Abstract: Implementations claimed and described herein provide systems and methods for generating an user interface in response to a request associated with a product or service. The systems and methods use one or more machine learning models to generate the user interface. The user interface is transmitted to a user device for display.

Abstract: Techniques are described herein that are capable of detecting an algorithmic attack against a hosted artificial intelligence (AI) system based on inputs (e.g., queries) and outputs of the hosted AI system. In a first example, a feature-based classifier model is used to generate a classification score based on features that are derived from numerical representations of the queries and the outputs, and an algorithmic attack is detected based on the classification score being greater than or equal to a score threshold. In a second example, a transformer-based model is used to generate a vector by providing a multivariate time series, which is based on attribute(s) of the inputs and attribute(s) of the outputs, as an input to the transformer-based model, and an algorithmic attack is detected based on a distance between the vector and a point corresponding to a reference vector being less than or equal to a distance threshold.

Abstract: Implementations claimed and described herein provide systems and methods for responding to a query associated with a product or service. The systems and methods use a machine learning model to generate a recommendation and a user interface. The recommendation is transmitted to a user device for display via the user interface.

Abstract: Implementations claimed and described herein provide systems and methods for predicting demand associated with a product or service. The systems and methods use a machine learning model to determine a potential consumer is likely to purchase a product or a service. A notification is generated based on the determination that the potential consumer is likely to purchase the product or the service.

Abstract: An ultrasound system includes a transmit-receive switch. The transmit-receive switch includes a combined transmit-receive and return-to-zero (RTZ) path. The combined transmit-receive and RTZ path includes a transistor with a first current terminal, a second current terminal, and a control terminal. The second current terminal of the transistor is coupled to a ground node via a first switch and is coupled to a receive node via a second switch. The ultrasound system also includes a receiver front-end circuit coupled to the receive node.

Abstract: The present disclosure discloses a method and a system for identifying fault resolution steps for an equipment. Method captures multi-modal diagnosis data associated with at least one primary part in the equipment. Method obtains multi-modal features of the at least one primary part from the multi-modal diagnosis data. Method detects a condition state of the at least one primary part using the multi-modal features and a trained object fault detection model. Method determines location of a fault on the at least one primary part using a trained fault location prediction model when the condition state is detected as a faulty state. Method identifies primary fault resolution steps for the at least one primary part based on historic data associated with the at least one primary part and the location of the fault. Method identifies secondary resolution steps for secondary parts based on the primary fault resolution steps before rendering.

Abstract: A system may include a storage array comprised of one or more array member drives. The storage array may be partitioned into stripes of data across the one or more array member drives. In operation, a DMA controller may move source data from a processor memory and may write the data to an accelerator memory. The accelerator may perform a parity calculation and a DMA controller may write the parity result and the source data to at least one of the array member drives. The parity information may be used to reconstruct data in the one or more array member drives in the case of damage or loss of data.

Abstract: Methods, apparatus, systems, and articles of manufacture are described corresponding to current limit circuitry with controlled current variation. An example circuit includes an amplifier having an input terminal and an output terminal; a capacitor having a first terminal and a second terminal, the first terminal of the capacitor coupled to the input terminal of the amplifier, the second terminal of the capacitor coupled to the output terminal of the amplifier; and diode circuitry having a first terminal and a second terminal, the first terminal of the diode circuitry coupled to the first terminal of the capacitor and the input terminal of the amplifier, the second terminal of the diode circuitry coupled to the second terminal of the capacitor and the output terminal of the amplifier.

Abstract: Certain aspects of the present disclosure provide techniques for interference management reference signal (RS) transmission. An example method, performed at a first network entity, generally includes applying a frequency domain function to a first copy of a first reference signal (RS) sequence to obtain a modified first copy of the first RS sequence, and outputting, to a second network entity, frequency domain samples of the modified first copy of the first RS sequence and frequency domain samples of a second copy of the first RS sequence.

