Abstract: In at least one embodiment, a microphone assembly including a substrate, a printed circuit board (PCB), a micro-electro-mechanical systems (MEMS) transducer, a first lid, and a second lid is provided. The substrate defines a first port that extends completely therethrough. The PCB defines a sound opening that extends completely therethrough. The MEMS transducer is positioned on a first side of the substrate. The first lid defines a second port and covers the MEMS transducer and the first port. The first lid and the substrate define a front volume of air that surrounds the MEMS transducer. The second lid is positioned on the second side of the PCB. A cavity of the second lid, the sound opening of the PCB, the sound opening of the PCB, and the first port of the substrate define a back volume of air that is greater than the front volume of air.

Abstract: Methods and systems for in-memory metadata reduction in cloud storage system are provided. According to an aspect, a method comprises receiving a first command to write a data stream to a storage device; writing the data stream into a plurality of fragments having logical addresses corresponding to physical addresses on the storage device; and generating an index for individual fragment of the plurality of fragments, the index indicating information to locate the physical addresses of the individual fragment. Individual records in the individual fragment have a same pre-set logical size and all individual records in the individual fragment are continuous, and the index indicates the information including at least: an offset value of the individual record in the individual fragment; the pre-set logical size of the individual record; and a pre-set physical size of the individual record.

Abstract: A storage engine may obtain one or more object access properties of an object to be received, and determine a type of storage device that is suitable or desirable for storing the object from among different types of storage devices based at least in part on the one or more object access properties of the object to be received. In response to determining the type of storage device, the storage engine may allocate a storage device of such type for the object. The storage engine may then receive the object, and store the object into the allocated storage device.

Abstract: Embodiments of the present disclosure a computer-implemented method comprising receiving sensor data from one or more vibration sensors that are embedded within a seat, where the sensor data includes data associated with speech by an occupant of the seat, and at least one of processing, transmitting, or storing the sensor data based on the data associated with the speech.

Abstract: A method for detecting a sound field in an outside environment of a solid structure is provided. The solid structure comprises at least one sound transducing element, which is a part of the solid structure and forms a part of an outer surface of the solid structure, wherein the incident sound falls at least partly onto the sound transducing element. The method comprises obtaining sensor data from at least one vibration sensor, which is mounted onto a mounting surface of the at least one sound transducing element, wherein the at least one vibration sensor detects vibrations in the at least one sound transducing element, which are induced into the at least one sound transducing element by the incident sound field, processing the sensor data to generate output data, and providing the output data.

Abstract: Provided in the present invention are an imaging device and method for turbid oil abrasive particles based on polarized image enhancement, which belong to the technical field of image processing. The imaging device of the present invention includes a CMOS fixing device, a Stokes polarized CMOS sensor, an optical lens sleeve and an optical magnifying glass, where the Stokes polarized CMOS sensor includes a CMOS sensor and a Stokes analyzer. By using a polarization characteristic difference between scattered light of lubricating oil media and reflected light of abrasive particles, Stokes vector information is measured in real time under turbid lubricating oil scenes, the scattered light of the media is filtered out, image quality of the abrasive particles is improved, coverage duration for monitoring abrasive particle imaging is effectively prolonged.

Abstract: A sound and vibration sensor having a sealed cavity defined between a microphone element and structure panel and an area below the microphone element wherein the microphone element senses a change in acoustic pressure inside the sealed cavity caused by sound waves striking the structure panel.

Abstract: A sound and vibration sensing device comprising a housing, a printed circuit board assembly (PCBA) including a sensor element supported inside the housing. A flexible and non-porous structural membrane is arranged to seal the housing at one end creating a first closed air volume below the sensor element and a second closed air volume above the sensor element wherein the sensor element measures changes in air pressure in the first closed air volume caused by vibration of the structural membrane induced by external acoustic pressure and structural vibration to output a combined sound and vibration signal.

Abstract: When performing a recycling operation on a storage device, a storage system may use or create a data buffer in the storage device, and designate the data buffer to temporarily store data of data blocks to be recycled in the storage device using direct memory access (DMA) operations that are performed internally in the storage device, without the need of reading the data of the data blocks from the storage device and writing the data into a host memory of the storage system, thereby saving or reducing the consumptions of the communication bandwidth of a communication channel between the storage system and the storage device, and the memory bandwidth of the host memory.

Abstract: Embodiments of the present disclosure a computer-implemented method comprising receiving sensor data from one or more vibration sensors that are embedded within a seat, where the sensor data includes data associated with speech by an occupant of the seat, and at least one of processing, transmitting, or storing the sensor data based on the data associated with the speech.

