Abstract: Methods and systems for managing unlocking of an electronic device are provided. A method includes receiving an unlocking input from a user, authenticating the unlocking input, generating a first pre-wakeup command of initializing a progress action for the electronic device, generating a second pre-wakeup command of whether to complete the progress action or to retrogress the progress action based on the authenticating of the unlocking input, and determining whether to unlock the electronic device based on the second pre-wakeup command.

Abstract: The techniques disclosed herein enable systems to perform repeatable and iterative load testing and performance benchmarking for artificial intelligence models deployed in a cloud computing environment. This is achieved by utilizing load profiles and representative workloads generated based on the load profiles to evaluate an artificial intelligence model under various workload contexts. The representative workload is then executed by the artificial intelligence model utilizing available computing infrastructure. Performance metrics are extracted from the execution and analyzed to provide insight into various performance dynamics such as the relationship between latency and data throughput. In addition, load profiles and input datasets are dynamically adjusted to evaluate different scenarios and use cases enabling the system to automatically test the artificial intelligence model across diverse applications.

Abstract: Disclosed herein are formulations, substrates, and arrays. Also disclosed herein are methods for manufacturing and methods of assuring a uniformly high quality of a microarray of features that are attached to a surface of the microarray at positionally-defined locations.

Abstract: Exemplary embodiments provide distributed parallel training of a machine learning model. Multiple processors may be used to train a machine learning model to reduce training time. To synchronize trained model data between the processors, data is communicated between the processors after some number of training cycles. To improve the communication efficiency, exemplary embodiments synchronize data among a set of processors after a predetermined number of training cycles, and synchronize data between one or more processors of each set of the processors after a predetermined number of training cycles. During the first synchronization among a set of processors, compressed model gradient data generated after performing the training cycles may be communicated. During the second synchronization between the set of processors, trained models or full model gradient data generated after performing the training cycles may be communicated.

Abstract: The invention relates to method and system for classifying faces of a Boundary Representation (B-Rep) model using Artificial Intelligence (AI). The method includes extracting topological information corresponding to each of a plurality of data points of a B-Rep model of a product; determining a set of parameters based on the topological information corresponding to each of the plurality of data points; transforming the set of parameters corresponding to each of the plurality of data points of the B-Rep model into a tabular format to obtain a parametric data table; and assigning each of the plurality of faces of the B-Rep model a category from a plurality of categories based on the parametric data table using an AI model.

Abstract: Disclosed herein are formulations, substrates, and arrays. Also disclosed herein are methods for manufacturing and methods of assuring a uniformly high quality of a microarray of features that are attached to a surface of the microarray at positionally-defined locations.

Abstract: The disclosure relates to method and system for identifying a weldment feature. The method may include extracting a plurality of wire bodies from a sheet-metal model, and identifying from the plurality of wire bodies, a set of wire bodies associated with a face of the sheet-metal model. The method may further include generating one or more potential weldment features from the set of wire-bodies. Each of the one or more potential weldment features may be analyzed with respect to the face of the sheet-metal model. The method may further include identifying from the one or more potential weldment features, at least one related pair of weldment features. Weldment features of each related pair may include one of a contacting relationship and crossing relationship with each other.

Abstract: Disclosed herein are formulations, substrates, and arrays. Also disclosed herein are methods for manufacturing and using the formulations, substrates, and arrays. Also disclosed are methods for identifying peptide sequences useful for diagnosis and treatment of disorders, and methods for using the peptide sequences for diagnosis and treatment of disorders, e.g., celiac disorder. In certain embodiments, substrates and arrays comprise a porous layer for synthesis and attachment of polymers or biomolecules.

Abstract: The invention relates to method and system for automatically identifying tube elements in a Boundary Representation (B-Rep)-based Computer Aided Design (CAD) model of a tube. The method includes extracting information corresponding to the B-Rep-based CAD model of the tube; validating geometrical features of the B-Rep based CAD model of the tube based on the extracted information; classifying each of a plurality of faces of the tube into one of a set of face types; determining one or more regions on the tube using a set of connected top faces of the tube; generating a plurality of primary tube elements and a set of secondary tube elements based on shapes of the plurality of regions; determining a plurality of element parameters for each of the plurality of tube elements of the tube; and determining a plurality of tube parameters for the tube based on the plurality of element parameters.

Abstract: The invention relates to method and system for classifying faces of a Boundary Representation (B-Rep) model using Artificial Intelligence (AI).

Abstract: Disclosed herein are methods for collecting data from chip arrays and for piecewise real-time scanning and stitching of said data. Such arrays can comprise a plurality of microarrays, each microarray comprising biomolecular features that are attached to a surface of the microarray.

