Abstract: The disclosure provides an apparatus for substrate polishing, the apparatus includes a housing member, a carrier member, and a distal force assembly. The carrier member is coupled to and disposed radially outward of the housing member. The distal force assembly disposed radially inward of an exterior portion of the carrier member and radially outward of the housing member. The distal force assembly includes a bladder seal, a seal ring in contact with the bladder seal. The seal ring has a seal ring face and one or more grooves disposed therein. The grooves include a plateau radius and a ridge radius disposed opposite the plateau radius at an intersection between the seal ring face and the grooves, wherein the plateau radius and the ridge radius are between about 0.012 to about 0.018 inches. The distal force assembly also includes a transfer ring coupled to the seal ring opposite the bladder.

Abstract: An electrode microstructure and a method for fabricating the same are provided. The electrode microstructure includes a plastic part and a surface structural layer disposed on a surface of the plastic part. The surface structural layer includes a one-dimensional metal nanocrystal structure, the surface structural layer includes a plurality of microstructures, each of the plurality of microstructures is spaced apart from each other and distributed on the surface of the plastic part, and each of the plurality of microstructures includes at least one conical portion for tip discharge. The conical portion of the microstructures for tip discharge enhance the process efficiency of the subsequent electroplating electrode microstructure, and the conical portion of the microstructures can provide larger surface area to react with the electroplating material.

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: Implementations of this disclosure provide for automated monitoring of configuration parameters of a primary data intake and query system instance operating within a distributed deployment environment. Further implementations provide for automatically generating instructions in response to a detected change in a configuration parameter of the primary data intake and query system instance and transmitting those instructions to one or more secondary data intake and query system instances. The instructions, upon execution by one or more processors, cause the configuration parameters of the one or more secondary data intake and query system instances to be updated in accordance with the detected change in the configuration parameter of the primary data intake and query system instance.

Abstract: The present invention provides a biochip for tracking postoperative recurrence status of a patient with lung adenocarcinoma. The biochip comprises a bare plate layer, the bare plate layer comprises a sensing electrode, and the sensing electrode comprises a biological agent capable of measuring an expression amount of a GPNMB gene. The present invention further provides a method for tracking the postoperative recurrence status of a patient with lung adenocarcinoma. The method comprises the following steps: step one, providing a sample from a patient with lung adenocarcinoma; step two: contacting the sample with a carrier capable of detecting an expression amount of a GPNMB gene; and step three: analyzing a change of the expression amount of the GPNMB gene to track the postoperative recurrence status of the patient with lung adenocarcinoma.

Abstract: Techniques are described for automating the configuration of a simple network management protocol (SNMP) manager device for enabling collection of SNMP data from one or more SNMP-enabled devices. Based upon SNMP object identifiers (OIDs) received from an SNMP-enabled device, processing is performed to map the OIDs to one or more SNMP management information bases (MIBs) corresponding to the OIDs. The identification of the OIDs and mapping the OIDs to one or more MIBs is performed in an automated manner and substantially free of any human or manual intervention. The identified one or more MIBs are then used to configure the SNMP manager to enable SNMP communications between the SNMP-enabled device and the SNMP manager. In certain implementations, the identified one or more MIBs are loaded into system memory by the SNMP manager.

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: Techniques are described herein for drawing conclusions using free form texts and external resources. In various embodiments, free form input data (202) may be segmented (504) into a plurality of input data segments. A first input data segment may be compared (510) with an external resource (304) to identify a first candidate conclusion. A reinforcement learning trained agent (310) may be applied (512) to make a first determination of whether to accept or reject the first candidate conclusion. Similar actions may be performed with a second input data segment to make a second determination of whether to accept or reject a second candidate conclusion. A final conclusion may be presented (522) based on the first and second determinations of the reinforcement learning trained agent with respect to at least the first candidate conclusion and the second candidate conclusion.

Abstract: A system is provided for reducing friction during user interactions with a natural language processing system, such as voice assistant systems. The system determines a pre-trained model using dialog session data corresponding to multiple user profiles. The system determines a fine-tuned model using the pre-trained model and a fine-tuning dataset that corresponds to a particular task, such as query rewriting. The system uses the fine-tuned model to process a user input and determine an alternative representation of the input that can result in a desired response from the natural language processing system.

