Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to implement parallel architectures for neural network classifiers. An example non-transitory computer readable medium comprises instructions that, when executed, cause a machine to at least: process a first stream using first neural network blocks, the first stream based on an input image; process a second stream using second neural network blocks, the second stream based on the input image; fuse a result of the first neural network blocks and the second neural network blocks; perform average pooling on the fused result; process a fully connected layer based on the result of the average pooling; and classify the image based on the output of the fully connected layer.

Abstract: The present disclosure relates to a lithium-ion battery, comprising: a silicon anode comprising an essentially pure amorphous porous silicon film deposited onto a current collector, and comprising a plurality of columnar structures; an electrolyte layer comprising one or more solid components, and a cathode layer.

Abstract: A new approach of systems and methods to support automatic generation of multiple platform-dependent instruction sets from a single specification of an integrated circuit (IC). First, a specification compiler accepts as input a first instruction set of a plurality of first instructions in a specification format, wherein the first instruction set defines a design pattern of one or more specifications and/or requirements of the IC and is independent of any implementation or platform of the IC. The design tool then converts the first instruction set into a second instruction set of a plurality of second instructions in an intermediate format. A language compiler then accepts and compiles the second instruction set into a plurality of third instruction sets, wherein each of the plurality of third instruction sets comprises a plurality of third instructions in a specific language for a specific platform targeting a specific implementation or application of the IC.

Abstract: Techniques are described for replicating encryption keys using a write ahead log (WAL). An example method can include receiving a request from a user device to transmit encryption keys stored in a first virtual vault of a first hardware security module (HSM) of a first data center to a second virtual vault of a second HSM of a second data center, the request comprising an account identifier. The method can further include identifying a first account-specific WAL of a plurality of account-specific WALs based at least in part on the account identifier, each account-specific WAL corresponding to the first HSM, and configured to record changes to a respective virtual vault of the plurality of virtual vaults. The method can further include accessing the encryption keys from the first account-specific WAL of the first HSM. The method can further include transmitting the encryption keys to the second data center.

Abstract: Disclosed herein are systems, methods, and software for providing a platform for complex image data analysis using artificial intelligence and/or machine learning algorithms. One or more subsystems allow for the capturing of user input such as eye gaze and dictation for automated generation of findings. Additional features include quality metric tracking and feedback, and worklist management system and communications queueing.

Abstract: A method of providing access to a hardware security module (HSM) partition may include receiving request for access to the HSM partition from a client device. The request may include a leaf certificate signed with a public key associated with a user and a secret key associated with the client device. The method may include verifying the request using the leaf certificate and a trust anchor certificate signed with a public key associated with the client device. The method may include a first connection between the HSM partition and the client device. The method may include verifying the request using the leaf certificate and an authentication certificate stored on the HSM partition. The method may include establishing a second connection between the client device and the HSM partition such that the computing system is isolated from the second connection.

Abstract: A method may include receiving a request for a secure partition on an HSM from a client device and provisioning the secure partition on the HSM. The method may include generating a control server and a load balancer. The method may include generating, by a certificate service, a CSR signed by the certificate service. The method may include transmitting the CSR to the client device and receiving a first certificate including the public key of the first public private key pair and a private key of a second public private key pair. The method may include receiving a second certificate generated by an external certificate authority and signed with a public key of the second public private key pair. The method may include storing the first certificate and the second certificate on the secure partition in a location such that the second is accessible by the control server.

Abstract: In various examples, an autonomous system may use a multi-stage process to solve three-dimensional (3D) manipulation tasks from a minimal number of demonstrations and predict key-frame poses with higher precision. In a first stage of the process, for example, the disclosed systems and methods may predict an area of interest in an environment using a virtual environment. The area of interest may correspond to a predicted location of an object in the environment, such as an object that an autonomous machine is instructed to manipulate. In a second stage, the systems may magnify the area of interest and render images of the virtual environment using a 3D representation of the environment that magnifies the area of interest. The systems may then use the rendered images to make predictions related to key-frame poses associated with a future (e.g., next) state of the autonomous machine.

Abstract: Systems and methods for implementing an RPA (robotic process automation) cloud suite comprising a plurality of RPA related services are provided. Each of the plurality of RPA related services of the RPA cloud suite is associated with one of a plurality of nodes of a hierarchical model. A routing address for each respective RPA related service of the plurality of RPA related services is defined according to a standardized format for the RPA cloud suite based on the node associated with the respective RPA related service.

Abstract: Disclosed embodiments provide techniques for a digital platform for automated content generation. One or more digital betting platforms are accessed. The digital betting platforms each provide one or more betting odds opportunities for online users, including odds opportunities for sporting events. The sporting events data is cross-validated with content from other websites to confirm that sporting events are active. A content trigger event corresponding to at least one of the betting odds opportunities is detected. Data from the betting opportunities is aggregated, and the data is used to generate a human-readable online article. The aggregated data is used to select one or more dynamic article features related to the betting odds opportunities. The dynamic article features are used as part of the human-readable online article. The online article is published and refreshed on a fixed-time cadence.

