Abstract: The present document relates to audio source coding systems. In particular, the present document relates to audio source coding systems which make use of linear prediction in combination with a filterbank. A method for estimating a first sample (615) of a first subband signal in a first subband of an audio signal is described. The first subband signal of the audio signal is determined using an analysis filterbank (612) comprising a plurality of analysis filters which provide a plurality of subband signals in a plurality of subbands from the audio signal, respectively.

Abstract: A computer system and method for machine inductive learning on a graph is provided. In the inductive learning computational approach, an iterative approach is used for sampling a set of seed nodes and then considering their k-degree (hop) neighbors for aggregation and propagation. The approach is adapted to enhance privacy of edge weights by adding noise during a forward pass and a backward pass step of an inductive learning computational approach. Accordingly, it becomes more technically difficult for a malicious user to attempt to reverse engineer the edge weight information. Applicants were able to experimentally validate that acceptable privacy costs could be achieved in various embodiments described herein.

Abstract: A system is provided that includes an event site device having a display and an imaging device. The event site device is configured to capture an image of a user's face for identification. The system further includes a server configured to associate ticketing information with an identity of the user, receive the image of the user's face from the event site device, determine the identity of the user based on facial feature information stored for the user, retrieve the ticketing information associated with the user, and transmit the ticketing information to the event site device for presentation on the display.

Abstract: Training a neural network (NN) may include training a NN N, and for S, a version of N to be sparsified (e.g. a copy of N), removing NN elements from S to create a sparsified version of S, and training S using outputs from N (e.g. “distillation”). A boosting or reintroduction phase may follow sparsification: training a NN may include for a trained NN N and S, a sparsified version of N, re-introducing NN elements previously removed from S, and training S using outputs from N. The boosting phase need not use a NN sparsified by “distillation.” Training and sparsification, or training and reintroduction, may be performed iteratively or over repetitions.

Abstract: An example embodiment includes: an extraction unit that extracts a determination object image including a side part of an outer circumference of an iris from an image including an eye; and a determination unit that determines whether or not a colored contact lens is worn based on the determination object image.

Abstract: Techniques for determining predictions on a top-down representation of an environment based on vehicle action(s) are discussed herein. Sensors of a first vehicle (such as an autonomous vehicle) can capture sensor data of an environment, which may include object(s) separate from the first vehicle (e.g., a vehicle or a pedestrian). A multi-channel image representing a top-down view of the object(s) and the environment can be generated based on the sensor data, map data, and/or action data. Environmental data (object extents, velocities, lane positions, crosswalks, etc.) can be encoded in the image. Action data can represent a target lane, trajectory, etc. of the first vehicle. Multiple images can be generated representing the environment over time and input into a prediction system configured to output prediction probabilities associated with possible locations of the object(s) in the future, which may be based on the actions of the autonomous vehicle.

Abstract: An example embodiment includes: a determination unit that, based on an image including an eye of a recognition subject, determines whether or not a colored contact lens is worn; and a processing unit that, when it is determined by the determination unit that the colored contact lens is worn, performs a process of improving accuracy of iris matching on the recognition subject.

Abstract: A learning apparatus that can realize efficient machine learning is provided. A learning apparatus that learns a set value in a machine learning model based on predetermined image data for learning includes an inverting unit that inverts data of at least a part of respective channels in the image data for learning, an input unit that inputs the inverted data to the machine learning model, an output unit that can compare data obtained by inverting data output from the machine learning model with training data, and/or data output from the machine learning model with data obtained by inverting training data, and a learning process executing unit that learns the set value according to a result of the comparison.

Abstract: A system and method for de novo drug discovery using machine learning algorithms. In a preferred embodiment, de novo drug discovery is performed via data enrichment and interpolation/perturbation of molecule models within the latent space, wherein molecules with certain characteristics can be generated and tested in relation to one or more targeted receptors. Filtering methods may be used to determine active novel molecules by filtering out non-active molecules and contain activity predictors to better navigate the molecule-receptor domain. The system may comprise neural networks trained to reconstruct known ligand-receptors pairs and from the reconstruction model interpolate and perturb the model such that novel and unique molecules are discovered. A second preferred embodiment trains a variational autoencoder coupled with a bioactivity model to predict molecules exhibiting a range of desired properties.

