Abstract: Some embodiments of the disclosure provide an air intake bypass recirculation structure with adjustable air entraining amount and controllable broadband noise which includes main body of the air intake bypass recirculation structure and an air entraining amount adjusting structure. In some examples, an air intake bypass recirculation cavity is formed in the main body of the air intake bypass recirculation structure. An air inlet of an air intake pipe and an air outlet of the air intake pipe are formed in two ends of the main body of the air intake bypass recirculation structure respectively. An airflow inlet of the air intake bypass recirculation structure and an airflow outlet of the air intake bypass recirculation structure are formed in the inner side of the main body of the air intake bypass recirculation structure. The air entraining amount adjusting structure is arranged in the air intake bypass recirculation cavity.

Abstract: A method of training a conversational computing system. The method includes providing an initial query to a machine learning model previously trained to generate actions for responding to a query. Based on the initial query, a plurality of candidate actions are received from the machine learning model and presented via a training interface. Based on input provided by the human annotator, a selected candidate action is recognized and associated with the initial query in a labeled data example.

Abstract: A system and a method of black start for a combined operation of wind power, photovoltaic power, energy storage, and thermal power includes a renewable energy alternating current microgrid module, a power transmission and distribution module, and a thermal power generation module, the power transmission and distribution module is coupled to the renewable energy alternating current microgrid module and the thermal power generation module respectively. The renewable energy alternating current microgrid module includes a photovoltaic unit, a wind power unit, and an energy storage unit. The power transmission and distribution module is configured to transmit the electrical energy generated by the renewable energy alternating current microgrid module to the thermal power generation module. The thermal power generation module is configured to start an auxiliary equipment using the electrical energy transmitted by the power transmission and distribution module.

Abstract: The system for frequency modulation based on Direct Current (DC) controllable load for a high-voltage plant includes a frequency modulator and a DC-controllable load for the high-voltage plant connected to the frequency modulator. The frequency modulator includes a centralized rectifier configured to adjust power of loads in the DC-controllable load in response to a frequency modulation command. The DC-controllable load is configured to respond to an adjustment of the power of loads performed by the centralized rectifier.

Abstract: A computer-implemented method of responding to a conversational event is presented. The method comprises receiving a conversational event at a conversational computing interface. Based on the received conversational event, an applicable generation rule of a plurality of candidate generation rules is selected. The applicable generation rule is configured with one or more parameters. A computer-executable plan is then selected based on the selected generation rule. The one or more parameters are passed from the selected generation rule to one or more additional generation rules. The one or more additional generation rules configured with the one or more parameters are recursively applied to extend the selected computer-executable plan. One or more candidate responses to the conversational event are output via the conversational computing interface based on the recursive application of the one or more additional generation rules configured with the one or more parameters.

Abstract: An injection control device executes a current-drive of a fuel injection valve to inject fuel into an engine. The injection control device includes: a drive controller that controls energization by correcting an energization instruction time when injecting the fuel by executing the current-drive of the fuel injection valve; an estimated energization time correction amount calculation unit that calculates an estimated energization time correction amount; and a deterioration determination unit that determines deterioration of the charging capacitor based on a calculated estimated energization time correction amount and the energization time correction amount.

Abstract: Embodiments of the disclosure provide methods and systems for representation learning from a biomedical image with a sparse convolution. The exemplary system may include a communication interface configured to receive the biomedical image acquired by an image acquisition device. The system may further include at least one processor, configured to extract a structure of interest from the biomedical image. The at least one processor is also configured to generate sparse data representing the structure of interest and input features corresponding to the sparse data. The at least one processor is further configured to apply a sparse-convolution-based model to the biomedical image, the sparse data, and the input features to generate a biomedical processing result for the biomedical image. The sparse-convolution-based model performs one or more neural network operations including the sparse convolution on the sparse data and the input features.

Abstract: A computer-implemented method of responding to a conversational event is presented. The method comprises receiving a conversational event at a conversational computing interface. Based on the received conversational event, an applicable generation rule of a plurality of candidate generation rules is selected. The applicable generation rule is configured with one or more parameters. A computer-executable plan is then selected based on the selected generation rule. The one or more parameters are passed from the selected generation rule to one or more additional generation rules. The one or more additional generation rules configured with the one or more parameters are recursively applied to extend the selected computer-executable plan. One or more candidate responses to the conversational event are output via the conversational computing interface based on the recursive application of the one or more additional generation rules configured with the one or more parameters.

