Abstract: A computer-implemented method for performing machine condition monitoring for fault diagnosis includes collecting multivariate time series data from a plurality of sensors in a machine and partitioning the multivariate time series data into a plurality of segment clusters. Each segment cluster corresponds to one of a plurality of class labels related to machine condition monitoring. Next, the segment clusters are clustered into segment cluster prototypes. The segment clusters and the segment cluster prototypes are used to learn a discriminative model that predicts a class label. Then, as new multivariate time series data is collected from the sensors in the machine, the discriminative model may be used to predict a new class label corresponding to segments included in the new multivariate time series data. If the new class label indicates a potential fault in operation of the machine, a notification may be provided to one or more users.

Abstract: In one embodiment, a device makes a first determination as to whether a time series of a performance metric for a first network path exhibits seasonality. The device makes a second determination as to whether the time series exhibits a trend. The device predicts, based on the first determination and on the second determination, the performance metric for the first network path during a future time period. The device assesses, based on the performance metric predicted for the first network path, whether a measure of application experience will degrade for application traffic sent via the first network path. The device causes the application traffic to be proactively switched to a second network path, when the measure of application experience is expected to degrade.

Abstract: A method for determining a position of a geological feature in a formation includes acquiring a seismic dataset, wherein the seismic dataset is based on signals of one or more seismic sensors and determining a set of indicators of candidate discontinuities in the formation based on the seismic dataset. The method also includes labeling a subset of the set of indicators of candidate discontinuities using a neural network with a label based on the set of indicators of candidate discontinuities, wherein the label distinguishes an indicator of a candidate discontinuity between being an indicator of a target discontinuity or being an indicator of a non-target discontinuity and determining the position of the geological feature in the formation, wherein the geological feature in the formation is associated with at least one target discontinuity based on the subset of the set of indicators of candidate discontinuities.

Abstract: A method performed by a device may include identifying a plurality of samples of textual content; performing tokenization of the plurality of samples to generate a respective plurality of tokenized samples; performing embedding of the plurality of tokenized samples to generate a sample matrix; determining groupings of attributes of the sample matrix using a convolutional neural network; determining context relationships between the groupings of attributes using a bidirectional long short term memory (LSTM) technique; selecting predicted labels for the plurality of samples using a model, wherein the model selects, for a particular sample of the plurality of samples, a predicted label of the predicted labels from a plurality of labels based on respective scores of the particular sample with regard to the plurality of labels and based on a nonparametric paired comparison of the respective scores; and providing information identifying the predicted labels.

Abstract: Assistance is provided to policy exception approvers that make decisions on whether to grant or deny policy exception requests. A determination can be made regarding an amount of impact associated with granting a policy exception request based on a comparison of conditions before and after policy enactment. The probability of an impact occurring can be determined as a result of granting the policy exception based on past impact occurrences. A risk rating can be computed as a function of the amount of impact and the probability of the impact, and a visual representation of the risk rating can be generated and displayed to the policy exception approvers.

Abstract: Tags may be used in decisions by an access management service regarding access of computing resources (“resources”) by principals (e.g., users, roles, etc.). The tags may also be used to determine cost information, for grouping resources and/or principals, and for other reasons. The tags may be assigned to principals, to resources, or both. The resource may be a virtual or physical type of computing resource. Tags may be metadata, which may include a key-value pair. Tags may include email addresses, cost centers, project identifiers, location, team name, etc. The value may be a number, letters, or a combination of both. In some embodiments, the values may be limited to certain numbers or bytes, and some numbers and/or letter combinations may be excluded for special use.

Abstract: A storm damage response system includes a storm ensemble database that stores ensemble forecast models associated with respective potential storm paths of a given storm across a geographic area and an equipment database that stores equipment data associated with location and characteristics of power-providing equipment in the geographic area. A storm damage model algorithm generates a storm response plan comprising an operational procedure for repairing or maintaining power transmission and distribution electric systems to mitigate storm damage impact based on generating a probabilistic model for each of the ensemble forecast models based on the equipment data and calculating a statistical impact value associated with the probabilistic model based on an aggregate of iterative probabilistic simulations for the respective ensemble forecast model. The storm response plan can be generated based on the relative statistical impact value of the probabilistic model of each of the ensemble forecast models.

