Abstract: Methods and systems are described for recognizing, based on a voice input, a user and/or a voice command. An algorithm is described herein that processes data associated with a voice input. The data may indicate characteristics of the voice such as a gender, an age, or accent associated with the voice and other metadata. For example, the system may process the data and determine the gender of a voice. The determined characteristics may be used as an input into a voice recognition engine to improve the accuracy of identifying the user who spoke the voice input and identifying a voice command associated with the voice input. For example, the determined gender may be used as a parameter to improve the accuracy of an identified user (e.g., the speaker) or command. The algorithm may adjust, based on gender, parameters such as confidence thresholds used to match voices and voice commands.

Abstract: An apparatus for displaying profile information in a virtual environment comprises a processor associated with a server. The processor is configured to generate an authorization token configured to assign a first avatar to a first user, wherein the authorization token is stored within the plurality of transfers of the blockchain record. The processor is further configured to receive session data associated with the first avatar, wherein the session data comprises at least one gesture for a session in a virtual environment and to compare the at least one gesture of the session data to one or more authorized gestures to identify a first authorized gesture. The processor is further configured to display profile information in the virtual environment that is stored in a first user profile associated with the first user in response to identifying the first authorized gesture.

Abstract: A system for generating notifications to greet and facilitate an avatar to conduct interactions comprises a processor associated with a server. The processor identifies the avatar associated with a user device in a virtual environment and receives a metaverse identifier of the avatar. The processor queries a database to determine whether the metaverse identifier is linked to a user identifier of a user profile. The processor retrieves a user name associated with the user identifier from the database if the metaverse identifier is linked to the user identifier. The processor presents a greeting notification with the user name to a user to grant access to the virtual environment for the avatar. The processor queries the database to obtain interaction data to determine a pending task associated with the user identifier. The processor presents an interaction notification corresponding to a pending task associated with the user identifier to the user device.

Abstract: A system includes a plurality of computing nodes that form a blockchain network comprising a first user device of a user and a centralized exchange server that stores a first data file of a first type for the user comprising a private key used to perform data interactions relating to data objects of the user stored in the blockchain network. The central exchange server comprises a processor that receive a first request to switch the first data file from a first type to a second type, exports the private key to the first user device and deletes the private key from the first data file. The processor receives a second request to switch the second data file of the second type to the first data file of the first type, imports the private key to the first data file and deletes the private key from the second data file.

Abstract: Systems, methods and products for associating arbitrary configuration tags to configuration item for a service so that items grouped by the tags can be unconfigured or manipulated as a group with minimal touchpoints. In one embodiment, a method is provided for managing the configuration of per-tenant features in a server system. The method includes identifying a configuration feature of the server system to be configured for a specific tenant. A configuration command is received to configure the configuration feature for the specific tenant, wherein the configuration command includes a configuration tag associated with the specific tenant. The configuration command is stored in a configuration of the server system and is applied to the server system. Tag-based commands are provided which are operable to modify a subset of configuration features corresponding to a designated configuration tag.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating speech from text. One of the systems includes one or more computers and one or more storage devices storing instructions that when executed by one or more computers cause the one or more computers to implement: a sequence-to-sequence recurrent neural network configured to: receive a sequence of characters in a particular natural language, and process the sequence of characters to generate a spectrogram of a verbal utterance of the sequence of characters in the particular natural language; and a subsystem configured to: receive the sequence of characters in the particular natural language, and provide the sequence of characters as input to the sequence-to-sequence recurrent neural network to obtain as output the spectrogram of the verbal utterance of the sequence of characters in the particular natural language.

Abstract: Methods, systems, and techniques for providing visual expertise to objectively measure, evaluate, and visualize aesthetic change are provided. Example embodiments provide an Aesthetic Delta Measurement System (“ADMS”), which enables users to objectively measure and visualize aesthetic health and wellness and treatment outcomes and to continuously supplement a knowledge repository of objective aesthetic data based upon a combination of automated machine learning and surveyed human input data. The ADMS provides a labeling platform for labeling aesthetic health and wellness over large populations of individuals and a personal analysis application for viewing an individuals aesthetic changes over time. The ADMS provides labeling of images using guides with corresponding discrete scalar values or using pairwise comparison techniques.

