Abstract: The apparatuses described herein relate to preparation of a meat product. Apparatuses, systems comprising the apparatuses, and methods of making and use the systems and apparatuses are described herein. These are useful for controlling one or more of growth on and separation of a meat product from an enclosed substrate. The apparatuses and systems are configured to receive fluid and grow the meat product and/or separate the meat product from the substrate in a scalable manner.

Abstract: A system and method generate answers to user queries by providing natural language responses containing direct citations to primary sources. The system comprises a data collection pipeline that ingests, processes, and organizes data from multiple sources, and a retrieval mechanism that processes user queries, identifies relevant data, and employs a machine learning model, such as a Large Language Model (LLM), to generate natural language responses based on the retrieved data. The generated responses are augmented with direct references to the primary sources, ensuring accurate attribution and up-to-date information. This system combines the natural language capabilities of LLMs with the direct connections to primary sources provided by traditional search engines, delivering real-time, dynamic processing of resources without incurring high re-training costs.

Abstract: Described herein are methods and systems for improved domain name resolution/routing. Routing data associated with domain names (e.g., websites) may be cached by a Domain Name System (DNS) based on historical domain name queries. The historical domain name queries may be analyzed to determine a ranking (e.g., popularity) for the domain names at multiple time intervals throughout a day, week, etc. Routing data for the highest ranked domain names during one or more time intervals may be cached for a period(s) of time corresponding to the one or more time intervals (e.g., times during which those domain names are most popular).

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing data identifying a first image as input to a machine learning model trained using training data identifying a plurality of composite images that each include one or more constituent images, and determining, using one or more outputs of the trained machine learning model, that the first image is a composite image that includes a first constituent image, wherein at least a portion of the first constituent image is in a spatial area of the first image, and wherein the first constituent image corresponds to a frame of a video embedded into the first image.

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing data identifying a first image as input to a machine learning model trained using training data identifying a plurality of composite images that each include one or more constituent images, and determining, using one or more outputs of the trained machine learning model, that the first image is a composite image that includes a first constituent image, wherein at least a portion of the first constituent image is in a spatial area of the first image, and wherein the first constituent image corresponds to a frame of a video embedded into the first image.

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing pixel data of a first image as input to the trained machine learning model, obtaining one or more outputs from the trained machine learning model, and extracting, from the one or more outputs, an indication that the first image is a composite image that includes a constituent image, wherein at least a portion of the constituent image is in a spatial area of the first image.

Abstract: A synthesis procedure learns program transformations for a text document, on-the-fly during an edit session, from examples of concrete edits made during the edit session and from an unsupervised set of additional inputs. The additional inputs are derived from explicit feedback from the user and inferred feedback from the user's behavior during the edit session. A reward score, based on anti-unification and provenance analysis, is used to classify the additional inputs as either a positive input or a negative input. Outputs are generated for the positive inputs that are consistent with the existing examples and then used to synthesize a new program transformation. The program transformations are then used to generate code edit suggestions during the edit session.

Abstract: A synthesis procedure learns program transformations for a text document, on-the-fly during an edit session, from examples of concrete edits made during the edit session and from an unsupervised set of additional inputs. The additional inputs are derived from explicit feedback from the user and inferred feedback from the user's behavior during the edit session. A reward score, based on anti-unification and provenance analysis, is used to classify the additional inputs as either a positive input or a negative input. Outputs are generated for the positive inputs that are consistent with the existing examples and then used to synthesize a new program transformation. The program transformations are then used to generate code edit suggestions during the edit session.

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing pixel data of a first image as input to the trained machine learning model, obtaining one or more outputs from the trained machine learning model, and extracting, from the one or more outputs, an indication that the first image is a composite image that includes a constituent image, wherein at least a portion of the constituent image is in a spatial area of the first image.

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing pixel data of a first image as input to the trained machine learning model, obtaining one or more outputs from the trained machine learning model, and extracting, from the one or more outputs, a level of confidence that (i) the first image is a composite image that includes a constituent image, and (ii) at least a portion of the constituent image is in a particular spatial area of the first image.

