Abstract: A method of performing open world object detection includes receiving object data, that includes embeddings data corresponding to a plurality of embeddings for known objects in a first input image, projecting the embeddings into a hyperbolic embedding space that includes embeddings in a plurality of categories of objects each including one or more classes of objects, regularizing the projected embeddings within the hyperbolic embedding space by moving each of the projected embeddings closer to embeddings in a same category of the plurality of categories and further away from embeddings in different categories of the plurality of categories, receiving an unmatched query corresponding to an object in a second input image, and generating, based on the hyperbolic embedding space including the regularized embeddings, an output signal that indicates whether the object in the second input image corresponds to an unknown object in one of the classes of objects.

Abstract: A method includes extracting, by an analysis computer, a plurality of first datasets from a plurality of graph snapshots using a structural self-attention module. The analysis computer can then extract at least a second dataset from the plurality of first datasets using a temporal self-attention module across the plurality of graph snapshots. The analysis computer can then perform graph context prediction with at least the second dataset.

Abstract: A method of obtaining an uncertainty attribution of a prediction of objects in an input image includes receiving an input image, generating a prediction of objects in the input image, estimating an uncertainty associated with the prediction of the objects in the input image, calculating an uncertainty attribution that represents regions of the input image that cause the estimated uncertainty, including generating a plurality of adversarial gradients each corresponding to a modification of the input image configured to change the estimated uncertainty, and generating an output indicative of the calculated uncertainty attribution.

Abstract: A method of performing data pre-selection for an object detection system includes receiving a first dataset that includes unlabeled data corresponding to one or more images, providing the first dataset and a plurality of learnable prompt vectors to a pre-training model. The learnable prompt vectors include text inputs. The method further includes generating, using the pre-training model, an unsupervised learning prompt based on the first dataset and the plurality of learnable prompt vectors. The unsupervised learning prompt corresponds to a multi-modal feature of the one or more images of the first dataset. The method further includes extracting features from either of the first dataset and a second dataset based on the unsupervised learning prompt, selecting and labeling a subset of instances of the extracted features, and generating and outputting a labeled dataset based on the labeled subset of instances.

Abstract: The present disclosure provides a device for full-wave field seismic source based on a gas explosion technology and a method for acquiring seismic data. The device includes a cylindrical explosion-proof metal outer barrel, and four sides of the explosion-proof metal outer barrel are fixedly connected to four high-strength steel plates. The device also includes a cylindrical explosion-proof metal gas explosion inner barrel and pipelines for injecting high-pressure air and high-pressure gas into the gas explosion inner barrel. A center of the gas explosion inner barrel is installed with an electronic ignition gun, which is connected to a GPS timing module connected to the electronic ignition gun. The device further includes a controller configured to control a seismic source of a gas explosion full-wave field.

Abstract: In some implementations, the device may receive, for a plurality of stations, processing times indicating a time required for a part to be processed by each station, and waiting times indicating how long the part waited before moving to a subsequent one of the plurality of stations. In addition, the device may determine, cycle times for a predetermined window of time, where the cycle times indicates an average number of parts processed by the plurality of stations during the predetermined window of time. The device may determine one of the stations as a potential bottleneck station. Moreover, the device may display, to a user, the potential bottleneck station as a visualization which includes the processing time, the waiting time, and the cycle time of the potential bottleneck station. Also, the device may receive, from the user, a user feedback related to the potential bottleneck station.

Abstract: A method includes extracting, by an analysis computer, a plurality of first datasets from a plurality of graph snapshots using a structural self-attention module. The analysis computer can then extract at least a second dataset from the plurality of first datasets using a temporal self-attention module across the plurality of graph snapshots. The analysis computer can then perform graph context prediction with at least the second dataset.

Abstract: A method for performing at least one perception task associated with autonomous vehicle control includes receiving a first dataset and identifying a first object category of objects associated with the plurality of images, the first object category including a plurality of object types. The method also includes identifying a current statistical distribution of a first object type of the plurality of object types and determining a first distribution difference between the current statistical distribution of the first object type and a standard statistical distribution associated with the first object category. The method also includes, in response to a determination that the first distribution difference is greater than a threshold, generating first object type data corresponding to the first object type, configuring at least one attribute of the first object type data, and generating a second dataset by augmenting the first dataset using the first object type data.

Abstract: A computer-implemented system and method relates to language-guided self-supervised semantic segmentation. A modified image is generated by performing data augmentation on a source image. A machine learning model generates first pixel embeddings based on the modified image. First segment embeddings are generated using the first pixel embeddings. A pretrained vision-language model generates second pixel embeddings based on the source image. Second segment embeddings are generated by applying segment contour data from the first pixel embeddings to the second pixel embeddings after the data augmentation is performed on the second pixel embeddings. Embedding consistent loss data is generated by comparing the first segment embeddings in relation to the second segment embeddings. Combined loss data is generated that includes the embedding consistent loss data. Parameters of the machine learning model are updated based on the combined loss data.

Abstract: A computer system can perform a semi-supervised machine learning processes to cluster a plurality of entities within a population based on their features and associated labels. The computer system can generate visualization data representing the clusters of entities and associated labels for displaying on a user interface. A user can review the clustering of entities and use the user interface to add or modify the labels associated with a particular entity or set of entities. The computer system can use the user's feedback to update the labels and then re-determine the clustering of entities using the semi-supervised machine learning process with the updated labels as input. As such, the computer system can use the user's feedback to improve the accuracy of the machine learning model without requiring a larger amount of labeled input data.

