Abstract: Disclosed systems and methods may register a first plugin server as a primary server of a cluster management plugin (CMP) for a user interface (UI) of a virtualization platform client and a second plugin server as an auxiliary server of the CMP. The first plugin server may be associated with a first cluster of the virtualization platform and the second plugin server is associated with a second cluster of the visualization platform. The clusters may include one or more multi-node hyperconverged infrastructure (HCI) clusters. The first cluster may be managed by a first instance of a cluster manager while the second cluster may be managed by a second instance of the cluster manager. A CMP manifest, indicative of user interface extension points defined by the CMP, is loaded into a browser from the primary server.

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.

Abstract: A capacitor is provided. The capacitor includes a first electrode layer and a second electrode layer; and a first dielectric layer and a second dielectric layer, wherein the first dielectric layer and the second dielectric layer are disposed between the first electrode layer and the second electrode layer. The first dielectric layer includes a first dielectric powder and a first organic resin, and the second dielectric layer includes a second dielectric powder and a second organic resin. In particular, the weight ratio of the first dielectric powder to the first organic resin is greater than the weight ratio of the second dielectric powder to the second organic resin.

Abstract: Techniques disclosed herein relate to 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: A method for presenting do-it-yourself (DIY) videos to a user related to a user task by a DIY video system, including: receiving a user query including a first image file and a text question from a user regarding the current state of the user task; extracting entities from the first image file to create entity data; extracting question information from the text question; extracting from a DIY video index a video segment related to the user task based upon the entity data and the question information; and presenting the extracted video segment to the user.

Abstract: A capacitor is provided. The capacitor includes a first electrode layer and a second electrode layer; and a first dielectric layer and a second dielectric layer, wherein the first dielectric layer and the second dielectric layer are disposed between the first electrode layer and the second electrode layer. The first dielectric layer includes a first dielectric powder and a first organic resin, and the second dielectric layer includes a second dielectric powder and a second organic resin. In particular, the weight ratio of the first dielectric powder to the first organic resin is greater than the weight ratio of the second dielectric powder to the second organic resin.