Abstract: Techniques for template generation from image content includes extracting information associated with an input image. The information comprises: 1) layout information indicating positions of content corresponding to a content type of a plurality of content types within the input image; and 2) text attributes indicating at least a font of text included in the input image.

Abstract: Techniques for template generation from image content includes extracting information associated with an input image. The information comprises: 1) layout information indicating positions of content corresponding to a content type of a plurality of content types within the input image; and 2) text attributes indicating at least a font of text included in the input image.

Abstract: Techniques for template generation from image content includes extracting information associated with an input image. The information comprises: 1) layout information indicating positions of content corresponding to a content type of a plurality of content types within the input image; and 2) text attributes indicating at least a font of text included in the input image. A user-editable template having the characteristics of the input image is generated based on the layout information and the text attributes.

Abstract: A CAM array of compare memory cell circuits includes a decode column corresponding to each set, and each set includes at least one row of the compare memory cell circuits. Each decode column receives a set clock signal addressing the corresponding set and generates a set match signal in each row of the corresponding set. A column compare circuit generates compare data indicating a bit of a compare tag. A row match circuit generates, for each row, in response to the set match signal, a row match signal indicating the compare tag matches the binary tag stored in the row. Circuits and loads in a decode column employed to generate the set clock signal correspond to circuits generating the row match signal in each column of the CAM array to reduce a timing margin of the match indication and decrease the access time for the CAM array.

Abstract: A method and apparatus for performing a convolution of a N×N matrix. A weights matrix for a N×N Convolutional Neural Network (CNN) is received and is divided into 3×3 weights matrixes. Lines of image values are read and are stored in a buffer as sets of image values. A 3×3 convolution is performed to generate a 3×3 convolution value. All 3×3 convolution values associated with a particular N×N convolution and a particular set of image values are summed. The 3×3 convolutions and the summing are repeated until all columns in the set of image values have been processed; and the reading, the storing, the performing 3×3 convolutions, the summing and the repeating performing 3×3 convolutions are repeated until all lines of image values have been processed. The sums associated with a particular N×N convolution are added together to generate an N×N convolution value for each of the N×N convolutions.

Abstract: A CAM array of compare memory cell circuits includes a decode column corresponding to each set, and each set includes at least one row of the compare memory cell circuits. Each decode column receives a set clock signal addressing the corresponding set and generates a set match signal in each row of the corresponding set. A column compare circuit generates compare data indicating a bit of a compare tag. A row match circuit generates, for each row, in response to the set match signal, a row match signal indicating the compare tag matches the binary tag stored in the row. Circuits and loads in a decode column employed to generate the set clock signal correspond to circuits generating the row match signal in each column of the CAM array to reduce a timing margin of the match indication and decrease the access time for the CAM array.

Abstract: Systems and methods for training an object detection network are described. Embodiments train an object detection network using a labeled training set, wherein each element of the labeled training set includes an image and ground truth labels for object instances in the image, predict annotation data for a candidate set of unlabeled data using the object detection network, select a sample image from the candidate set using a policy network, generate a labeled sample based on the selected sample image and the annotation data, wherein the labeled sample includes labels for a plurality of object instances in the sample image, and perform additional training on the object detection network based at least in part on the labeled sample.

Abstract: A method, apparatus, and computer program product for executing a relational database management system (RDBMS) in a computer system, wherein the RDBMS manages a relational database comprised of at least one column-partitioned base table storing data. Column values from at least one column of the column-partitioned base table are stored in one or more containers spread across one or more data blocks. Metadata comprising summarized information about the column values in the containers is stored in a metadata index subtable. A query with a filtering condition on the column is applied to the metadata index subtable before the column-partitioned base table is accessed, so that only qualified containers and data blocks are accessed, and unqualified containers and data blocks are eliminated, when responding to the query.

Abstract: A magnetic resonance pulse sequence technique may acquire a water reference spectrum and two water suppressed metabolite spectra and with frequency selective inversion pulse centered at either single frequency, at multiple frequencies, or in a single acquisition. Subtraction of the inverted from non-inverted water suppressed metabolite spectrum results in single or a combination of specific metabolite peak/peaks alone with a flat baseline for easier quantification.

Abstract: Certain embodiments involve using machine-learning tools that include Bidirectional Encoder Representations from Transformers (“BERT”) language models for predicting emotional responses to text by, for example, target readers having certain demographics. For instance, a machine-learning model includes, at least, a BERT encoder and a classification module that is trained to predict demographically specific emotional responses. The BERT encoder encodes the input text into an input text vector. The classification module generates, from the input text vector and an input demographics vector representing a demographic profile of the reader, an emotional response score.