Abstract: Systems, methods, and storage media for performing actions in response to a determined spoken command of a user are disclosed.

Abstract: A system and method for optimizing the latent space in generative machine learning models, and applications of the optimizations for use in the de novo generation of molecules for both ligand-based and pocket-based generation. The ligand-based optimizations comprise a tunable reward system based on a multi-property model and further define new measurable metrics: molecular novelty and uniqueness. The pocket-based optimizations comprise an initial multi-property optimization followed up by either a seed-based optimization or a relaxed-based optimization.

Abstract: Graphical elements in a user interface (UI) may be detected in robotic process automation (RPA) using convolutional neural networks (CNNs). Such processes may be particularly well-suited for detecting graphical elements that are too small to be detected using conventional techniques. The accuracy of detecting graphical elements (e.g., control objects) may be enhanced by providing neural network-based processing that is robust to changes in various UI factors, such as different resolutions, different operating system (OS) scaling factors, different dots-per-inch (DPI) settings, and changes due to UI customization of applications and websites, for example.

Abstract: Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

Abstract: A method of controlling a nuclear power plant includes obtaining sensor data from one or more sensors of the nuclear power plant, providing the sensor data and a desired plant response to a neural network, wherein the neural network has been previously trained using a simulated nuclear power plant and is structured to determine at least one control system setting to achieve the desired plant response, determining at least one control system setting to achieve the desired plant response with the neural network, and setting or changing at least one control system setting of a control system of the nuclear power plant to the at least one control system setting determined by the neural network.

Abstract: Aspects relate to systems and methods for parsing and correlating solicitation video content. An exemplary system includes a computing device configured to receive a solicitation video related to a subject, where the solicitation video includes at least an image component and at least an audio component, where the audio component includes audible verbal content related to at least an attribute of the subject, transcribe at least a keyword as a function of the audio component, and associate the subject with at least a job description as a function of the at least a keyword.

Abstract: A system and method for accelerating the calculations of free energy differences by automating FEP-path-decision-making and replacing the standard series of alchemical interpolations typically created by molecular dynamic (MD) simulations with voxelated interpolated states. A novel machine learning approach comprising a restricted variational autoencoder (ResVAE) is used which can reduce the computational-cost associated with interpolations by restricting the dimensions of a molecular latent space. The ResVAE generates a model based on flow-based transformations of a 3D-VAE latent point that is trained to maximize the log-likelihood of MD samples which enables the model to compute transformations more efficiently between molecules and also handle deletions of atoms more efficiently during iterative FEP calculation steps.

Abstract: Detecting a chewing noise from a user during a chewing session, triggering operation of a camera, obtaining image data capturing a food product, identifying the food product based on image data, determining a measurement of the chewing session, determining a volume of the food product based on the measurement of the chewing session, and determining a calorie intake based on the food product, the volume of the food product, and the measurement of the chewing session.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for status monitoring using machine vision and machine learning. In some implementations, image data representing a monitored area is obtained. Input data based on the image data is provided to one or more machine learning models trained to detect different properties of the monitored area. Output of the one or more machine learning models is received. The output of the one or more machine learning models is evaluated to detect a condition present in the monitored area. Output is provided indicating the detected condition present in the monitored area.

Abstract: An apparatus for driving a motor comprising a first plurality of neurons of neural network circuitry, motor circuitry, and a second plurality of neurons of the neural network circuitry. The first plurality of neurons is configured to generate a first cycle value based on a target speed. The motor circuitry is configured to control, based on the first cycle value, a set of switching elements to drive the motor. The second plurality of neurons is configured to train the second plurality of neurons to generate, based on a resulting speed value for the motor that occurs when the motor circuitry has controlled the set of switching elements to drive the motor based on the first cycle value, a second cycle value to minimize a difference between the second cycle value and the first cycle value.

Abstract: A system and method for patient health data prediction and analysis which utilizes an automated text mining tool to automatically format ingested electronic health record data to be added to a knowledge graph, which enriches the edges between nodes of the knowledge graph with fully interactive edge data, which can extract a subgraph of interest from the knowledge graph, and which analyzes the subgraph of interest to generate a set of variables that define the subgraph of interest. The system utilizes a knowledge graph and data analysis engine capabilities of the data platform to extract deeper insights based upon the enriched edge data.