Methods and systems for performing diagnostic functions for a deep learning model are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a deep learning model configured for determining information from an image generated for a specimen by an imaging tool. The one or more components also include a diagnostic component configured for determining one or more causal portions of the image that resulted in the information being determined and for performing one or more functions based on the determined one or more causal portions of the image.

A system for authenticating a user attempting to access a computing device or a software application executing thereon. A data storage device stores one or more digital images or frames of video of face(s) of authorized user(s) of the device. The system subsequently receives from a first video camera one or more digital images or frames of video of a face of the user attempting to access the device and compares the image of the face of the user attempting to access the device with the stored image of the face of the authorized user of the device. To ensure the received video of the face of the user attempting to access the device is a real-time video of that user, and not a forgery, the system further receives a first photoplethysmogram (PPG) obtained from a first body part (e.g., a face) of the user attempting to access the device, receives a second PPG obtained from a second body part (e.g., a fingertip) of the user attempting to access the device, and compares the first PPG with the second PPG. The system authenticates the user attempting to access the device based on a successful comparison of (e.g., correlation between, consistency of) the first PPG and the second PPG and based on a successful comparison of the image of the face of the user attempting to access the device with the stored image of the face of the authorized user of the device.

A device can receive parking metadata that includes location data indicating that a portion of a vehicle is located outside of a designated parking area (DPA). The device can process the parking metadata to identify values that are to be used when determining actions to perform. The device can obtain supplemental events data associated with events occurring near the DPA. The device can determine the actions to perform based on the parking metadata and the supplemental events data. The device can provide, as one of the actions and to one or more other devices or to the vehicle, a message indicating that the portion of the vehicle is located outside of the DPA. This can cause the one or more other devices or the vehicle to: move the vehicle from the DPA, reposition the vehicle within the DPA, or penalize an owner of the vehicle.

Provided are a classification model training method, system, electronic device, and storage medium. The method includes: determining sampling rates of first-class samples and second-class samples in a data set, and setting the samples with a sampling rate less than a preset value as target samples (S101); determining data distribution feature information of the target samples based on Euclidean distances between all the samples in the data set (S102); wherein the data distribution feature information is information describing the number of same-class samples in nearest neighbor samples, and the nearest neighbor samples are two samples at a Euclidean distance less than a preset distance; generating new samples corresponding to the target samples based on the data distribution feature information (S103); and training the classification model using the first-class samples, the second-class samples and the new samples (S104).

One variation of a method for deploying sensors within an agricultural facility includes: accessing scan data of a set of modules deployed within the agricultural facility; extracting characteristics of plants occupying the set of modules from the scan data; selecting a first subset of target modules from the set of modules, each target module in the set of target modules containing a group of plants exhibiting characteristics representative of plants occupying modules neighboring the target module; for each target module, scheduling a robotic manipulator within the agricultural facility to remove a particular plant from a particular plant slot in the target module and load the particular plant slot with a sensor pod from a population of sensor pods deployed in the agricultural facility; and monitoring environmental conditions at target modules in the first subset of target modules based on sensor data recorded by the first population of sensor pods.

Methods and apparatuses for remote electrical tilt (RET) control are disclosed. According to an embodiment, a network entity obtains beam reports indicating beam candidates suitable for serving terminal devices in a serving area of an access network node. The network entity determines a spatial distribution of the beam candidates based on the beam reports. The network entity determines one or more boundaries dividing the beam candidates into a plurality of groups, based on the spatial distribution. The network entity determines control information related to RET for an antenna array of the access network node, based on the one or more boundaries.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data acquired continuously by a plurality of types of sensors deployed on the vehicle are first received, where the plurality of types of sensor data provide information about surrounding of the vehicle. Based on at least one model, one or more items are tracked from a first of the plurality of types of sensor data acquired by one or more of a first type of the plurality of types of sensors, wherein the one or more items appear in the surrounding of the vehicle. At least some of the one or more items are then automatically labeled on-the-fly via either cross modality validation or cross temporal validation of the one or more items and are used to locally adapt, on-the-fly, the at least one model in the vehicle.

An example apparatus for semantic image segmentation includes a receiver to receive an image to be segmented. The apparatus also includes a gated dense pyramid network including a plurality of gated dense pyramid (GDP) blocks to be trained to generate semantic labels for respective pixels in the received image. The apparatus further includes a generator to generate a segmented image based on the generated semantic labels.

A data processing method for a convolutional neural network includes: (a) obtaining a matrix parameter of an eigenmatrix; (b) reading corresponding data in an image data matrix from a first buffer space based on the matrix parameter through a first bus, to obtain a next to-be-expanded data matrix, and sending and storing the to-be-expanded data matrix to a second preset buffer space through a second bus; (c) reading the to-be-expanded data matrix, and performing data expansion on the to-be-expanded data matrix to obtain expanded data; (d) reading a preset number of pieces of unexpanded data in the image data matrix, sending and storing the unexpanded data to the second preset buffer space, and updating, based on the unexpanded data, the to-be-expanded data matrix; and (e). repeating (c) and (d) until all data in the image data matrix is completely read out on the to-be-expanded data matrix.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.