Abstract: A building system including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to receive an indication to execute an artificial intelligence (AI) agent of a digital twin, the digital twin including the AI agent and a graph data structure, the graph data structure including nodes representing entities of a building and edges between the nodes representing relationships between the entities of the building. The instructions cause the one or more processors to execute the AI agent based on data of the building to generate an inference that is a prediction of a future data value of a data point of the building for a future time and store at least one of the inference, or a link to the inference, in the graph data structure.

Abstract: A system comprises a network of computers comprising a master computer and slave computers. For a machine learning problem that is partitioned into a number of correlated sub-problems, each master computer is configured to store tasks associated with the machine learning problem, and each of the slave computers is assigned one of the correlated sub-problems. Each slave computer is configured to store variables or parameters or both associated with the assigned one of the correlated sub-problems; obtain information about one or more tasks stored by the master computer without causing conflict with other slave computers with regard to the information; perform computations to update the obtained information and the variables or parameters or both of the assigned sub-problem; send the updated information to the master computer to update the information stored at the master computer; and store the updated variables or parameters or both of the assigned sub-problem.

Abstract: Methods, and systems, including computer programs encoded on computer storage media for generating data items. A method includes reading a glimpse from a data item using a decoder hidden state vector of a decoder for a preceding time step, providing, as input to a encoder, the glimpse and decoder hidden state vector for the preceding time step for processing, receiving, as output from the encoder, a generated encoder hidden state vector for the time step, generating a decoder input from the generated encoder hidden state vector, providing the decoder input to the decoder for processing, receiving, as output from the decoder, a generated a decoder hidden state vector for the time step, generating a neural network output update from the decoder hidden state vector for the time step, and combining the neural network output update with a current neural network output to generate an updated neural network output.

Abstract: A system includes a memory having instructions therein and at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to acquire phytomorphological field data via a sensor component of a mobile robot, generate, based on the phytomorphological field data and via a machine learning agent, a predicted likelihood of whether a hypothetical action by the mobile robot against a found plant would be directed against a true Toxicodendron plant, conduct a non-phytomorphological assessment of the found plant via the mobile robot and based on the predicted likelihood being below a first threshold and above a second threshold, and, via the mobile robot and based on the non-phytomorphological assessment, attack the found plant, mark a site of the found plant, and/or document a context of the site.

Abstract: A method of generating an outcome for a sporting event is disclosed herein. A computing system retrieves tracking data from a data store. The computing system generates a predictive model using a deep neural network. The one or more neural networks of the deep neural network generates one or more embeddings comprising team-specific information and agent-specific information based on the tracking data. The computing system selects, from the tracking data, one or more features related to a current context of the sporting event. The computing system learns, by the deep neural network, one or more likely outcomes of one or more sporting events. The computing system receives a pre-match lineup for the sporting event. The computing system generates, via the predictive model, a likely outcome of the sporting event based on historical information of each agent for the home team, each agent for the away team, and team-specific features.

Abstract: A method and corresponding system identify missing interactions in incompletely known datasets represented as complex networks. The method identifies missing connections in a complex network. The method accesses an electronic representation of the network. The network includes nodes and links, the nodes represent entities, and the links represent interactions between the entities. For each pair of nodes not directly connected by a link, the method determines a number of paths connecting the pair of nodes and calculates a prediction score for the pair of nodes based on the number of paths connecting the pair of nodes. The method ranks the pairs of nodes based on the prediction scores, resulting in an ordered list of node pairs, and selects at least a subset of the pairs of nodes based on the ordered list of node pairs. The selected pairs of nodes represent missing connections in the network.

Abstract: The present disclosure relates to a method and an apparatus for object preference prediction, and a computer readable medium. The method includes: acquiring evaluation information indicating preference values of partial users in a user set for partial objects in an object set; acquiring auxiliary information of at least one of the user set and the object set, wherein the auxiliary information indicates an attribute of at least one of a corresponding user in the user set and a corresponding object in the object set; determining a user feature representation and an object feature representation using a matrix decomposition model, based on the evaluation information and the auxiliary information; and determining a preference prediction value of a target user in the user set for a target object in the object set based on the user feature representation and the object feature representation.

Abstract: A method, computer system, and a computer program product for assessing energy consumption is provided. The present invention may include determining a first set of critical energy consumption units (ECUs) involved in a target production process, the pool of the critical ECUs being obtained based on a plurality of reference production processes. The present invention may then include determining a second set of critical ECUs involved in the candidate production process. The present invention may also include determining a first set of non-critical ECUs involved in the target production process, the pool of the non-critical ECUs being obtained based on the plurality of reference production processes. The present invention may then include determining, a second set of non-critical ECUs involved in the candidate production process. The present invention may further include determining the process similarity.

