Abstract: The computing device transforms lab data and field data into a first format suitable for execution with a supervised machine learning model to determine an input variable importance for a first set of input variables in predicting a field outcome, generates one or more logical rules of decision metrics, selects the one or more input variables that yields a higher input variable importance, generates one or more pass-fail indicators, combines the one or more pass-fail indicators generates one or more prediction factor rules, transforms the field data and the one or more prediction factor rules into a second format suitable for execution with a model to determine a treatment effect for the one or more prediction factor rules, and selects the prediction factor rule that maximizes the treatment effect of predicting the field outcome of a performance of compounds or biological actives within a range of uncertainty.

Abstract: An apparatus includes a processor to: within a performance container, execute a performance routine to derive an order of performance of tasks of a job flow based on dependencies, begin performing the tasks, and store, within a job queue, a job performance status indication including task performance statuses; identify a set of sub flows within the job flow based on branches in the job flow; correlate each of the task performance statuses to a corresponding sub flow performance status; reduce the job performance status indication size by, for each sub flow in which all tasks have been completed, replace the corresponding task performance statuses with the corresponding sub flow performance status of completed, and for each sub flow with no task performed, replace the corresponding task performance statuses with the corresponding sub flow performance status of not executed; and transmit the job performance status indication to the requesting device.

Abstract: A computing device responds to a membership overlap query. A list of unique member identifiers included in a plurality of datasets is created. A list of datasets of the plurality of datasets is defined for each unique member identifier. Each dataset included in the list of datasets includes a unique member associated with a respective unique member identifier. A unique list of datasets is defined from each list of datasets. A number of occurrences of each unique list of datasets is determined. A number of datasets included in each unique list of datasets is determined. Intersection data is created that includes a dataset list of each unique list of datasets in association with the number of occurrences of each respective, unique list of datasets and with the number of datasets included in each respective, unique list of datasets. An overlap response is determined using the created intersection data.

Abstract: A computing system creates interaction features from variable values in a transformed dataset that includes a variable value computed for each variable of transformed variables computed from a prior execution of a transformation flow applied to an input dataset. An interaction transformation flow definition indicates a subset of the transformed variables, a synthesis definition, and interaction transformation operations to apply to the transformed variables. The synthesis definition describes how the subset of the transformed variables are combined to compute a value input to the interaction transformation operations. A plurality of variable combinations of the subset is defined. A computation is defined for each combination and interaction transformation operation. An operation data value is computed for each computation from the transformed dataset. An observation vector is read from the transformed dataset and a current interaction variable value is synthesized for each combination.

Abstract: An apparatus includes processor(s) to: receive a request to test goodness-of-fit of a spatial process model; generate a KD tree from observed spatial point dataset including locations within a region at which instances of an event occurred; derive, from the observed spatial point dataset, multiple quadrats into which the region is divided; receive, from multiple processors, current levels of availability of processing resources including quantities of currently available execution threads; select, based on the quantity of currently available execution threads, a subset of the multiple processors to perform multiple iterations of a portion of the test in parallel; provide, to each processor of the subset, the KD tree, the spatial process model, and the multiple quadrats; receive, from each processor of the subset, per-quadrat data portions indicative of results of an iteration; derive a goodness-of-fit statistic from the per-quadrat data portions; and transmit an indication of goodness-of-fit to another device

Abstract: An apparatus includes a processor to: within each reading thread, retrieve a data set part and corresponding part metadata from storage device(s), analyze row group metadata for each row group within the data set part to identify candidate row group(s) meeting specified criteria, and store the candidate row group(s) and corresponding row group metadata within a data buffer of a queue; operate the queue as a FIFO buffer; within each provision thread, retrieve one of multiple row groups and corresponding metadata from within the data buffer, use information in the metadata to identify rows meeting the criteria, and provide those rows to the requesting device or an application; and in response to each instance of storage of a data set part within a data buffer of the queue, analyze the availability of storage space and/or of processing resources to determine whether to dynamically adjust the quantity of reading threads.