Abstract: Implementations relate to generating multi-modal response(s) through utilization of large language model(s) (LLM(s)). Processor(s) of a system can: receive natural language (NL) based input, generate a multi-modal response that is responsive to the NL based output, and cause the multi-modal response to be rendered. In some implementations, and in generating the multi-modal response, the processor(s) can process, using a LLM, LLM input (e.g., that includes at least the NL based input) to generate LLM output, and determine, based on the LLM output, textual content for inclusion in the multi-modal response and multimedia content for inclusion in the multi-modal response. In some implementations, the multimedia content can be obtained based on a multimedia content tag that is included in the LLM output and that is indicative of the multimedia content. In various implementations, the multimedia content can be interleaved between segments of the textual content.

Abstract: Implementations relate to a method implemented by one or more processors, the method including: receiving natural language (NL) based input associated with a client device; generating, using a large language model (LLM) and based on processing the NL based input, LLM output; determining, based on the LLM output, a sequence of LLM responses, the sequence of LLM responses including at least one intermediate LLM response and a final LLM response. In some implementations, the method may further include causing the final LLM response to be rendered at the client device. In additional or alternative implementations, the method may further include storing, as an instance of training data for fine-tuning the LLM or an additional LLM, the NL based input along with the final LLM response.

Abstract: In at least one embodiment, a microphone assembly including a substrate, a printed circuit board (PCB), a micro-electro-mechanical systems (MEMS) transducer, a first lid, and a second lid is provided. The substrate defines a first port that extends completely therethrough. The PCB defines a sound opening that extends completely therethrough. The MEMS transducer is positioned on a first side of the substrate. The first lid defines a second port and covers the MEMS transducer and the first port. The first lid and the substrate define a front volume of air that surrounds the MEMS transducer. The second lid is positioned on the second side of the PCB. A cavity of the second lid, the sound opening of the PCB, the sound opening of the PCB, and the first port of the substrate define a back volume of air that is greater than the front volume of air.

Abstract: A smart insect collection device comprising: a trapping device; a sensing device having a through opening in fluid connection with the trapping device; a processing unit to receive electrical signals from the sensing device; and a collecting module comprising: a selection plate having at least one through hole configured to engage an opening of a collection container for collection of insects therein, the sensing device provided between the trapping device and the selection plate, the selection plate movable between at least one non-collecting position and at least one collecting position; and a controller in communication with the processing unit and with a motor provided to move the selection plate, the controller programmable to activate the motor to move the selection plate to the collecting position in response to the processing unit identifying an insect passing through the through opening to be an insect of a predetermined kind for collection.

Abstract: A memory and an electronic device are provided in the present application. The memory includes a bank. The bank includes two planes. Each of the two planes includes a plurality of memory resources and a plurality of redundant replacement resources. A bad memory resource of one of the two planes is capable of being replaced by the redundant replacement resource of the other of the two planes for performing a normal Read/Write operation. By the above-mentioned way, the redundant replacement resources in different planes are capable of being shared, thereby improving the flexibility of redundancy and the yield of the memory.

Abstract: Implementations relate to improving instruction following capabilities of large language models (LLMs) using instruction decomposition, self-evaluation, and optionally progressive refinement. Processor(s) of a system can: obtain natural language (NL) based input, generate a plurality of candidate responses and evaluate the candidate responses based on instructions included in the NL based input, using an LLM, and progressively refine the candidate responses until it is determined that one or more termination criteria are satisfied. In some implementations, the NL based input can be received from a client device. In these implementations, a given candidate response that is progressively refined can be rendered for presentation at the client device and responsive to the NL base input. In additional or alternative implementations, the NL based input can be obtained from database(s). In these implementations, a given candidate response that is progressively refined can be utilized in fine-tuning of the LLM.

Abstract: Implementations described herein relate to using self-evaluation when utilizing a large language model (LLM) to generate a response to a natural language (NL) based input. The LLM can be used to process the NL based input to generate a plurality of responses, and to generate a critique of those responses by comparing the responses to a set of response evaluation criteria. One of the responses can then be selected based on the comparison with the set of response evaluation criteria which can vary from one NL based input to another. If the NL based input was obtained a user of a client device during an inference stage, then the selected response can be rendered for presentation to the user. If the NL based input was obtained during a training stage, then the selected response can be stored as a training instance and optionally in association with additional data.

Abstract: At least utilizing a custom corpus of documents to condition a large language model (LLM) when generating a response to a user query. In some implementations, a user query associated with a client device is received. An API query for an external application is generated by an LLM based on the user query. The external application has access to a custom corpus of documents comprising a plurality of documents. The external application is queried using the API query. Data representative of one or more documents in the custom corpus of documents is received from the external application in response to the API query. The LLM generates a response to the query that is conditioned on the data representing one or more of the documents in the custom corpus of documents received from the external application. The response to the user query is caused to be rendered on the client device.

Abstract: A data processing method, a storage medium, and a processor are provided that solve the technical problem of low efficient of data storage. The data processing method is applied to a storage engine, and involves determining that target data is transmitted from a target application to a first persistent memory region of a storage device, and sending a target instruction to the storage device. The target instruction is used for instructing the storage device to write the target data from the first persistent memory region into a memory of the storage device.