Abstract: A method for selecting a target cell by a user equipment (UE), includes: detecting a movement of the UE based on a change in a location of the UE and a change in a measured signal power of a serving cell; determining personalized data of a user based on a plurality of user contextual parameters; predicting a destination of the user, and one or more target cells across a path of the user to the destination based on the determined personalized data of the user and the detected movement of the UE; and selecting the target cell from the one or more target cells, based on an artificial intelligence (AI) model. The AI model is configured to update weights of one or more network quality parameters.

Abstract: Systems and methods for detecting and aligning land property boundaries on aerial imagery are provided. The system receives an aerial imagery having land properties. The system applies a feature encoder having a plurality of levels to the aerial imagery. A first level of the plurality of levels includes a convolution block and a discrete wavelet transform layer. The discrete wavelet transform layer decomposes an input feature tensor to the first level into a low-frequency band and a high-frequency band. The high-frequency band is cached and processed with side-convolutional blocks before the high-frequency band are passed to a feature decoder. The system applies the feature decoder to an output of the feature encoder based at least in part on one of inverse discrete wavelet transform layers. The system determines boundaries of the one or more land properties based at least in part on a boundary cross-entropy loss function.

Abstract: A processing device receives one or more inputs for design verification of an integrated circuit using an emulation compiler. The processing device determines a type of compiler for processing the one or more inputs. In response to determining that the type of compiler is a simulation compiler, the processing device modifies the simulation compiler according to the one or more inputs to form a modified simulation compiler to match one or more emulation semantics associated with the emulation compiler. The processing device performs a design verification using the modified simulation compiler.

Abstract: Computer vision systems and methods for noisy contour alignment are provided. The system generates a loss function and trains a convolutional neural network with the loss function and a plurality of images of a dataset to learn to align contours with progressively increasing complex forward and backward transforms over increasing scales. The system can align an attribute of an image of the dataset by the trained neural network.

Abstract: Systems and methods for detecting and aligning land property boundaries on aerial imagery are provided. The system receives an aerial imagery having land properties. The system applies a feature encoder having a plurality of levels to the aerial imagery. A first level of the plurality of levels includes a convolution block and a discrete wavelet transform layer. The discrete wavelet transform layer decomposes an input feature tensor to the first level into a low-frequency band and a high-frequency band. The high-frequency band is cached and processed with side-convolutional blocks before the high-frequency band are passed to a feature decoder. The system applies the feature decoder to an output of the feature encoder based at least in part on one of inverse discrete wavelet transform layers. The system determines boundaries of the one or more land properties based at least in part on a boundary cross-entropy loss function.

Abstract: Computer vision systems and methods for noisy contour alignment are provided. The system generates a loss function and trains a convolutional neural network with the loss function and a plurality of images of a dataset to learn to align contours with progressively increasing complex forward and backward transforms over increasing scales. The system can align an attribute of an image of the dataset by the trained neural network.

Abstract: Systems and methods for detecting and aligning land property boundaries on aerial imagery are provided. The system receives an aerial imagery having land properties. The system applies a feature encoder having a plurality of levels to the aerial imagery. A first level of the plurality of levels includes a convolution block and a discrete wavelet transform layer. The discrete wavelet transform layer decomposes an input feature tensor to the first level into a low-frequency band and a high-frequency band. The high-frequency band is cached and processed with side-convolutional blocks before the high-frequency band are passed to a feature decoder. The system applies the feature decoder to an output of the feature encoder based at least in part on one of inverse discrete wavelet transform layers. The system determines boundaries of the one or more land properties based at least in part on a boundary cross-entropy loss function.

Abstract: Computer vision systems and methods for noisy contour alignment are provided. The system generates a loss function and trains a convolutional neural network with the loss function and a plurality of images of a dataset to learn to align contours with progressively increasing complex forward and backward transforms over increasing scales. The system can align an attribute of an image of the dataset by the trained neural network.

Abstract: A method for managing offers for prepaid telephony accounts of a plurality of users is provided. The method includes obtaining information related to user profiles and usage patterns of a plurality of telecommunication services offered to the plurality of users through their respective prepaid telephony accounts. The method also includes customizing prepaid telephony offers for each of the plurality of users based upon the information related to the user profiles and the usage patterns and delivering the customized prepaid telephony offers to a mobile device of each of the plurality of users via unstructured supplementary services data (USSD) messages.