Abstract: An example operation may include at least one of receiving, from a source dataset, a plurality of labeled images, receiving, from a target dataset, a plurality of images associated with a different visual domain, extracting, from each of the plurality of labeled images and each of the plurality of images from the target dataset, one or more feature representations indicative of visual characteristics, grouping the plurality of labeled images into a plurality of image clusters based on similarity among the one or more feature representations, comparing the one or more feature representations of each of the plurality of image clusters to the one or more feature representations of the plurality of images from the target dataset to determine a similarity ranking for each image cluster, selecting, from the plurality of image clusters, a subset of labeled images based on the similarity ranking and a selection limit, and storing the subset of labeled images in a memory.

Abstract: An example operation may include one or more of retrieving an annotated source dataset from a storage via a software application, retrieving a non-annotated target dataset from the storage via the software application, identifying a subset of data from the annotated source dataset, wherein the subset is configured to include source dataset data that is similar to the non-annotated target dataset, reducing the subset of data from the annotated source dataset by using a classifier to remove redundant data from the subset of data from the annotated source dataset, and classifying data from the non-annotated target dataset by a trained artificial intelligence (AI) model.

Abstract: An example operation may include at least one of determining, by a transformer encoder trained on annotated image-text data, first latents for a first dataset stored in a memory, and second latents for a second dataset stored in the memory, generating a similarity matrix based on comparisons between the first latents and the second latents, constructing a graph comprising nodes corresponding to the first latents and edges based on pairwise similarity exceeding a threshold, identifying connected components in the graph and selecting, from each component, at least one latent having a highest score from a classifier trained to approximate divergence between the first dataset and the second dataset, forming a reduced dataset comprising the at least one latent, providing the reduced dataset to a model training module, and training an image classifier using the reduced dataset and the second dataset.

Abstract: An example operation may include at least one of converting an annotated dataset loaded from a storage into a first set of latents, converting a non-annotated dataset loaded from the storage into a second set of latents creating an aligned subset of data from the annotated dataset comprising: clustering the first set of latents into a plurality of clusters, determining a discrepancy score for each cluster in the plurality of clusters and the second set of latents, creating a refined subset of data from the annotated dataset by including at least one data from each cluster of the plurality of clusters, wherein adding the at least one data lowers the discrepancy score of the refined subset of data and the second set of latents, determining a similarity score between latents in the first set of latents and the second set of latents, wherein the aligned subset of data is created from the annotated dataset by parsing the refined subset into pairs of latents and for each of the pairs of latents, including a latent

Abstract: An example operation may include one or more of receiving a query parameter input via an interface of a software application, querying the feature store based on the query parameter, wherein the querying comprises identifying one or more vectors stored in the features store that match the query parameter via execution of a query on the feature store, executing a machine learning model on the one or more vectors identified in the feature store to generate a predicted output, and displaying the predicted output via the interface of the software application.

Abstract: An example operation may include one or more of querying data of a merchant read from a point of sale (POS) system of the merchant and converting the data into an encoding, executing a machine learning model on the input encoding to generate a vector that comprises vectorized values corresponding to latent features of the merchant embedded within slots of the vector, respectively, generating an entry comprising an identifier of the merchant, context of the merchant, and the generated vector, and storing the entry in the feature store.

Abstract: The application proposes a bracket, a bracket assembly, a device arranged on vehicle roof and a vehicle, which are applied in the field of automobile technology. The bracket comprises: a first bracket body mounted to the top of the vehicle; and a connecting body comprising: a first connecting piece arranged on the left side of the first bracket body and mounted with a first image sensor, and a second connecting piece arranged on the right side of the first bracket body and mounted with a second image sensor, wherein the first connecting piece and the second connecting piece are arranged above A pillars of the vehicle.

Abstract: The application proposes a bracket, a bracket assembly, a device arranged on vehicle roof and a vehicle, which are applied in the field of automobile technology. The bracket comprises: a first bracket body mounted to the top of the vehicle; and a connecting body comprising: a first connecting piece arranged on the left side of the first bracket body and mounted with a first image sensor, and a second connecting piece arranged on the right side of the first bracket body and mounted with a second image sensor, wherein the first connecting piece and the second connecting piece are arranged above A pillars of the vehicle.

Abstract: Techniques for database versioning are described. In one embodiment, an apparatus may comprise a database change management component operative to compare a developer table to a reference table to determine a database change set, wherein both the developer table and the reference table are based on a target table; a database conflict management component operative to compare the database change set to the target table to determine a conflicting change set; and a user interface component operative to display the conflicting change set where the conflicting change set comprises one or more conflicting changes; and indicate a conflict-free change set where the conflicting change set is empty. Other embodiments are described and claimed.

Abstract: Improved methods and apparatus for stripping photoresist and removing ion implant related residues from a work piece surface are provided. According to various embodiments, the workpiece is exposed to a passivation plasma, allowed to cool for a period of time, and then exposed to an oxygen-based or hydrogen-based plasma to remove the photoresist and ion implant related residues. Aspects of the invention include reducing silicon loss, leaving little or no residue while maintaining an acceptable strip rate. In certain embodiments, methods and apparatus remove photoresist material after high-dose ion implantation processes.

Abstract: Improved methods for stripping photoresist and removing etch-related residues from dielectric materials are provided. In one aspect of the invention, methods involve removing material from a dielectric layer using a hydrogen-based etch process employing a weak oxidizing agent and fluorine-containing compound. Substrate temperature is maintained at a level of about 160° C. or less, e.g., less than about 90° C.

Abstract: Improved methods for stripping photoresist and removing etch-related residues from dielectric materials are provided. In one aspect of the invention, methods involve removing material from a dielectric layer using a hydrogen-based etch process employing a weak oxidizing agent and fluorine-containing compound. Substrate temperature is maintained at a level of about 160° C. or less, e.g., less than about 90° C.

Abstract: Improved methods and apparatus for stripping photoresist and removing ion implant related residues from a work piece surface are provided. According to various embodiments, the workpiece is exposed to a passivation plasma, allowed to cool for a period of time, and then exposed to an oxygen-based or hydrogen-based plasma to remove the photoresist and ion implant related residues. Aspects of the invention include reducing silicon loss, leaving little or no residue while maintaining an acceptable strip rate. In certain embodiments, methods and apparatus remove photoresist material after high-dose ion implantation processes.

Abstract: Improved methods for stripping photoresist and removing etch-related residues from dielectric materials are provided. In one aspect of the invention, methods involve removing material from a dielectric layer using a hydrogen-based etch process employing a weak oxidizing agent and fluorine-containing compound. Substrate temperature is maintained at a level of about 160° C. or less, e.g., less than about 90° C.

Abstract: Improved methods for stripping photoresist and removing ion implant related residues from a work piece surface are provided. According to various embodiments, plasma is generated using elemental hydrogen, a fluorine-containing gas and a protectant gas. The plasma-activated gases reacts with the high-dose implant resist, removing both the crust and bulk resist layers, while simultaneously protecting exposed portions of the work piece surface. The work piece surface is substantially residue free with low silicon loss.