Abstract: One embodiment provides a method of using a computing device for image-to-image translation including accessing an image file containing a first amount of data. The computing device inputs the image file into a convolutional neural network (CNN). The CNN includes multiple Fourier layers. Each Fourier layer includes a Fourier transform, a linear feature transformation in a frequency domain and an inverse Fourier transform. Each linear feature transformation in the frequency domain is shared by different frequency components to reduce a number of parameters. The CNN outputs an output image file that includes contents that are translated from the input image file.

Abstract: The present invention provides a system for dynamically registering plugin with an Enterprise Performance Management (EPM) system. The system provides a database module operationally coupled to a repository for storing information on registered enterprises, one or more user devices mapped to the registered enterprises, and a set of rules corresponding to each user device. The system further comprises a data identification module which is configured to receive data from one or more user devices and a converter framework comprising a converter module and a plugin module for conversion of data from a user device. The system further provides a registration module which is operationally coupled to said data identification module and the database module and is configured to receive a plugin registration request from a user device including a plugin, identify the user device and its data type, and determine if the plugin is already registered with the repository.

Abstract: The present invention provides a system for dynamically registering plugin with an Enterprise Performance Management (EPM) system. The system provides a database module operationally coupled to a repository for storing information on registered enterprises, one or more user devices mapped to the registered enterprises, and a set of rules corresponding to each user device. The system further comprises a data identification module which is configured to receive data from one or more user devices and a converter framework comprising a converter module and a plugin module for conversion of data from a user device. The system further provides a registration module which is operationally coupled to said data identification module and the database module and is configured to receive a plugin registration request from a user device including a plugin, identify the user device and its data type, and determine if the plugin is already registered with the repository.

Abstract: The present disclosure provides a system for routing data to the converter framework of an enterprise performance management (EPM) system. The EPM system includes a database module operatively connected to a repository that stores information of one or more registered user devices, and a set of rules corresponding to each registered device. The system further includes a data identification module which is operatively coupled to the database module. The data identification module is configured to receive data from one or more user devices and, thereafter, communicate with the database module to identify if the user device is a registered device or unregistered device. When the user device is a registered device, the data identification module routes the data to the converter framework according to the rules specified in the database module.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: encoding one or more instance of a received image with spectral mask data, wherein the spectral mask data specifies spectral information of the received image to be masked; training one or more predictive model in dependence on the encoding; querying the one or more predictive model with a query image; and performing processing in dependence on an output from the querying.

Abstract: In a method for intelligently executing predictive simulator, a processor may input a previous input vector of conditions for a predictive simulator collected at a first time into a machine-learning (ML) model. A processor may input a current input vector of conditions for the predictive simulator collected at a second time into the ML model. A processor may determine using the ML model, a binary similarity index. The binary similarity index represents a prediction of similarity between a first output from the predictive simulator based on the previous input and a second output from the predictive simulator based on the current input.

Abstract: One embodiment provides a method of using a computing device for image-to-image translation including accessing an image file containing a first amount of data. The computing device inputs the image file into a convolutional neural network (CNN). The CNN includes multiple Fourier layers. Each Fourier layer includes a Fourier transform, a linear feature transformation in a frequency domain and an inverse Fourier transform. Each linear feature transformation in the frequency domain is shared by different frequency components to reduce a number of parameters. The CNN outputs an output image file that includes contents that are translated from the input image file.

Abstract: Method, apparatus, and computer program product are provided for estimating crop pest risk and/or crop disease risk at sub-farm level. In some embodiments, a farm region is determined based on farm definition data, and input data associated with the farm region are retrieved from a plurality of data sources. The input data may include a plurality of pixel sets. In some embodiments, for each pixel set, crop risk data are determined based on the input data using one or more spatiotemporal regression models. The crop risk data may include an estimate of crop pest risk and/or crop disease risk for each pixel set. In some embodiments, the farm region is categorized into a plurality of sub-farms each defining a risk level category for that sub-farm based on the crop risk data. In some embodiments, the sub-farms are displayed as a visual heat-map, along with recommended antidote options.

Abstract: Methods, systems, and computer program products for stochastic compression of raster data are provided herein. A computer-implemented method includes obtaining at least one compression ratio and at least one error value for a given set of raster data; compressing at least a portion of the given set of raster data based at least in part on the at least one compression ratio and the at least one error value; transmitting the compressed raster data, to at least one given destination, based at least in part on a given transmission speed variable; and performing one or more automated actions based at least in part on the transmitted compressed raster data.

Abstract: One embodiment provides a method for recommending model characteristics to be used in developing a target geo-spatial physical model for a target geographic location utilizing historical lineage data corresponding to historical geo-spatial physical models, including: receiving information related to the target geographic location, wherein the information describes geographical and domain features of the target geographic location; identifying, using at least one similarity algorithm, at least one other geographic location that is similar to the target geographic location, wherein the at least one geographic location has at least one corresponding historical geo-spatial physical model; and recommending, using at least one machine-learning model and based upon the at least one other geographic location, initial model characteristics for developing and deploying the target geo-spatial physical model.

