Abstract: A data processing apparatus is provided that includes forecast circuitry for generating a forecast of an aspect of a system for a next future time and for one or more subsequent future times following the next future time. Measurement circuitry generates, at the next future time, a new measurement of the aspect of the system. Aggregation circuitry produces an aggregation of the forecast of the aspect of the system for the next future time and of the new measurement of the aspect of the system. The forecast circuitry revises the forecast of the aspect of the system for the one or more subsequent future times using the aggregation.

Abstract: Embodiments of a system and method for sorting and delivering articles in a processing facility based on image data are described. Image processing results such as rotation notation information may be included in or with an image to facilitate downstream processing such as when the routing information cannot be extracted from the image using an unattended system and the image is passed to an attended image processing system. The rotation notation information may be used to dynamically adjust the image before presenting the image via the attended image processing system.

Abstract: A method and an apparatus for training a transferable vision transformer (TVT) for unsupervised domain adaption (UDA) in heterogeneous devices are provided. The method includes that a heterogeneous device including one or more graphic processing units (GPUs) loads multiple patches into the TVT which includes a transferability adaption module (TAM). Furthermore, a patch-level domain discriminator in the TAM assigns weights to the multiple patches and determines one or more transferable patches based on the weights. Moreover, the heterogeneous device generates a transferable attention output for an attention module in the TAM based on the one or more transferable patches.

Abstract: Apparatuses and methods of operating such apparatuses are disclosed. An apparatus comprises feature dataset input circuitry to receive a feature dataset comprising multiple feature data values indicative of a set of features, wherein each feature data value is represented by a set of bits. Class retrieval circuitry is responsive to reception of the feature dataset from the feature dataset input circuitry to retrieve from class indications storage a class indication for each feature data value received in the feature dataset, wherein class indications are predetermined and stored in the class indications storage for each permutation of the set of bits for each feature. Classification output circuitry is responsive to reception of class indications from the class retrieval circuitry to determine a classification in dependence on the class indications. A predicated class may thus be accurately generated from a simple apparatus.

Abstract: A computer-implemented method and a system for computer guided surgery, which include a transposition of an action, planned in a virtual environment with respect to a virtual referential RP, to a physical action performed with a surgical tool in a real operating theatre environment for orthopedic surgery of a patient.

Abstract: Methods and systems are disclosed for performing operations comprising: receiving, by one or more processors, an image that includes a depiction of a face of a user; computing a real-world scale of the face of the user based on a selected subset of landmarks of the face of the user; obtaining an augmented reality graphical element comprising augmented reality eyewear; scaling the augmented reality graphical element based on the computed real-world scale of the face; and positioning the scaled augmented reality graphical element within the image on the face of the user.

Abstract: A method for determining a target image to be labeled includes: obtaining an original image and an autoencoder (AE) set, the original image being an image having not been labeled, the AE set including N AEs; obtaining an encoded image set corresponding to the original image by using the AE set, the encoded image set including N encoded images, the encoded images being corresponding to the AEs; obtaining the encoded image set and a segmentation result set corresponding to the original image by using an image segmentation network, the image segmentation network including M image segmentation sub-networks, and the segmentation result set including [(N+1)*M] segmentation results; determining labeling uncertainty corresponding to the original image according to the segmentation result set; and determining whether the original image is a target image according to the labeling uncertainty.

Abstract: According to an embodiment, a learning device includes one or more processors. The processors calculate a latent vector of each of a plurality of first target data, by using a parameter of a learning model configured to output a latent vector indicating a feature of a target data. The processors calculate, for each first target data, first probabilities that the first target data belongs to virtual classes on an assumption that the plurality of first target data belong to the virtual classes different from each other. The processors update the parameter such that a first loss of the first probabilities, and a second loss that is lower as, for each of element classes to which a plurality of elements included in each of the plurality of first target data belong, a relation with another element class is lower, become lower.

Abstract: A plurality of on-road vehicles moving along streets of a city, while utilizing onboard cameras for autonomous driving functions, are bound to capture, over time, random images of various city objects such as structures and street signs. Images of a certain city object may be captured by many of the vehicles at various times while passing by that object, thereby resulting in a corpus of imagery data containing various random images and other representations of that certain object. Per each of at least some of the city objects appearing more than once in the corpus of imagery data, appearances are detected and linked, and then analyzed to uncover behaviors associated with that city object, thereby eventually, over time, revealing various city dynamics associated with many city objects.

Abstract: Methods and systems for capturing motion and/or determining the shapes and positions of one or more objects in 3D space utilize cross-sections thereof. In various embodiments, images of the cross-sections are captured using a camera based on reflections therefrom or shadows cast thereby.

Abstract: A method for training a machine learning model to recognize an object topology of an object from an image of the object. The method includes: obtaining a 3D model of the object; determining a descriptor component value for each vertex of the grid; generating training data image pairs each having a training input image and a target image. The target image is generated by determining the vertex positions in the training input image; assigning the descriptor component value determined for the vertex at the vertex position to the position in the target image; and adapting at least some of the descriptor component values assigned to the positions in the target image or adding descriptor component values to the positions of the target image.

