A system and method for assisting drivers of vehicles are described. The systems and methods provide an extended view of the area surrounding the driver's vehicle while providing real-time object trajectory for objects and other vehicles that may enter the driver's reactionary zone. The system and methods capture images of the area surrounding the driver's vehicle and create a composite image of that area in real-time and using Augmented Reality (AR) create a 3-D overlay to warn the driver as objects or other vehicles enter the driver's reactionary zone so that a driver can make more informed driving decisions.

A weight estimation system includes: an image capturer that captures an image of an inside of a poultry house; a calculator that calculates a flocking behavior feature quantity of chickens in the poultry house by performing image processing on the image captured by the image capturer; and an estimator that estimates a weight for each chicken in the poultry house, based on the flocking behavior feature quantity calculated.

An information processing device includes a processor. The processor obtains an input image, inputs the input image to a machine learning model that executes classification likelihood calculation processing to obtain, for each of candidate objects in the input image, likelihoods of belonging to the plurality of classes, executes first determination on whether or not each of the candidate objects is classified as a first class of the plurality of classes using a likelihood of belonging to the first class that is a likelihood having a negative correlation with likelihoods of belonging to other classes, executes second determination on whether or not each of the candidate objects that have been determined in the first determination as a non-first class is classified as the other classes, and outputting a result of classifying the candidate objects included in the input image using a result of the second determination.

A method may include applying, to a corpus of data, a first machine learning technique to identify candidate domains of an ontology mapping brain structure to mental function. The corpus of data may include textual data describing a plurality of mental functions and spatial data corresponding to a plurality of brain structures. A second machine technique may be applied to optimize a quantity of domains included in the ontology and/or a quantity of mental function terms included in each domain. The ontology may be applied to phenotype an electronic medical record and predict a clinical outcome for a patient associated with the electronic medical record. Related systems and articles of manufacture, including computer program products, are also provided.

An apparatus to facilitate partially-frozen neural networks for efficient computer vision systems is disclosed. The apparatus includes a frozen core to store fixed weights of a machine learning model, one or more trainable cores coupled to the frozen core, the one or more trainable cores comprising multipliers for trainable weights of the machine learning model, and wherein the alpha blending layer includes a trainable alpha blending parameter, and wherein the trainable alpha blending parameter is a function of a trainable parameter, a sigmoid function, and outputs of frozen and trainable blocks in a preceding layer of the machine learning model.

A method for face recognition based vehicle access control. The method may include acquiring one or more images of at least a face of a person that is positioned outside the vehicle and accesses a door of the vehicle in order to determine, based on the one or more images, whether the person is allowed to enter the vehicle. The determination may include matching the face of the person to a database of faces of allowed persons. The database is generated during a machine learning process that involves monitoring persons within the vehicle; and then granting access to the vehicle when determining that the person is allowed to enter the vehicle.

A method for face recognition based vehicle access control. The method may include acquiring one or more images of at least a face of a person that is positioned outside the vehicle and accesses a door of the vehicle in order to determine, based on the one or more images, whether the person is allowed to enter the vehicle. The determination may include matching the face of the person to a database of faces of allowed persons. The database is generated during a machine learning process that involves monitoring persons within the vehicle; and then granting access to the vehicle when determining that the person is allowed to enter the vehicle.

A device for processing data sequences by means of a convolutional neural network is configured to carry out the following steps: receiving an input sequence comprising a plurality of data items captured over time using a sensor, each of said data items comprising a multi-dimensional representation of a scene, generating an output sequence representing the input sequence processed item-wise by the convolutional neural network, wherein generating the output sequence comprises: generating a grid-generation sequence based on a combination of the input sequence and an intermediate grid-generation sequence representing a past portion of the output sequence or the grid-generation sequence, generating a sampling grid on the basis of the grid-generation sequence, generating an intermediate output sequence by sampling from the past portion of the output sequence according to the sampling grid, and generating the output sequence based on a weighted combination of the intermediate output sequence and the input sequence.

Refined searching based on detected object configurations is provided by training a machine learning model to identify non-naturally occurring object configurations, acquiring images of an initial search area based on scanning it using a camera-equipped autonomous aerial vehicle operating in accordance with an initial automated flight plan defining the initial search area, analyzing the acquired images using the trained machine learning model and identifying that an object configuration is a non-naturally occurring object configuration, then based on identifying the non-naturally occurring object configuration, refining the initial automated flight plan to obtain a modified automated flight plan defining a different search area as compared to the initial search area, and initiating autonomous aerial scanning of the different search area in accordance with the modified automated flight plan.

Refined searching based on detected object configurations is provided by training a machine learning model to identify non-naturally occurring object configurations, acquiring images of an initial search area based on scanning it using a camera-equipped autonomous aerial vehicle operating in accordance with an initial automated flight plan defining the initial search area, analyzing the acquired images using the trained machine learning model and identifying that an object configuration is a non-naturally occurring object configuration, then based on identifying the non-naturally occurring object configuration, refining the initial automated flight plan to obtain a modified automated flight plan defining a different search area as compared to the initial search area, and initiating autonomous aerial scanning of the different search area in accordance with the modified automated flight plan.