Provided is an information processing apparatus that performs processing of sensor information using artificial intelligence. The information processing apparatus includes: a collection unit that collects first sensor information detected by a first sensor and second sensor information detected by a second sensor; a model generation unit that generates a learned model for estimating second sensor information corresponding to first sensor information on the basis of the first sensor information and the second sensor information that have been collected; an accumulation unit that accumulates the learned model; and a providing unit that provides a result of a service based on the learned model. The providing unit provides the learned model to the second apparatus in response to a request from the second apparatus.

Embodiments include a patch-type, ultrasound sensor system and method to monitor the function and motion of a patients anatomical structure, comprising processing at least one received ultrasound image using one or more analytical tools, including radon transformation, higher-order spectra techniques, and/or active contour models, to generate at least one processed ultrasound image; inputting the at least one processed ultrasound image into a deep learning Convolutional Neural Network to obtain an automatic classification result selected from two or more classes indicating the functional state of the anatomical structure. The patch-type, ultrasound sensor system can communicate via a wireless or wired connection. The monitoring can be at rest or during surgery or other procedure or whilst the subject is exposed to any physiological stressors as part of medical examinations, and can be adapted for use in monitoring the function of body structures including the heart, blood vessels, lungs or joints.

Systems, devices, and methods related to using model architecture search for hardware configuration are provided. An example apparatus includes an input node to receive an input signal; a pool of processing units to perform one or more arithmetic operations and one or more signal selection operations, wherein each of the processing units in the pool is associated with at least one parameterized model corresponding to a data transformation operation; and a control block to configure, based on a first parameterized model, a first subset of the processing units in the pool, where the first subset of the processing units processes the input signal to generate a first signal.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting an optimal feature point in a continuous domain for a group of agents. A computer-implemented system obtains, for each of a plurality of agents, respective training data that comprises a respective utility score for each of a plurality of discrete points in the continuous domain. The system trains, for each of the plurality of agents and on the respective training data for the agents, a respective neural network that is configured to receive an input comprising a point in the continuous domain and to generate as output a predicted utility score for the agent at the point. And the system identifies the optimal point by optimizing an approximation of the shared outcome function that is defined by, for any given point in the continuous domain, a combination of the predicted utility scores generated by the respective neural networks for each of the plurality of agents by processing an input comprising the given point.

The present invention relates to a method for determining a data item's membership in a database, the method comprising: a supervised training phase to obtain three trained neural networks, a phase of preparing the database by application of the first trained network to each data item of the base, and a utilization phase comprising the step of: using the first network on the data item, obtaining a binary value representative of the identity between the data item and a data item of the base by application of the third network, and selecting of those data items of the database for which the binary value obtained corresponds to an identity between the data item and the data items.

A deep learning based compression (DLBC) system trains multiple models that, when deployed, generates a compressed binary encoding of an input image that achieves a reconstruction quality and a target compression ratio. The applied models effectively identifies structures of an input image, quantizes the input image to a target bit precision, and compresses the binary code of the input image via adaptive arithmetic coding to a target codelength. During training, the DLBC system reconstructs the input image from the compressed binary encoding and determines the loss in quality from the encoding process. Thus, the models can be continually trained to, when applied to an input image, minimize the loss in reconstruction quality that arises due to the encoding process while also achieving the target compression ratio.

A system for the detection and classification of live microorganisms in a sample includes a light source and an incubator holding one or more sample-containing growth plates. A translation stage moves the image sensor and/or the growth plate(s) along one or more dimensions to capture time-lapse holographic images of microorganisms. Image processing software executed by a computing device captures time-lapse holographic images of the microorganisms or clusters of microorganisms on the one or more growth plates. The image processing software is configured to detect candidate microorganism colonies in reconstructed, time-lapse holographic images based on differential image analysis. The image processing software includes one or more trained deep neural networks that process the time-lapsed image(s) of candidate microorganism colonies to detect true microorganism colonies and/or output a species associated with each true microorganism colony.

An authentication method comprising creating electrocardiogram data of users; calculating a similarity between electrocardiogram data of each user and template data created by averaging electrocardiogram data of each user; creating and training a first NNmodel for every user by using similarities between electrocardiogram data of a user and template data of the same user and similarities between electrocardiogram data of a user and template data of another user, and creating and training second NNmodels for users by using similarities between electrocardiogram data of a user and template data of the user and similarities between electrocardiogram data of the user and template data of another user; and executing a first step in which the similarities calculated using electrocardiogram data for authentication of a user to be authenticated and template data are input to the first NNmodel, and executing a second step in which the similarities are input to the second NNmodels.

Among other things, we describe techniques for detecting objects in the environment surrounding a vehicle. A computer system is configured to receive a set of measurements from a sensor of a vehicle. The set of measurements includes a plurality of data points that represent a plurality of objects in a 3D space surrounding the vehicle. The system divides the 3D space into a plurality of pillars. The system then assigns each data point of the plurality of data points to a pillar in the plurality of pillars. The system generates a pseudo-image based on the plurality of pillars. The pseudo-image includes, for each pillar of the plurality of pillars, a corresponding feature representation of data points assigned to the pillar. The system detects the plurality of objects based on an analysis of the pseudo-image. The system then operates the vehicle based upon the detecting of the objects.

This disclosure relates to a system and a method for automated recognition of drugs. This disclosure also relates to a system for automated recognition of drugs comprising a hyper-spectral imaging system. This disclosure also relates to a hyper-spectral imaging system configured to automatically recognize drugs by using a neural network. This disclosure relates to training the neural network to identify a drug type (e.g., the name of the drug) based on an image (e.g., normal visible image and/or hyperspectral image) of the drug.