Abstract: Techniques for generating machine learning training data which corresponds to one or more downstream tasks are disclosed. In one example, a computer implemented method comprises generating one or more synthetic data instances for training a machine learning model, and determining a value of respective ones of the one or more synthetic data instances with respect to at least one task. One or more additional synthetic data instances for training the machine learning model are generated based at least in part on the values of the respective ones of the one or more synthetic data instances.

Abstract: Techniques for anomaly detection are described. An exemplary method includes receiving one or more requests to train an anomaly detection machine learning model using feedback-based training, the request to indicate one or more of a type of analysis to perform, a model selection indication, and a configuration for a training dataset; training the anomaly detection machine learning model according to the one or more requests using the training data; performing feedback-based training on the trained anomaly detection machine learning model; and using the retrained anomaly detection machine learning model.

Abstract: In a case where the operation program is started, a CPU of the mini-batch learning apparatus functions as a calculation unit, a specifying unit, and an evaluation unit. The calculation unit calculates an area ratio of each of a plurality of classes in mini-batch data. The specifying unit specifies, as a correction target class, a rare class of which the area ratio is lower than a setting value. The evaluation unit evaluates the class determination accuracy of the machine learning model by using a loss function. As correction processing, the evaluation unit sets a weight for a loss value of the rare class to be larger than a weight for a loss value of a class other than the rare class.

Abstract: Speed of first work is compared with speed of second work based on a first working period when a worker is caused to perform the first work of setting annotation data to first image data and a second working period when the worker is caused to perform the second work of correcting advance annotation data set based on a recognition result obtained by causing a predetermined recognizer to recognize the first image data, and, in a case where the first work is faster than the second work, the worker is requested to correct second image data in which advance annotation data is not set, while, in a case where the second work is faster than the first work, the worker is requested to correct advance annotation data set based on a recognition result obtained by causing the recognizer to recognize the second image data.

Abstract: A system is provided that considers allele fraction shifts as a function of copy number and clonal heterogeneity. The system leverages differences between allele frequencies to differentiate between somatic and normal variants in impure tumor samples. In solid tumors, stromal cells and infiltrating lymphocytes are typically interspersed among the tumor cells. The normal cell contamination in tumors can be leveraged to differentiate somatic from germline variants. We explicitly model allelic copy number and clonal sample fractions so that we can examine how these factors impact the power to detect somatic variants. The system models the copy number alterations, which can also affect the allele frequencies of both somatic and germline variants. The expected allele frequencies can be calculated. The expected allele frequencies for somatic and germline differ with tumor content for different copy number alterations.

Abstract: Improved (e.g., high-throughput, low-noise, and/or low-artifact) X-ray Microscopy images are achieved using a deep neural network trained via an accessible workflow. The workflow involves selection of a desired improvement factor (x), which is used to automatically partition supplied data into two or more subsets for neural network training. The neural network is trained by generating reconstructed volumes for each of the subsets. The neural network can be trained to take projection images or reconstructed volumes as input and output improved projection images or improved reconstructed volumes as output, respectively. Once trained, the neural network can be applied to the training data and/or subsequent data—optionally collected at a higher throughput—to ultimately achieve improved de-noising and/or other artifact reduction in the reconstructed volume.

Abstract: Systems and methods are disclosed for parsing resume documents using computer vision and optical character recognition technology in combination with a user feedback interface system to facilitate user feedback to improve the overall processing quality of the resumes that are imported into computer resume processing systems. In at least one embodiment, the system and method prompt a user to upload an input resume document, which is processed with a first parsing pass to generate initial resume data by extracting a plurality of resume text blocks. Further processing identifies an initial set of bounding blocks and to visually displays the initial resume data for user review and feedback to regroup one or more of the initial set of bounding blocks into a regrouped bounding block. Additional processing consolidates into a group text block each of the resume text blocks corresponding to the regrouped one or more of the initial set of bounding blocks.

Abstract: Systems and methods are provided for identifying a product in an image and outputting stock keeping units of the product. The system comprises three main components: a database server, a data analytics system and a standard dashboard. The database server contains real-time inventory images as well as historical images of each product type. The data analytics system is executed by a computer processor configured to apply a multi-head self-supervised learning-based classifier to detect product information captured by the image. The data analytics system is also configured to determine hierarchical classification categories for the product. The standard dashboard is configured to output a report regarding the product information.

Abstract: An artificial intelligence (AI) system utilizing a machine learning algorithm such as deep learning for controlling an electronic device when a video is reproduced and a user's voice instruction is received, to acquire a frame corresponding to the time point when the input of the user's voice instruction is received, and obtain a search result for information on objects in the frame using an AI model trained according to at least one of machine learning, a neural network or a deep learning algorithm.