Abstract: Systems and methods described herein are directed to minimizing the resource requirements for edge and network while keeping the accuracy of machine learning classifier by utilizing simulated test data. Once sufficient measured test data is collected by the server, the server instructs the edge computer to reduce the transmission of data received from the corresponding sensors.

Abstract: In acoustic well logging, for each inversion depths of a well at which logging of data occurs, acoustic log signals representative of waveforms received at acoustic receivers are processed in a frequency domain to derive field dispersion curve(s). A neural net is operated to generate formation shear slowness value(s) from the curve(s), and resulting signal(s) indicative of shear slowness values are saved, transmitted, plotted, printed or processed. An apparatus for carrying out the method includes a logging tool having at least one activatable acoustic wave source; spaced and acoustically isolated therefrom in the logging tool an array of acoustic detectors that on the detection of acoustic wave energy generate electrical or electronic log signal(s) characteristic of acoustic energy waves detected by the acoustic detector(s); and at least one processing device associated with or forming part of the logging tool for processing the log signal(s).

Abstract: The present disclosure relates to a method and system for registering multi-modality images. The method may include: acquiring a first image relating to one or more reference objects; acquiring a second image relating to the one or more reference objects; determining a set of reference points based on the one or more reference objects; determining a set of mapping data corresponding to the set of reference points in the first image and the second image; and determining one or more registration parameters by comparing a plurality of back-projection errors generated in a plurality of iterations that are performed based on the set of mapping data.

Abstract: Systems and methods for adaptive switching between multiple content delivery networks during adaptive bitrate streaming. In one embodiment, an adaptive content delivery network switching device includes a processor, a network interface, and a memory connected to the processor, where the memory contains a content delivery network switching application and, the content delivery network switching application directs the processor to receive content from a first content delivery network, determine a minimum performance threshold based on playback variables from the received content, receive new content from a second content delivery network if the minimum performance threshold is not satisfied.

Abstract: Certain aspects of the present disclosure provide techniques for detecting and preventing failures in a cluster of systems. Embodiments include receiving a set of performance metrics from each respective system of the cluster of systems. Embodiments include determining a score for each respective system of the cluster of systems based on the set of performance metrics received from the respective system. Embodiments include comparing the score for each respective system of the cluster of systems to a threshold. Embodiments include identifying, based on the comparing, an aberrant system of the cluster of systems. Embodiments include performing a remedial action on the aberrant system.

Abstract: In general, embodiments are disclosed herein for tracking and allocating graphics hardware resources. In one embodiment, a software and/or firmware process constructs a cross-application command queue utilization table based on one or more specified command queue quality of service (QoS) settings, in order to track the target and current utilization rates of each command queue on the graphics hardware over a given frame and to load work onto the graphics hardware in accordance with the utilization table. Based on the constructed utilization table for a given frame, any command queues that have exceed their respective target utilization value may be moved to an “inactive” status for the duration of the current frame. For any command queues that remain in an “active” status for the current frame, work from those command queues may be loaded on to slots of the appropriate data masters of the graphics hardware in any desired order.

Abstract: Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to select an optimum recovery model by matching the feature of the learning signal set X with the recovery models generated through the recovery in formation P to estimate and recover normal signals for the some tags.

Abstract: The system receives a set of present time-series signals gathered from sensors in the asset. Next, the system uses an inferential model to generate estimated values for the set of present time-series signals, and performs a pairwise differencing operation between actual values and the estimated values for the set of present time-series signals to produce residuals. The system then performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms with associated tripping frequency (TF). While the TF exceeds a TF threshold, the system iteratively adjusts sensitivity parameters for the SPRT to reduce the TF, and performs the SPRT again on the residuals. The system then uses a logistic regression model to compute a risk index for the asset based on the TF. If the risk index exceeds a threshold, the system generates a notification indicating that the asset needs to be replaced.

Abstract: Embodiments include method, systems and computer program products for compressing instrumentation data. Aspects include defining an intermediate region of memory. Instrumentation data associated with a processing device is received and stored in the intermediate region of the memory. The instrumentation data is compressed in the intermediate region of memory and stored in a sample region of memory.

