Abstract: Embodiments of the disclosed technologies receive first event data associated with a first party application, receive second event data representing a click, in the first party application, on a link to a third party application, receive third event data from the third party application, convert the third event data to a label, map a compressed format of the labeled third event data to the first event data and the second event data to create multi-party attribution data, group multiple instances of the multi-party attribution data into a batch, add noise to the compressed format of the labeled third event data in the batch, and send the noisy batch to a second computing device. A debiasing algorithm can be applied to the noisy batch. The debiased noisy batch can be used to train at least one machine learning model.

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

Abstract: Disclosed are a thin film transistor structure, a display panel and a display device. The thin film transistor structure includes a base, a source electrode, a drain electrode configured to connect to a pixel electrode and a grid electrode. The source electrode, the drain electrode and the grid electrode are provided on the base. and a channel is formed between the source electrode and the drain electrode. The thin film transistor structure further includes an insulating layer and a slow-release electrode. The insulating layer is provided on a side of the source electrode and the drain electrode, and filled in the channel. The slow-release electrode is provided in the insulating layer. At least a part of the slow-release electrode is provided inside the channel.

Abstract: A method for training a machine learning model. The method comprises receiving a training dataset that includes a plurality of images. The method also includes identifying, by a machine learning model, at least one portion of at least one image of the plurality of images in the training dataset associated with a first object type. The method further includes identifying other images having at least one portion that includes the first object type. The method also includes grouping the identified other images into a first image group. The method also includes generating for display a first user interface, that at least includes a rank matrix, wherein a first row of the rank matrix represents the images of the first image object. The user may provide feedback for the visualization using the first interface. The method may also include training the machine learning model based on the user feedback.

Abstract: Disclosed are a thin film transistor structure, a display panel and a display device. The thin film transistor structure includes a base, a source electrode, a drain electrode configured to connect to a pixel electrode and a grid electrode. The source electrode, the drain electrode and the grid electrode are provided on the base. and a channel is formed between the source electrode and the drain electrode. The thin film transistor structure further includes an insulating layer and a slow-release electrode. The insulating layer is provided on a side of the source electrode and the drain electrode, and filled in the channel. The slow-release electrode is provided in the insulating layer. At least a part of the slow-release electrode is provided inside the channel.

Abstract: A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.

Abstract: Methods and systems for performing function testing for moveable objects. One system includes an electronic processor configured to access a driving scene including a moveable object. The electronic processor is also configured to perform spatial representation learning on the driving scene. The electronic processor is also configured to generate an adversarial example based on the learned spatial representation. The electronic processor is also configured to retrain the deep learning model using the adversarial example and the driving scene.

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: Methods and systems for performing concept-based adversarial generation with steerable and diverse semantics. One system includes an electronic processor configured to access an input image. The electronic processor is also configured to perform concept-based semantic image generation based on the input image. The electronic processor is also configured to perform concept-based semantic adversarial learning using a set of semantic latent spaces generated as part of performing the concept-based semantic image generation. The electronic processor is also configured to generate an adversarial image based on the concept-based semantic adversarial learning. The electronic processor is also configured to test a target model using the adversarial image.

Abstract: A computer-implemented system and method for semantic localization of various objects includes obtaining an image from a camera. The image displays a scene with a first object and a second object. A first set of 2D keypoints are generated with respect to the first object. First object pose data is generated based on the first set of 2D keypoints. Camera pose data is generated based on the first object pose data. A keypoint heatmap is generated using the camera pose data. A second set of 2D keypoints is generated with respect to the second object based on the keypoint heatmap. Second object pose data is generated based on the second set of 2D keypoints. First coordinate data of the first object is generated in world coordinates using the first object pose data and the camera pose data. Second coordinate data of the second object is generated in the world coordinates using the second object pose data and the camera pose data. The first object is tracked based on the first coordinate data.

