A discrete three-dimensional (3-D) processor comprises stacked first and second dice. The first die comprises three-dimensional memory (3D-M) arrays, whereas the second die comprises at least a portion of a logic/processing circuit and an off-die peripheral-circuit component of the 3D-M array(s). The preferred 3-D processor can be used to compute non-arithmetic function/model. In other applications, the preferred 3-D processor may also be a 3-D configurable computing array, a 3-D pattern processor, or a 3-D neuro-processor.

A technique for arranging nodes on a landscape based on a viewpoint desired by a user is provided. One aspect of the present disclosure relates to a node processing apparatus for synthesizing and extracting feature quantities that meet the needs of a user analysis from a plurality of types of feature quantities assigned for each node of a node set, and the node processing apparatus includes a receiving unit configured to receive, from a user, a designation related to an arrangement of nodes selected from the node set on an analysis axis assumed by the user, and a node processing unit configured to synthesize and extract feature quantities based on the arrangement of the received designation.

Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

A information processing apparatus (2000) includes a determination unit (2160) and a deletion unit (2180). The determination unit (2160) determines whether feature information to be determined satisfies a predetermined condition. When feature information to be determined is determined to satisfy the predetermined condition, the deletion unit (2180) deletes the feature information to be determined from the storage apparatus (120).

Smart sensor methods and systems are described that improve on prior systems. An example device includes a sensor, a memory, a network connection, and two processing units, wherein a first processing unit compares current data provided by the first sensor to the reference data previously provided by the first sensor. Based on the result of the comparison, a second processing unit may be enabled to process the current data, or may be disabled to prevent the second processing unit from processing the current data.

A computational microscope and a method for its operation are disclosed. In some embodiments, the microscope maps points on a sample to point in an intensity pattern on a one-to-many basis. The microscope utilizes illumination angle coding, polarization coding, amplitude coding, and phase coding to capture more information than prior art computational microscopes. Although the resulting intensity patterns are not human-interpretable images of the sample, they contain more information about the sample, by virtue of the aforementioned coding techniques, than is captured by prior-art microscopes. Machine-learning algorithms, such as neural networks, are used to analyze the intensity patterns and extract useful information, such as cellular events or cell behavior.

The present disclosure relates to computer-implemented methods, software, and systems for predicting failure event occurrence for a machine asset. Run-to-failure sequences of time series data that include an occurrence of a failure event for the machine asset are received. One or more candidate cut-off values are determined based on iterative evaluation of a plurality of potential cut-off points. A candidate cut-off value is identified as substantially corresponding to a local peak point for calculated distances between relative frequency distributions of positive and negative sub-sequences. A failure prediction model is iteratively trained to iteratively extract sets of relevant features to determine a prediction horizon for an occurrence of the failure event for the machine asset. A candidate cut-off value associated with a model of highest quality from a set of failure prediction models determined during the iterations is selected to determine the prediction horizon for the machine asset.

An apparatus including an interface and a processor. The interface may be configured to receive pixel data generated by a capture device. The processor may be configured to generate video frames in response to the pixel data, perform computer vision operations on the video frames to detect objects, perform a classification of the objects detected based on characteristics of the objects, determine whether the classification of the objects corresponds to a user-defined event and generate encoded video frames from the video frames. The encoded video frames may be communicated to a cloud storage service. The encoded video frames may comprise a first sample of the video frames selected at a first rate when the user-defined event is not detected and a second sample of the video frames selected at a second rate while the user-defined event is detected. The second rate may be greater than the first rate.

An object recognition method of a refrigerator is disclosed. The disclosed object recognition method of a refrigerator comprises the steps of: obtaining a captured image of a storage compartment of a refrigerator; checking the change in the imaging direction of an image capturing device which has captured the image of the storage compartment, when a change in the captured image is confirmed compared to a previously stored image; and performing an object recognition operation of the captured image when the imaging direction is maintained.

Techniques for detecting attributes and/or gestures associated with pedestrians in an environment are described herein. The techniques may include receiving sensor data associated with a pedestrian in an environment of a vehicle and inputting the sensor data into a machine-learned model that is configured to determine a gesture and/or an attribute of the pedestrian. Based on the input data, an output may be received from the machine-learned model that indicates the gesture and/or the attribute of the pedestrian and the vehicle may be controlled based at least in part on the gesture and/or the attribute of the pedestrian. The techniques may also include training the machine-learned model to detect the attribute and/or the gesture of the pedestrian.