Abstract: The automatic operation system includes a plurality of learned imitation models and a model selecting unit. The learned imitation models are constructed by machine learning of operation history data, the operation history data being classified into several groups by an automatic classification system algorithm, the operation history data of each group being learned by the imitation model corresponding to the group. The operation history data include data indicating a surrounding environment and data indicating an operation of an operator in the surrounding environment. The model selecting unit selects one imitation model from several imitation models based on a result of classifying data indicating a given surrounding environment by the automatic classification algorithm of the classification system.

Abstract: Methods and apparatuses for feature-driven communications are described. A set of features describing an observed subject is transmitted by a transmitting electronic device (ED) to a base station (BS). The BS translates the received features to another set of transmission features to be transmitted to a receiving ED. The receiving ED recovers information about the subject from the features received from the BS.

Abstract: Systems, methods, and apparatus for beam steering and switching are disclosed. In one or more examples, a method for operating a communication system comprises switching, at least one switch in a rearrangeable switch network, to control input levels to power amplifiers in a power distribution network. The method further comprises outputting, by the power amplifiers in the power distribution network, power to a plurality of antenna elements. Further, the method comprises steering and distributing power, by the antenna elements, in beams associated with each of the antenna elements according to a level of the power in each of the antenna elements.

Abstract: An information processing apparatus includes a hardware processor which (i) performs learning by a learning data set associated with a correct answer label for a preset problem and creates a machine learning model for estimating a correct answer to the preset problem for input data, (ii) estimates the correct answer to the preset problem for the input data by using the machine learning model, (iii) in response to a user operation, determines a label indicating a result of the estimation as a correct answer label of the input data or corrects the label to determine the corrected label as a correct answer label of the input data, and (iv) additionally registers the determined correct answer label as learning data in association with the input data in the learning data set.

Abstract: A lesion detection ensemble machine learning model architecture comprising a plurality of trained machine learning (ML) computer models is provided. A first decoder of a lesion detection ML model processes a medical image input to generate a first lesion mapping prediction. A second decoder of the lesion detection ML model processes the medical image input to generate a second lesion mapping prediction. Combinational logic combines the first and second lesion mapping predictions to generate a combined prediction. Final lesion mapping output logic generates a final lesion prediction based on the combined lesion mapping prediction. The final lesion mapping output logic outputs the final lesion prediction for further downstream computing operations. The first decoder is trained with a first loss function that is configured to counterbalance a training of the second decoder that is trained using a second loss function different from the first loss function.

Abstract: A wearable article, system, and methods may include a structure configured to enclose a human body part. A first antenna, positioned with respect to the structure, is tuned to communicate, while the wearable article is being worn, according to a first wireless communication modality with a first external antenna. A second antenna, positioned with respect to the structure, is tuned to communicate according to a second wireless communication modality with a second external antenna different than the first external antenna, the second communication modality being different than the first communication modality. A transceiver, coupled to at least one of the first antenna and the second antenna, is configured to communicate via one of the first and second antennas based, at least in part, on the one of the first and second antennas coming into wireless communication contact with a corresponding one of the first and second external antennas.

Abstract: Systems and methods for automatically detecting, classifying, and processing objects captured in an images or videos are provided. In one embodiment, the system receives an image from an image source and detects one or more objects in the image. The system performs a high-level classification of the one or more objects in the image. The system performs a specific classification of the one or more objects, determines a price of the one or more objects, and generates a pricing report comprising a price of the one or more objects. In another embodiment, the system captures at least one image or video frame and classifies an object present in the image or video frame using a neural network. The system adds the classified object and an assigned object code to an inventory and processes the inventory to assign the classified object a price.

Abstract: In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.

Abstract: A method of operating a video camera system comprises recording video frames of a scene that includes a zone defined as an area within a field-of-view of the video capture element; performing a first analysis of recorded video frames showing the zone, determining, based on the first analysis, a first difference of composition thereof; performing a second analysis of an area outside of the zone; determining, based on the second analysis, a second difference of composition thereof; characterizing one or more properties corresponding to the second difference in composition; determining whether the one or more properties are associated with the first difference in composition within the zone and if so, characterizing the first difference in composition within the zone based on the one more properties and performing an action based on the characterization of the first difference in composition within the zone based on the one or more properties.

Abstract: A system of configuring a new device may include a new device that is not configured with one or more settings. The new device includes a short range communication transmitter and programming instructions configured to cause the new device to operate in a discoverable mode. The system includes an existing device that is configured with the settings, and that includes a short range communication receiver and programming instructions. The programming instructions are configured to cause the existing device to receive instructions to set up the new device, in response to receiving the instructions, detect, by the short range communication receiver, a presence of the new device by detecting the broadcast signal within a communication range of the short range communication receiver, and in response to detecting the presence of the new device, transmit at least a portion of the one or more settings directly to the new device.