Abstract: A system and method for monitoring performance of a repeated activity is described. The system comprises a motion sensing system and a processing system. The motion sensing system includes sensors configured to measure or track motions corresponding to a repeated activity. The processing system is configured to process motion data received from the motion sensing system to recognize and measure cycle durations in the repeated activity. In contrast to the conventional systems and methods, which may work for repeated activities having a high level of standardization, the system advantageously enables recognition and monitoring of cycle durations for a repeated activity, even when significant abnormal motions are present in each cycle. Thus, the system can be utilized in a significantly broader set of applications, compared conventional systems and methods.

Abstract: A method and system for an augmented reality assistant that recognizes a step in a product assembly process and assists in the installation of a constituent component into a base component. That system having a prepopulated database of templates, the templates being generated based off of two-dimensional images and the related three-dimensional models. The template database is used to train a first machine learning model, that model configured to identify the step in the product assembly process based on an image captured from an image capture device. Verifying that determination by a second machine learning model. Presenting an AR assistant to the user to assist with that step based on the related template.

Abstract: Methods and systems for determining a 6D pose of an object in an image are disclosed. In embodiments, an input image is received from a sensor, wherein the input image includes an object in the image. A trained image encoder transforms the input image into a normal map and an instance segmentation map. The normal map is encoded with pointwise 2D features. A 3D CAD model is selected from memory that resembles the object in the image. The 3D CAD model is encoded with pointwise 3D features. The pointwise 2D features are matched with the pointwise 3D features to obtain correspondences between the 2D features and the 3D features. The 6D pose of the object is then determined based on the correspondences.

Abstract: A method and system for an augmented reality assistant that recognizes a step in a product assembly process and assists in the installation of a constituent component into a base component. That system having a prepopulated database of templates, the templates being generated based off of two-dimensional images and the related three-dimensional models. The template database is used to train a first machine learning model, that model configured to identify the step in the product assembly process based on an image captured from an image capture device. Verifying that determination by a second machine learning model. Presenting an AR assistant to the user to assist with that step based on the related template.

Abstract: A helmet and a method and system for controlling a digital visor of a helmet are disclosed herein. The helmet includes a visor screen having a plurality of liquid crystal display (LCD) pixels, with each LCD pixel configured to alter in transparency. The helmet also includes a light sensor configured to detect incident light. The helmet also includes a controller coupled to the visor screen and the light sensor. The controller is configured to alter the transparency of the plurality of LCD pixels based on the incident light. In embodiments, the controller can alter the transparency of the LCD pixels based on the direction and/or intensity of the incident light.

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 and method are disclosed herein for developing robust semantic mapping models for estimating semantic maps from LiDAR scans. In particular, the system and method enable the generation of realistic simulated LiDAR scans based on two-dimensional (2D) floorplans, for the purpose of providing a much larger set of training data that can be used to train robust semantic mapping models. These simulated LiDAR scans, as well as real LiDAR scans, are annotated using automated and manual processes with a rich set of semantic labels. Based on the annotated LiDAR scans, one or more semantic mapping models can be trained to estimate the semantic map for new LiDAR scans. The trained semantic mapping model can be deployed in robot vacuum cleaners, as well as similar devices that must interpret LiDAR scans of an environment to perform a task.

Abstract: A method and device for performing a perception task are disclosed. The method and device incorporate a dense regression model. The dense regression model advantageously incorporates a distortion-free convolution technique that is designed to accommodate and appropriately handle the varying levels of distortion in omnidirectional images across different regions. In addition to distortion-free convolution, the dense regression model further utilizes a transformer that incorporates an spherical self-attention that use distortion-free image embedding to compute an appearance attention and uses spherical distance to compute a positional attention.

Abstract: A system and method are disclosed herein for developing a machine perception model in the omnidirectional image domain. The system and method utilize the knowledge distillation process to transfer and adapt knowledge from the perspective projection image domain to the omnidirectional image domain. A teacher model is pre-trained to perform the machine perception task in the perspective projection image. A student model is trained by adapting the pre-existing knowledge of the teacher model from the perspective projection image domain to the omnidirectional image domain. By way of this training, the student model learns to perform the same machine perception task, except in the omnidirectional image domain, using limited or no suitably labeled training data in the omnidirectional image domain.