged in multiple layers and employing corresponding activation functions. The method further includes assigning a random activation threshold value to each of the corresponding activation functions, determining activated neurons in each layer for a majority of input data in the input dataset based on the random activation threshold value for each of the corresponding activation functions, identifying removable layers based on a number of activated neurons and a pre-defined threshold value, evaluating a relative loss of the ANN model upon removing each removable layer from the ANN model and for a random input data in the input dataset, and deriving a modified ANN model by removing one or more of the removable layers based on the evaluation.

Abstract: A method includes obtaining an array of sorted identifiers to be stored in a designated portion of a memory of a given computing system, determining a segment size for splitting elements of the array into a plurality of segments, splitting the array into the plurality of segments based at least in part on the determined segment size, and compressing the plurality of segments to create a plurality of compressed segments. The method also includes generating a balanced binary search tree comprising a plurality of nodes each identifying a range of elements of the array corresponding to a given one of the segments and comprising a pointer to a given compressed segment corresponding to the given segment. The method further includes maintaining the balanced binary search tree and the compressed segments in the designated portion of the memory, and processing queries to the array utilizing the balanced binary search tree.

Abstract: The present invention provides a knowledge graph construction method and device. The method includes: obtaining structured data, where the structured data includes a first entity name of a first entity and attribute information corresponding to the first entity name, and the attribute information includes a first attribute and a first attribute value; performing, based on measurement of a similarity between the first entity and a second entity in a knowledge graph, entity alignment processing on the first entity, where the measurement of the similarity includes at least one of the following types: measurement of a character similarity, measurement of a structure similarity of a classification tree on which an entity is located, and measurement of an attribute similarity; and importing the structured data into the knowledge graph according to an entity alignment processing result. Embodiments may ensure correctness of data in the knowledge graph.

Abstract: A system and method include generation of modified imaging data and modified acquisition parameters of a training example based on the target imaging data and target acquisition parameters of each of a plurality of target examples, and generation, for each training example, of an initial reconstructed image based on the modified imaging data and modified acquisition parameters of the training example. A first network stage of a multi-stage network is trained based on the modified imaging data, modified acquisition parameters and initial reconstructed image of each training example, a first output image is generated for each training example by inputting the modified imaging data, modified acquisition parameters and initial reconstructed image of the training example to the trained first network stage, and a second network stage of the multi-stage network is trained based on the modified imaging data, modified acquisition parameters and first output image of each training example.

Abstract: Systems, devices, and methods of the present invention calculate a rhetorical relationship between one or more sentences. In an example, a computer-implemented method accesses a sentence comprising a plurality of fragments. At least one fragment includes a verb and a words. Each word includes a role of the words within the fragment. Each fragment is an elementary discourse unit. The method generates a discourse tree that represents rhetorical relationships between the sentence fragments. The discourse tree includes nodes including nonterminal and terminal nodes, each nonterminal node representing a rhetorical relationship between two of the sentence fragments, and each terminal node of the nodes of the discourse tree is associated with one of the sentence fragments. The method matches each fragment that has a verb to a verb signature, thereby creating communicative discourse tree.

Abstract: A query directed at a source table organized into a set of batch units is received. The query comprises a regular expression search pattern. The regular expression search pattern is converted to a pruning index predicate comprising a set of substring literals extracted from the regular expression search pattern. A set of N-grams is generated based on the set of substring literals extracted from the regular expression search pattern. A pruning index associated with the source table is accessed. The pruning index indexes distinct N-grams in each column of the source table. A subset of batch units to scan for data matching the query are identified based on the pruning index and the set of N-grams. The query is processed by scanning the subset of batch units.

