A system maintains a knowledge layout to support the analysis of active events and determination of epicenter and aftershock nodes via an event reach stack. At an input layer of the event reach stack, the system may receive active event data. At a semantic layer, the system may parse the active event data to determine event phrases. Based on the event phrases, the system may identify epicenter nodes directly affected by the active event. At an analytic model layer, the system may successively determine aftershock nodes by traversing the knowledge layout outward from the epicenter nodes. The system then directs the response to the active event to the aftershock and epicenter nodes, via action at a focus response layer of the event reach stack.

A compression policy to produce compression profiles for compressing trained machine learning models may be trained using reinforcement learning. An iterative reinforcement learning may be performed response to a search request. Different prospective compression profiles may be generated for received machine learning models according to a compression policy being trained. Performance of compressed versions of the trained neural networks according to the compression profiles may be caused using data sets used to train the machine learning models. The compression policy may be updated according to reward signal determined from an application of a reward function for performance criteria to performance results of the different versions of the machine learning models. When a search criteria is satisfied, the trained compression policy may be provided.

The present disclosure relates to a computer-implemented method and an apparatus for classifying anomalies of one or more feature-associated anomalies in network data traffic between devices in a first part of a network and devices in a second part of the network. The method comprises retrieving at least one network data traffic sample and determining one or more feature-associated anomaly scores for the retrieved at least one network data traffic sample. The method further comprises determining feature importance of each feature of a feature-associated anomaly score and classifying one or more anomalies based on the determined one or more feature-associated anomaly scores and the determined feature importance.

An approach is provided in which a method, system, and program product display, on a user interface, at least one of a set of node split parameters in response to receiving a first user selection that selects a node in a decision tree. The selected node branches to a set of child nodes in the decision tree based on the set of node split parameters. The method, system, and program product adjust at least one of the set of node split parameters of the selected node in response to receiving a second user selection. The method, system, and program product modify the decision tree based on the adjusted set of node split parameters. The modified decision tree includes a modified set of child nodes that branch from the selected node based on the adjusted set of node split parameters.

A share allocation (SA) computing device includes a processor in communication with a database. The processor is configured to execute a computational model including a plurality of model layers. The plurality of model layers includes a fractional node layer configured to assign each candidate investor of a plurality of candidate investors to a corresponding node. Each node is associated with a weight, and the nodes define an interconnected neural network. The fractional node layer is also configured to apply a machine learning algorithm configured to adjust the weights of the nodes in response to respective fitness values input to the nodes, and convert the adjusted weight for each node into a corresponding fraction. The fractional node layer is further configured to allocate, to each candidate investor, a respective fractional share of an offering, the fractional share corresponding to the fraction associated with the corresponding node.

Techniques for data pattern analysis using deterministic finite automaton are described herein. In one embodiment, a number of transitions from a current node to one or more subsequent nodes representing one or more sequences of data patterns is determined, where each of the current node and subsequent nodes is associated with a deterministic finite automaton (DFA) state. A data structure is dynamically allocated for each of the subsequent nodes for storing information associated with each of the subsequent nodes, where data structures for the subsequent nodes are allocated in an array maintained by a data structure corresponding to the current node if the number of transitions is greater than a predetermined threshold. Other methods and apparatuses are also described.

Techniques for data pattern analysis using deterministic finite automaton are described herein. In one embodiment, a number of transitions from a current node to one or more subsequent nodes representing one or more sequences of data patterns is determined, where each of the current node and subsequent nodes is associated with a deterministic finite automaton (DFA) state. A data structure is dynamically allocated for each of the subsequent nodes for storing information associated with each of the subsequent nodes, where data structures for the subsequent nodes are allocated in an array maintained by a data structure corresponding to the current node if the number of transitions is greater than a predetermined threshold. Other methods and apparatuses are also described.

A vehicle having one or more cameras, configured to record one or more images of a person approaching the vehicle. The camera(s) can be configured to send biometric data derived from the image(s). The vehicle can include a computing system configured to receive the biometric data and to determine a risk score of the person based on the received biometric data and an AI technique, such as an ANN or a decision tree. The received biometric data or a derivative thereof can be input for the AI technique. The computing system can also be configured to determine whether to notify a driver of the vehicle of the risk score based on the risk score exceeding a risk threshold. The vehicle can also include a user interface, configured to output the risk score to notify the driver when the computing system determines the risk score exceeds the risk threshold.

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.