Abstract: In example implementations, an apparatus is provided. The apparatus includes a validator, a decimator, and a processor. The processor is communicatively coupled to the validator and the decimator to control operation of the validator and the decimator. The validator is to detect errors in a computer generated three-dimensional (3D) object model and mark locations that have errors. The decimator is to reduce a resolution of the computer generated 3D object model excluding the locations that are marked with the errors to generate an optimized mesh representation of the computer generated 3D object model.

Abstract: In one example in accordance with the present disclosure, a system is described. The system includes a property determiner to determine visual properties of a part to be formed. A formation determiner of the system determines characteristics of a formation of the part and a label to be disposed thereon. The system also includes a label generator to select label properties based on the visual properties of the part and the characteristics of the formation of the part and label. The system also includes a controller that controls formation of the label on the part.

Abstract: Methods, systems, and computer storage media for providing command recommendations for an analysis-goal, using analytics system operations in an analytics systems. In operation, an analytics client is configured to provide an analytics interface for receiving a selection of analysis-goal information that corresponds to an analysis-goal model. A goal engine selects an analysis-goal based on the analysis-goal information. A command engine is configured to use the analysis-goal and goal-driven models to predict probable commands for the analysis goal. The command engine also selects a next command recommendation from the probable commands. The command engine generates additional command recommendation data based on a loss function fine tuner. The additional command recommendation data can include a goal orientation score that quantifies a degree to which a command aligns with the analysis-goal.

Abstract: CEST imaging technique and MR scanning are used as an MRI method for detecting levels of lactate in vivo by exploiting the exchange of —OH protons on lactate with bulk water. In accordance with this method, one first obtains a lactate CEST MRI map of a slice of the body of a patient. A contrast agent such as pyruvate, glucose or glutamine is administered and a post-administration CEST MRI map is obtained. The difference in the spatial maps indicates the level of expression of lactate in the tissue of interest.

Abstract: A task-aware command recommendation system and related techniques are described herein. The task-aware command recommendation system can provide a user of a software application (e.g., an analytics application or other software application) with guidance by predicting commands that can be executed to accomplish a given task. For example, an ongoing task being performed by a user can be determined based on commands that have been performed by the user up to a current point in time. Information about the task can be incorporated into one or more command recommendation models, which can determine one or more commands to recommend to the user for performing the task. In some cases, the task-aware command recommendation system can include a help prediction model that can anticipate when the user is having difficulties completing a task, and can provide help for the user to continue performing the task.

Abstract: In embodiments, there is disclosed, systems, methods, and computer program products for discovering a physical host or a virtual host in a storage network comprising: querying a name server database; obtaining from the name server database a port name and a port worldwide number for a port connected to a switch, wherein the switch is part of the storage network; determining using the name server database if the port is, an initiator port or a target port; and for an initiator port, determining using the name server database a host name corresponding to the physical host or virtual host, wherein the physical host or the virtual host is connected to the initiator port.

Abstract: A non-invasive imaging approach using CEST and MRS may be used to monitor the cleavage of the poly-L-glutamate (PLG) backbone. The cleavage of PLG by cathespsin B can expose exchangeable —NH2 protons in the PLG that are then monitored non-invasively through CEST. The technique can provide direct information on malignant tissue and tumor aggressiveness, and can also be used to monitor treatment.

Abstract: Embodiments of a wellhead assembly have a tubular body with a removable bottom cap to house a tubing rotator comprising a multi-enveloping worm gear assembly which provides sufficient torque to rotate tubing strings in deep and/or deviated wellbores. The bottom cap is supported on a wellhead having a dognut for supporting a production string therefrom in a wellbore. The bottom cap rotatably supports a mandrel and multi-enveloping worm wheel thereon, the mandrel being connected to the dognut and tubing string for co-rotation therewith. The tubular body supports a multi-enveloping worm for engagement with the worm wheel when the tubular body is lowered axially onto the bottom cap. When the tubular body and worm are lifted axially from the bottom cap, the worm wheel and mandrel are exposed for repair or replacement without need to pull the production string from the wellbore.

Abstract: In one example implementation, a system for ranking target dimensions includes a model engine to generate a three-dimensional (3D) model of an object from a scan of the object, an analysis engine to analyze the 3D model of the object and determine target dimensions of the object, a ranking engine to rank the target dimensions based on target dimension characteristics, historical scan information related to the target dimension characteristics, or a combination thereof, and a display engine to display information relating to the ranked target dimensions of the object.

Abstract: A method for controlling the drive current in a stepper motor includes measuring stepper motor current, computing a load angle of the stepper motor, calculating a torque ratio of the stepper motor, generating a reference current as a function of the torque ratio and a maximum current setting for the stepper motor, and setting the drive current of the stepper motor as a function of the reference current.