Abstract: Techniques are described for automating the configuration of a simple network management protocol (SNMP) manager device for enabling collection of SNMP data from one or more SNMP-enabled devices. Based upon SNMP object identifiers (OIDs) received from an SNMP-enabled device, processing is performed to map the OIDs to one or more SNMP management information bases (MIBs) corresponding to the OIDs. The identification of the OIDs and mapping the OIDs to one or more MIBs is performed in an automated manner and substantially free of any human or manual intervention. The identified one or more MIBs are then used to configure the SNMP manager to enable SNMP communications between the SNMP-enabled device and the SNMP manager. In certain implementations, the identified one or more MIBs are loaded into system memory by the SNMP manager.

Abstract: A system is provided for reducing friction during user interactions with a natural language processing system, such as voice assistant systems. The system determines a pre-trained model using dialog session data corresponding to multiple user profiles. The system determines a fine-tuned model using the pre-trained model and a fine-tuning dataset that corresponds to a particular task, such as query rewriting. The system uses the fine-tuned model to process a user input and determine an alternative representation of the input that can result in a desired response from the natural language processing system.

Abstract: Techniques are provided for generating and applying a granular attention hierarchical neural network model to classify a document. In various embodiments, data indicative of the document may be obtained (102) and processed (104) into a first layer of two or more layers of a hierarchical network model using a dual granularity attention mechanism to generate first layer output data, wherein the dual granularity attention mechanism weighs some portions of the data indicative of the document more heavily. Some portions of the data indicative of the document are integrated into the hieratical network model during training of the dual granularity attention mechanism. The first layer output data may be processed (106) in the second of two or more layers of the hierarchical network model to generate second layer output data. A classification label can be generated (108) from the second layer output data.

Abstract: Various embodiments described herein relate to a method, system, and non-transitory machine-readable medium including one or more of the following: extracting a first concept from input data presented for processing by a downstream function; identifying external data from an external resource based on the first concept; extracting a second concept from the external data; revising the first concept based on the second concept to produce a revised concept, wherein revising includes: applying a machine learning agent to determine whether to keep the first concept or adopt the second concept, and adopting the second concept in place of the first concept for use as the revised concept based on a decision by the machine learning agent to adopt the second concept; and further processing the revised concept according to the downstream function to generate an output.

Abstract: A system, method and device for determining and notifying a clinician of information relevant to the clinician. The method that is performed by the device or system includes identifying at least one keyword in a user profile of a clinician, identifying at least one content word in a new information item, determining a relevance score between the new information item and the clinician based on the at least one keyword and the at least one content word and when the relevance score is above a predetermined threshold value, generating a notification for the clinician indicating the new information item.

Abstract: The present disclosure discloses an antimicrobial tape comprising a tape layer, a connection layer, and an antimicrobial layer. The connection layer is disposed on the tape layer, and the connection layer consists of angstrom-level silicon dioxide. The antimicrobial layer is disposed on the connection layer. The tape layer and the antimicrobial layer are connected by the connection layer consisting of angstrom-level silicon dioxide to increase the connection strength between the tape layer and the antimicrobial layer. The tape layer may be used to cover a surface of an object, and the antimicrobial layer is located on an outer surface. Therefore, after a user touching and using the object, the object has been sterilized through the antimicrobial layer of the antimicrobial tape.

Abstract: Techniques described herein relate to visual question answering (“VQA”) using trained machine learning models.

Abstract: A method (100) for generating a textual description of a medical image, comprising: receiving (130) a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting (140) the anatomical region in the received medical image from a remainder of the image; identifying (150) at least one of the one or more abnormalities in the segmented anatomical region; extracting (160) one or more features from the identified abnormality; generating (170), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (180), via a user interface of the system, the generated textual description of the identified abnormality.

Abstract: A method of generating embeddings for a machine learning model, including: extracting a character embedding and a word embedding from a first textual data; generating a domain knowledge embedding from a domain knowledge dataset; combining the character embedding, the word embedding, and the domain knowledge embedding into a combined embedding; and providing the combined embedding to a layer of the machine learning model.

Abstract: A method for generating a textual description from a medical image, comprising: receiving a medical image having a first modality to a system configured to generate a textual description of the medical image; determining, using an imaging modality classification module, that the first modality is a specific one of a plurality of different modalities; determining, using an anatomy classification module, that the medical image comprises information about a specific portion of an anatomy; identifying, by an orchestrator module based at least on the determined first modality, which of a plurality of different text generation models to utilize to generate a textual description from the medical image; generating, by a text generation module utilizing the identified text generation model, a textual description from the medical image; and reporting, via a user interface of the system, the generated textual description.