Abstract: Systems, apparatuses, and methods for implementing a safety monitor framework for a safety-critical inference application are disclosed. A system includes a safety-critical inference application, a safety monitor, and an inference accelerator engine. The safety monitor receives an input image, test data, and a neural network specification from the safety-critical inference application. The safety monitor generates a modified image by adding additional objects outside of the input image. The safety monitor provides the modified image and neural network specification to the inference accelerator engine which processes the modified image and provides outputs to the safety monitor. The safety monitor determines the likelihood of erroneous processing of the original input image by comparing the outputs for the additional objects with a known good result. The safety monitor complements the overall fault coverage of the inference accelerator engine and covers faults only observable at the network level.

Abstract: The present disclosure relates to a class-agnostic object segmentation system that automatically detects, segments, and selects objects within digital images irrespective of object semantic classifications. For example, the object segmentation system utilizes a class-agnostic object segmentation neural network to segment each pixel in a digital image into an object mask. Further, in response to detecting a selection request of a target object, the object segmentation system utilizes a corresponding object mask to automatically select the target object within the digital image. In some implementations, the object segmentation system utilizes a class-agnostic object segmentation neural network to detect and automatically select a partial object in the digital image in response to a target object selection request.

Abstract: Methods, systems, and media for modifying user interface colors are provided. In some embodiments, the method comprises: receiving a video and color palette information, wherein each color of the color palette information indicates a color of an element of a user interface in which the video is to be presented; identifying a first color for the element, wherein the first color corresponds to a first portion of the video; causing the first portion of the video to be presented, wherein the element of the user interface having the first color is presented; identifying a second color for the element, wherein the second color corresponds to a second portion of the video; and modifying an appearance of the element by changing the color of the element from the first color to the second color while presenting the second portion of the video.

Abstract: According to an aspect of an embodiment, a method may include obtaining a data set that includes categories (or features), and a target criteria. The method may further include obtaining a first decision tree model using the data set. The method may further include ranking the categories based on the first decision tree model and removing low-ranking categories from the data set. The method may further include generating a second decision tree model using the data set. The second decision tree model may include branch nodes. Each of branch nodes may represent a branch criteria. The method may further include pruning a branch node. The method may further include designating a remaining branch nodes as a rule node. The method may further include generating a rule based on the branch criteria of the rule node and presenting the rule in a graphical user interface.

Abstract: The described technology is generally directed towards validating content selection graphs for use in an in-memory content selection graph data store. When a content selection graph data is generated, the graph nodes can correspond to prebuilt response data. The response data for any of the nodes can be verified against rules based on the type of a node, for example, as well as based on client-specific information for types of clients. The overall validation process can validate the content selection graph for subsequent use, can result in errors or warnings being logged (which may or may not render the graph unusable, e.g., depending on severity), or can fail the content selection graph. If validated, the content selection graph can be used to rapidly return response data when content from the graph is requested.

Abstract: An information processor can logically support prediction based on past statistical information even though the information contains qualitative or non-numerical data. The processor determines whether an input pattern corresponding to an input object (a determination target) belongs to a specific class among multiple classes, based on feature subsets of any combination of a plurality of features, each feature comprises multiple categories. The processor includes a storage storing the input pattern corresponding to the input object and samples corresponding to respective sample objects and a classification determiner determining whether the input pattern belongs to the specific class.

Abstract: A computing device of the present invention estimates an unknown parameter ? of a model formula when distributed sample data is acquired, wherein when an estimated quantity of ? is acquired, a function g is estimated using a random forest model, such that when an estimated quantity of g is acquired, the estimated quantity of g and the estimated quantity of ? are used so as estimate a function G as a prediction formula for new data corresponding to a specific item such that an estimated quantity of G is acquired, and thus new data xnew is received, thereby enabling the class of the specific item to be classified from the calculated value.

Abstract: An augmented reality/mixed reality system that provides a more immersive user experience. That experience is provided with increased speed of update for occlusion data by using depth sensor data augmented with lower-level reconstruction data. When operating in real-time dynamic environments, changes in the physical world can be reflected quickly in the occlusion data. Occlusion rendering using live depth data augmented with lower-level 3D reconstruction data, such as a raycast point cloud, can greatly reduce the latency for visual occlusion processing. Generating occlusion data in this way may provide faster operation of an XR system using less computing resources and enabling the system to be packaged in a battery operated wearable device.

