Abstract: The present disclosure is directed to a rotating detonation combustor that includes a forward wall, a radially inner wall, and a radially outer wall. The forward wall is disposed at an inlet end of the rotating detonation combustor. The radially inner wall surrounds a longitudinal axis and extends downstream from the forward wall to an outlet end of the rotating detonation combustor. The radially outer wall extends downstream from the forward wall to the outlet end and surrounds the radially inner wall to define at least one annular plenum between the radially inner wall and the radially outer wall. At least one partition is proximate to the inlet end and defines at least two mixing zones. A plurality of oxidizer inlets and a plurality of fuel inlets are disposed at the inlet end in fluid communication with the at least two mixing zones.

Abstract: Retrieving content items in response to a query in a way that increases user satisfaction and increases chances of users consuming a retrieved content item is not trivial. One retrieval strategy may include dividing the content items into buckets according to a dimension about the content items and retrieving a top K number of items from different buckets to balance semantic affinity and the dimension. Choosing an optimal K for different buckets for a given query can be a challenge. Reinforcement learning can be used to train and implement an agent model that can choose the optimal K for different buckets.

Abstract: Pre-trained large language models may be trained on a large data set which may not necessarily align with specific tasks, business goals, and requirements. Pre-trained large language models can solve generic semantic relationship or question-answering type problems but may not be suited for content item retrieval or recommendation of content items that are semantically relevant to a query. It is possible to build a machine learning model while using transfer learning to learn from pre-trained large language models. Training data can significantly impact the performance of machine learning models, especially machine learning models developed using transfer learning. The training data can impact a model's performance, generalization, fairness, and adaptation to specific domains. To address some of these concerns, a popularity bucketing strategy can be implemented to debias training data. Optionally, an ensemble of models can be used to generate diverse training data.

Abstract: An example apparatus described herein to implement cancellation pulse generation includes a first memory storing first subsets of data samples of a single pulse cancellation waveform. The example apparatus includes a second memory storing second subsets of data samples of the single pulse cancellation waveform, the second subsets including different data samples of the single pulse cancellation waveform than the first subsets. The example apparatus includes first circuitry coupled to the first memory and to the second memory in parallel. The example apparatus includes a plurality of buffers. The example apparatus includes second circuitry coupled to the plurality of buffers.

Abstract: Training data can significantly impact the performance of machine learning models. Its impact may be more significant in transfer learning. Different data sources can be used to generate training data used in transfer learning. The training data originating from user interaction logs may be subject to presentation bias. The training data originating from model generated labeled data may have false positives. Poor quality training data may cause the machine learning model to perform poorly. To address some of these concerns, a checker having one or more models can check for false positives and for labeled data entries that may have been subject to presentation bias. Such entries may be removed or modified. In some cases, the checker can generate a test that can be used to test the machine learning model and penalize the machine learning model if the model generates an incorrect prediction.

Abstract: A content retrieval system may receive a query associated with a plurality of content items in a repository. For each content item of the plurality of content items: a respective first and second similarity score may be generated based on a similarity between embeddings indicative of a first and second data type generated from the query and for the content item; and a respective normalized similarity score may be generated based on a combination of the respective first and second similarity scores. A set of content items with respective normalized similarity scores that satisfy a similarity score threshold may be identified. An exact-match (lexical) search may yield respective mapping scores for content items that may also be ranked. An output indicative of content items that are identified in the set of content items with high-ranking similarity scores and identified in the set of content items with high-ranking mapping scores.

Abstract: An example apparatus to reduce crests in an input signal includes: memory; and programmable circuitry configured to: store a first copy and a second copy of a normalized window waveform in the memory, the first copy of the normalized window waveform including more data points than the second copy of the normalized window waveform; use the second copy of the normalized window waveform to generate a weight corresponding to a peak in the input signal; use the weight and the first copy of the normalized window waveform to generate an output waveform; generate a peak limiting waveform responsive to the output waveform; and combine the peak limiting waveform with the input signal to reduce an amplitude of the peak.

Abstract: A dual-screen computing device includes two separate displays that are coupled to an interconnecting hinge. A hinge detector detects movement or position of the hinge, and the positions of the displays may be determined based on the hinge movement or position. The positions of the displays relative to each other may then be used to determine which mode of operation the dual-screen computing device is operating (e.g., tent mode, open, closed, etc.). Additionally, the dual-screen computing device may include various sensors that detect different environmental, orientation, location, and device-specific information. Applications are configured to operate differently based on the mode of operation and, optionally, the sensor data detected by the sensors.

Abstract: A content retrieval system may receive a query associated with a plurality of content items in a repository. For each content item of the plurality of content items: a respective first and second similarity score may be generated based on a similarity between embeddings indicative of a first and second data type generated from the query and for the content item; and a respective normalized similarity score may be generated based on a combination of the respective first and second similarity scores. A set of content items with respective normalized similarity scores that satisfy a similarity score threshold may be identified. An exact-match (lexical) search may yield respective mapping scores for content items that may also be ranked. An output indicative of content items that are identified in the set of content items with high-ranking similarity scores and identified in the set of content items with high-ranking mapping scores.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for ranking a plurality of content items for presentation to a user. An embodiment generates a ranking score for each content item by: providing input to a deep machine learning (ML) model, the input including at least one or more query features and one or more content item features, determining, by the deep ML model and based at least on the input, a first probability of a first type of interaction between the user and the content item and a second probability of a second type of interaction between the user and the content item, and calculating the ranking score for the content item based at least on the first and second probabilities. An embodiment ranks the content items for presentation based on the ranking score associated with each content item.