Abstract: The present disclosure proposes a modeling method and apparatus for diagnosing an ophthalmic disease based on artificial intelligence, and a storage medium. The modeling method includes: establishing a data collection of ophthalmic images and a data collection of non-image ophthalmic disease diagnosis questionnaires; training a first neural network model by employing the data collection of the ophthalmic images to obtain a first classification model; training a second classification model by employing the data collection of non-image ophthalmic disease diagnosis questionnaires; and merging the first classification model and the second classification model to obtain a target classification network model, in which, a test result outputted by the target classification network model is used as a diagnosis result of the ophthalmic disease.

Abstract: In one example, a remote tuner module includes: a first tuner to receive, process and demodulate a first radio frequency (RF) signal to output an analog audio signal, and to receive and process a second RF signal to output a first downconverted modulated signal; a second tuner to receive and process the second RF signal to output a second downconverted modulated signal; a demodulator circuit coupled to the first and second tuners to demodulate and link the first and second modulated signals, to output a linked demodulated signal. The remote tuner module may further include a gateway circuit coupled to at least the demodulator circuit to output the analog audio signal and the linked demodulated signal.

Abstract: A beverage container lid can include an audio speaker; a wireless transceiver to transmit and receive wireless signals; and a hardware processor to process wireless signals received by the wireless transceiver and to output audible signals through the audio speaker. The beverage container lid can also include a water-tight housing, the wireless transceiver and hardware processor contained within the water-tight housing; a drinking aperture; and a fluid channel, the fluid channel to allow fluid to pass through the drinking aperture, and the water-tight housing to prevent fluid passing through the fluid channel from contacting the wireless transceiver and hardware processor.

Abstract: In various embodiments, a computer-implemented method of training a neural network for relighting an image is described. A first training set that includes source images and a target illumination embedding is generated, the source images having respective illuminated subjects. A second training set that includes augmented images and the target illumination embedding is generated, where the augmented images corresponding to the source images. A first autoencoder is trained using the first training set to generate a first output set that includes estimated source illumination embeddings and first reconstructed images that correspond to the source images, the reconstructed images having respective subjects that are i) from the corresponding source image, and ii) illuminated based on the target illumination embedding.

Abstract: The present disclosure describes a method, an apparatus, and storage medium for recognizing medical image. The method includes obtaining, by a device, a medical image. The device includes a memory storing instructions and a processor in communication with the memory. The method further includes determining, by the device, the medical image through a first recognition model to generate a lesion recognition result used for indicating whether the medical image comprises a lesion; and in response to the lesion recognition result indicating that the medical image comprises a lesion, recognizing, by the device, the medical image through a second recognition model to generate a lesion degree recognition result of the medical image used for indicating a degree of the lesion. Manual analysis and customization of a feature extraction solution are not required, so that the efficiency and accuracy of medical image recognition are improved.

Abstract: The subject matter discussed herein relates to systems and methods for generating a clinical outcome based on creating a task-specific model associated with processing raw image(s). In one such example, input raw data is acquired using an imaging system, a selection input corresponding to a clinical task is received, and a task-specific model corresponding to the clinical task is retrieved. Using the task-specific model, the raw data is mapped onto an application specific manifold. Based on the mapping of the raw data onto the application specific manifold the clinical outcome is generated, and subsequently providing the clinical outcome for review.

Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of images using iterative image inpainting. In particular, iterative inpainting utilize a confidence analysis of predicted pixels determined during the iterations of inpainting. For instance, a confidence analysis can provide information that can be used as feedback to progressively fill undefined pixels that comprise the holes, regions, and/or portions of an image where information for those respective pixels is not known. To allow for accurate image inpainting, one or more neural networks can be used. For instance, a coarse result neural network (e.g., a GAN comprised of a generator and a discriminator) and a fine result neural network (e.g., a GAN comprised of a generator and two discriminators).

Abstract: Techniques are provided for providing a user with retrieved images of a specific article of clothing or accessory worn by models having a particular body type and pose as selected by the user. The images have been analyzed to identify both a body type and pose of the model wearing the article of clothing in each of the images. The images are labeled based on at least body type and pose of the model and clustered based on their labels, and are thus available for retrieval by subsequent requests by a user. In particular, a user that is interested in the article of clothing can input one or more requests for images of models having a selected body type and pose. Any of the images labeled with the selected body type and pose can then be provided to the user in any number ways.

Abstract: The present invention discloses methods and systems for communicating at a cellular router between a first wireless communication module and a first subscriber identity module (SIM). The cellular router receives a first request from a first wireless communication module and encapsulates the first request in a first modified request. The cellular router then sends the first modified request to a first SIM card in a first communication apparatus and waits for a first modified reply. While waiting for the first modified reply the cellular router sends at least one halt message to the first wireless communication module after a first time threshold. After receiving the first modified reply, the cellular router decapsulates the first modified reply to retrieve a first reply and sends the first reply to the first wireless communication module where the first modified reply is a reply to the first modified request.