Abstract: A system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. A method may include providing pixel data of a first image as input to the trained machine learning model, obtaining one or more outputs from the trained machine learning model, and extracting, from the one or more outputs, a level of confidence that (i) the first image is a composite image that includes a constituent image, and (ii) at least a portion of the constituent image is in a particular spatial area of the first image.

Abstract: A system and methods are disclosed for training a machine learning model to identify constituent images within composite images. In one implementation, a composite image is generated, where the composite image comprises a first portion containing pixel data of a first constituent image, and a second portion containing pixel data of a second constituent image. A first training input comprising pixel data of the composite image and a first target output for the first training input are generated, where the first target output identifies a position of the first portion within the composite image. The training data is provided to train the machine learning model on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output.

Abstract: A system and methods are disclosed for training a machine learning model to identify constituent images within composite images. In one implementation, a composite image is generated, where the composite image comprises a first portion containing pixel data of a first constituent image, and a second portion containing pixel data of a second constituent image. A first training input comprising pixel data of the composite image and a first target output for the first training input are generated, where the first target output identifies a position of the first portion within the composite image. The training data is provided to train the machine learning model on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output.

Abstract: Provided content is determined to contain an asset represented by reference content by comparing digital fingerprints of the provided content and the reference content. The fingerprints of the reference content and the provided content are generated using a convolutional neural network (CNN). The CNN is trained using a plurality of frame triplets including an anchor frame representing the reference content, a positive frame which is a transformation of the anchor frame, and a negative frame representing content that is not the reference content. The provided content is determined to contain the asset represented by the reference content based on a similarity measure between the generated fingerprints. If the provided content is determined to contain the asset represented by the reference content, a policy associated with the asset is enforced on the provided content.

Abstract: Systems and techniques are provided for a ranking approach to train deep neural nets for multilabel image annotation. Label scores may be received for labels determined by a neural network for training examples. Each label may be a positive label or a negative label for the training example. An error of the neural network may be determined based on a comparison, for each of the training examples, of the label scores for positive labels and negative labels for the training example and a semantic distance between each positive label and each negative label for the training example. Updated weights may be determined for the neural network based on a gradient of the determined error of the neural network. The updated weights may be applied to the neural network to train the neural network.

Abstract: A computer-implemented method includes receiving a first visual media article from an entity that provides content sources, identifying a first content item of the first visual media article, and identifying in a database a second visual media article that includes a second content item, wherein the second content item is substantially similar to the first content item. The method further includes extracting from logging data one or more keywords that yield a listing of a content source that includes the second visual media article, and suggesting the extracted one or more keywords to the entity.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for clustering images. In one aspect a system includes one or more computers configured to, for each of a plurality of digital images, associate extrinsic image-related information with each individual image, the extrinsic image-related information including text information and co-click data for the individual image, assign images from the plurality of images to one or more of the clusters of images based on the extrinsic information associated with each of the plurality of images, receive in the search system a user query from a user device, identify by operation of the search system one or more clusters of images that match the query, and provide one or more cluster results, where each cluster result provides information about an identified cluster.

Abstract: A classifier training system trains unified classifiers for categorizing videos representing different categories of a category graph. The unified classifiers unify the outputs of a number of separate initial classifiers trained from disparate subsets of a training set of media items. The training process takes into account the relationships that exist between the various categories of the category graph by relating scores associated with related categories, thus enhancing the accuracy of the unified classifiers.

Abstract: Techniques for a shape descriptor used for object recognition are described. Tokens of an object in digital image data are captured, where tokens can be edges, interest points or even parts. Geometric configurations of the tokens are captured by describing portions of the shape of the object. The shape of such configurations is finely quantized and each configuration from the image is assigned to a quantization bin. Objects are recognized by utilizing a number of quantization bins as features. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Abstract: An image quality subsystem computes quality scores for images that represent a measure of visual quality of the images. Initial quality scores and query specific quality scores can be computed for the images based on image feature values for the images and a transformation factor that represents a measure of importance of image quality for computing relevance scores for images. The initial quality scores are query independent quality scores that are computed for the images and can be used as a factor for computing relevance scores for the image relative to any query. Query specific quality scores are computed for images that are identified as relevant for a particular query based on the initial quality scores and a query specific transformation factor for the particular query. Adjusted relevance scores for the images can be computed based on the initial quality scores or the query specific quality scores.