Abstract: In accordance with an aspect of the disclosure, an electronic device comprises a communication circuit; a display; at least one processor operatively connected to the display and the communication circuit; and a memory operatively connected to the at least one processor, wherein the memory stores instructions that, when executed, cause the at least one processor to perform a plurality of operations comprising: storing first raw image data in the memory; generating first small raw image data smaller in size than the first raw image data, from the first raw image data; transmitting the first small raw image data to a first external electronic device via the communication circuit; receiving information about an object region for identifying at least one object of the first small raw image data from the first external electronic device via the communication circuit; transmitting a first region of the first raw image data to a second external electronic device via the communication circuit based on a user input an

Abstract: This disclosure is directed to an image sensor. The image sensor includes a two-dimensional pixel array divided into a plurality of blocks, each of the plurality of blocks comprising pixels arranged in at least two different rows and two different columns, and a shutter mechanism that exposes the plurality of blocks sequentially, with all pixels in each block being exposed synchronously.

Abstract: Systems and methods for improving the quality of ultrasound images made up of a combination of multiple sub-images include giving more weight to sub-image information that is more likely to improve a combined image quality. Weighting factor information may be determined from the geometry (e.g., angle or path length) of a location of one or more specific transducer elements relative to a specific point within a region of interest or a region of an image. In some embodiments, any given pixel (or other discrete region of an image) may be formed by combining received echo data in a manner that gives more weight to data that is likely to improve image quality, and/or discounting or ignoring data that is likely to detract from image quality (e.g., by introducing noise or by increasing point spread).

Abstract: This disclosure is directed to an image sensor. The image sensor includes a two-dimensional pixel array divided into a plurality of blocks, each of the plurality of blocks comprising pixels arranged in at least two different rows and two different columns, and a shutter mechanism that exposes the plurality of blocks sequentially, with all pixels in each block being exposed synchronously.

Abstract: A two-stage person searching method combining face and appearance features, comprises: detecting a face of a person utilizing a face detector, and outputting a face representation vector based on a face comparing model; ranking person sets to be matched according to an Euclidean distance to acquire a face ranking result; selecting a plurality of samples as multi-matching targets at the next stage according to the ranking result; using the selected multi-matching targets of different persons at the next stage in the same data set as mutual negative samples, so as to compress the sample space matched at the next stage; and finally re-recognizing multi-target persons, and ranking the image sets to be matched according to an average distance or similarity with multiple targets to output a final result.

Abstract: A testing environment in which offline simulations can be run to identify policies and/or prediction models that result in more valuable content being included in content pages is described herein. For example, the offline simulations can be run in an application executed by an experiment device using data gathered by a production content delivery system. The simulation application can test any number of different policies and/or prediction models without impacting users that use a production content delivery system to request content.

Abstract: A fusion-based classifier, classification method, and classification system, wherein the classification method includes: generating a plurality of probability vectors according to input data, wherein each of the plurality of probability vectors includes a plurality of elements corresponding to a plurality of class respectively; selecting, from the plurality of probability vectors, a first probability vector having an extremum value corresponding to a first class-of-interest according to the first class-of-interest; and determining a class of the input data according to the first probability vector.

Abstract: A data searching apparatus receives a searching character string with respect to an encoding document; referring to relevant information in which encoded data of a word according to an encoding rule of the encoding document is associated with a basic form of the word and a relevant word relevant to the basic form, with respect to a plurality of words, acquires a plurality of codes relevant to the basic form of the searching character string and a relevant word of the basic form and searches the encoding document by using an automaton in which the plurality of codes are set in a searching target.

Abstract: A learning assistance device acquires a plurality of learned discriminators obtained by causing learning discriminators provided in a plurality of respective terminal devices to perform learning using image correct answer data, acquires a plurality of discrimination results obtained by causing a plurality of learned discriminators to discriminate the same input image, determines the correct answer data of the input image on the basis of the plurality of discrimination results, causes the discriminator to perform learning the input image and the correct answer data, and outputs a result thereof as a new learning discriminator to each terminal device.

