Abstract: Adapting a machine learning model to process data that differs from training data used to configure the model for a specified objective is described. A domain adaptation system trains the model to process new domain data that differs from a training data domain by using the model to generate a feature representation for the new domain data, which describes different content types included in the new domain data. The domain adaptation system then generates a probability distribution for each discrete region of the new domain data, which describes a likelihood of the region including different content described by the feature representation. The probability distribution is compared to ground truth information for the new domain data to determine a loss function, which is used to refine model parameters. After determining that model outputs achieve a threshold similarity to the ground truth information, the model is output as a domain-agnostic model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model on training data. In one aspect, one of the methods include: obtaining a training data set comprising a plurality of training inputs; obtaining data defining an original search space of a plurality of candidate data augmentation policies; generating, from the original search space, a compact search space that has one or more global hyperparameters; and training the machine learning model on the training data using one or more final data augmentation policies generated from the compact search space.

Abstract: In implementations of systems for training language models and preserving privacy, a computing device implements a privacy system to predict a next word after a last word in a sequence of words by processing input data using a machine learning model trained on training data to predict next words after last words in sequences of words. The training data describes a corpus of text associated with clients and including sensitive samples and non-sensitive samples. The machine learning model is trained by sampling a client of the clients and using a subset of the sensitive samples associated with the client and a subset of the non-sensitive samples associated with the client to update parameters of the machine learning model. The privacy system generates an indication of the next word after the last word in the sequence of words for display in a user interface.

Abstract: Provided are transaminase mutants and uses thereof. The transaminase mutant is obtained by one or more amino acid mutations occurring in SEQ ID NO: 2 or is a mutant with a conserved amino acid mutation obtained by taking the sequence SEQ ID NO: 1 of a wild-type CvTA transaminase as a reference. Compared with wild-type transaminases, the catalytic activity of the mutant is improved to different degrees, so that the production efficiency of chiral amine compound synthesis may be improved.

Abstract: An encoder includes circuitry and memory coupled to the circuitry. The circuitry, in operation: determines whether a size of a current block, which is a unit for which a vector candidate list including vector candidates is generated, is less than or equal to a threshold; when the size of the current block is less than or equal to the threshold, generates the vector candidate list by registering a history-based motion vector predictor (HMVP) vector candidate in the vector candidate list from an HMVP table without performing a first pruning process; when the size of the current block is greater than the threshold, generates the vector candidate list by performing the first pruning process and registering the HMVP vector candidate in the vector candidate list from the HMVP table; and encodes the current block using the vector candidate list.

Abstract: An encoder determines, based on a width and a height of a block, whether or not to disable a prediction mode in which the block is split along a partitioning line defined by a distance and an angle and then prediction is performed; and encodes the block with the prediction mode disabled or not disabled according to a result of the determination on whether or not to disable the prediction mode. Here, the distance is the shortest distance between the center of the block and the partitioning line, and the angle is an angle representing a direction from the center of the block toward the partitioning line in the shortest distance. The encoder determines to disable the prediction mode when (i) a width-to-height ratio is at least 8 or (ii) a height-to-width ratio is at least 8.

Abstract: In various examples, a table recognition model receives an image of a table and generates, using a first encoder of the table recognition machine learning model, an image feature vector including features extracted from the image of the table; generates, using a first decoder of the table recognition machine learning model and the image feature vector, a set of coordinates within the image representing rows and columns associated with the table, and generates, using a second decoder of the table recognition machine learning model and the image feature vector, a set of bounding boxes and semantic features associated with cells the table, then determines, using a third decoder of the table recognition machine learning model, a table structure associated with the table using the image feature vector, the set of coordinates, the set of bounding boxes, and the semantic features.

Abstract: The present application provides a preparation method for a core-shell structured tungsten/gadolinium oxide functional fiber for X and ? ray protection, comprising: first preparing a core-shell structured tungsten/gadolinium oxide powder; preparing a W@Gd2O3/PP blended melt from the powder; and preparing a W@Gd2O3/PP composite fiber from the blended melt. The core-shell structured tungsten/gadolinium oxide functional fiber prepared by the method can play a role in synergistic protection in the aspect of radiation protection, eliminate a weak protection area, and effectively absorb secondary radiation generated by radiation. Secondly, the prepared functional fiber has the characteristics of no lead and light weight, and has good application prospects in the aspect of X and ? ray radiation protection.

Abstract: A decoder includes circuitry which, in operation, parses a first flag indicating whether a CCALF (cross component adaptive loop filtering) process is enabled for a first block located adjacent to a left side of a current block; parses a second flag indicating whether the CCALF process is enabled for a second block located adjacent to an upper side of the current block; determines a first index associated with a color component of the current block; and derives a second index indicating a context model, using the first flag, the second flag, and the first index. The circuitry, in operation, performs entropy decoding of a third flag indicating whether the CCALF process is enabled for the current block, using the context model indicated by the second index; and performs the CCALF process on the current block in response to the third flag indicating the CCALF process is enabled for the current block.