Abstract: The present invention discloses an edge calculation-oriented reparametric neural network architecture search method, including the following steps: S1: designing linear operators and multi-branch block structures; S2: constructing a hypernetwork by stacking the multi-branch block structures; S3: training the hypernetwork through a gradient-based first-stage search algorithm; S4: deleting redundant branches in the hypernetwork to construct an optimal subnetwork; S5: converting the multi-branch optimal subnetwork into a single-branch network; and S6: completing task reasoning by using the single-branch network. The method is used to search the neural network structure capable of performing reparameterization, and ensures the reasoning real-time performance and the high efficiency of model operation while ensuring the reasoning precision.

Abstract: An example method for updating convolutional neural network includes: obtaining a sample with a classification label; performing a first operation on the sample based on parameters of each layer of front-end network, to obtain a first operation result; performing a second operation on the sample based on the first operation result and the parameters of each layer of back-end network that the first GPU has, to obtain a second operation result; separately sending the first operation result to the other GPUs; receiving a third operation result obtained after each other GPU performs a third operation on the sample based on their parameters of each layer of back-end network and the first operation result; combining the second and third operation results to obtain a classification result; determining a prediction error based on the classification result and the classification label; and updating the convolutional neural network based on the prediction error.

Abstract: A mechanism is described for facilitating storage management for machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting one or more components associated with machine learning, where the one or more components include memory and a processor coupled to the memory, and where the processor includes a graphics processor. The method may further include allocating a storage portion of the memory and a hardware portion of the processor to a machine learning training set, where the storage and hardware portions are precise for implementation and processing of the training set.

Abstract: According to one embodiment, when a predicted trajectory is received, a set of one or more features are extracted from at least some of the trajectory points of the predicted trajectory. The predicted trajectory is predicted using a prediction method or algorithm based on perception data perceiving an object within a driving environment surrounding an autonomous driving vehicle (ADV). The extracted features are fed into a predetermined DNN model to generate a similarity score. The similarity score represents a difference or similarity between the predicted trajectory and a prior actual trajectory that was used to train the DNN model. The similarity score can be utilized to evaluate the prediction method that predicted the predicted trajectory.

Abstract: This present disclosure relates to systems and methods for providing an Adaptive Analytical Behavioral and Health Assistant. These systems and methods may include collecting one or more of patient behavior information, clinical information, or personal information; learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms; identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns; preparing a plan based on the collected information and the identified interventions; and/or presenting the plan to a user or executing the plan.

Abstract: A data processing system processes data sets (such as low-resolution transaction data) into high-resolution data sets by mapping generic information into attribute-based specific information that may be processed to identify frequent sets therein. When association rules are generated from such frequent sets, the complexity and/or quantity of such rules may be managed by removing redundancies from the rules, such as by filtering subsumed rules from the generated rule set that have a confidence metric value that does not exceed a first confidence metric value for a subsuming rule by more than a scaled lift threshold value that is calculated by determining a complement of the first confidence metric value, squaring the complement to obtain a squared value and multiplying the squared value by a scaling factor.

Abstract: A computer-implemented method optimizes a neural network. One or more processors define layers in a neural network based on neuron locations relative to incoming initial inputs and original outgoing final outputs of the neural network, where a first defined layer is closer to the incoming initial inputs than a second defined layer, and where the second defined layer is closer to the original outgoing final outputs than the first defined layer. The processor(s) define parameter criticalities for parameter weights stored in a memory used by the neural network, and associate defined layers in the neural network with different memory banks based on the parameter criticalities for the parameter weights. The processor(s) store parameter weights used by neurons in the first defined layer in the first memory bank and parameter weights used by neurons in the second defined layer in the second memory bank.

Abstract: A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.

Abstract: A computer implemented method for adapting a model for recognition processing to a target-domain is disclosed. The method includes preparing a first distribution in relation to a part of the model, in which the first distribution is derived from data of a training-domain for the model. The method also includes obtaining a second distribution in relation to the part of the model by using data of the target-domain. The method further includes tuning one or more parameters of the part of the model so that difference between the first and the second distributions becomes small.

Abstract: An optimal power flow computation method based on multi-task deep learning is provided, which is related to the field of smart power grids.

Abstract: Embodiments include techniques for detection of data offloading through instrumentation analysis, where the techniques include monitoring, via a processor, an execution of a job, and analyzing processes associated with the job to determine a pattern. The techniques also include determining whether the pattern of the job is associated with a pattern for a workload type, and classifying the job based at least in part on the determination.

Abstract: A method includes fusing multi-modal sensor data from a plurality of sensors having different modalities. At least one region of interest is detected in the multi-modal sensor data. One or more patches of interest are detected in the multi-modal sensor data based on detecting the at least one region of interest. A model that uses a deep convolutional neural network is applied to the one or more patches of interest. Post-processing of a result of applying the model is performed to produce a post-processing result for the one or more patches of interest. A perception indication of the post-processing result is output.