Abstract: Geospatial data can be converted into audio outputs. For example, a system can receive a dataset indicating geospatial locations of objects within a region. Based on the dataset, the system can generate a virtual map representing the region and including virtual points representing the objects. The virtual points can be spatially positioned at locations in the virtual map corresponding to the geospatial locations of the objects in the region. The system can receive a user input via a user input device for interacting with a particular virtual point among the virtual points in the virtual map. The system can determine one or more sound characteristics for a sound based on receiving the user input. The system can then transmit an audio signal to an audio device for causing the audio device to generate the sound having the one or more sound characteristics, which may assist with exploring the virtual map.

Abstract: An apparatus includes processor(s) to: perform preprocessing operations of a segmentation technique including divide speech data set into data chunks representing chunks of speech audio, use an acoustic model with each data chunk to identify pauses in the speech audio, and analyze a length of time of each identified pause to identify a candidate set of likely sentence pauses in the speech audio; and perform speech-to-text operations including divide the speech data set into data segments that each representing segments of the speech audio based on the candidate set of likely sentence pauses, use the acoustic model with each data segment to identify likely speech sounds in the speech audio, analyze the identified likely speech sounds to identify candidate sets of words likely spoken in the speech audio, and generate a transcript of the speech data set based at least on the candidate sets of words likely spoken.

Abstract: An apparatus includes processor(s) to: perform pre-processing operations including derive an audio noise level of speech audio of a speech data set, derive a first relative weighting for first and second segmentation techniques for identifying likely sentence pauses in the speech audio based on the audio noise level, and select likely sentence pauses for a converged set of likely sentence pauses from likely sentence pauses identified by the first and/or second segmentation techniques based on the first relative weighting; and perform speech-to-text processing operations including divide the speech data set into data segments representing speech segments of the speech audio based on the converged set of likely sentence pauses, and derive a second relative weighting based on the audio noise level for selecting words indicated by an acoustic model or by a language model as being most likely spoken in the speech audio for inclusion in a transcript.

Abstract: A computing system displays an initial graph with icons. Each icon graphically represents data associated with a respective entity. The first icon is connected in the initial graph to other icon(s). The system receives an indication of a graphical network pattern. The graphical network pattern is defined by a user selection of a second icon in the initial graph and: a user selection of a third icon in the initial graph; or a user selection of a graphical representation in the initial graph of a relationship between the second icon and the third icon. The system sends computer instructions indicating a network pattern query for searching an electronic database for electronic record(s) corresponding to a queried network pattern. The system receives a dataset indicating located electronic record(s) corresponding to the queried network pattern. The system generates output data indicating an output graph for a graphical representation of the located record(s).

Abstract: A computing system trains a classification model using distributed training data. In response to receipt of a first request, a training data subset is accessed and sent to each higher index worker computing device, the training data subset sent by each lower index worker computing device is received, and a first kernel matrix block and a second kernel matrix block are computed using a kernel function and the accessed or received training data subsets. (A) In response to receipt of a second request from the controller device, a first vector is computed using the first and second kernel matrix blocks, a latent function vector and an objective function value are computed, and the objective function value is sent to the controller device. (A) is repeated until the controller device determines training of a classification model is complete. Model parameters for the trained classification model are output.

Abstract: A computing system receives a request for a computer-generated design of an experiment. A design space is defined by candidate inputs for each factor of the experiment. The system receives a disallowed combination indication indicating a user-defined disallowed combination for the design space. The disallowed combination constrains a first set of values from a first set of candidate inputs from being assigned to the first factor if the second factor is assigned one of a second set of values from a second set of candidate inputs. The system determines additional constraint(s) on computer generation of the design. The system evaluates if a user-defined model can be generated according to the disallowed combination and the constraint(s). The system generates a computer suggested modification of the user-defined model such that the design can be generated according to the computer suggested modification.

Abstract: An apparatus includes processor(s) to: generate a set of candidate n-grams based on probability distributions from an acoustic model for candidate graphemes of a next word most likely spoken following at least one preceding word spoken within speech audio; provide the set of candidate n-grams to multiple devices; provide, to each node device, an indication of which candidate n-grams are to be searched for within the n-gram corpus by each node device to enable searches for multiple candidate n-grams to be performed, independently and at least partially in parallel, across the node devices; receive, from each node device, an indication of a probability of occurrence of at least one candidate n-gram within the speech audio; based on the received probabilities of occurrence, identify the next word most likely spoken within the speech audio; and add the next word most likely spoken to a transcript of the speech audio.