Abstract: Crest factor reduction circuitry includes: a peak neighborhood analyzer; peak detection circuitry and a controller. The peak neighborhood analyzer is configured to: receive an input signal; analyze the input signal to determine whether a peak larger than a target threshold is expected within an interval; and provide a first control signal responsive to determining that a peak larger than the target threshold is expected within the interval. The controller is configured to: receive the first control signal; and gate a clock or data to the peak detection circuitry responsive to the first control signal.

Abstract: An example apparatus includes: crest factor reduction circuitry having a signal input and a peak cancellation waveform input; and peak cancellation waveform generator circuitry including: carrier profile analyzer circuitry having a signal input coupled to the signal input of the crest factor reduction circuitry, and having a carrier profile output; waveform construction circuitry having a carrier profile input coupled to the carrier profile output of the carrier profile analyzer circuitry, having a second input, and having a peak cancellation waveform output coupled to the peak cancellation waveform input of the crest factor reduction circuitry; and profile change detector circuitry having a carrier profile input coupled to the carrier profile output of the carrier profile analyzer circuitry, and having an output coupled to the second input of the waveform construction circuitry.

Abstract: Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for generating a prediction based on a query. An embodiment operates by providing a query to a deep machine learning (ML) model. The deep ML model generates a plurality of query projection embeddings by projecting the query into each of a plurality of different query embedding spaces and generates the prediction based at least on the plurality of query projection embeddings. Each of a plurality of query projection embedding layers of the deep ML may generate a corresponding one of the query projection embeddings by applying a hash function associated with the query projection layer to the query to generate a vector representation of the query, and applying a set of weights associated with the query projection layer to the vector representation to generate a query projection embedding in the plurality of query projection embeddings.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for model customization for domain-specific tasks. An embodiment may select a pre-trained embedding model trained with a first dataset. The embodiment may determine a second dataset for a target domain. Based on target embeddings for data indicative of the target domain. The embodiment may transform the second dataset from a first format to a second format associated with the target domain. The embodiment may modify the weights of the pre-trained embedding model based on the transformed second dataset. Based on the modified weights, the embodiment may transform the pre-trained embedding model into a target embedding model for the target domain. The embodiment may then generate an efficacy score for the target embedding model based on a task of the target domain performed by the target embedding model.

Abstract: Disclosed are system, method and/or computer program product embodiments that retrieve items for a user based on a query using a two-tower deep machine learning model. An example embodiment provides input to a context tower, wherein the input includes the query and one or more of a query embedding corresponding to the query or a graph user embedding corresponding to the user. The context tower generates a context embedding in a vector space based on the input. The model determines a measure of similarity between the context embedding and each of a plurality of item embeddings in the vector space that are generated by an item tower and represent a plurality of candidate items. A relevancy score is calculated for each candidate item based on the measure of similarity between the context embedding and the corresponding item embedding. The relevancy scores are used for item retrieval and/or ranking.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for training a heterogenous graph neural network (GNN) to generate user embeddings corresponding to users and item embeddings corresponding to items. An example embodiment generates a first user interaction graph for a first time window and a second user interaction graph for a second time window, wherein each graph represents users and items as nodes and user-item interactions within the respective time window as edges, samples user-item node pairs from the second user interaction graph, and trains the heterogeneous GNN based on user-item node pairs from the first user interaction graph that correspond to the sampled user-item node pairs from the second user interaction graph. User and item embeddings generated by the trained GNN may be used to determine a relevancy of a given item with respect to a given user.

Abstract: A dual-screen computing device includes two separate displays that are coupled to an interconnecting hinge. A hinge detector detects movement or position of the hinge, and the positions of the displays may be determined based on the hinge movement or position. The positions of the displays relative to each other may then be used to determine which mode of operation the dual-screen computing device is operating (e.g., tent mode, open, closed, etc.). Additionally, the dual-screen computing device may include various sensors that detect different environmental, orientation, location, and device-specific information. Applications are configured to operate differently based on the mode of operation and, optionally, the sensor data detected by the sensors.

Abstract: A system and model by which to manage edits to localized versions of base-language electronic content. The model determines the likelihood of whether a modification made to a document is substantive and should be propagated back to the base electronic content and assists users in identifying these edits. The proposed method can significantly improve workflow efficiency and allow users to feel more comfortable in the development and use of their electronic content across multiple authoring platforms.

Abstract: A content retrieval system may receive a query associated with a plurality of content items in a repository. For each content item of the plurality of content items; a respective first and second similarity score may be generated based on a similarity between embeddings indicative of a first and second data type generated from the query and for the content item; and a respective normalized similarity score may be generated based on a combination of the respective first and second similarity scores. A set of content items with respective normalized similarity scores that satisfy a similarity score threshold may be identified. An exact-match (lexical) search may yield respective mapping scores for content items that may also be ranked. An output indicative of content items that are identified in the set of content items with high-ranking similarity scores and identified in the set of content items with high-ranking mapping scores.

Abstract: A deep-learning based method evaluates similarities of entities in decentralized identity graphs. One or more processors represent a first identity profile as a first identity graph and a second identity profile as a second identity graph. The processor(s) compare the first identity graph to the second identity graph, which are decentralized identity graphs from different identity networks, in order to determine a similarity score between the first identity profile and the second identity profile. The processor(s) then implement a security action based on the similarity score.