Abstract: Methods and systems for circuit generation include generating a circuit design. Paths are extracted from the circuit design, with the paths representing sequences of connected circuit components from one terminal of the circuit to another. The extracted paths are embedded as respective vectors in a latent space. A property of the circuit design is determined using an ensemble of trained surrogate models that accept a sequence of the vectors as input.

Abstract: A tubing hanger recovery tool and a recovery method are provided. The tubing hanger recovery tool includes a mandrel, a locking part and a hook claw part; a lower end face of the mandrel is in contact with an inner side of the locking part, and when the mandrel moves towards the tubing hanger, the locking part is squeezed and locked with the tubing hanger; the hook claw part surrounds the mandrel, and when the mandrel moves towards the tubing hanger, it drives the hook claw part to move towards the tubing hanger, and the hook claw part is clamped with the setting ring of the tubing hanger.

Abstract: A computing device includes a processor and a storage device coupled to the processor. The storage device stores instructions to cause the processor to perform acts to provide a circuit performance modeling. The acts include identifying and extracting paths of an electric circuit between a plurality of designated components that represent the electric circuit; converting at least one of the extracted paths to a path embedding comprising a vector of a fixed length; and predicting, by a circuit representation-learning model, characteristics of the designated components that represent the electric circuit based on an input of circuit parameters of the electric circuit.

Abstract: Described aspects include a system for optimizing performance of a functional circuit unit, a method of optimizing performance of a functional circuit unit, and a computer program product. In one embodiment, the system may include a functional circuit unit having an associated cooling device and power converter, one or more sensors for the functional circuit unit, the one or more sensors including a power sensor and a temperature sensor, and a first machine learning model. The first machine learning model may be adapted to receive temperature data and power data from the one or more sensors, and to generate control signals for the cooling device and the power converter to optimize performance of the functional circuit unit.

Abstract: A method, system, and computer program product for circuit design automation. The method identifies a set of circuit components for a proposed circuit design. A subset of circuit components is selected to generate an initial topology for the proposed circuit design. A set of subsequent topologies are iteratively generated by a heuristic search algorithm based on the subset of circuit components and the initial topology. A set of valid topologies of the set of subsequent topologies are determined by a circuit simulator based on the subset of circuit components and a set of connections within the set of subsequent topologies. The method generates the proposed circuit design from the set of valid topologies.

Abstract: Described aspects include a system for optimizing performance of a functional circuit unit, a method of optimizing performance of a functional circuit unit, and a computer program product. In one embodiment, the system may include a functional circuit unit having an associated cooling device and power converter, one or more sensors for the functional circuit unit, the one or more sensors including a power sensor and a temperature sensor, and a first machine learning model. The first machine learning model may be adapted to receive temperature data and power data from the one or more sensors, and to generate control signals for the cooling device and the power converter to optimize performance of the functional circuit unit.

Abstract: A method includes receiving input from a client at least partially specifying one or more characteristics, wherein the initial input includes a seed image and a natural language statement describing a desired change to the seed image; predicting one or more attributes of the seed image by operation of a neural network on the seed image; and parsing the natural language statement to identify desired changes to the one or more attributes of the seed image. The method also includes generating an interim target image by changing the one or more attributes of the seed image, according to the parsed natural language statement; selecting a set of images from an image database for output to the client, each of said set of images being determined to at least partially satisfy the one or more changed attributes of the seed image; and displaying the set of images to the client.

Abstract: A current observation expressed in natural language is received. Entities in the current observation are extracted. A relevant historical observation is retrieved, which has at least one of the entities in common with the current observation. The current observation and the relevant historical observation are combined as observations. The observations and a template list specifying a list of verb phrases to be filled-in with at least some of the entities are input to a neural network, which can output the template list of the verb phrases filled-in with said at least some of the entities. The neural network can include attention mechanism. A reward associated with the neural network's output can be received and fed back to the neural network for retraining the neural network.

Abstract: Dual-modality relation networks for audio-visual event localization can be provided. A video feed for audio-visual event localization can be received. Based on a combination of extracted audio features and video features of the video feed, informative features and regions in the video feed can be determined by running a first neural network. Based on the informative features and regions in the video feed determined by the first neural network, relation-aware video features can be determined by running a second neural network. Based on the informative features and regions in the video feed, relation-aware audio features can be determined by running a third neural network. A dual-modality representation can be obtained based on the relation-aware video features and the relation-aware audio features by running a fourth neural network. The dual-modality representation can be input to a classifier to identity an audio-visual event in the video feed.

Abstract: A method and a system for learning and applying neuro-symbolic multi-hop rules are provided. The method includes inputting training texts into a neural network as well as pre-defined entities. The training texts and the entities relate to a specific domain. The method also includes generating an entity graph made up of nodes and edges. The nodes represent the pre-defined entities, and the edges represent passages in the training texts with co-occurrence of the entities connected together by the edges. The method further includes determining a relation based on the passages for each of the pre-defined entities connected together by the edges, calculating a probability relating to the relation, generating a potential reasoning path between a head entity and a target entity. The method also includes learning a neuro-symbolic rule by converting the edges along the potential reasoning path into symbolic rules and combining those rules into the neuro-symbolic rule.