Abstract: A neural network-based classification method, including: obtaining a neural network and a first classifier; inputting input data to the neural network to generate a feature map; cropping the feature map to generate a first cropped part and a second cropped part of the feature map; inputting the first cropped part to the first classifier to generate a first probability vector; inputting the second cropped part to a second classifier to generate a second probability vector, wherein weights of the first classifier are shared with the second classifier; and performing a probability fusion on the first probability vector and the second probability vector to generate an estimated probability vector for determining a class of the input data.

Abstract: The learning assistance device acquires a plurality of learned discriminators obtained by causing learning discriminators provided in a plurality of respective terminal devices to perform learning using image correct answer data, sets the learned discriminator having the highest correct answer rate among the plurality of learned discriminators as a new learning discriminator, and outputs the learning discriminator and identification information capable of identifying the image correct answer data used for learning. The plurality of terminal devices repeatedly performs a process of outputting a plurality of learned discriminators obtained by causing the learning discriminators to perform learning using image correct answer data different from image correct answer data indicated by the identification information.

Abstract: A method, apparatus, system, computer program product, and computer readable storage medium provide the ability to parallel process hierarchical data. Hierarchical data is obtained. The hierarchical data is organized in a relationship graph having two or more nodes (consisting of parent nodes that are dependent on child nodes). The relationship graph requires bottom-up processing. Cycles are identified in the relationship graph. The relationship graph is leveled by traversing the graph and assigning all graph nodes into levels. The hierarchical data is processed by parallel processing the nodes in a first level of the one or more levels before parallel processing the nodes in a subsequent level.

Abstract: An image processing device is provided. An image processing device includes: a program executing part that applies parameter groups to an image processing program which searches a search object using an image group with different resolutions created from an input image, acquires a search result for each of the parameter groups, and acquires an image group which is acquired in the course of execution of the image processing program for each parameter group; a synthesis part that creates a synthetic image from each of a plurality of acquired image groups; a display part that displays the plurality of created synthetic images and displays the plurality of acquired search results to overlap the plurality of synthetic images; and a setting part that sets the parameter group corresponding to the synthetic image selected from the plurality of synthetic images displayed on the display part by an operation in the image processing program.

Abstract: A method and apparatus is disclosed for entering text on a computer screen with a virtual keyboard and limited space, such as would be found on a mobile device such as smartphone, tablet, handheld computer or an automobile dashboard, or an appliance with a small screen. The method requires a sequence of events in which text is selected on the virtual keyboard, and a last key selected generates a new limited set of responsive keys, potentially based on word completion word fragments, for display on the screen, allowing responsive keys to be dynamically larger, variable in shape, or positioned on the virtual keyboard based on priority and availability of adjacent space to maximize the ease of use of the highest priority responsive keys. The user selects one of the responsive keys to partially complete or complete text entry.

Abstract: An image processing system automatically segments and labels an image using a hierarchical classification model. A global classification model determines initial labels for an image based on features of the image. A label-based descriptor is generated based on the initial labels. A local classification model is then selected from a plurality of learned local classification model based on the label-based descriptor. The local classification model is applied to the features of the input image to determined refined labels. The refined labels are stored in association with the input image.

Abstract: The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed create and store a decision tree, the nodes of which include classifiers that each recognizes the symbol that corresponds to a symbol image. Input of a symbol image to the decision tree and processing of the symbol image through one or more nodes of the decision tree returns a symbol corresponding to the symbol image.

Abstract: Systems and methods are provided, such as those that enable identification of data flows and corresponding results in a pattern-recognition processor. In one embodiment, a system may include the pattern-recognition processor and a flow identification register, wherein a unique flow identifier for each data flow is stored in the register. The system may include a results buffer that stores the results data and the flow identifier for each data flow, so that the results data may be related to a specific data flow.