Abstract: In various examples, a machine may generate, using sensor data capturing one or more views of an environment, a virtual environment including a 3D representation of the environment. The machine may render, using one or more virtual sensors in the virtual environment, one or more images of the 3D representation of the environment. The machine may apply the one or more images to one or more machine learning models (MLMs) trained to generate one or more predictions corresponding to the environment. The machine may perform one or more control operations based at least on the one or more predictions generated using the one or more MLMs.

Abstract: Disclosed herein are systems, methods, and software for providing a platform for complex image data analysis using artificial intelligence and/or machine learning algorithms. One or more subsystems allow for the capturing of user input such as eye gaze and dictation for automated generation of findings. Additional features include quality metric tracking and feedback, and worklist management system and communications queueing.

Abstract: Apparatuses, systems, and techniques to generate a prompt for one or more machine learning processes. In at least one embodiment, the machine learning process(es) generate(s) a plan to perform a task (identified in the prompt) that is to be performed by an agent (real world or virtual).

Abstract: Disclosed herein are systems, methods, and software for providing a platform for complex image data analysis using artificial intelligence and/or machine learning algorithms. One or more subsystems allow for the capturing of user input such as eye gaze and dictation for automated generation of findings. Additional features include quality metric tracking and feedback, and worklist management system and communications queueing.

Abstract: A method includes determining, using at least one processing device of an electronic device, a target embedding vector for each class of a plurality of classes. The method also includes generating, using the at least one processing device, an utterance embedding vector using a pre-trained language model, where the utterance embedding vector represents an input utterance associated with an expected class. The method further includes obtaining, using the at least one processing device, a predicted class associated with the input utterance based on distances of the utterance embedding vector to spatial parameters representing the plurality of classes, where the spatial parameter of each class is based on the target embedding vector associated with that class. In addition, the method includes updating, using the at least one processing device, parameters of the language model based on a difference between the predicted class and the expected class.

Abstract: A method includes receiving an input utterance that is a continuation of a previous utterance. The method also includes, using a trained Siamese network, determining input utterance embeddings representing tokens from the input utterance, pooling the input utterance embeddings with a context token embedding representing a class associated with the previous utterance to generate a representative input utterance embedding, and determining a representative embedding associated with each of multiple possible classes. Each possible class is associated with first and second threshold boundaries. The method further includes, using the trained Siamese network, determining a similarity score for each possible class based on a distance between the representative input utterance embedding and a selected threshold boundary of the representative embedding for that possible class and identifying a class for the input utterance based on the determined similarity scores.

Abstract: A system creates and searches knowledge base (KB) articles inside an organization while supporting operation as a SaaS (Software as-a-Service) across a plurality of organizations. The system implements a real-time online learning-to-rank (L2R) algorithm for learning relevance scoring that is customized to each organization. This algorithm incorporates rich lexical features using a query similarity kernel. A scoring function includes a pairwise static module, which may be trained off-line using training data, and a lexical adaptive module, which is trained on-line based on user feedback. The scoring function makes the system easy to deploy, modify, and suitable for handling events that naturally happen over the lifecycle of any KB deployment, without manual training.

Abstract: An exemplary embodiment of the present disclosure provides a localized heating, ventilation, or air conditioning (HVAC) system comprising a moveable air delivery system comprising. The moveable air delivery system further comprising an air inlet configured to receive air and an air outlet configured to output the air into a space. The localized HVAC further comprising a movement system configured to move the air outlet in a generally planar manner. The movement system further comprising an air delivery support system configured to support the air outlet, the air delivery support system configured to move the air outlet in an air delivery plane.

Abstract: An organization has or uses an SLA system that selects a service-level agreement (SLA) that should apply to a given user request. The SLA system then can monitor the state of the ticket created based on the user request and provide messages or other feedback to the agent to aid the agent in meeting any performance goals associated with the SLA. The SLA system can determine the SLA to associate with a given user request by inferring a type of the request and selecting the SLA based on the type, or by using a model to directly associate an SLA to a given user request, without the need to infer an intermediate type for the user request, which eliminates the need for administrators to create and maintain metadata to guide the association between types and SLAs.

Abstract: A communications server apparatus and method for forecasting demand for services is provided, the server managing the provision of services based on received queries. Data records comprising data instances of service queries within a past time period are received. Modified data records are derived by modifying a value of a feature of one or more of the data instances to a standardized value. The feature is a parameter relating to an offer, by the server, to provide a service in response to a query. The data instances of the modified data records are input into a classification model, wherein the classification model predicts whether or not a service booking will be made. Classification predictions are output by the classification model and an estimate of potential demand for the past time period is determined based on the classification predictions, for improved forecasting of demand for services.