Abstract: A computer platform for hosting applications utilizes a computing device to manage seasonal performance metric alerts. The computer device may include a memory and at least one processor coupled to the memory. The computer device may collect a time series of an application performance metric for a period of less than two weeks. The computer device may determine a daily distributions each day within the period. The computer device may apply a radial basis function (RBF) kernel-based change point detection to the time series to determine that the daily distributions include a weekend time period that has a different daily distribution than a time period before or after the weekend time period. The computer device may adjust a baseline prediction of the metric for the weekend time period. The computer device may send an alert based on a deviation of a value of the metric from the adjusted baseline prediction.

Abstract: A method for detecting outliers is provided, the method comprising: receiving a digitized text corpus comprising a plurality of data points; identifying k clusters of the plurality of data points; sampling a data point among the plurality of data points as a first cluster center of the k clusters; determining sampling probability of each of remaining data points of the plurality of data points; sampling the next cluster center based on the sampling probability and iterate the process of determining sampling probability and the process of sampling the next cluster center until k cluster centers are sampled; generating weightage for each of the k cluster centers; determining sensitivity scores of the data points belonging to each of the k cluster centers; and labeling a data point having a sensitivity score greater than a threshold value as an outlier and removing the outlier from the digitized text corpus.

Abstract: Techniques are provided for assigning a bias rating to a service based on bias and/or anti-bias testing. For example, unbiased data can be input to the service to, e.g., determine whether the service introduces elements of bias. As another example, biased data can be input to the service to determine whether the service provides elements of anti-bias. In one example, a computer-implemented method comprises selecting, by a device operatively coupled to a processor, source data configured according to a bias specification indicating a criterion that defines bias. The computer-implemented method can comprise determining whether bias exists in output data of the service and determining a bias rating based on the determining whether bias exists in the output data. The computer-implemented method can further comprise assigning the bias rating to the service.

Abstract: A test suite is executed to test a computer program for a plurality of variables. A variable value is generated for each variable of the plurality of variables at a predetermined location of the computer program. Whether a test included in the test suite is successful is determined. If the test is successful, the variable value is recorded as part of a success value set. If the test is not successful, the variable value is recorded as part of a failure value set. A confidence metric is calculated for the variable value. A relevance metric for each variable of the plurality of variables is measured, based on a Jaccard distance between the success value set and the failure value set. The failure value set, the success value set, the confidence metric for the variable value, and the relevance metric for each variable of the plurality of variables are presented.

Abstract: A test suite is executed to test a computer program for a plurality of variables. A variable value is generated for each variable of the plurality of variables at a predetermined location of the computer program. Whether a test included in the test suite is successful is determined. If the test is successful, the variable value is recorded as part of a success value set. If the test is not successful, the variable value is recorded as part of a failure value set. A confidence metric is calculated for the variable value. A relevance metric for each variable of the plurality of variables is measured, based on a Jaccard distance between the success value set and the failure value set. The failure value set, the success value set, the confidence metric for the variable value, and the relevance metric for each variable of the plurality of variables are presented.

Abstract: Techniques are disclosed for conducting poll-based reviews of various businesses and service providers and searching for the businesses and service providers having at least one review. A search module receives a search inquiry related to one or more reviewees having at least one review from one or more user devices. A filter module determines whether the one or more reviewees is associated with at least one search criteria in the search inquiry in order to identify reviewees that are associated with the received search criteria. Thereafter, the search module populates reviewees meeting the search criteria and displays the reviewees meeting the search criteria in a user interface. A trend analysis module performs a trend analysis for the reviewees meeting the search criteria.

Abstract: A method is disclosed. The method includes obtaining an access request associated with a user for a software application; obtaining a plurality of verification attributes associated with the user; generating a fraud score for the access request by feeding a supervised machine learning (ML) classifier with a feature vector for the user that is based on the plurality of verification attributes; selecting a first unsupervised ML anomaly detector of a plurality of unsupervised ML anomaly detectors based on the fraud score; generating an anomaly score for the access request by feeding the first unsupervised ML anomaly detector with an augmented feature vector for the user that is based on the plurality of verification attributes and the fraud score; and processing the access request based on the anomaly score.

Abstract: Managing performance metrics includes: obtaining a plurality of performance metrics associated with a plurality of sources on a network; aggregating, at a first rate, the plurality of performance metrics associated with the plurality of sources to generate a plurality of first aggregated results; maintaining at least some of the plurality of first aggregated results in one or more memories; aggregating, at a second rate, the plurality of first aggregated results to generate a plurality of second aggregated results, the second rate being a lower rate than the first rate; and maintaining at least some of the plurality of second aggregated results in the one or more memories.

