Abstract: A system may display a set of images to a user, the set of images includes a plurality of synthetic images output by a generative adversarial network (GAN) includes a generator and a discriminator, and a plurality of non-synthetic images, detect a user response to the set of images, the user response includes at least a gaze of the user relative to the set of images, and train the GAN based at least on the user response, including tuning the generator based on the gaze of the user.

Abstract: A computing platform may train, using historical information indicating a plurality of regimes for LLM outputs, an LLM steward model, which may configure the LLM steward model to generate LLM validation information indicating classifications of LLM outputs as acceptable/tolerable/non-acceptable. The computing platform may input, into an LLM, an LLM prompt, which may cause the LLM to generate an LLM output. The computing platform may input the LLM output into the LLM steward model, which may cause the LLM steward model to output the LLM validation information. Based on outputting LLM validation information indicating that the LLM output is acceptable/tolerable, the computing platform may send the LLM output to a user device for presentation. Based on outputting LLM validation information indicating that the LLM output is non-acceptable, the computing platform may update the LLM output to conform with a corresponding subset of the plurality of regimes.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks using a preference function that compares a measure of quality between network outputs. According to one aspect there is provided a method for training a target neural comprising: at each of a plurality of training steps, receiving one or more network inputs; for each of the network inputs, processing the network input using the target neural network to generate a first network output, processing the network input using an alternative neural network for the training step to generate a second network output, and applying a preference function to the first and second network outputs to generate a preference score comparing the first and second network outputs; and updating the target neural network weights using an objective function that encourages the first network outputs to be preferred over the second network outputs.

Abstract: A data fusion method and apparatus, and a device and a storage medium are provided. The method includes: acquiring, from a first data center, a plurality of pieces of first data to be compared that are within a preset time period, and acquiring, from a second data center, a plurality of pieces of second data to be compared that are within the preset time period; then, determining the same data unique identifier in the plurality of pieces of first data to be compared and the plurality of pieces of second data to be compared, and taking the same data unique identifier as a first data identifier; and acquiring first data to be compared that corresponds to the first data identifier, and acquiring second data to be compared that corresponds to the first data identifier.

Abstract: A quantum system can include a reconfigurable array of quantum sensors; and one or more processors configured to perform operations, the operations including: configuring the quantum system including the reconfigurable array of quantum sensors; obtaining a signal from a quantum sensor exposed to a signal for a period of time; transducing at least one state of the quantum sensor into quantum memory; encoding the at least one transduced state using an error-correcting code; processing the encoded states by quantum operations to extract at least one measurement value; and processing the extracted at least one measurement value to determine one or more classical values.

Abstract: There is described a quantum controller for interfacing between a host computer and a quantum processing unit (QPU) having a plurality of qubits. The quantum controller comprises signal processing hardware configured for transforming instructions from the host computer into control signals readable by the QPU, the signal processing hardware comprising programmable logic and signal conversion circuits; hardware accelerator components dedicated to tasks offloaded from the programmable logic, the hardware accelerator components comprising at least one processor different from the QPU; and a carrying substrate on which the signal processing hardware and the hardware accelerator components are coupled, the carrying substrate providing power and signal routing to the signal processing hardware and the hardware accelerator components.

Abstract: Methods, systems and computer program products for distributed machine learning are provided. Such methods, systems and products may comprise, or may comprise instructions operable to configure one or more processors to perform, a set of acts. Such acts may comprise a plurality of nodes performing a set of training round activities, and a server performing a set of network topology design activities. The network topology design activities may comprise the server generating update data based on data from the plurality of nodes. The plurality of nodes may use that data to update control values used to exchange and combine machine learning models. After the set of training round activities and the set of network topology design activities have been repeated one or more times, the plurality of nodes may send machine learning models to the server, and the server may use them to create an aggregated machine learning model.

Abstract: Systems, methods, and computer-readable storage media for combining machine learning models which respectfully use public and private data using a third machine learning model. Upon training a public data machine learning model and a private data machine learning model, the system trains a public and private data machine learning model using a combination of: (1) historical public data machine learning predictions output by the public data machine learning model, and (2) historical private data machine learning predictions output by the private data machine learning model. The system then executes the public and private data machine learning models, resulting in a public data machine learning prediction and a private data machine learning prediction, then executes the public and private data machine learning model using the public data machine learning prediction and the private data machine learning prediction as inputs, resulting in a final prediction.

Abstract: A scanning window is used to scan an image frame of a sensor when doing object detection. In one approach, positions within the image frame are stored in memory. Each position corresponds to an object detection at that position for a prior frame of data. A first area of the image frame is determined based on the stored positions. When starting to analyze a new frame of data, the first area is scanned to detect at least one object. After scanning within the first area, at least one other area of the new image frame is scanned.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a model, obtaining input audio, processing the input audio to generate a transcript, obtaining at least one summary based on the transcript, identifying at least one error or inconsistency in the transcript or the at least one summary based on the model, and implementing, based on the identifying, a correction or a clarification in respect of the at least one error or inconsistency. Other aspects are disclosed.

