Abstract: Techniques for determining predictions on a top-down representation of an environment based on object movement are discussed herein. Sensors of a first vehicle (such as an autonomous vehicle) may capture sensor data of an environment, which may include object(s) separate from the first vehicle (e.g., a vehicle, a pedestrian, a bicycle). A multi-channel image representing a top-down view of the object(s) and the environment may be generated based in part on the sensor data. Environmental data (object extents, velocities, lane positions, crosswalks, etc.) may also be encoded in the image. Multiple images may be generated representing the environment over time and input into a prediction system configured to output a trajectory template (e.g., general intent for future movement) and a predicted trajectory (e.g., more accurate predicted movement) associated with each object. The prediction system may include a machine learned model configured to output the trajectory template(s) and the predicted trajector(ies).

Abstract: In some embodiments, a method inputs a set of images into a network and trains the network based on a classification of the set of images to one or more characters in a set of characters. The method obtains a set of encodings for the one or more characters based on a layer of the network that restricts the output of the layer to a number of values. Then, the method stores the set of encodings for the one or more characters, wherein an encoding in the set of encodings is retrievable when a corresponding character is determined.

Abstract: In order to improve a user's task of documenting items that are identified by the user while at a construction site, a computing system may be configured to (i) analyze media content captured at a construction site (e.g., an audio or audiovisual recording) that is descriptive of at least one given on-site item identified by a user while at the construction site, (ii) extract, from the captured media content, a set of information for the given on-site item, (iii) based on the extracted set of information for the given on-site item, generate a data record for the given on-site item, and (iv) cause the generated data record for the given on-site item to be presented to the user for review.

Abstract: Disclosed are a method and an apparatus for recommending clothing coordination information in the 5G communication environment by executing an artificial intelligence (AI) algorithm and/or a machine learning algorithm, which have been mounted therein.

Abstract: A system and method for an automated pharmaceutical research utilizing contextual workspaces comprising a workspace drive engine, a data analysis engine, one or more machine and deep learning modules, a knowledge graph, and a workspace interface, which can create a virtual research workspace where data files containing biochemical data related to current research can be uploaded, which automatically processes and analyzes the uploaded data file to autonomously extract a plurality of information related to the uploaded data file, which performs various similarity searches on the uploaded data, and which formats and displays all the extracted information in the workspace, such that the workspace may provide a deeper contextualized view of the uploaded biochemical data.

Abstract: A system and method that produces an accurate probability distribution representative of a target molecule that may be used in pharmacokinetics and analogous applications. A generator is seeded from a variational autoencoder during training and is then used after training in series with a second variational autoencoder to produce the probability distributions from molecular tensors.

Abstract: A computer-implemented method for aligning a set of images which have shared structural characteristics, such as images of an official document. The method comprises acquiring image data comprising a set of images and applying, using a first deep neural network, at least one image transform to the image data to form aligned image data in which each image of the set of images is substantially aligned with a template image. Then, the aligned image data is compressed and the image data reconstructed from the compressed image data, and a set of aligned images is output from the reconstructed image data. The set of aligned images may be annotated for automated official document authentication.

Abstract: A system and method for biomarker-outcome prediction and medical literature exploration which utilizes a data platform to analyze, optimize, and explore the knowledge contained in or derived from clinical trials. The system utilizes a knowledge graph and data analysis engine capabilities of the data platform. The knowledge graph may be used to link biomarkers with molecules, proteins, and genetic data to provide insight into the relationship between biomarkers, outcomes, and adverse events. The system uses natural language processing techniques on a large corpus of medical literature to perform advanced text mining to identify biomarkers associated with adverse events and to curate a comprehensive profile of biomarker-outcome associations. These associations may then be ranked to identify the most-common biomarker-outcome association pairs.

Abstract: A system and method that predicts whether a given protein-ligand pair is active or inactive, the ground-truth protein-ligand complex crystalline-structure similarity, and an associated bioactivity value. The system and method further produce 3-D visualizations of previously unknown protein-ligand pairs that show directly the importance assigned to protein-ligand interactions, the positive/negative-ness of the saliencies, and magnitude. Furthermore, the system and method make enhancements in the art by accurately predicting protein-ligand pair bioactivity from decoupled models, removing the need for docking simulations, as well as restricting attention of the machine learning between protein and ligand atoms only.