Abstract: In one implementation, a method includes obtaining time series data. The time serious data includes a plurality of network utilization measurements. The plurality of network utilization measurements is indicative of a plurality of utilizations of one or more resources of a network resource at a plurality of times. The method also includes determining whether the time series data comprises a plurality of segments. Each segment of the plurality of segments is associated with a separate regression model and each segment includes a portion of the time series data. The method further includes identifying a current segment from the time series data when the time series data comprises the plurality of segments. The method further includes determining an estimated network utilization based on a current regression model associated with the current segment.

Abstract: A system for authenticating a user associated with a plurality of user devices using a plurality of types of authentication information. The system includes an electronic computing device including an electronic processor. The electronic processor is configured to receive, from a user device, a request to access sensitive information and send, to the user device, a request for a first accuracy measurement for a first type of authentication information. When first accuracy measurement is below a predetermined threshold, the electronic processor sends to the user device a request for a second accuracy measurement for a second type of authentication information. When the second accuracy measurement is above or equal to the predetermined threshold, the electronic processor authenticates the user and lowers the predetermined threshold for each user device associated with a user profile, records an anomaly associated with the first type of the authentication information, or both.

Abstract: A graphical event model method, system, and computer program product, include learning statistical and causal co-occurrence relationships among multiple event-types of data, requiring no complex input, and generating a representation that explains a mutual dynamic of the multiple event-types in a form of a graphical event model.

Abstract: An apparatus and method for monitoring a stability of a spectrum are provided. The apparatus for monitoring stability of a spectrum includes a spectroscope configured to measure a spectrum of a sample and a processor configured to calculate a similarity change index of the measured spectrum and to determine the stability of the measured spectrum by analyzing the calculated similarity change index.

Abstract: An intelligent cash drawer unit or cash register is operable to process signals from load cells beneath note/coin cups when the drawer is open. It then determines the weight/value of cash in the cups, in order to either (a) detect an accurate weight in a cup comprising a poorly placed note, or (b) allow a float to be added, or a lift to be made, stepwise, whilst the drawer remains open. An intelligent cash drawer unit or cash register is also described which can detect a poorly placed note, by either (a) comparing signals from the load cells when the drawer is open, and when the drawer is closed or (b) monitoring stable readings, over a predetermined period when the drawer is closed and comparing the monitored values and where the apparent weight determined by the transducer appears to reduce by more than a predetermined amount, determining that a note is poorly placed.

Abstract: A set of vectors may be obtained. The vectors may be multi-dimensional vectors that are associated with and describe tokens from a first set of tokens from a corpus of sources. The description may be based in part on the relationship of the token to at least a portion of the remainder of the corpus. A set of sentiment scores may be obtained. The sentiment scores in the set of sentiment scores may describe a sentiment associated with a corresponding token that is described by a vector from the set of vectors. The set of vectors and the set of sentiment scores may be input into a pattern-recognizer pathway in a first neural network. A probability value of a potential future event may then be generated by the first neural network. The probability value may be based on the set of vectors and the set of sentiment scores.

Abstract: A smart vehicle system is disclosed, which relates to technology for increasing driving stability of a vehicle by performing a test related to reliability of the vehicle. The smart vehicle system includes a test information storage circuit, a controller, and a test circuit. The test information storage circuit classifies a test control information, a test condition information, and a test result information according to a plurality of test levels, and to stores a classified test result information therein. The controller determines at least one parameter value for performing a reliability test in response to a sensing information, the test control information, and the test condition information, and to generates a test enable signal in response to the parameter value. The test circuit tests a characteristic of a storage circuit in response to the test enable signal.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate transformation of an enterprise application into a cloud native container are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analysis component that employs a model to discover attributes of an enterprise application. The computer executable components can further comprise a transformation component that generates artifacts based on the attributes to transform the enterprise application into a cloud native container.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: According to an embodiment, a training apparatus is a training apparatus training an inference model performing predetermined inference based on data, the training apparatus changes, according to a predefined distance for a selected piece of data selected based on predefined scores given to a plurality of pieces of data in a predefined classification space, the predefined score of a piece of data other than the selected piece of data, gives labels in the predefined classification space to pieces of data including the piece of data the predefined score of which is changed, builds training data from the pieces of data to which the labels are given, and updates the inference model using the training data.

