Abstract: Methods and systems for simulating fulfillment of digital orders within a retail supply chain are disclosed. One method includes receiving a selection of a first operational parameter of a supply chain model. The supply chain simulation model is a transaction-level model representative of a digital order fulfillment process within a retail supply chain network. The selection of the first operational parameter includes a default value for the first operational parameter and an experimental value for the first operational parameter that is different from the default value. Simulations of a set of predicted digital orders within the retail supply chain network, using the supply chain simulation model as modified in accordance with the first operational parameter, are performed. Scenario evaluations including predicted metrics associated with each of a cost, a capacity, and a guest experience for the digital order fulfillment process may be output and displayed on a user interface.

Abstract: Various embodiments of a smart non-pneumatic tire and methods for generating a mean vibration characteristic of the smart non-pneumatic tire are described. In one embodiment, a method for measuring the mean vibration characteristic includes receiving tire-road contact acceleration data from an accelerometer that is secured near a tire-surface contact region, where the tire-road contact acceleration data includes data captured by the accelerometer over a duration of time while a tread along an outer periphery of a sector of the tire contacts a surface. The method further includes receiving velocity data for the tire and load data for the tire over the duration of time. The method further includes generating a mean vibration characteristic based on the above-mentioned data. The method also includes changing a stiffness of spokes of the tire based on the generated mean vibration characteristic in some cases.

Abstract: Methods and systems for simulating fulfillment of digital orders within a retail supply chain are disclosed. One method includes receiving a selection of a first operational parameter of a supply chain model. The supply chain simulation model is a transaction-level model representative of a digital order fulfillment process within a retail supply chain network. The selection of the first operational parameter includes a default value for the first operational parameter and an experimental value for the first operational parameter that is different from the default value. Simulations of a set of predicted digital orders within the retail supply chain network, using the supply chain simulation model as modified in accordance with the first operational parameter, are performed. Scenario evaluations including predicted metrics associated with each of a cost, a capacity, and a guest experience for the digital order fulfillment process may be output and displayed on a user interface.

Abstract: Methods and systems for simulating fulfillment of digital orders within a retail supply chain are disclosed. One method includes receiving a selection of a first operational parameter of a supply chain model. The supply chain simulation model is a transaction-level model representative of a digital order fulfillment process within a retail supply chain network. The selection of the first operational parameter includes a default value for the first operational parameter and an experimental value for the first operational parameter that is different from the default value. Simulations of a set of predicted digital orders within the retail supply chain network, using the supply chain simulation model as modified in accordance with the first operational parameter, are performed. Scenario evaluations including predicted metrics associated with each of a cost, a capacity, and a guest experience for the digital order fulfillment process may be output and displayed on a user interface.

Abstract: A method, apparatus, and non-transitory computer readable medium for chart question answering are described. The method, apparatus, and non-transitory computer readable medium may receive a text query about a chart, identify a plurality of chart elements in the chart, associate a text string from the text query with corresponding chart elements from the plurality of chart elements, replace the text string in the text query with arbitrary rare words based on the association to produce an encoded query, generate an embedded query based on the encoded query, generate an image feature vector based on the chart, combine the embedded query and the image feature vector to produce a combined feature vector, compute an answer probability vector based on the combined feature vector, and provide an answer to the text query based on the answer probability vector.

Abstract: A system and method for updating a cluster of a virtual computing system includes receiving a maintenance window from a user during which to upgrade the cluster, determining available upgrades for the cluster, presenting one or more upgrade plans to the user, such that each of the one or more upgrade plans is created to be completed within the maintenance window and includes one or more of the available upgrades selected based on a total upgrade time computed for each of the available upgrades, receiving selection of one of the one or more upgrade plans from the user, and upgrading the cluster based on the one of the one or more upgrade plans that is selected.

Abstract: A method, apparatus, and non-transitory computer readable medium for chart question answering are described. The method, apparatus, and non-transitory computer readable medium may receive a text query about a chart, identify a plurality of chart elements in the chart, associate a text string from the text query with corresponding chart elements from the plurality of chart elements, replace the text string in the text query with arbitrary rare words based on the association to produce an encoded query, generate an embedded query based on the encoded query, generate an image feature vector based on the chart, combine the embedded query and the image feature vector to produce a combined feature vector, compute an answer probability vector based on the combined feature vector, and provide an answer to the text query based on the answer probability vector.

Abstract: The present invention provides a method and system for distributed probabilistic matrix factorization. In accordance with a disclosed embodiment, the method may include partitioning a sparse matrix into a first set of blocks on a distributed computer cluster, whereby a dimension of each block is MB rows and NB columns. Further, the method shall include initializing a plurality of matrices including first mean matrix ?, a first variance matrix ?, a first prior variance matrix ?P, a second mean matrix V, a second variance matrix {tilde over (V)}, and a second prior variance matrix {tilde over (V)}P, by a set of values from a probability distribution function. The plurality of matrices can be partitioned into a set of blocks on the distributed computer cluster, whereby each block can be of a shorter dimension K, and the plurality of matrices can be updated iteratively until a cost function of the sparse matrix converges.

Abstract: System and methods and memory media for identifying a rogue base station. A receiver captures an input signal that includes a mixture of the rogue station's transmission and the transmission of a victim base station. The victim station's transmission is removed from the mixture. A plurality of channel signals are extracted from the residual signal. The channel signals are analyzed which of them are “valid”, i.e., contain a GMSK-modulated GSM signal. The “valid” channel signals are low pass filtered and then analyzed to determine which of them corresponds to the beacon channel. Information identifying the rogue base station is extracted from the beacon channel.

Abstract: System and methods and memory media for identifying a rogue base station. A receiver captures an input signal that includes a mixture of the rogue station's transmission and the transmission of a victim base station. The victim station's transmission is removed from the mixture. A plurality of channel signals are extracted from the residual signal. The channel signals are analyzed which of them are “valid”, i.e., contain a GMSK-modulated GSM signal. The “valid” channel signals are low pass filtered and then analyzed to determine which of them corresponds to the beacon channel. Information identifying the rogue base station is extracted from the beacon channel.