Abstract: Systems and methods for estimating a layout of a room are disclosed. The room layout can comprise the location of a floor, one or more walls, and a ceiling. In one aspect, a neural network can analyze an image of a portion of a room to determine the room layout. The neural network can comprise a convolutional neural network having an encoder sub-network, a decoder sub-network, and a side sub-network. The neural network can determine a three-dimensional room layout using two-dimensional ordered keypoints associated with a room type. The room layout can be used in applications such as augmented or mixed reality, robotics, autonomous indoor navigation, etc.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for composed image retrieval. In one aspect, a method performed by one or more computers is described. The method includes: receiving a query including: (i) an image depicting a scene, and (ii) a text prompt describing a context of the scene; processing the image, using a visual encoder, to generate a visual embedding of the image; processing the visual embedding of the image, using a mapping neural network, to generate one or more language tokens of the image; generating multiple language tokens of the text prompt; processing the language tokens of the image and text prompt, using a language encoder, to generate a language embedding of the query; and selecting, from a number candidate images, one or more of the candidate images using the language embedding of the query.

Abstract: The present invention related to a saccharide nanomaterial for inhibiting DNA polymerase activity. The saccharide nanomaterial is produced by dry heating of saccharides and comprises graphene-like nanosheets and the saccharide. The graphene-like nanosheets contain at least a portion of the carbonized product of the saccharide, and form a cross-linked supramolecular structure with the saccharide. The saccharide nanomaterial can be applied to hot-start polymerase chain reaction (PCR), improving the sensitivity and specificity of the reaction.

Abstract: Efficiently routing requests among multiple generative models with varying computational costs. A request is initially processed by an initial generative model, which can optionally be the most computationally efficient of the generative models. During processing of the request using the initial generative model, but prior to completing processing of the request utilizing the initial generative model and prior to initiating processing of the request utilizing any additional generative model of the generative models, intermediate output, from an intermediate layer of the initial generative model, is processed using an early exit (EE) head to generate EE output. A routing decision is made based on the EE output. The routing decision includes determining whether to continue utilizing the initial generative model or to instead initiating processing of the request utilizing an alternative generative model of the set of generative models.

Abstract: Implementations disclose selecting, in response to receiving a generative model request and from among multiple candidate generative models, a particular generative model to utilize in generating a response to the generative model request. Various implementations identify an indication of a submitting entity of the generative model request. The particular generative model can be selected based on processing the generative model request and custom selection feature(s) provided by the submitting entity (e.g., provided well in advance of the generative model request). Different submitting entities (e.g., a first and second entities) can have different custom selection features. Accordingly, even if the first and second submitting entities submit the same generative model request, different generative models are selected to process the generative model request, resulting in two different responses, one responsive to the first entity and the other responsive to the second entity.

Abstract: A solid-state image sensor is provided. The solid-state image sensor includes a first photoelectric conversion element and a second photoelectric conversion element adjacent to the first photoelectric conversion element. The solid-state image sensor also includes a color filter layer disposed above the first photoelectric conversion element and the second photoelectric conversion element. The solid-state image sensor further includes a converging structure and a diverging structure disposed on the color filter layer. The converging structure corresponds to the first photoelectric conversion element. The diverging structure corresponds to the second photoelectric conversion element.

Abstract: A computer-implemented method includes receiving an anomaly clustering request that requests data processing hardware to assign each image of a plurality of images into one of a plurality of groups. The method also includes obtaining a plurality of images. For each respective image, the method includes extracting a respective set of patch embeddings from the respective image, determining a distance between the respective set of patch embeddings and each other set of patch embeddings, and assigning the respective image into one of the plurality of groups using the distances between the respective set of patch embeddings and each other set of patch embeddings.

Abstract: Systems, methods, and computer readable media to implementing an end-to-end room layout estimation are described. A room layout estimation engine performs feature extraction on an image frame to generate a first set of coefficients for a first room layout class and a second set of coefficients for a second room layout class. Afterwards, the room layout estimation engine generates a first set of planes according to the first set of coefficients and a second set of planes according to the second set of coefficients. The room layout estimation engine generates a first prediction plane according to the first set of planes and a second prediction plane according to the second set of planes. Afterwards, the room layout estimation engine merges the first prediction plane and the second prediction plane to generate a predicted room layout for the room.

Abstract: Semiconductor processing apparatuses and methods are provided in which a pre-clean chamber receives a semiconductor wafer from a metal gate layer deposition chamber and at least partially removes an oxide layer on a metal gate layer. In some embodiments, a semiconductor processing apparatus includes a plurality of metal gate layer deposition chambers. Each of the metal gate layer deposition chambers is configured to form a metal gate layer on a semiconductor wafer. At least one pre-clean chamber of the apparatus is configured to receive the semiconductor wafer from one of the metal gate layer deposition chamber and at least partially remove an oxide layer on the metal gate layer.

Abstract: Semiconductor processing apparatuses and methods are provided in which a pre-clean chamber receives a semiconductor wafer from a metal gate layer deposition chamber and at least partially removes an oxide layer on a metal gate layer. In some embodiments, a semiconductor processing apparatus includes a plurality of metal gate layer deposition chambers. Each of the metal gate layer deposition chambers is configured to form a metal gate layer on a semiconductor wafer. At least one pre-clean chamber of the apparatus is configured to receive the semiconductor wafer from one of the metal gate layer deposition chamber and at least partially remove an oxide layer on the metal gate layer.