Abstract: Methods, systems, and computer program products for auto-discovery of reasoning knowledge graphs in supply chains are provided herein. A computer-implemented method includes obtaining a spatiotemporal query related to a demand of at least one product in a supply chain; analyzing the spatiotemporal query to identify one or more parameters affecting the demand of the at least one product, wherein the one or more parameters comprise at least one of one or more climate parameters and one or more disruptive event parameters; generating a knowledge graph comprising information indicating an impact on the demand of the at least one product for at least a portion of the one or more parameters; and outputting, to a user interface, an explanation of a predicted demand forecast for the at least one product based at least in part on the knowledge graph.

Abstract: Embodiments described herein relate to a method for probabilistically forecasting the state of hardware components. The method may include obtaining data items corresponding to a hardware component and performing an analysis of the hardware component. The analysis may include making a variety of probability predictions as to whether a label from among a set of possible labels is likely to be the correct label. The set of probabilities from the aforementioned analysis are then analyzed to determine which predicted label has the tightest range, and the prediction with the tightest range for a certain label is displayed to a user in a ranked fashion that includes a quantity of such probability prediction ranges. Such a display may allow an administrator to take action as to which hardware components should be replaced and in what order.

Abstract: In a method for intelligently executing predictive simulator, a processor may input a previous input vector of conditions for a predictive simulator collected at a first time into a machine-learning (ML) model. A processor may input a current input vector of conditions for the predictive simulator collected at a second time into the ML model. A processor may determine using the ML model, a binary similarity index. The binary similarity index represents a prediction of similarity between a first output from the predictive simulator based on the previous input and a second output from the predictive simulator based on the current input.

Abstract: A method and system generate news headlines from user input parameters. The user input parameters include a specified geographic region of interest and an industry of interest. Climate data and carbon emissions data for the specified geographic region of interest is retrieved. Supply chain dependencies are determined. A machine learning model is generated using the specified geographic region of interest, the industry, the climate data, the carbon emissions data, and the supply chain dependencies. The machine learning model performs an impact analysis on a supply chain based on the climate data and the carbon emissions data. The machine learning model predicts a supply chain performance for the industry based on the impact analysis. A news headline is automatically generated describing the predicted supply chain performance. The news headline includes an underlying basis for the predicted supply chain performance.

Abstract: Methods, systems, and computer program products for stochastic compression of raster data are provided herein. A computer-implemented method includes obtaining at least one compression ratio and at least one error value for a given set of raster data; compressing at least a portion of the given set of raster data based at least in part on the at least one compression ratio and the at least one error value; transmitting the compressed raster data, to at least one given destination, based at least in part on a given transmission speed variable; and performing one or more automated actions based at least in part on the transmitted compressed raster data.

Abstract: Techniques for using machine learning to model climatic data are disclosed. In one example, a computer implemented method comprises receiving climate data comprising a plurality of spatial components and a plurality of temporal components, and masking a portion of the climate data. A machine learning model is trained, wherein the training is based at least in part on the masked portion of the climate data. A vector representation of the climate data is generated via the machine learning model.

Abstract: One embodiment provides a method, including: obtaining information related to farming activities of a farmer; predicting an annotation category for the information, wherein the annotation category identifies a topic of the information; selecting an annotator for annotating the information based upon the annotation category, wherein the selecting comprises utilizing (i) a social proximity constraint identifying a social connection between the farmer and another farmer and (ii) a farm signature constraint identifying a similarity of the farmer to another farmer; assigning the annotator to annotate the obtained information; and receiving annotations for the information.

Abstract: One embodiment provides a computer implemented method of estimating replication completion time. The method includes creating a historical dataset of prior replication data; determining a set of replication parameters to consider; inputting the historical dataset and the set of replication parameters to a replication completion time estimator module; generating a replication completion time prediction based on the historical dataset and the set of replication parameters; and generating a confidence prediction corresponding to the replication completion time prediction.

Abstract: A computer-implemented method for effective agriculture and environment monitoring. The method may comprise measuring a desired variable over an area of interest using a remote inspection platform according to an inspection plan, predicting an occlusion of the remote inspection platform, and in response to the predicted occlusion, determining whether to invoke a local inspection platform to complete the inspection plan. The occlusion in some embodiments interrupts the inspection plan for the remote inspection platform.

Abstract: In an approach to jointly learning uncertainty-aware trend-informed neural network for a demand forecasting model, a machine learning model is trained to capture uncertainty in input forecasts. The uncertainty in a latent space is represented using an auto-encoder based neural architecture. The uncertainty-aware latent space is modeled and optimized to generate an embedding space. A time-series regressor model is learned from the embedding space. A machine learning model is trained for trend-aware demand forecasting based on said time-series regressor model.