Systems, devices, and methods discussed herein provide improved autonomous agent applications that are configured to provide explanations in response to user-submitted questions. Training data comprising a question, and an explanation pair may be accessed. A discourse tree and an explanation chain can be constructed from the explanation. The explanation chain may identify logical relationships between two entities of elementary discourse units identified from the discourse tree. A query may be submitted for the two entities, and a set of search results can be mined to identify text linking the two entities. An additional discourse tree can be generated from the text of a search result. The additional discourse tree can be combined with the original discourse tree to generate a complete discourse tree. A model may be trained using this augmented data (e.g., the complete discourse tree) to improve the quality of explanations provided by the autonomous agent application.

An embodiment of a method 100 for predicting a future state of a biological system is provided. The method 100 comprises receiving 101a microscope image depicting the biological system at an associated time and receiving 102 metadata corresponding to the microscope image. The method 100 further comprises extracting 103 features from the microscope image having information on a state of the biological system and using 104 the features and the metadata to predict the future state of the biological system.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

Aspects of the invention include detecting an error alert from a target computer system. In response to detecting the error alert, performance data is then retrieved from the target computer system. A gated recurrent unit (GRU) neural network is used to generate a prediction of a root cause of the error alert based on the performance data. The weights of a reset gate of the GRU neural network are adjusted based on received feedback of the prediction.

Techniques for monitoring a computing environment for anomalous activity are presented. An example method includes receiving a request to invoke an action within the computing environment. An anomaly score is generated for the received request by applying a probabilistic model to properties of the request. The anomaly score generally indicates a likelihood that the properties of the request correspond to historical activity within the computing environment for a user associated with the request. The probabilistic model generally comprises a model having been trained using historical activity within the computing environment for a plurality of users, the historical activity including information identifying an action performed in the computing environment and contextual information about a historical request. Based on the generated anomaly score, one or more actions are taken to process the request such that execution of requests having anomaly scores indicative of unexpected activity may be blocked pending confirmation.

The technology described herein obfuscates image content using a local neural network and a remote neural network. The local network runs on a local computer system and a remote classifier runs in a remote computing system. Together, the local network and the remote classifier are able to classify images, while the image never leaves the local computer system. In aspects of the technology, the local network receives a local image and creates a transformed object. The transformed object may be generated by processing the image with a local neural network to generate a multidimensional array and then randomly shuffling data locations within a multidimensional array. The transformed object is communicated to the remote classifier in the remote computing system for classification. The remote classifier may not have the seed used to deterministically scramble the spatial arrangement of data within the multidimensional array.

The present disclosure describes transaction-enabling systems and methods. A system can include a controller and a fleet of machines, each having at least one of a compute task requirement, a networking task requirement, and an energy consumption task requirement. The controller may include a resource requirement circuit to determine an amount of a resource for each of the machines to service the task requirement for each machine, a forward resource market circuit to access a forward resource market, and a resource distribution circuit to execute an aggregated transaction of the resource on the forward resource market.

A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

A vehicular trailer hitching assist system includes a camera disposed at a rear portion of a vehicle and viewing at least rearward of the vehicle. During a reversing maneuver of the vehicle toward a trailer that is spaced from the vehicle at a distance from the vehicle, the camera views at least a portion of a front profile of the trailer. An electronic control unit (ECU) includes an image processor operable to process image data captured by the camera. The vehicular trailer hitching assist system, via image processing at the ECU of image data captured by the camera during the reversing maneuver of the vehicle toward the trailer, determines a plurality of landmarks corresponding to the front profile of the trailer. Based at least in part on the determined plurality of landmarks, the vehicular trailer hitching assist system determines location of a trailer coupler of the trailer.

Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.