Abstract: This disclosure discloses a method and system for predicting disease quantification parameters for an anatomical structure. The method includes extracting a centerline structure based on a medical image. The method further includes predicting the disease quantification parameter for each sampling point on the extracted centerline structure by using a GNN, with each node corresponds to a sampling point on the extracted centerline structure and each edge corresponds to a spatial constraint relationship between the sampling points. For each node, a local feature is extracted based on the image patch for the corresponding sampling point by using a local feature encoder, and a global feature is extracted by using a global feature encoder based on a set of image patches for a set of sampling points, which include the corresponding sampling point and have a spatial constraint relationship defined by the centerline structure.

Abstract: This disclosure discloses a method for analyzing clinical data. The Method includes extracting a first feature information by applying a neural network to the clinical data; predicting a disease status related parameter by applying a regression model to the extracted first feature information; generating a second feature information based on the extracted first feature information and the disease status related parameter; and predicting a disease status classification result by applying a classification model to the second feature information. The method can improve the prediction accuracy and the diagnosis efficiency of doctors.

Abstract: The present disclosure relates to a method and a system for generating anatomical labels of an anatomical structure. The method includes receiving an anatomical structure with an extracted centerline, or a medical image containing the anatomical structure with the extracted centerline; and predicting the anatomical labels of the anatomical structure based on the centerline of the anatomical structure, by utilizing a trained deep learning network. The deep learning network includes a branched network, a Graph Neural Network, a Recurrent Neural Network and a Probability Graph Model, which are connected sequentially in series. The branched network includes at least two branch networks in parallel. The method in the disclosure can automatically generate the anatomical labels of the whole anatomical structure in medical image end to end and provide high prediction accuracy and reliability.

Abstract: Embodiments of the disclosure provide systems and methods for analyzing medical images using a learning model. The system receives a medical image acquired by an image acquisition device. The system may additionally include at least one processor configured to apply the learning model to perform an image analysis task on the medical image. The learning model is trained jointly with an error estimator using training images comprising a first set of labeled images and a second set of unlabeled images. The error estimator is configured to estimate an error of the learning model associated with performing the image analysis task.

Abstract: A computer-implemented method of responding to a conversational event. The method comprises enacting, by a conversational computing interface, an initial computer-executable plan based on a conversational event received by the conversational computing interface, wherein the initial computer-executable plan is configured to output an initial value based on the conversational event. The method further comprises selecting, by the conversational computing interface, an extended computer-executable plan based on determining that the initial value is insufficient for generating an extended description responsive to the conversational event. The method further comprises enacting, by the conversational computing interface, the extended computer-executable plan to output additional information beyond what the initial computer-executable plan is configured to output, the additional information sufficient for generating the extended description responsive to the conversational event.

Abstract: An injection control device executes a current-drive of a fuel injection valve to inject fuel into an engine. The injection control device includes: a drive controller that controls energization by correcting an energization instruction time when injecting the fuel by executing the current-drive of the fuel injection valve; an estimated energization time correction amount calculation unit that calculates an estimated energization time correction amount; and a deterioration determination unit that determines deterioration of the charging capacitor based on a calculated estimated energization time correction amount and the energization time correction amount.

Abstract: A method comprising recognizing a user utterance including an ambiguity. The method further comprises using a previously-trained code-generation machine to produce, from the user utterance, a data-flow program including a search-history function. The search-history function is configured to select a highest-confidence disambiguating concept from one or more candidate concepts stored in a context-specific dialogue history.

Abstract: A method of training a conversational computing system. The method includes providing an initial query to a machine learning model previously trained to generate actions for responding to a query. Based on the initial query, a plurality of candidate actions are received from the machine learning model and presented via a training interface. Based on input provided by the human annotator, a selected candidate action is recognized and associated with the initial query in a labeled data example.

Abstract: A computer-implemented method of responding to a conversational event. The method comprises enacting, by a conversational computing interface, an initial computer-executable plan based on a conversational event received by the conversational computing interface, wherein the initial computer-executable plan is configured to output an initial value based on the conversational event. The method further comprises selecting, by the conversational computing interface, an extended computer-executable plan based on determining that the initial value is insufficient for generating an extended description responsive to the conversational event. The method further comprises enacting, by the conversational computing interface, the extended computer-executable plan to output additional information beyond what the initial computer-executable plan is configured to output, the additional information sufficient for generating the extended description responsive to the conversational event.

Abstract: A method comprising recognizing a user utterance including an ambiguity. The method further comprises using a previously-trained code-generation machine to produce, from the user utterance, a data-flow program including a search-history function. The search-history function is configured to select a highest-confidence disambiguating concept from one or more candidate concepts stored in a context-specific dialogue history.