Abstract: Aspects of the disclosed technology include computer-implemented systems and methods for adapting machine-learned models using high-quality synthetic data that is tailored to elicit improved instruction-following abilities for particular target instruction distributions and models. A model adaptation system can obtain instruction metadata indicative of at least one use case and at least one skill associated with a particular computing task to be performed by a target machine-learned model. The system can generate a metadata-conditioned synthetic instruction by prompting a generative model system including one or more machine-learned generative models with the instruction metadata as one or more constraints. The system can generate a model response by prompting the generative model system with the metadata-conditioned synthetic instruction.

Abstract: A machine-learning system is described for effectively and efficiently identifying computing tools that are relevant to processing a query for a sequence processing model. A system can store, for each computing tool, data associated with at least one synthetic query generated by a machine-learned sequence processing model based on tool documentation for the computing tools. The system can determine a subset of computing tools relevant to a particular user query based on the synthetic query for each of the plurality of computing tools. The system can generate at least one prompt including the user query and a processing result from each of the subset of computing tools in response to the user query. The system can generate a response to the particular user query based at least in part on an output of at least one machine-learned sequence processing model in response to the at least one prompt.

Abstract: Using a large language model to comply with a user request. The large language model receives tool documentation for each of one or more tools, and analyzes the tool documentation for each of the one or more tools to determine, for each tool, one or more tasks that the tool is operable to perform. Upon receiving a request from a user, the large language model generates a plan for complying with the request by using one or more of the tools, the plan including performance of one or more of the tasks.

Abstract: Systems and methods for providing a structure-aware sequence model that can interpret a document's text without first inferring the proper reading order of the document. In some examples, the model may use a graph convolutional network to generate contextualized “supertoken” embeddings for each token, which are then fed to a transformer that employs a sparse attention paradigm in which attention weights for at least some supertokens are modified based on differences between predicted and actual values of the order and distance between the attender and attendee supertokens.

Abstract: Semiconductor processing apparatuses and methods are provided in which a pre-clean chamber receives a semiconductor wafer from a metal gate layer deposition chamber and at least partially removes an oxide layer on a metal gate layer. In some embodiments, a semiconductor processing apparatus includes a plurality of metal gate layer deposition chambers. Each of the metal gate layer deposition chambers is configured to form a metal gate layer on a semiconductor wafer. At least one pre-clean chamber of the apparatus is configured to receive the semiconductor wafer from one of the metal gate layer deposition chamber and at least partially remove an oxide layer on the metal gate layer.

Abstract: Semiconductor processing apparatuses and methods are provided in which a pre-clean chamber receives a semiconductor wafer from a metal gate layer deposition chamber and at least partially removes an oxide layer on a metal gate layer. In some embodiments, a semiconductor processing apparatus includes a plurality of metal gate layer deposition chambers. Each of the metal gate layer deposition chambers is configured to form a metal gate layer on a semiconductor wafer. At least one pre-clean chamber of the apparatus is configured to receive the semiconductor wafer from one of the metal gate layer deposition chamber and at least partially remove an oxide layer on the metal gate layer.

Abstract: A method for extracting entities comprises obtaining a document that includes a series of textual fields that includes a plurality of entities. Each entity represents information associated with a predefined category. The method includes generating, using the document, a series of tokens representing the series of textual fields. The method includes generating an entity prompt that includes the series of tokens and one of the plurality of entities and generating a schema prompt that includes a schema associated with the document. The method includes generating a model query that includes the entity prompt and the schema prompt and determining, using an entity extraction model and the model query, a location of the one of the plurality of entities among the series of tokens. The method includes extracting, from the document, the one of the plurality of entities using the location of the one of the plurality of entities.

Abstract: Methods and systems for meta-learning are described for automating learning of child tasks with a single neural network. The order in which tasks are learned by the neural network can affect performance of the network, and the meta-learning approach can use a task-level curriculum for multi-task training. The task-level curriculum can be learned by monitoring a trajectory of loss functions during training. The meta-learning approach can learn to adapt task loss balancing weights in the course of training to get improved performance on multiple tasks on real world datasets. Advantageously, learning to dynamically balance weights among different task losses can lead to superior performance over the use of static weights determined by expensive random searches or heuristics. Embodiments of the meta-learning approach can be used for computer vision tasks or natural language processing tasks, and the trained neural networks can be used by augmented or virtual reality devices.

Abstract: Systems and methods for estimating a layout of a room are disclosed. The room layout can comprise the location of a floor, one or more walls, and a ceiling. In one aspect, a neural network can analyze an image of a portion of a room to determine the room layout. The neural network can comprise a convolutional neural network having an encoder sub-network, a decoder sub-network, and a side sub-network. The neural network can determine a three-dimensional room layout using two-dimensional ordered keypoints associated with a room type. The room layout can be used in applications such as augmented or mixed reality, robotics, autonomous indoor navigation, etc.

Abstract: The present invention relates to a method for treating a side effect caused by bacillus Calmette-Guérin (BCG) perfusion therapy for bladder cancer. The method includes: administering a Chinese medicine composition to a subject in need thereof; wherein the Chinese medicine composition is an extract of a first mixture comprising Gentiana scabra, Scutellariae Radix, Gardeniae Fructus, Angelicae Sinensis Radix, Rehmanniae radix, Akebiae Caulis, Bupleurum chinense, Plantaginis Semen, Atractylodes lancea, Rhizoma alismatis, and Glycyrrhiza uralensis.