Abstract: A method of labeling data and training a model is provided. The method includes obtaining a set of images. The set of images includes a first subset and a second subset. The first subset is associated with a first set of labels. The method also includes generating a set of pseudo labels for the set of images and a second set of labels for the second subset based on the first subset, the second subset, a first machine learning model, and a domain adaption model. The method further includes generating second machine learning model. The second machine learning model is generated based on the set of images, the set of pseudo labels, the first set of labels, and the second set of labels. The second set of labels is updated based on one or more inferences generated by the second machine learning model.

Abstract: In one embodiment, a method is provided. The method includes obtaining sensor data indicative of a set of objects detected within an environment. The method also includes determining a set of positions of the set of objects and a set of properties of the set of objects based on the sensor data. The method further includes generating a state graph based on the sensor data. The state graph represents the set of objects and the set of positions of the set of objects. The state graph includes a set of object nodes to represent the set of objects and a set of property nodes to represent the set of properties of the set of objects. The state graph is provided to a graph enhancement module that updates the state graph with additional data to generate an enhanced state graph.

Abstract: A system and method for translating a 3D visualization to a 2D visualization is provided. Data for a 3D visualization is received and includes layers of voxels that are processed to determine a type of terrain and color associated with the terrain type. Each voxel in a base layer of the 3D visualization is transformed into a tile of pixels for a 2D visualization. The color associated with the layers is assigned to the tiles by identifying, for each such layer, a marker for each voxel in that layer that indicates a presence or absence of the terrain type for that layer and applying the color associated with the layer to at least a portion of the tiles based on the markers. When multiple colors are applied to one of the tiles, the color associated with the layer furthest from the base layer is selected. The 2D visualization is output.

Abstract: Data representations are formed of a target substrate and a plurality of donor coupons that are incompletely filled with functional chips. The data representations are abstracted into a current state description of the target substrate and the donor coupons and input into a machine learning model that has been trained on previous mass transfer sequences. An optimal output of the machine learning model defines at least a selected one or more of the donor coupons and corresponding functional chips of the selected one or more of the donor coupons used to fill the vacancies. A parallel transfer of the corresponding functional chips is performed to fill the vacancies on the target substrate using the selected one or more of the donor coupons.

Abstract: A system and method for translating a 2D image to a 3D image is provided. A 2D image having pixels grouped into tiles is obtained. Contour lines associated with an altitude value are located on the 2D image. The altitude values are determined. Each tile in the 2D image is represented using at least one voxel. A height map of the voxels is generated based on the contour lines and altitude values as a base layer for a 3D image. A terrain type of each of the voxels of the 3D image is determined and objects are placed in the 3D image. The 3D image is output.

Abstract: A method of labeling training data includes inputting a plurality of unlabeled input data samples into each of a plurality of pre-trained neural networks and extracting a set of feature embeddings from multiple layer depths of each of the plurality of pre-trained neural networks. The method also includes generating a plurality of clusterings from the set of feature embeddings. The method also includes analyzing, by a processing device, the plurality of clusterings to identify a subset of the plurality of unlabeled input data samples that belong to a same unknown class. The method also includes assigning pseudo-labels to the subset of the plurality of unlabeled input data samples.

Abstract: A method utilizes a target substrate has an array of chips on a carrier with a plurality of vacancies and a plurality of donor coupons are incompletely filled with functional chips. A bounding box is defined that encompasses the vacancies on the target substrate. Outcomes are simulated by overlapping a representation of the bounding box over a representation of each of a plurality of donor coupons at a plurality of translational offsets on a substrate plane to determine matches. An optimal one of the outcomes is found at a selected one or more of the donor coupons corresponding one or more offsets. A parallel transfer of the matching functional chips fills the vacancies on the target substrate using the one or more selected donor coupons and corresponding one or more offsets.

Abstract: A method is provided. The method includes obtaining sensor data indicative of a set of objects detected within an environment. The method also includes generating a state graph based on the sensor data. The state graph includes a set of object nodes and a set of property nodes. The method further includes obtaining user input data generated based on a natural language input. The method further includes updating the state graph based on the user input data to generate an enhanced state graph. The enhanced state graph includes additional nodes generated based on the user input data. The method further includes generating a set of instructions for a set of mechanical systems based on the enhanced state graph. The method further includes operating the set of mechanical systems to achieve a set of objectives based on the set of instructions.

Abstract: A method includes defining a model for a liquid while the liquid is positioned at least partially within a nozzle of a printer. The method also includes synthesizing video frames of the liquid using the model to produce synthetic video frames. The method also includes generating a labeled dataset that includes the synthetic video frames and corresponding model values. The method also includes receiving real video frames of the liquid while the liquid is positioned at least partially within the nozzle of the printer. The method also includes generating an inverse mapping from the real video frames to predicted model values using the labeled dataset. The method also includes reconstructing the liquid in the real video frames based at least partially upon the predicted model values.

Abstract: A system for generating a labelled image data set for use in object detection training includes a three-dimensional scan of an object and its environment in a coordinate space generated using a set of images taken by a user device. The three-dimensional scan has an annotation of the object in the coordinate space. Each image in the set of images has position information of the user device in the coordinate space. A computing system is adapted to annotate the set of images by projecting the annotation of the object onto each image using the position information. The computing system is adapted to generate a labelled image set for teaching object detection using the annotated set of images.