Abstract: An information handling system determines zones within a datacenter that exhibit poor component life. Components of the information handling system provide error indications and have error thresholds and warranty durations. The components organized into zones.

Abstract: Incremental generation of models with dynamic clustering. A first set of data is received. A first set of clusters based on the first set of data is generated. A respective first set of models for the first set of clusters is created. A second set of data is received. A second set of clusters, based on the second set of data and based on a subset of the first set of data, is generated. A respective second set of models for the second set of clusters, based on a subset of the first set of models and based on the second set of data, is created.

Abstract: An abnormality detection system, support device, and model generation method are provided for generating a more highly accurate abnormality detection model before an actual operation. A model generation part of an abnormality detection system includes a section for generating plural feature values from state values provided from a state value storage part, a section for selecting a combination of one or plural feature values among the plural generated feature values, a section for generating an extra learning data set having at least part of a data series of the feature values of the selected combination and a data series of statistically generated virtual feature values, and a section for evaluating a detection accuracy of a model corresponding to the feature values of the selected combination using the extra learning data set.

Abstract: An analysis device for analyzing services on a network includes: a storage unit configured to store, maintenance information collected, from the network, at a predetermined time interval and for each of the services, application rules describing a technique for applying a plurality of types of analysis logics classified by use, and a catalog describing specifications of the analysis logics; a rule application unit configured to, upon receipt of a request related to an analysis target service, apply the maintenance information over a predetermined period of the analysis target service to the application rules; an analysis logic selection unit configured to select the analysis logics for each use based on a result of the application to the application rules; and a collaborative catalog generation unit configured to generate a collaborative catalog including a combination of the catalogs describing the specifications of the selected analysis logics.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallel time series analysis. One of the methods includes receiving a plurality of data elements. The plurality of data elements are partitioned into a plurality of chunks, wherein the plurality of chunks, including a first chunk and a plurality of additional chunks, have an ordering according to the data elements included in each chunk. Each chunk is assigned to a particular segment of a plurality of segments. A first iteration of an autoregressive integrated moving average is computed for each chunk assigned to each segment. A second iteration of the autoregressive integrated moving average is computed for each chunk assigned to each segment, wherein computing uses the result data for a corresponding preceding chunk in the first iteration. One or more additional iterations of the autoregressive integrated moving average are computed until stopping criteria has been satisfied.

Abstract: One or more non-transitory computer readable media contain program instructions that, when executed, cause one or more processors to: receive first raw data including one or more first data points generated by a first object of a plurality of objects associated with one or more buildings; generate first input timeseries according to the one or more data points; access a database of interconnected smart entities, the smart entities including object entities representing each of the plurality of objects and data entities representing stored data, the smart entities being interconnected by relational objects indicating relationships between the smart entities; identify a first object entity representing the first object from a first identifier in the first input timeseries; identify a first data entity from a first relational object indicating a relationship between the first object entity and the first data entity; and store the first input timeseries in the first data entity.

Abstract: A method, computer system, and computer program product to detect anomalies in a multivariate or multidimensional time series data set. The time series data set is retrieved from a monitored device. A pair of neural networks are trained simultaneously using the retrieved time series data set by implementing an adversarial training process, to generate a generative neural network and a discriminative neural network. The anomalies in the time series data set of the monitored device are detected by implementing one or both of the generative neural network and the discriminative neural network to monitor the time series data set.

Abstract: One or more non-transitory computer readable media contain program instructions that, when executed, cause one or more processors to: receive first raw data including one or more first data points generated by a first object of a plurality of objects associated with one or more buildings; generate first input timeseries according to the one or more data points; access a database of interconnected smart entities, the smart entities including object entities representing each of the plurality of objects and data entities representing stored data, the smart entities being interconnected by relational objects indicating relationships between the smart entities; identify a first object entity representing the first object from a first identifier in the first input timeseries; identify a first data entity from a first relational object indicating a relationship between the first object entity and the first data entity; and store the first input timeseries in the first data entity.

