A training machine assembly comprises at least one control device and at least one training resistance. Each of the at least one training resistance comprises at least one training resistance value, such as a force applied towards the user contact element, e.g. a handle. The training resistance value can also comprise a function or a vector, for example a function linking a speed of movement of a user and/or a user contact element and a force applied against said movement. The control device can be a control device for controlling the training machine assembly. The training resistance can comprise an actuator. The actuator can comprise an electric motor. The training resistance can comprise a weight. The training resistance can comprise an element configured to provide a resistance against a movement of the user. The training machine assembly can comprise at least one camera.

A data processing system for training a neural network, the data processing system comprising: a first set of one or more processing units running one model of the neural network, a second set of one or more processing units running another model of the neural network, a data storage, and an interconnect between the first set of one or more processing units, the second set of processing units and the data storage, wherein the data storage is configured to provide over the interconnect, training data to the first set of one or more processing units and the second set of one more processing units, wherein each of the first and second set of processing units is configured to, when performing the training, evaluate loss for the respective training iteration including a measure of the dissimilarity between the output values calculated based on the different modes running on the first and second set of processing units, wherein the dissimilarity measure is weighted in the evaluation of the loss in accordance with a parameter that is updated between different training iterations.

A method and system for performing indicia recognition includes obtaining, at an image sensor, an image of an object of interest and identifying at least one region of interest in the image. The region of interest contains one or more indicia indicative of the object of interest. The processor then determines positions of each region of interest and further determines a geometric shape based on the positions of each of the regions of interest. An orientation classification is identified for each region of interest is based on a respective position relative to the geometric shape for reach region of interest. The processor then identifies and performs one or more transformations for each region of interest, with each transformation determined by each regions respective orientation classification. The processor then performs indicia recognition on each of the one or more transformed regions of interest.

Various embodiments include a method for product quality inspection on a group of products. The method may include: getting for each product in the group of products: image, value for each known fabrication parameter affecting quality of the group of products, and quality evaluation result; training a neural network. A layer of the neural network comprises at least one first neuron and at least one second neuron; each first neuron represents a known fabrication parameter affecting quality of the group of products and each second neuron represents an unknown fabrication parameter affecting quality of the group of products; and the images of the group of products are input to the neural network, the quality evaluation results are output of the neural network, and the value of each first neuron is set to the value for the known fabrication parameter the first neuron representing.

An electronic apparatus includes a processor configured to acquire a plurality of characteristic data of a plurality of users through a communication interface circuitry; identify a plurality of categories and reference characteristics for analyzing the plurality of characteristic data according to an input received through the communication interface circuitry; identify specific characteristic data that corresponds to the reference characteristics, among the plurality of characteristic data for each of the plurality of categories; identify a specific user having the specific characteristic data, among the plurality of users; and output an analysis result of the specific characteristic data of the specific user.

A method includes generating a plurality of vector sequences based on input signals of an electric circuit design and encoding the plurality of vector sequences. The method also includes clustering the plurality of encoded vector sequences into a plurality of clusters and selecting a set of encoded vector sequences from the plurality of clusters. The method further includes selecting a first set of vector sequences corresponding to the selected set of encoded vector sequences, selecting a second set of vector sequences from the plurality of vector sequences not in the first set of encoded vector sequences, and training, by a processing device, a machine learning model to predict power consumption using the first and second sets of vector sequences.

A vehicle terminal system for processing a message includes: a portable device for receiving the message; and a vehicle terminal for analyzing a text of the message received from the portable device to yield an analysis result, and for determining a recommended operation corresponding to the analysis result. Thus, the vehicle terminal does not output pop-up images for all messages received from the portable device and recommends a message suitable for the user.

A client device configured with a neural network includes a processor, a memory, a user interface, a communications interface, a power supply and an input device, wherein the memory includes a trained neural network received from a server system that has trained and configured the neural network for the client device. A server system and a method of training a neural network are disclosed.

Systems and methods for video analysis are provided. The systems and methods may utilize machine learning to recognize steps of a medical procedure as they are being performed, and compare them with expected steps. The systems and methods may aid in supporting a medical practitioner before the procedure, during the procedure, as well as providing feedback after the procedure has been completed.

Disclosed herein is a system for leveraging telemetry data representing usage of a component installed on a group of sampled computing devices to confidently infer the quality of a user experience and/or the behavior of the component (e.g., an operating system) on a larger group of unsampled computing devices. The system is configured to use a propensity score matching approach to identify a sampled computing device that best represents an unsampled computing device using configuration data that is collected from both the sampled and unsampled computing devices. The quality of the user experience and/or the behavior of the component may be captured by a metric of interest (e.g., a QoS value). Accordingly, the system is configured to use the known metric of interest, determined from the telemetry data collected for the sampled computing device, to determine or predict the metric of interest for the unsampled computing device.