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 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: 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.

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: 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 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 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: A liquid storage assembly includes: a liquid storage cavity for storing to-be-vaporized liquid; a reference element at least partially located in the liquid storage cavity and including a reference electrode; at least one detection element at least partially located in the liquid storage cavity, the at least one detection element being spaced apart from the reference element and including a detection electrode; and a detection circuit electrically connected to the reference element and the at least one detection element, the detection circuit detecting capacitance between the reference element and the at least one detection element or resistance of the to-be-vaporized liquid, and determining a remaining volume of the to-be-vaporized liquid according to the detected capacitance or resistance.

Abstract: The present disclosure discloses an image denoising method, an electronic device and a storage medium, the method including: acquiring a preset quantity of full dose images and low dose images; training a preset deep learning model according to the full dose images and the low dose images, wherein the deep learning model includes a multi-frequency band denoising network, and the multi-frequency band denoising network includes one multi-frequency band separation module, N denoising modules and one summation module; and acquiring an image to be denoised, and denoising the image to be denoised through the multi-frequency band denoising network in a trained deep learning model.

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: A liquid storage assembly includes: a liquid storage cavity for storing to-be-vaporized liquid; a reference element at least partially located in the liquid storage cavity and including a reference electrode; at least one detection element at least partially located in the liquid storage cavity, the at least one detection element being spaced apart from the reference element and including a detection electrode; and a detection circuit electrically connected to the reference element and the at least one detection element, the detection circuit detecting capacitance between the reference element and the at least one detection element or resistance of the to-be-vaporized liquid, and determining a remaining volume of the to-be-vaporized liquid according to the detected capacitance or resistance.

Abstract: A method for testing gas produced by fermentation of intestinal flora includes placing a sample bottle into a sample bin; inserting the sampling needle into a sample bottle for sampling; communicating the sample bottle with a gas detecting cavity, and quantitatively extracting the fermentation gas from the sample bottle; detecting the concentration of a certain fermentation gas using a sensor to deduce the concentration of the fermentation gas in the sample bottle; repeating the detection by using a step-type injection mode until the detection in preset gradients of all the fermentation gas of gut flora is completed, and the detection data of each fermentation gas of gut flora conforms to the detection range of the preset gas sensor; and carrying out reduced calculation of the concentration of each fermentation gas of gut flora in the sample bottle according to the collected data. An instrument carries the test method.

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 an other of the two planes for performing a normal Read/Write operation. A first priority level of replacing the bad memory resource in the one of the two planes by the redundant replacement resource in a same plane is greater than a second priority level of replacing the bad memory resource by the redundant replacement resource in a different plane of the two planes.

Abstract: An index creation method, a computing device, and a storage medium are provided. The method comprises: dividing storage records in logic sub-storage areas on the basis of the storage records and the logic sub-storage areas in a logic storage area divided in a physical storage area, the storage records comprising storage data; determining, from among multiple preset index formats according to the divided storage records, an index format matched with a record attribute; and creating a corresponding sub-index according to the determined index format to search for storage records according to the sub-index, the index format being able to cause the generated corresponding sub-index to occupy memory space smaller than a preset capacity. The index format matched with the record attribute is determined from among the multiple preset index formats, the index format can cause the generated corresponding sub-index to occupy memory space smaller than the preset capacity.

Abstract: A data processing method, system and device, and a medium are provided. The method comprises: receiving a data stream; dicing the data stream to obtain a plurality of segmented data blocks; respectively calculating verification information corresponding to the plurality of segmented data blocks; and sending the segmented data blocks and the corresponding check information as a target logic block to a storage device.

Abstract: The present disclosure discloses an image denoising method, an electronic device and a storage medium, the method including: acquiring a preset quantity of full dose images and low dose images; training a preset deep learning model according to the full dose images and the low dose images, wherein the deep learning model includes a multi-frequency band denoising network, and the multi-frequency band denoising network includes one multi-frequency band separation module, N denoising modules and one summation module; and acquiring an image to be denoised, and denoising the image to be denoised through the multi-frequency band denoising network in a trained deep learning model.

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: Embodiments of the present disclosure provide a data processing method and system, a device, a storage system, and a medium. The data processing method includes: receiving a logic address corresponding to data to be processed; storing the logic address as a first access unit in a first storage pool; and in a case that the quantity of the stored first access units in the first storage pool meets a requirement, combining a plurality of first access units into a logic address group and storing the logic address group as a second access unit in a second storage pool so as to process the data based on the second access unit.

Abstract: In at least one embodiment, a multi-stage sound and vibration sensor is provided. The multi-stage sound and vibration sensor includes a housing, a first piezo-diaphragm and a second piezo diaphragm. The first piezo-diaphragm and the second piezo-diaphragm are positioned in the housing to detect an input signal including audio or vibrations. The first piezo-diaphragm and the second piezo-diaphragm provide a first resonance frequency and a second resonance frequency in response to detecting the audio or the vibrations.

Abstract: In at least one embodiment, a microphone assembly including an enclosure, a first printed circuit board (PCB) and a microphone assembly is provided. The microphone assembly includes a sub-casing, a micro-electro-mechanical systems (MEMS) transducer, and a second PCB. The MEMS transducer positioned in the sub-casing and the second PCB supports the MEMS transducer. The first PCB defines a first acoustic path positioned below the second PCB and the MEMS transducer. The second PCB defines a first audio port positioned directly below the MEMS transducer. The enclosure defines a first acoustic opening that is positioned directly below the first acoustic path to enable an audio input signal to pass through the first audio port and to an underside of the MEMS transducer. The enclosure defines a second acoustic opening that is positioned at a distance of between 3 to 30 mm from the first acoustic opening.