Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing sequences using convolutional neural networks. One of the methods includes, for each of the time steps: providing a current sequence of audio data as input to a convolutional subnetwork, wherein the current sequence comprises the respective audio sample at each time step that precedes the time step in the output sequence, and wherein the convolutional subnetwork is configured to process the current sequence of audio data to generate an alternative representation for the time step; and providing the alternative representation for the time step as input to an output layer, wherein the output layer is configured to: process the alternative representation to generate an output that defines a score distribution over a plurality of possible audio samples for the time step.

Abstract: Systems and methods may generate a boundary of a FOU for an interval type-2 MF based on a transformation of another boundary of the FOU. The systems and methods may receive a plurality of parameters for a type-1 MF that defines a boundary of the FOU for the interval type-2 MF and may receive at least one other parameter. The systems and methods may generate, based on a transformation of the type-1 MF utilizing the at least one parameter, a type-1 MF that defines a different boundary of the FOU. The system and methods may adjust the plurality of parameters and the at least one second parameter to adjust the FOU for use in a model representing, for example, a real-world physical system, where execution of the model executes a fuzzy inference system and generates results representing a behavior of the real-world physical system.

Abstract: A neural processing device comprising processing circuitry are provided. A neural processing device comprises a plurality of processing engine groups; a first memory shared by the plurality of engine groups; a first interconnection configured to transmit data between the first memory and the plurality of processing engine groups. The neural processing device is configured to provide hardware resource to the plurality of processing engine groups. The at least one of the plurality of processing engine groups comprises a plurality of processing engines, each of the plurality of processing engines comprising an array of a plurality of processing elements interconnected by a mesh style network, the processing elements being reconfigurable; a second memory shared by the plurality of processing engines; and a second interconnection configured to transmit data between the second memory and the plurality of processing engines.

Abstract: System and method for synthesizing a controller for a dynamical system includes a feeder neural network trained to estimate an ordinary differential equation (ODE) from time series training data (X) of a trajectory having embedded angular data and configured to learn dynamics of a physical system by encoding a generalization of a Hamiltonian representation of the dynamics using a constant external control term (u). A neural ODE solver receives the estimate of the ODE from the feeder neural network and synthesizes a controller to control the system to track a reference configuration.

Abstract: In some implementations, a computing device determines an event timeline that comprises one or more finance-related events associated with a person. A production classifier may be used to determine (i) an individual contribution of each event in the event timeline to a financial capacity of the person and (ii) a first decision regarding whether to extend credit to the person. A bias monitoring classifier may, based on the event timeline, determine a second decision whether to extend credit to the person. The bias monitoring classifier may be trained using pseudo-unbiased data. If a difference between the first decision and the second decision satisfies a threshold, the production classifier may be modified to reduce bias in decisions made by the production classifier.

Abstract: An optimization calculation apparatus may comprise an algorithm module obtaining a plurality of first solutions (S1) from a plurality of input data, obtaining second solutions (S2) from the first solutions (S1), and repeating the process to derive an optimal solution (Sm). The optimization calculation apparatus may further comprise a similarity determination module connected to the algorithm module and computing a similarity of ith solutions in order to obtain (i+1)th solutions (1?i?m?1).

Abstract: Industrial smart data tags conforming to structured data types serve as the basis for creating a digital twin of an industrial asset. The digital twin can comprise an automation model and a mechanical model or other type of non-automation model, both of which reference the smart tags in connection with digitally modeling the industrial asset. The structured data topology offered by the smart tags allows the digital twin to be readily interfaced with artificial intelligence (AI) systems. AI analysis can leverage the smart tags to discover new relationships between key performance indicators and other variables of the asset and encode these relationships in the smart tags themselves. These enhanced smart tags can also be leveraged to perform AI-based validation the digital twin. Additional contextualization provided by the enhanced smart tags can simplify AI analysis and assist in quickly converging on desired analytic results.

Abstract: An apparatus for producing a target strategy comprising at least a processor and a memory communicatively connected to the at least a processor. The memory is configured to instruct the processor to receive a history datum. The memory also is configured to instruct the processor to identify a pattern datum using a quantitative field machine learning model. The quantitative field machine learning model is configured to train the quantitative field machine learning model using a quantitative field training data. The quantitative field machine learning model is also configured to identify the pattern datum as a function of the history datum. The memory then instructs the processor to generate a modified target as a function of the pattern datum. Finally, the memory instructs the processor to generate a target strategy as a function of the modified target.

Abstract: A system for identifying a longevity element to optimize supplement decisions is disclosed. The system includes a computing device configured to capture an identifier of a first longevity element using a data capturing device. The computing device is configured to receive a longevity inquiry from a remote device generating a longevity inquiry from the identifier, the longevity query identifying the first longevity element. The system retrieves a biological extraction pertaining to a user and identifies a longevity element associated with a user. The system selects an ADME model utilizing a biological extraction. The system generates a machine-learning algorithm utilizing the selected ADME model to input a longevity element associated with a user as an input and output an ADME factor. The system identifies a tolerant longevity element utilizing an ADME factor. A method for identifying a longevity element to optimize supplement decisions is also disclosed.