Abstract: Systems and methods for image searching are described. The systems and methods include receiving a search query comprising user input for a reference image; converting the user input for the reference image to a preference statement using a machine learning model; encoding the preference statement in an embedding space to obtain an encoded preference statement; combining the encoded preference statement with an encoded reference image representing the reference image in the embedding space to obtain a multi-modal search encoding; and performing a search operation using the multi-modal search encoding to retrieve a second image, wherein the second image differs from the reference image based on the user input for the reference image.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that fill in digital documents using user identity models of client devices. For instance, in one or more embodiments, the disclosed systems receive a digital document comprising a digital fillable field. The disclosed systems further retrieve, for a client device associated with the digital document, a decentralized identity credential comprising a user attribute established under a decentralized identity framework. Using the user attribute of the decentralized identity credential, the disclosed systems modify the digital document by filling in the digital fillable field.

Abstract: An information processing method, an interconnection device, and a computer-readable storage medium are provided. The interconnection device includes a request processing module configured for: receiving a data access request from at least one processor, wherein the data access request comprises a merge bit, a multicast group identifier (MGID), and a multicast transaction identifier (MTID); determining whether the data access request is a multicast request; determining whether the interconnection device receives other multicast requests if it is determined that the data access request is a multicast request based on the MGID, the MTID, and a static routing policy of a multicast group; and obtaining the other multicast requests if it is determined that the interconnection device receives the other multicast requests, merging the multicast request with the other multicast requests into a merged request, and forwarding the merged request to a next-hop device of the interconnection device.

Abstract: One example method involves operations for a processing device that include receiving, by a machine learning model trained to generate a search result, a search query for a text input. The machine learning model is trained by receiving pre-training data that includes multiple documents. Pre-training the machine learning model by generating, using an encoder, feature embeddings for each of the documents included in the pre-training data. The feature embeddings are generated by applying a masking function to visual and textual features in the documents. Training the machine learning model also includes generating, using the feature embeddings, output features for the documents by concatenating the feature embeddings and applying a non-linear mapping to the feature embeddings. Training the machine learning model further includes applying a linear classifier to the output features. Additionally, operations include generating, for display, a search result using the machine learning model based on the input.

Abstract: An information processing method, an interconnection device, and a computer-readable storage medium are provided. The interconnection device includes a request processing module configured for: receiving a data access request from at least one processor, wherein the data access request comprises a merge bit, a multicast group identifier (MGID), and a multicast transaction identifier (MTID); determining whether the data access request is a multicast request; determining whether the interconnection device receives other multicast requests if it is determined that the data access request is a multicast request based on the MGID, the MTID, and a static routing policy of a multicast group; and obtaining the other multicast requests if it is determined that the interconnection device receives the other multicast requests, merging the multicast request with the other multicast requests into a merged request, and forwarding the merged request to a next-hop device of the interconnection device.

Abstract: Embodiments are provided for facilitating multimodal extraction across multiple granularities. In one implementation, a set of features of a document for a plurality of granularities of the document is obtained. Via a machine learning model, the set of features of the document are modified to generate a set of modified features using a set of self-attention values to determine relationships within a first type of feature and a set of cross-attention values to determine relationships between the first type of feature and a second type of feature. Thereafter, the set of modified features are provided to a second machine learning model to perform a classification task.

Abstract: Systems and methods for machine learning based multipage scanning are provided. In one embodiment, one or more processing devices perform operations that include receiving a video stream that includes image frames that capture a plurality of pages of a document. The operations further include detection, via a machine learning model that is trained to infer events from the video stream detects, a new page event. Detection of the new page event indicates that a page of the plurality of pages available for scanning has changed from a first page to a second page. Based on the detection of the new page event, the one or more processing devices capture an image frame of the page from the video stream. In some embodiments, the machine learning model detects events based on a weighted use of video data, inertial data, audio samples, image depth information, image statistics and/or other information.

Abstract: The technology described herein is directed to an adaptive sparse attention pattern that is learned during fine-tuning and deployed in a machine-learning model. In aspects, a row or a column in an attention matrix with an importance score for a task that is above a threshold importance score is identified. The important row or the column is included in an adaptive attention pattern used with a machine-learning model having a self-attention operation. In response to an input, a task-specific inference is generated for the input using the machine-learning model with the adaptive attention pattern.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a natural language model that provides user-entity differential privacy. For example, in one or more embodiments, a system samples sensitive data points from a natural language dataset. Using the sampled sensitive data points, the system determines gradient values corresponding to the natural language model. Further, the system generates noise for the natural language model. The system generates parameters for the natural language model using the gradient values and the noise, facilitating simultaneous protection of the users and sensitive entities associated with the natural language dataset. In some implementations, the system generates the natural language model through an iterative process (e.g., by iteratively modifying the parameters).

Abstract: Reaction products of amines and polymers containing saturated heterocyclic moieties may be used as levelers in metal electroplating baths. The reaction products may plate metal with good surface properties and good physical reliability.

Abstract: There is provided a semiconductor device 100, comprising: at least one semiconductor chip 5, and a structure 2 thermally coupled to the at least one semiconductor chip 5, wherein the structure 2 comprises a surface located within an interior of the semiconductor device, and the surface comprises a groove 12; and a sensor 16 comprising an optical fibre 13 passing through the groove 12, wherein the sensor 16 is configured to sense a temperature of the at least one semiconductor chip 5.