Abstract: An apparatus includes a processor to: based on data dependencies specified in a job flow definition, identify first and second tasks of the corresponding job flow to be performed sequentially, wherein the first task outputs a data object used as an input to the second; store, within a task queue, at least one message conveying at least an identifier of the first task, and an indication that the data object is to be exchanged through a shared memory space; within a task container, in response to storage of the at least one message within the task queue, sequentially execute first and second task routines to sequentially perform the first and second tasks, respectively, and instantiate the shared memory space to be accessible to the first and second task routines during their executions; and upon completion of the job flow, transmit an indication of completion to another device via a network.

Abstract: Unclassified observations are classified. Similarity values are computed for each unclassified observation and for each target variable value. A confidence value is computed for each unclassified observation using the similarity values. A high-confidence threshold value and a low-confidence threshold value are computed from the confidence values. For each observation, when the confidence value is greater than the high-confidence threshold value, the observation is added to a training dataset and, when the confidence value is greater than the low-confidence threshold value and less than the high-confidence threshold value, the observation is added to the training dataset based on a comparison between a random value drawn from a uniform distribution and an inclusion percentage value. A classification model is trained with the training dataset and classified observations. The trained classification model is executed with the unclassified observations to determine a label assignment.

Abstract: Testing for software applications can be implemented according to some aspects described herein. For example, a system can receive override data, including a location of a logical statement in source code and an override command, that is associated with a software application. The system can generate debugging data based on the override data, the debugging data including a breakpoint associated with the location and a debugger command corresponding to the override command. The system can then provide the debugging data as input to debugging software, the debugging software being configured to monitor execution of the software application during a software test. The debugging software can determine that the breakpoint has been reached and responsively execute the debugger command for testing a target portion of source code for the software application.

Abstract: A computing system determines a design space for designing a design system. The computing system receives a request to identify input(s) that will provide a response of the design system that advances a user-defined goal for the design system. The computing system, responsive to the request to identify input(s), generates a design for the design space that provides design cases for generating the response of the design system and obtains responses of the design system generated for multiple design cases of the design for the design system. The computing system selects at least one design case based on obtained responses of the design system for the multiple design cases, and based on the at least one design case, outputs an indication of suggested input(s) for the design system. The suggested input(s) advances the user-defined goal for the design system and is not disallowed according to disallowed combination(s).

Abstract: A computing device trains a neural network machine learning model. A forward propagation of a first neural network is executed. A backward propagation of the first neural network is executed from a last layer to a last convolution layer of a plurality of convolutional layers to compute a gradient vector for first weight values of the last convolution layer using observation vectors. A discriminative localization map is computed for each observation vector with the gradient vector using a discriminative localization map function. A forward and a backward propagation of a second neural network is executed to compute a second weight value for each neuron of the second neural network using the discriminative localization map computed for each observation vector. A predefined number of iterations of the forward and the backward propagation of the second neural network is repeated.

Abstract: Various embodiments are generally directed to techniques for image content extraction. Some embodiments include extracting contextually structured data from document images, such as by automatically identifying document layout, document data, document metadata, and/or correlations therebetween in a document image, for instance. Several embodiments include extracting contextually structured data from table images, such as gridded and non-gridded tables. For example, the contents of cells may be extracted from a table image along with structural context including the corresponding row and column information. Many embodiments are directed to generating and utilizing a document template database for automatically extracting document image contents into a contextually structured format. Several embodiments are directed to automatically identifying and associating document metadata with corresponding document data in a document image to generate a machine-facilitated annotation of the document image.

Abstract: A computing system responsive to obtaining original image data, detects a set of data point(s), in the original image data, that indicates an object. The system determines, based on the set of data point(s), a set of pixels associated with the object in the original image data. The system generates an alternative visual identifier for the object that provides a unique identifier for the set of pixels absent in the original image data. The system generates, autonomously from intervention by any user of the computing system, pixel information to conceal feature(s) of the object. The system obtains modified image data comprising the alternative visual identifier. The modified image data further comprises the feature(s) of the object in the original image data visually concealed in the modified image data according to the pixel information. The system outputs an image representation of a trajectory of the object through the modified image data.