Abstract: Some embodiments provide a neural network inference circuit for executing a neural network that includes multiple nodes that use state data from previous executions of the neural network. The neural network inference circuit includes (i) a set of computation circuits configured to execute the nodes of the neural network and (ii) a set of memories configured to implement a set of one or more registers to store, while executing the neural network for a particular input, state data generated during at least two executions of the network for previous inputs. The state data is for use by the set of computation circuits when executing a set of the nodes of the neural network for the particular input.

Abstract: Systems, apparatuses and methods may provide for technology that identifies a cognitive space that is to be a compressed representation of activations of a neural network, maps a plurality of activations of the neural network to a cognitive initial point and a cognitive destination point in the cognitive space and generates a first cognitive trajectory through the cognitive space, wherein the first cognitive trajectory traverses the cognitive space from the cognitive initial point to the cognitive destination point.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a sequence to sequence model that is recurrent in depth while employing self-attention to combine information from different parts of sequences.

Abstract: An apparatus is configured to identify a plurality of temporal ranges, associated with a plurality of first identifiers and a plurality of sets of descriptive data, generate a plurality of temporal sections, wherein generating further includes dividing each temporal range into at least a temporal section of the plurality of temporal sections, receive at least a second identifier, wherein the at least a second identifier is associated with at least a temporal constraint and a set of second identifier data, classify, as a function of the inputs, the at least a first identifier to a particular temporal section of the plurality of temporal sections as a function of the plurality of sets of descriptive data and the at least a set of second identifier data and output the particular temporal section.

Abstract: A novel and useful quantum computing machine includes classic computing and quantum computing cores. A programmable pattern generator executes instructions that control the quantum core. A pulse generator generates the control signals input to the quantum core to perform quantum operations. A partial readout of the quantum state is re-injected into the quantum core to extend decoherence time. Access gates control movement of quantum particles in the quantum core. Errors are corrected from the readout before being re-injected into the quantum core. Internal and external calibration loops calculate error syndromes and calibrate control pulses input to the quantum core. Control of the quantum core is provided from an external support unit via the pattern generator or retrieved from classic memory where sequences of commands are stored in memory. A cryostat unit functions to cool the quantum computing core to approximately 4 Kelvin.

Abstract: An extracted hypothesis has logical formulas leading to a possible conclusion. If the logical formulas include one including a first parameter a value obtained from observation data and a second parameter that does not have a value obtained from the observation data, the logical formula is set as a target logical formula, and a value of the second parameter acquired based on the observation data is input. If there is no such logical formula, but there is a logical formula that has, for one or more parameters, a value set in advance or obtained using inference knowledge, whether this logical formula is true or false is determined. When whether the logical formula is true or false is determined, or, when the value of the second parameter is input, the logical formula is added to the observation data, and a hypothesis is derived again.

Abstract: Generally, the present disclosure is directed to systems and methods that perform adaptive optimization with improved convergence properties. The adaptive optimization techniques described herein are useful in various optimization scenarios, including, for example, training a machine-learned model such as, for example, a neural network. In particular, according to one aspect of the present disclosure, a system implementing the adaptive optimization technique can, over a plurality of iterations, employ an adaptive effective learning rate while also ensuring that the effective learning rate is non-increasing.

Abstract: A method for determining an allocation decision for at least one elevator includes using an existing calls in an elevator system as a first input in a machine learning module, processing the first input with the machine learning module to provide a first output comprising a first allocation decision, using the first output as a second input in an iterative module, processing the second input with the iterative module to provide a second ouput comprising a second allocation decision, and providing the second allocation decision to an elevator control module and to an allocation decision storage for further machine learning module training.

Abstract: Embodiments of the present disclosure are directed to methods, computer program products, and computer systems of a robotic apparatus with robotic instructions replicating a food preparation recipe.

Abstract: Disclosed are a data sample label processing method and apparatus.

Abstract: The present disclosure discloses a method, apparatus, and server for information recommendation. Search behavior data, browsing behavior data, and click behavior data on recommended content of a specified user in a forum are acquired. A preprocessing on the search behavior data, the browsing behavior data, and the click behavior data on recommended content is performed respectively to obtain a first recommendation result, a second recommendation result, and a third recommendation result. Distribution and integration on the first recommendation result, the second recommendation result, and the third recommendation result are performed according to weights to obtain recommended content to be recommended to the specified user. Search behavior data, browsing behavior data, and click behavior data on recommended content are taken into comprehensive consideration, data used in recommendation is enriched, and accuracy of recommendation is improved.