Abstract: A method of labeling a dataset includes inputting a testing set comprising a plurality of input data samples into a plurality of pre-trained machine learning models to generate a set of embeddings output by the plurality of pre-trained machine learning models. The method further includes performing an iterative cluster labeling algorithm that includes generating a plurality of clusterings from the set of embeddings, analyzing the plurality of clusterings to identify a target embedding with a highest duster quality, analyzing the target embedding to determine a compactness for each of the plurality of clusterings of the target embedding, and identifying a target cluster among the plurality of clusterings of the target embedding based on the compactness. The method further includes assigning pseudo-labels to the subset of the plurality of input data samples that are members of the target duster.

Abstract: A three-dimensional (3D) printer includes a nozzle and a camera configured to capture a real image or a real video of a liquid metal while the liquid metal is positioned at least partially within the nozzle. The 3D printer also includes a computing system configured to perform operations. The operations include generating a model of the liquid metal positioned at least partially within the nozzle. The operations also include generating a simulated image or a simulated video of the liquid metal positioned at least partially within the nozzle based at least partially upon the model. The operations also include generating a labeled dataset that comprises the simulated image or the simulated video and a first set of parameters. The operations also include reconstructing the liquid metal in the real image or the real video based at least partially upon the labeled dataset.

Abstract: A method of image annotation includes obtaining a candidate annotation map for an annotation task for an image from each of a set of annotation models wherein each of the candidate annotation maps includes suggested annotations for the image, receiving user selections or modifications of at least one of the suggested annotations from one or more of the candidate annotation maps, and generating a final annotation map based on the user selections or modifications from the one or more of the candidate annotation maps.

Abstract: Disclosed are methods and systems of controlling the placement of micro-objects on the surface of a micro-assembler. Control patterns may be used to cause phototransistors or electrodes of the micro-assembler to generate dielectrophoretic (DEP) and electrophoretic (EP) forces which may be used to manipulate, move, position, or orient one or more micro-objects on the surface of the micro-assembler. A set of micro-object may be analyzed. Geometric properties of the set of micro-objects may be identified. The set of micro-objects may be divided into multiple sub-sets of micro-objects based on the one or more geometric properties and one or more control patterns.

Abstract: A method of labeling data and training a model is provided. The method includes obtaining a set of images. The set of images includes a first subset and a second subset. The first subset is associated with a first set of labels. The method also includes generating a set of pseudo labels for the set of images and a second set of labels for the second subset based on the first subset, the second subset, a first machine learning model, and a domain adaption model. The method further includes generating second machine learning model. The second machine learning model is generated based on the set of images, the set of pseudo labels, the first set of labels, and the second set of labels. The second set of labels is updated based on one or more inferences generated by the second machine learning model.

Abstract: A method of image annotation includes selecting a plurality of annotation models related to an annotation task for an image, obtaining a candidate annotation map for the image from each of the plurality of annotation models, and selecting at least one of the candidate annotation maps to be displayed via a user interface, the candidate annotation maps comprising suggested annotations for the image. The method further includes receiving user selections or modifications of at least one of the suggested annotations from the candidate annotation map and generating a final annotation map based on the user selections or modifications.

Abstract: A method of labeling a dataset includes inputting a testing set comprising a plurality of input data samples into a plurality of pre-trained machine learning models to generate a set of embeddings output by the plurality of pre-trained machine learning models. The method further includes performing an iterative cluster labeling algorithm that includes generating a plurality of clusterings from the set of embeddings, analyzing the plurality of clusterings to identify a target embedding with a highest duster quality, analyzing the target embedding to determine a compactness for each of the plurality of clusterings of the target embedding, and identifying a target cluster among the plurality of clusterings of the target embedding based on the compactness. The method further includes assigning pseudo-labels to the subset of the plurality of input data samples that are members of the target duster.

Abstract: Disclosed are methods and systems of controlling the placement of micro-objects on the surface of a micro-assembler. Control patterns may be used to cause electrodes of the micro-assembler to generate dielectrophoretic (DEP) and electrophoretic (EP) forces which may be used to manipulate, move, position, or orient one or more micro-objects on the surface of the micro-assembler. The control patterns may be part of a library of control patterns.

Abstract: A method is provided. The method includes obtaining an enhanced state graph. The enhanced state graph represents a set of objects within an environment and a set of positions of the set of objects. The enhanced state graph includes a set of object nodes, a set of property nodes and a set of goal nodes to represent a set of objectives. The method also includes generating a set of instructions for a set of mechanical systems based on the enhanced state graph. The set of mechanical systems is configured to interact with one or more of the set of objects within the environment. The method further includes operating the set of mechanical systems to achieve the set of objectives based on the set of instructions.

Abstract: A method of labeling a dataset of input samples for a machine learning task includes selecting a plurality of pre-trained machine learning models that are related to a machine learning task. The method further includes processing a plurality of input data samples through each of the pre-trained models to generate a set of embeddings. The method further includes generating a plurality of clusterings from the set of embeddings. The method further includes analyzing, by a processing device, the plurality of clusterings to extract superclusters. The method further includes assigning pseudo-labels to the input samples based on analysis.