Abstract: A system, method, and computer program product for incorporating knowledge from more complex models in simpler models. A method may include obtaining first training data associated with a first set of features and second training data associated with a second set of features different than the first set of features; training a first model based on the first training data and the second training data; and training a second model, using a loss function that depends on an output of an intermediate layer of the first model and an output of the second model, based on the second training data.

Abstract: A method for training a model for determining graph similarity is disclosed. The method comprises receiving a first graph and a second graph as training inputs, the first graph and the second graph each including nodes connected by edges. The method further comprises applying a model to the first graph and the second graph to determine (i) pairs of aligned nodes between the first graph and the second graph and (ii) a first training loss. The method further comprises generating a first augmented graph by modifying the first graph depending on the pairs of aligned nodes. The method further comprises applying the model to the first graph and the first augmented graph to determine a second training loss. The method further comprises refining the model based on the first training loss and the second training loss.

Abstract: The present disclosure provides a device for full-wave field seismic source based on a gas explosion technology and a method for acquiring seismic data. The device includes a cylindrical explosion-proof metal outer barrel, and four sides of the explosion-proof metal outer barrel are fixedly connected to four high-strength steel plates. The device also includes a cylindrical explosion-proof metal gas explosion inner barrel and pipelines for injecting high-pressure air and high-pressure gas into the gas explosion inner barrel. A center of the gas explosion inner barrel is installed with an electronic ignition gun, which is connected to a GPS timing module connected to the electronic ignition gun. The device further includes a controller configured to control a seismic source of a gas explosion full-wave field.

Abstract: A computer-implemented system and method includes generating first pseudo segment data from a first augmented image and generating second pseudo segment data from a second augmented image. The first augmented image and the second augmented image are in a dataset along with other augmented images. A machine learning system is configured to generate pixel embeddings based on the dataset. The first pseudo segment data and the second pseudo segment data are used to identify a first set of segments that a given pixel belongs with respect to the first augmented image and the second augmented image. A second set of segments is identified across the dataset. The second set of segments do not include the given pixel. A local segmentation loss is computed for the given pixel based on the corresponding pixel embedding that involves attracting the first set of segments while repelling the second set of segments.

Abstract: A system, method, and computer program product for incorporating knowledge from more complex models in simpler models. A method may include obtaining first training data associated with a first set of features and second training data associated with a second set of features different than the first set of features; training a first model based on the first training data and the second training data; and training a second model, using a loss function that depends on an output of an intermediate layer of the first model and an output of the second model, based on the second training data.

Abstract: The present disclosure provides a method and a system for acquiring seismic data of a four-component ocean bottom node (OBN). The method is implemented by the system, comprising controlling installations of a plurality of ocean bottom submerged buoys and a plurality of four-component OBN seismic data acquisition instruments and sending positioning signals and timing signals to the plurality of ocean bottom submerged buoys through armored opto-electronic composite cables. The method also includes obtaining real-time and uninterrupted water temperature data, pressure data, density data, and salt saturation data along the armored opto-electronic composite cables from the ocean surface to locations of the plurality of ocean bottom submerged buoys, and calculating real-time and three-dimensional data of waters of a whole measurement work area through interpolation.

Abstract: The present disclosure provides a device for full-wave field seismic source based on a gas explosion technology and a method for acquiring seismic data. The device includes a cylindrical explosion-proof metal outer barrel, and four sides of the explosion-proof metal outer barrel are fixedly connected to four high-strength steel plates. The device also includes a cylindrical explosion-proof metal gas explosion inner barrel and pipelines for injecting high-pressure air and high-pressure gas into the gas explosion inner barrel. A center of the gas explosion inner barrel is installed with an electronic ignition gun, which is connected to a GPS timing module connected to the electronic ignition gun. The device further includes a controller configured to control a seismic source of a gas explosion full-wave field.

Abstract: Provided is a method for implementing reinforcement learning by a neural network. The method may include performing, for each epoch of a first predetermined number of epochs, a second predetermined number of training iterations and a third predetermined number of testing iterations using a first neural network. The first neural network may include a first set of parameters, the training iterations may include a first set of hyperparameters, and the testing iterations may include a second set of hyperparameters. The testing iterations may be divided into segments, and each segment may include a fourth predetermined number of testing iterations. A first pattern may be determined based on at least one of the segments. At least one of the first set of hyperparameters or the second set of hyperparameters may be adjusted based on the pattern. A system and computer program product are also disclosed.

Abstract: A computer-implemented method for a machine-learning network includes receiving an input dataset, wherein the input dataset is indicative of image information, tabular information, radar information, sonar information, or sound information, sending the input dataset to the machine-learning model to output predictions associated with the input data, identifying one or more slices associated with the input dataset and the machine learning model in a first iteration, wherein each of the one or more slices include input data from the input dataset and common attributes associated with each slice, outputting an interface that includes information associated with the one or more slices and performance measurements of the one or more slices of the first iteration and subsequent iterations identifying subsequent slices, wherein the performance measurements relate to the predictions associated with the first iteration and subsequent iterations.

Abstract: A computer-implemented method for a machine-learning network that includes receiving an input dataset, sending the input dataset to a first machine-learning model to output predictions associated with the input data, identifying one or more slices associated with the input dataset and a first machine learning model in a first iteration, wherein each of the one or more slices include input data from the input dataset and common attributes associated with each slice; upon selecting one or more slices of the input dataset, training a shallow regressor model configured to predict residuals associated with the model, create a representation associated with a ground-truth label and a second representation associated with a model prediction associated with each sample associated with each of the one or more slices, determine residuals associated with every prediction of the first machine learning model, training the shallow regressor to compute one or more predicted residuals of the selected slices, generate an opti