Abstract: A system and method of supply chain planning of process industry production include a processor and memory and are configured to model a supply chain planning problem for two or more products of a process industry, wherein a coproduct is produced for at least one of the products, group the two or more products into groups, receive a weight and a yield for each raw material that produces each of the products in at least one of the groups, cluster each of the raw materials using weight-yield clustering, generate BOM grouping, and assign one BOM grouping to each of the raw materials of a single cluster.

Abstract: Techniques are described herein that are capable of detecting an algorithmic attack against a hosted artificial intelligence (AI) system based on inputs (e.g., queries) and outputs of the hosted AI system. In a first example, a feature-based classifier model is used to generate a classification score based on features that are derived from numerical representations of the queries and the outputs, and an algorithmic attack is detected based on the classification score being greater than or equal to a score threshold. In a second example, a transformer-based model is used to generate a vector by providing a multivariate time series, which is based on attribute(s) of the inputs and attribute(s) of the outputs, as an input to the transformer-based model, and an algorithmic attack is detected based on a distance between the vector and a point corresponding to a reference vector being less than or equal to a distance threshold.

Abstract: An O-RU may receive a PRACH preamble and a PUSCH within a plurality of symbols of a slot, the PRACH and the PUSCH having different numerology. The O-RU may filter a PUSCH CP for each symbol of the PRACH preamble through a FFT window per symbol of the PRACH preamble, the FFT window extending from the end of the PUSCH CP within a symbol to the end of the symbol, and perform FFT per the FFT window of each symbol of the PRACH preamble. The O-RU may extract I/Q data in frequency domain corresponding to the PRACH preamble, adjust phase shift of the extracted I/Q data to generate the I/Q data of the PRACH preamble accounting for shift of the each FFT window in time domain compared to FFT windows of PRACH CP filtered PRACH preamble and send the I/Q data of the PRACH preamble to an O-DU.

Abstract: Systems and methods involving secure connections between security meshes are disclosed herein. One disclosed device includes a first security processor located within a first security mesh, a first casing having a connector and supporting the first security mesh and the applications processor, a second security processor located within a second security mesh, and a second casing connected to the first casing via the connector and supporting the second security mesh. The first security processor and second security processor are programmed to generate a unique pre-shared key independently on both the first security processor and the second security processor using an elliptic key exchange and establish a secure connection between the first security processor and the second security processor using the unique pre-shared key.

Abstract: Methods, systems, and computer-readable storage media for receiving a first document including structured data and unstructured data, providing a first sub-document and a second sub-document, the first sub-document including the structured data of the first document, the second sub-document including the unstructured data of the first document, generating a prompt using the second sub-document and a second document, inputting the prompt to a LLM, receiving a response from the LLM, providing a calibrated first document by merging the response into the first sub-document, and processing the calibrated first document and the second document using a ML model to provide a prediction, the prediction indicating a matching class between the first document and the second document.

Abstract: A system and method of supply chain planning of process industry production include a processor and memory and are configured to model a supply chain planning problem for two or more products of a process industry, wherein a coproduct is produced for at least one of the products, group the two or more products into groups, receive a weight and a yield for each raw material that produces each of the products in at least one of the groups, cluster each of the raw materials using weight-yield clustering, generate BOM grouping, and assign one BOM grouping to each of the raw materials of a single cluster.

Abstract: A method comprises reading data of one or more files and identifying one or more data types corresponding to the one or more files. In the method, at least one destination database for the data is recommended based at least in part on the one or more data types. A data model to use in connection with writing the data to the at least one destination database is generated. The generation of the data model is based at least in part on the one or more data types corresponding to the one or more files. At least the generation of the data model is performed using one or more machine learning algorithms.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a base station may receive, from a user equipment and at a physical layer entity of the base station, channel state information (CSI) that includes a CSI part 1 and a CSI part 2, the CSI part 1 having a length known to the base station and the CSI part 2 having a length that is based at least in part on a payload of the CSI part 1. The base station may decode, at the physical layer entity of the base station, a payload of the CSI part 2 based at least in part on a payload of the CSI part 1 and one or more parameters indicated by a medium access control (MAC) layer entity of the base station. Numerous other aspects are described.