Abstract: The embodiments of present disclosure herein address unresolved problems of handwritten notes or digital diaries for logging defects during quality inspection. Also, these kinds of inspections in the industries are time bound. In certain sensitive inspection cases inspectors are required to use gloves while inspecting which leads to added overhead of working with gloves while noting down the defects. Sharing of these defect logs also becomes difficult as they first must be digitized to be shared across the different units in the industry. Embodiments herein provide a method and system which logs the defects identified by inspectors using inspector's voice. The system identifies defect type, location and sub-section from inspector speech and marks the defect in an orthogonal view, a two-dimensional (2D) view, a three-dimensional (3D) view of the artifact being inspected for verification. The system also allows for marking the defects as resolved during the repair or rework process.

Abstract: As part of a dialog session between a user and an automated assistant, implementations can process, using a streaming ASR model, a stream of audio data to generate ASR output, process, using an NLU model, the ASR output to generate NLU output, and generate, based on the NLU output, a stream of fulfillment data. Further, implementations can further determine, based on processing the stream of audio data, audio-based characteristics associated with spoken utterance(s) captured in the stream of audio data. Based on a current state of the stream of NLU output, the stream of fulfillment data, and the audio-based characteristics, implementations can determine whether a next interaction state to be implemented is: (i) causing fulfillment output to be implemented; (ii) causing natural conversation output to be audibly rendered; or (iii) refrain from causing any interaction to be implemented, can cause the next interaction state to be implemented.

Abstract: An electronic device includes a display, an external device interface that communicates with a remote control device, a network interface that communicates with a server, a user input interface that transmits a signal related to a user input, and a controller, and the controller displays a preset text related to registration of identification information while performing a process of registering the identification information related to speech for a user account logged in to the server, upon receiving a speech signal related to the preset text, transmits data including the speech signal to the server, completes the process of registering the identification information based on processing of the speech signal related to the preset text by the server, and interrupts the process of registering the identification information upon receiving a predetermined input related to speech recognition from the remote control device during the process of registering the identification information.

Abstract: Systems and methods includes receiving a single-track audio input stream having a mixture of audio signals generated from a plurality of sources, training an audio source separation model using, at least in part, the received single-track audio input stream, and separating audio sources, using the audio source separation model, from the audio input stream in accordance with one or more processing recipes to generate a plurality of source separated output stems. The audio separation model is trained to receive the single-track audio input stream and generate a plurality of audio stems corresponding to one or more audio sources of the plurality of sources.

Abstract: A magnetic tape drive is used for a magnetic tape cartridge including a magnetic tape and a storage medium. Geometrical characteristic information is stored in the storage medium. The geometrical characteristic information is recorded on the magnetic tape. A processor of a magnetic tape drive erases first geometrical characteristic information from the storage medium to leave second geometrical characteristic information in the storage medium. The second geometrical characteristic information is geometrical characteristic information capable of specifying a recording position in the magnetic tape at which the geometrical characteristic information is recorded. The first geometrical characteristic information is geometrical characteristic information other than the second geometrical characteristic information.

Abstract: Method, system and computer program product for providing an output digital video stream enhanced by real-time analysis. A primary digital video stream is continuously captured and analyzed locally to detect initial events or patterns, generating a first control parameter with a first delay. A more complex image or audio analysis is performed remotely, identifying additional events and generating a second control parameter with a longer delay. The second parameter is applied to a later point in the stream, allowing seamless, real-time modification without playback interruption. The modified video stream is delivered to clients as a final output. The first primary digital video stream is continuously captured by a camera local to the participating client and a computer device performing the analysis and application of the first production control parameter. The second analysis is performed by a computer device remote to the computer device applying the first production control parameter.

Abstract: A power gating circuit for a memory includes a plurality of header switches, the plurality of header switches comprising: a first transistor having a source tied to an external power supply line; and a second transistor coupled to a drain of the first transistor and having its own drain coupled to an internal voltage line of the memory, wherein the second transistor is structured to be used as a transistor or a diode based on control of a gate of the second transistor; and a selection device coupled to the gate of the second transistor to controllably couple the gate of the second transistor in a first state to the drain of the second transistor for diode operation and in a second state to a second voltage line for transistor operation.

Abstract: A read circuitry for memory includes a column read-out multiplexer (MUX) coupled to a set of columns of the memory, wherein the column read-out MUX receives a column select signal from a control circuit to output a corresponding column output from the set of columns; and a single sense amplifier coupled to receive the corresponding column output of the column read-out MUX.

Abstract: Control circuitry for memory includes a state machine including a number of state elements corresponding to a maximum number of available columns of a blast operation for memory; a set of registers including a corresponding register for each state element; and a column selection control circuit that combines outputs of the state machine and the set of registers to trigger an appropriate column during an appropriate clock cycle, available at a start of a corresponding clock cycle. The state machine receives a clock and various inputs associated with a start of memory operations and provides intermediate state element outputs and a final state element output as the outputs. Each register of the set of registers is available to store, from an address enable signal, a value indicating that a column in memory to which that register corresponds is to be accessed.

Abstract: Embodiments disclosed herein are directed to a non-volatile storage system comprising a non-volatile memory including non-volatile storage elements and control circuitry. The control circuitry is configured to: perform a first read operation to access device parameter information for a first memory operation associated with a first storage region type, the device parameter information associated with the first storage region type stored in a first block of a plurality of blocks; perform the first memory operation, using the device parameter information associated with the first storage region type; perform a second read operation to access device parameter information for a second memory operation associated with a second storage region type, the device parameter information associated with the second storage region type stored in a second block of the plurality of blocks; and perform the second memory operation, using the device parameter information associated with the second storage region type.