Robotic visualization systems and methods include running and analyzing perception algorithms and models for robotic visualization systems on multiple computing platforms to obtain a successful complete an object processing request.

Robotic visualization systems and methods include running and analyzing perception algorithms and models for robotic visualization systems on multiple computing platforms to obtain a successful complete an object processing request.

Embodiments described herein provide a new approach to learning generative models of proteins based on sequence-to-sequence learning. Specifically, sequence modeling is formulated as a few-shot learning problem: a single encoder-decoder model receives an input of a protein family which is encoded into a protein representation and the protein representation is then decoded into a distribution over sequences from that family. The model is trained on tens of thousands of multiple sequence alignments representing known protein families and evaluated on unseen families heldout from training.

Embodiments described herein provide a new approach to learning generative models of proteins based on sequence-to-sequence learning. Specifically, sequence modeling is formulated as a few-shot learning problem: a single encoder-decoder model receives an input of a protein family which is encoded into a protein representation and the protein representation is then decoded into a distribution over sequences from that family. The model is trained on tens of thousands of multiple sequence alignments representing known protein families and evaluated on unseen families heldout from training.

A processor-implemented method includes: using an encoder, determining, for each of a plurality of tokens included in an input sequence, a self-attention weight based on a token and one or more tokens that precede the token in the input sequence; using the encoder, determining context information corresponding to the input sequence based on the determined self-attention weights; and using a decoder, determining an output sequence corresponding to the input sequence based on the determined context information.

A processor-implemented method includes: using an encoder, determining, for each of a plurality of tokens included in an input sequence, a self-attention weight based on a token and one or more tokens that precede the token in the input sequence; using the encoder, determining context information corresponding to the input sequence based on the determined self-attention weights; and using a decoder, determining an output sequence corresponding to the input sequence based on the determined context information.

A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, an adaptive prediction engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm. The adaptive prediction engine is further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm.

A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, an adaptive prediction engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm. The adaptive prediction engine is further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm.

Disclosed is a method of predicting user's position. This method comprises, creating information on a plurality of location clusters by processing a plurality of position data for a user with a probability based clustering algorithm; receiving a current position data of the user and determining a first location cluster to which the current data is mapped among the plurality of location clusters; and creating second information related to a probability that the user moves from the first location cluster to a second location cluster among the plurality of location clusters. The position data is a data tuple including latitude, longitude, and time. For all the plurality of location clusters, the information includes a determined representative position value of each of the location clusters.

A non-transitory computer-readable recording medium stores therein a learning program for causing a computer to execute a process comprising: referring to, at time of learning a computation model that is a target of deep learning and has a plurality of nodes, a storage unit in which route information that indicates a calculation route followed by a tensor in each stage of learning prior to the time of learning, and statistical information regarding a position of a decimal point used in the calculation route are associated with each other; acquiring, when executing each piece of calculation processing set in each of the plurality of nodes at the time of learning, the statistical information corresponding to the route information that reaches each of the plurality of nodes; and executing the each piece of calculation processing using the position of the decimal point specified by the acquired statistical information.