Abstract: The invention relates to a method for simulating an impact of particles/radiation on an object: (a) Providing a control memory including control memory locations and a mass memory including mass memory locations. (b) Building a simulated particle/radiation unit, wherein this particle or the radiation unit is designated for impact on the object. (c) Defining at least one property/energetic state of a particular, individual particle/radiation unit. (d) Carrying out a simulated impact of the particular individual particle/radiation unit on the object, wherein a distribution function of an impact site and/or of a velocity of the particular individual particle/radiation unit is used. (e) Determining or querying at least one local property and/or a local energetic state of at least one affected object element. (f) Determining or querying a collision event caused by the impact and acting on the individual particle/radiation unit and/or the affected object element or volume element.

Abstract: A computer system to detect data alerts includes a data preprocessing system and a data analysis system. The data preprocessing system obtains system data of the computer system, generates predicted data based on the system data, and generates differential data indicating a difference between the system data and the predicted data. The data analysis is in signal communication with the data preprocessing system and determines that the differential data is one of expected event or an anomaly event in response to adding at least one new value from the differential data to the predicted data.

Abstract: A pick and place nozzle performance analytics system streams production data from pick and place machines used in electronic assembly to a cloud platform as torrential data streams, and performs analytics on the production data to track, visualize, and predict performance of individual nozzles in terms of rejects or miss-picks. The analytics system generates a performance vector for each nozzle based on the collected production data, the performance vector tracking both the accumulated rejects and the percentage of rejects as respective dimensions of an x-y plane. The system monitors and analyzes the trajectory of this vector in the x-y plane to predict when performance degradation of the nozzle will reach a critical threshold. In response to predicting that nozzle performance degradation will exceed a threshold at a future time, the system can generate and deliver notifications to appropriate client devices.

Abstract: Representative apparatus, method, and system embodiments are disclosed for a self-scheduling processor which also provides additional functionality. Representative embodiments include a self-scheduling processor, comprising: a processor core adapted to execute a received instruction; and a core control circuit adapted to automatically schedule an instruction for execution by the processor core in response to a received work descriptor data packet. In another embodiment, the core control circuit is also adapted to schedule a fiber create instruction for execution by the processor core, to reserve a predetermined amount of memory space in a thread control memory to store return arguments, and to generate one or more work descriptor data packets to another processor or hybrid threading fabric circuit for execution of a corresponding plurality of execution threads. Event processing, data path management, system calls, memory requests, and other new instructions are also disclosed.

Abstract: A risk assessment manager for run-time patches receives a run-time patch to update one or more portions of a memory space, determines a first portion of memory of the one or more portions of the memory space to be updated by the run-time patch, and monitors the first portion of the memory space to detect accesses of the first portion of memory. The risk assessment manager determines a risk assessment value for the run-time patch in view of a number of accesses of the first portion of the memory space, and executes a test process for the run-time patch in view of the risk assessment value.

Abstract: Disclosed herein are systems, methods, and software for enhancing document productivity. In one implementation, various entries in a document are examined to identify at least an entry recognizable as an entity that is potentially related to at least one of various additional entities external to the document. At least a subset of the additional entities may be identified for surfacing in a user interface for potential inclusion in the document. In response to a selection of at least one of the subset of the additional entities, at least the one additional entity of the subset of the additional entities is included in the document in association with the entry.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to assess quality of produce at a facility. In some embodiments, there is provided a system for assessing quality of produce at a facility including a produce assessment station configured to provide a staging area to determine a quality classification of a target produce. By one approach, the produce assessment station includes a fixed surface; a rotatable base, a first arm comprising a microphone; a second arm comprising a tapping device; and a local control circuit. In one configuration, the local control circuit configured to rotate the rotatable base at a particular angle and at a particular time interval and receive audio data from the microphone. By one approach, the system includes a plurality of sensors and a portable device configured to provide a signal to the local control circuit to initiate the quality classification of the target produce.

Abstract: A method for correcting PET data includes acquiring first PET data at a time interval. The method also includes acquiring a first normalization coefficient corresponding to the first PET data. The method also includes determining a scale factor based at least partially on the first normalization coefficient. The method also includes determining second PET data based on the first PET data, the scale factor, and the second normalization coefficient. The method also includes determining a first dead time correction coefficient corresponding to the second PET data. The method also includes determining third PET data based on the second PET data and the first dead time correction coefficient. The method further includes reconstructing a first image based on the third PET data.