Abstract: Synthetic molecular tags are placed on an item at various points in a supply chain to create a molecular record of movement through the supply chain. Associations between each unique synthetic molecular tag and individual locations in the supply chain are stored in an electronic record which may be maintained in the cloud. The synthetic molecular tags are collected from the item and sequenced to determine movement of the item through the supply chain by reference to the electronic record. The synthetic molecular tags can be used for identifying recalled items based on locations in the supply chain associated with a recall. The synthetic molecular tags may be polynucleotides such as deoxyribose nucleic acid (DNA). The item may be any type of item including food.

Abstract: Disclosed herein are formulations, substrates, and arrays. Also disclosed herein are methods for manufacturing and using the formulations, substrates, and arrays. Also disclosed are methods for identifying peptide sequences useful for diagnosis and treatment of disorders, and methods for using the peptide sequences for diagnosis and treatment of disorders, e.g., celiac disorder. In certain embodiments, substrates and arrays comprise a porous layer for synthesis and attachment of polymers or biomolecules.

Abstract: Diagnostics and boot up for AV hardware and software of a computer system of an autonomous vehicle may be performed based at least on receiving a shutdown or power off indication, then a computing state of the computer system may be suspended with the computer system entering a low-power mode. The suspended computing state can be rapidly restored without requiring a reboot and diagnostics for key-on. To ensure the integrity of the saved computing state, the computer system may exit the low-power mode, rerun the diagnostics, reload the programs, and then reenter the low-power mode. Restoring the suspended computing state may be triggered by a user inserting an ignition key, pressing a button to turn on the vehicle, opening a door to the vehicle, remotely unlocking the vehicle, remotely starting the vehicle, etc.

Abstract: Diagnostics and boot up for AV hardware and software of a computer system of an autonomous vehicle may be performed based at least on receiving a shutdown or power off indication, then a computing state of the computer system may be suspended with the computer system entering a low-power mode. The suspended computing state can be rapidly restored without requiring a reboot and diagnostics for key-on. To ensure the integrity of the saved computing state, the computer system may exit the low-power mode, rerun the diagnostics, reload the programs, and then reenter the low-power mode. Restoring the suspended computing state may be triggered by a user inserting an ignition key, pressing a button to turn on the vehicle, opening a door to the vehicle, remotely unlocking the vehicle, remotely starting the vehicle, etc.

Abstract: A system configured to process speech commands may classify incoming audio as desired speech, undesired speech, or non-speech. Desired speech is speech that is from a same speaker as reference speech. The reference speech may be obtained from a configuration session or from a first portion of input speech that includes a wakeword. The reference speech may be encoded using a recurrent neural network (RNN) encoder to create a reference feature vector. The reference feature vector and incoming audio data may be processed by a trained neural network classifier to label the incoming audio data (for example, frame-by-frame) as to whether each frame is spoken by the same speaker as the reference speech. The labels may be passed to an automatic speech recognition (ASR) component which may allow the ASR component to focus its processing on the desired speech.

Abstract: Disclosed herein are formulations, substrates, and arrays. Also disclosed herein are methods for manufacturing and using the formulations, substrates, and arrays. Also disclosed are methods for identifying peptide sequences useful for diagnosis and treatment of disorders, and methods for using the peptide sequences for diagnosis and treatment of disorders, e.g., celiac disorder. In certain embodiments, substrates and arrays comprise a porous layer for synthesis and attachment of polymers or biomolecules.

Abstract: During functional/normal operation of an integrated circuit including multiple independent processing elements (such as processors), a selected independent processing element is taken offline (e.g., by stopping functional operation of the independent processing element), and the functionality of the selected independent processing element is then tested while the remaining independent processing elements continue functional operation (e.g., standard application-specific operations). This enables the selected processing element to be robustly tested without stopping the regular operation of the integrated circuit.

Abstract: Examples are disclosed that relate to an energy data platform. One example provides a method comprising receiving a first energy data set having a first data format, and a second energy data set having a second data format, and ingesting the first energy data set and the second energy data set by automatically converting one or more of the first energy data set and the second energy data set into a standard data format. The method further comprises receiving a request from a first application to provide the first energy data set in the first data format, and in response, providing the first energy data set in the first data format, and receiving a request from a second application to provide the first energy data set in the standard data format, and in response, providing the first energy data set in the standard data format.

Abstract: A computing device including a processor is provided. For one or more emissions factors, the processor may be configured to receive respective emissions factor data including sensor data received from one or more sensors and user-specified data received at a user interface. The processor may be further configured to receive a plurality of virtual sensor models. Each virtual sensor model may include one or more respective virtual sensor model parameters. At least in part by applying the virtual sensor models to the emissions factor data, the processor may be further configured to compute a plurality of virtual sensor readouts. The processor may be further configured to generate a greenhouse gas emissions estimate from the virtual sensor readouts. The processor may be further configured to convey an indication of the greenhouse gas emissions estimate to the user interface for output on one or more output devices.