Abstract: Aspects concern a data storage system comprising a data storage for storing data comprising a plurality of data elements, wherein each data element is associated with a data storage table, a data storage access interface configured to receive a request for an access to a data element from a data access client wherein the request comprises a identifier of the storage location of the data element and an access controller configured to determine a data storage table with which the data element is associated from the identifier of the storage location, determine whether the data access client has access rights to the determined data storage table allowing the access to the data element and grant the data access client access to the data element if the data access client has access rights to the determined data storage table allowing the access to the data element.

Abstract: This document generally describes a neural network training system, including one or more computers, that trains a recurrent neural network (RNN) to receive an input, e.g., an input sequence, and to generate a sequence of outputs from the input sequence. In some implementations, training can include, for each position after an initial position in a training target sequence, selecting a preceding output of the RNN to provide as input to the RNN at the position, including determining whether to select as the preceding output (i) a true output in a preceding position in the output order or (ii) a value derived from an output of the RNN for the preceding position in an output order generated in accordance with current values of the parameters of the recurrent neural network.

Abstract: An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.

Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.

Abstract: Systems and methods for increasing the speed with which a network device can process “heartbeat” packets that are transmitted between the network device and its peers to verify that the communication links between them are active, or to detect when the communication links go down (i.e., are inactive). Received heartbeat packets are processed primarily by a switching application specific integrated circuit (ASIC) rather than a CPU of the network device. The switching ASIC identifies heartbeat sessions corresponding to received heartbeat packets and resets aging timers for these sessions if the timers have not already expired. The reduced processing and faster timing mechanism of the switching ASIC enables the network device to accommodate spikes in the received packet rate.

Abstract: An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.

Abstract: Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating speech from text. One of the systems includes one or more computers and one or more storage devices storing instructions that when executed by one or more computers cause the one or more computers to implement: a sequence-to-sequence recurrent neural network configured to: receive a sequence of characters in a particular natural language, and process the sequence of characters to generate a spectrogram of a verbal utterance of the sequence of characters in the particular natural language; and a subsystem configured to: receive the sequence of characters in the particular natural language, and provide the sequence of characters as input to the sequence-to-sequence recurrent neural network to obtain as output the spectrogram of the verbal utterance of the sequence of characters in the particular natural language.

Abstract: A system for releasing analytes contained within a sample. The system comprises a housing enclosing an interior space. The system additionally comprises a sample holder having at least one receptacle configured to receive a sample containing analytes bound within the sample. The system further includes at least one light emitting diode configured to generate light sufficient to separate photo-cleavable bonds binding the analytes within the sample.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for speech recognition. One method includes obtaining an input acoustic sequence, the input acoustic sequence representing one or more utterances; processing the input acoustic sequence using a speech recognition model to generate a transcription of the input acoustic sequence, wherein the speech recognition model comprises a domain-specific language model; and providing the generated transcription of the input acoustic sequence as input to a domain-specific predictive model to generate structured text content that is derived from the transcription of the input acoustic sequence.

Abstract: User authentication and validation for performing transactions may be performed by a validation server of a service provider. For example, when a login request or purchase request is received, the request may be authenticated or validated before permitting the requested transaction. In some arrangements, the validation may be delegated to one or more devices or users external to the service provider. Multiple validation users may be consulted for each transaction request to determine a consensus validation decision. A consensus may be reached based on unanimous responses or based on a specified threshold level (e.g., more than 50% responding positively or negatively). The service provider may use this consensus determination to authorize or reject a transaction request.

Abstract: The disclosure features non-irritating pharmaceutical compositions containing CD101 in pharmaceutical acceptable salt (e.g., CD101 acetate) or neutral form. The pharmaceutical compositions can be intravenously administered to a subject to treat fungal infections (e.g., candidiasis) in the subject.

Abstract: Systems, methods, and apparatus are configured to enable a receiver to provide feedback. In one example, a method performed at a device coupled to a serial bus includes receiving a write command from the serial bus in a datagram, writing a data byte received in a first data frame of the datagram to a register address identified by the datagram, and using a second data frame of the datagram to provide feedback regarding the datagram. Feedback may be provided by driving a data line of the serial bus to provide a negative acknowledgement during the second data frame when a transmission error is detected in the datagram, and refraining from driving the data line of the serial bus during the second data frame when no transmission error is detected in the datagram, thereby providing an acknowledgement of the datagram.