Abstract: A search engine is modified to perform increasingly precise image searching using iterative Natural Language (NL) interactions. From an NL search input, the modification extracts a set of input features, which includes a set of response features corresponding to an NL statement in the NL search input and a set of image features from a seed image in the NL search input. The modification performs image analysis on an image result in a result set of a query including at least some of the input features. In a next iteration of NL interactions, at least some of the result set is provided. An NL response in the iteration is added to a cumulative NL basis, and a revised result set is provided, which includes a new image result corresponding to a new response feature extracted from the cumulative NL basis.

Abstract: A current observation expressed in natural language is received. Entities in the current observation are extracted. A relevant historical observation is retrieved, which has at least one of the entities in common with the current observation. The current observation and the relevant historical observation are combined as observations. The observations and a template list specifying a list of verb phrases to be filled-in with at least some of the entities are input to a neural network, which can output the template list of the verb phrases filled-in with said at least some of the entities. The neural network can include attention mechanism. A reward associated with the neural network's output can be received and fed back to the neural network for retraining the neural network.

Abstract: Dual-modality relation networks for audio-visual event localization can be provided. A video feed for audio-visual event localization can be received. Based on a combination of extracted audio features and video features of the video feed, informative features and regions in the video feed can be determined by running a first neural network. Based on the informative features and regions in the video feed determined by the first neural network, relation-aware video features can be determined by running a second neural network. Based on the informative features and regions in the video feed, relation-aware audio features can be determined by running a third neural network. A dual-modality representation can be obtained based on the relation-aware video features and the relation-aware audio features by running a fourth neural network. The dual-modality representation can be input to a classifier to identity an audio-visual event in the video feed.

Abstract: A method and a system for learning and applying neuro-symbolic multi-hop rules are provided. The method includes inputting training texts into a neural network as well as pre-defined entities. The training texts and the entities relate to a specific domain. The method also includes generating an entity graph made up of nodes and edges. The nodes represent the pre-defined entities, and the edges represent passages in the training texts with co-occurrence of the entities connected together by the edges. The method further includes determining a relation based on the passages for each of the pre-defined entities connected together by the edges, calculating a probability relating to the relation, generating a potential reasoning path between a head entity and a target entity. The method also includes learning a neuro-symbolic rule by converting the edges along the potential reasoning path into symbolic rules and combining those rules into the neuro-symbolic rule.

Abstract: A method includes receiving input from a client at least partially specifying one or more characteristics, wherein the initial input includes a seed image and a natural language statement describing a desired change to the seed image; predicting one or more attributes of the seed image by operation of a neural network on the seed image; and parsing the natural language statement to identify desired changes to the one or more attributes of the seed image. The method also includes generating an interim target image by changing the one or more attributes of the seed image, according to the parsed natural language statement; selecting a set of images from an image database for output to the client, each of said set of images being determined to at least partially satisfy the one or more changed attributes of the seed image; and displaying the set of images to the client.

Abstract: A method, system, and program product for generating and modifying a video response is provided. The method includes receiving an audio/video file. Parsed video features of the audio/video file are generated with respect to a first graph. Parsed audio features of the audio/video file are generated with respect to a second graph. The first graph is placed overlaying the second graph and at least one intersection point between the first graph and the second graph is determined. A natural language query is executed with respect to the audio/video file and a parsed query entity is generated from the natural language query. The parsed query entity is analyzed with respect to the intersection point and a node of the intersection point comprising similar features is determined with respect to the parsed query entity. A resulting natural language response with respect to the natural language query is generated.

Abstract: A method of using multiple artificial intelligence models for generating a high fidelity simulation includes generating, by a computing device, multiple artificial intelligence models. Each artificial intelligence model simulating an industry design process. The computing device further fusing the multiple artificial intelligence models to generate a best-fit proposed industry design process. The computing device utilizes a physics constraint model to determine whether the best-fit proposed industry design process is feasible. The best-fit proposed industry design process is displayed in response to determining that the best-fit proposed industry design process is feasible.

Abstract: A method includes: receiving initial input from a client at least partially specifying one or more characteristics sought by the client; selecting a set of images from an image database for output to the client; and determining after each set of images whether an end condition has occurred. The method also includes, until the end condition has occurred: responsive to each set of images output to the client, receiving additional input from the client further specifying the one or more characteristics sought by the client; and responsive to each input received from the client, selecting another set of images for presentation to the client, said set of images being determined to at least partially satisfy the one or more characteristics specified by all input received from the client, said determination being based at least in part on side information for respective images for at least the set of images.

Abstract: A search engine is modified to perform increasingly precise image searching using iterative Natural Language (NL) interactions. From an NL search input, the modification extracts a set of input features, which includes a set of response features corresponding to an NL statement in the NL search input and a set of image features from a seed image in the NL search input. The modification performs image analysis on an image result in a result set of a query including at least some of the input features. In a next iteration of NL interactions, at least some of the result set is provided. An NL response in the iteration is added to a cumulative NL basis, and a revised result set is provided, which includes a new image result corresponding to a new response feature extracted from the cumulative NL basis.

Abstract: A method, system, and program product for generating and modifying a video response is provided. The method includes receiving an audio/video file. Parsed video features of the audio/video file are generated with respect to a first graph. Parsed audio features of the audio/video file are generated with respect to a second graph. The first graph is placed overlaying the second graph and at least one intersection point between the first graph and the second graph is determined. A natural language query is executed with respect to the audio/video file and a parsed query entity is generated from the natural language query. The parsed query entity is analyzed with respect to the intersection point and a node of the intersection point comprising similar features is determined with respect to the parsed query entity. A resulting natural language response with respect to the natural language query is generated.