Abstract: A system and method that predicts whether a given protein-ligand pair is active or inactive and outputs a pose score classifying the propriety of the pose. A 3D bioactivity platform comprising a 3D bioactivity module and data platform scrapes empirical lab-based data that a docking simulator uses to generate a dataset from which a 3D-CNN model is trained. The model then may receive new protein-ligand pairs and determine a classification for the bioactivity and pose propriety of that protein-ligand pair. Furthermore, gradients relating to the binding affinity in the 3D model of the molecule may be used to generate profiles from which new protein targets may be determined.

Abstract: A smart device control method and apparatus, and a storage medium, relating to the technical field of smart devices.

Abstract: In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.

Abstract: A model training method and apparatus is disclosed, where the model training method acquires first output data of a student model for first input data and second output data of a teacher model for second input data and trains the student model such that the first output data and the second output data are not distinguished from each other. The student model and the teacher model have different structures.

Abstract: Methods and systems are provided for reducing anomalies in ultrasound images. One example method includes combining a plurality of sub-band components to attenuate anomalies in a first ultrasound image formed by the combination of the plurality of sub-band components, wherein the plurality of sub-band components are combined based on a plurality of adaptive weights output from a machine learning model and wherein the plurality of adaptive weights are associated with the plurality of sub-band components. The method further includes outputting for a display device, the first ultrasound image.

Abstract: Provided is an information processing system including: a comparison information acquisition unit that acquires comparison information regarding iris comparison generated based on an iris image including an iris of a recognition subject; and a display image generation unit that generates a display image including an image indicating a content of the comparison information in association with positions in the iris.

Abstract: Described is a system for action recognition through application of deep embedded clustering. For each image frame of an input video, the system computes skeletal joint-based pose features representing an action of a human in the image frame. Non-linear mapping of the pose features into an embedded action space is performed. Temporal classification of the action is performed and a set of categorical gesture-based labels is obtained. The set of categorical gesture-based labels is used to control movement of a machine.

Abstract: An apparatus, method, system and computer readable medium for video tracking. An exemplar crop is selected to be tracked in an initial frame of a video. Bayesian optimization is applied with each subsequent frame of the video by building a surrogate model of an objective function using Gaussian Process Regression (GPR) based on similarity scores of candidate crops collected from a search space in a current frame of the video. A next candidate crop in the search space is determined using an acquisition function. The next candidate crop is compared to the exemplar crop using a Siamese neural network. Comparisons of new candidate crops to the exemplar crop are made using the Siamese neural network until the exemplar crop has been found in the current frame. The new candidate crops are selected based on an updated surrogate model.

Abstract: A data extraction and expansion system receives documents with data to be processed, extracts a set of a specific type of entities from the received documents, expands the set of entities by retrieving additional entities of the specific type from an ontology and other external data sources to improve the match between the received documents. The ontology includes data regarding entities and relationships between entities. The ontology is built by extracting the entity and relationship information from external data sources and can be constantly updated. If the additional entities to expand the set of entities cannot be retrieved from the ontology then a real-time search of the external data sources is executed to retrieve the additional entities from the external data sources.

Abstract: Artificial intelligence systems are created for end users based on raw data received from the end users or obtained from any source. Training, validation and testing data is maintained securely and subject to authentication prior to use. A machine learning model is selected for providing solutions of any type or form and trained, verified and tested by an artificial intelligence engine using such data. A trained model is distributed to end users, and feedback regarding the performance of the trained model is returned to the artificial intelligence engine, which updates the model on account of such feedback before redistributing the model to the end users. When an end user provides data to an artificial intelligence engine and requests a trained model, the end user monitors progress of the training of the model, along with the performance of the model in providing quality artificial intelligence solutions, via one or more dashboards.

Abstract: Computer-implemented techniques for sematic image retrieval. According to one technique, digital images are classified into N number of categories based on their visual content. The classification provides a set of N-dimensional image vectors for the digital images. Each image vector contains up to N number of probability values for up to N number of corresponding categories. An N-dimensional image match vector is generated that projects an input keyword query into the vector space of the set of image vectors by computing the vector similarities between a word vector for the input query and a word vector for each of the N number of categories. Vector similarities between the image match vectors and the set of image vectors can be computed to determine images semantically relevant to the input query.