An encoder, connectable to a data-memory, for storing numerical values in the data-memory, which lie in a value range between a predefined-minimum-value and a predefined-maximum-value, the encoder including an assignment instruction, according to which the value range is subdivided into multiple discrete intervals, and the encoder being configured to classify a numerical value to be stored in exactly one interval and to output an identifier of this interval, the intervals varying in width on the scale of the numerical values. A decoder for numerical values, which are stored in a data-memory using an encoder, to assign according to one assignment instruction an identifier of a discrete interval retrieved from the data-memory a fixed numerical value belonging to this interval and to output it. Also described are an AI module including an ANN, an encoder and a decoder, and a method for manufacturing the AI module, and an associated computer program.

An encoder, connectable to a data-memory, for storing numerical values in the data-memory, which lie in a value range between a predefined-minimum-value and a predefined-maximum-value, the encoder including an assignment instruction, according to which the value range is subdivided into multiple discrete intervals, and the encoder being configured to classify a numerical value to be stored in exactly one interval and to output an identifier of this interval, the intervals varying in width on the scale of the numerical values. A decoder for numerical values, which are stored in a data-memory using an encoder, to assign according to one assignment instruction an identifier of a discrete interval retrieved from the data-memory a fixed numerical value belonging to this interval and to output it. Also described are an AI module including an ANN, an encoder and a decoder, and a method for manufacturing the AI module, and an associated computer program.

A method for diagnosing analog circuit fault based on vector-valued regularized kernel function approximation, includes steps of: step (1) acquiring a fault response voltage signal of an analog circuit; step (2) carrying out wavelet packet transform on the collected signal, and calculating a wavelet packet coefficient energy value as a characteristic parameter; step (3) utilizing a quantum particle swarm optimization algorithm to optimize a regularization parameter and kernel parameter of vector-valued regularized kernel function approximation, and training a fault diagnosis model; and step (4) utilizing the trained diagnosis model to recognize circuit faults.

A platform for updating one or more properties of one or more digital twins including receiving a request for one or more digital twins; retrieving the one or more digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models corresponding to one or more properties that are depicted in the one or more digital twins indicated by the request; selecting data sources from a set of available data sources based on the one or more inputs of the one or more dynamic models; obtaining data from selected data sources; determining one or more outputs using the retrieved data as one or more inputs to the one or more dynamic models; and updating the one or more properties of the one or more digital twins based on the one or more outputs of the one or more dynamic models.

The present disclosure relates to a computing apparatus, a method, an integrated circuit chip and an integrated circuit device for performing a neural network operation. The computing apparatus may be included in a combined processing apparatus. The combined processing apparatus may further include a general interconnection interface and other processing apparatus. The computing apparatus interacts with other processing apparatus to jointly complete calculation operations specified by users. The combined processing apparatus may further include a storage apparatus. The storage apparatus is respectively connected to the computing apparatus and other processing apparatus, and the storage apparatus is used for storing data of the computing apparatus and other processing apparatus. Solutions of the present disclosure may be widely applied to various floating-point data computations.

A topic for building a new intent on which to train a chatbot can be received. A database of chatbot training data can be searched for a candidate intent having meta-knowledge similar to the received topic. Utterances associated with the candidate intent can be extracted. The received topic and the extracted utterances can be input to a trained machine learning model. The trained machine learning model generates example utterances for the new intent. The new intent with the generated example utterances can be used as training data for training the chatbot.

The invention is notably directed to a sensor system for performing distributed sensing and classification of sensor data. The sensor system comprises a set of distributed sensor nodes for sensing the sensor data. The sensor system is configured to encode the sensor data of each sensor node of a set of distributed sensor nodes for sensing the sensor data as high-dimensional vectors and to transmit the high-dimensional vectors over a respective link between the respective sensor node and a receiver system. The sensor system is further configured to superpose the high-dimensional vectors of the sensor data from the set of sensor nodes by physical superposition, thereby generating a superposed high-dimensional vector and to classify the superposed high-dimensional vectors at the receiver system.

The present disclosure describes a technology platform for creating and updating records of resources in a ledger. To create a record, a tenant organization may prepare a record to write to the ledger that may be flagged as temporary. Metadata may be added to the record, which flags the record as temporary. The metadata may comprise a unique code and an identification of a user that can approve the temporary record. The unique code and the identification may be sent, by the technology platform, to a device associated with one or more approving devices. Upon receiving the code and the identification of the transaction, the device may sign the unique code and invoke a routine based on the identification. The routine may fetch the temporary record. The device may compare the unique code to a code stored in the metadata of the temporary record. Upon valid verification of the unique code, the device may indicate authorization of the write. Based on the authorization, a proxy node associated with the technology platform may write a definitive record to the ledger based on the temporary record.

An artificial intelligence-based curating method and a device for performing the same may include operations of allowing an artificial intelligence curating device to receive purchaser information, allowing the artificial intelligence curating device to determine recommended painting information on the basis of the purchaser information, and allowing the artificial intelligence curating device to transmit candidate sale painting information to a user device of a purchaser on the basis of the recommended painting information.

The present disclosure relates to cell populations and systems for detection of compounds in an environment. Specifically the disclosure relates to methods for generating reproducible genome-wide edited populations of microbes that display novel, defined, and reproducible phenotypes when exposed to one or more chemicals. In some applications, such phenotypes are read out by barcode amplicon and compared against population fingerprints. In other applications digital information is stored in such populations of microorganisms. The digital information can be retrieved from the microorganisms with Boolean logic.