Abstract: A computer-implemented method, computer program product, and computing system for synchronizing machine vision and audio is executed on a computing device and includes obtaining encounter information of a user encounter, wherein the encounter information includes machine vision encounter information and audio encounter information. The machine vision encounter information and the audio encounter information are temporally-aligned to produce a temporarily-aligned encounter recording.

Abstract: Disclosed solutions for improved machine learning (ML) employ knowledge balancing self-distillation with adaptive mutual information (AMI). Examples include: for a neural network (NN) having a plurality of modules, determining a task objective for at least a final module of the plurality of modules; for the NN, determining a balancing objective using at least an output of the final module and an output of a first intermediate module of the plurality of modules; determining an overall objective, wherein determining the overall objective comprises combining the task objective with the balancing objective; and adjusting weights of the NN to minimize the overall objective. Balancing information may combine mutual information (between an intermediate module output and the output of the final module) with self-information (for the intermediate module output) to produce AMI. Adjusting weights of the NN during training, using the AMI, results in knowledge balancing self-distillation.

Abstract: A computerized method for summarizing digital content based on a query from a user is described. An inference of the query is used to identify a website that includes non-structured content. The most relevant media within the website is identified based on the inference and content from the most relevant media is extracted. Using the inference, semantic summaries are generated from the extracted content, and an aggregation of the semantic summaries are presented to the user.

Abstract: Determining a location of an entity comprises: receiving a query comprising a 2D image depicting an environment of the entity; searching for a match between the query and a 3D map of the environment. The 3D map comprising a 3D point cloud, the match indicating the location of the entity in the environment. Searching for the match comprises: extracting descriptors from the 2D image referred to as image descriptors; extracting descriptors from the 3D point cloud referred to as point cloud descriptors; correlating the image descriptors with the point cloud descriptors to produce correspondences, wherein a correspondence is an image descriptor corresponding to a point cloud descriptor; estimating, using the correspondences, the location of the entity.

Abstract: A method, computer program product, and computing system for securely transmitting voice signals. A speech signal including a content component and a speaker component of a first voice is received at an encoder. The speaker component of the speech signal is processed, using machine learning, to generate a speaker embedding. The content component of the voice signal is processed, using machine learning and based at least on the speaker embedding, to generate a content embedding having minimized speaker information. The content embedding is transmitted to a decoder for restoring the received speech signal.

Abstract: Example solutions for image paragraph captioning use a first vision language model to generate visual information (comprising text) for an image. The visual information may include tags, an initial image caption, and information on objects within the image (e.g., further tags and captions, and object attributes and locations within the image). In some examples, the visual information further includes visual clues. A generative language model generates a plurality of image story caption candidates (e.g., descriptive paragraphs) from the visual information. A second vision language model evaluates the plurality of image story caption candidates and selects a caption as the final output caption.

Abstract: A computerized method for processing packets in parallel using a reorder queue is described. As packets are received, it is determined which packet is the first packet in the flow. Once identified, the first packet in the flow, along with all other packets in the flow, is sent to the reorder queue while only a copy of the first packet in the flow is sent to a packet scheduler for processing on one of a plurality of processing cores. After the copy of the first packet in the flow is processed, the reorder queue releases each of the packets for the flow from the reorder queue in an order in which they were received. Thereafter, each of the packets released from the reorder queue are processed based on the processing of the copy of the first packet.

Abstract: This disclosure provides a filtering mechanism to manage anomalous security data items. An anomalous security data item is provided to an analysis engine (such as a Large Language Model (LLM) or another form of generative language model) for interpretation. By curating a selection of one or more relevant non-anomalous security data items to provide with the anomalous data item, the filtering mechanism enables the analysis engine to perform with increased accuracy, without requiring the analyst engine to process large numbers of data items to ascertain their relevance to the anomalous security data item.

Abstract: A computerized method performs fusion of segmentation masks to generate a refined segmentation mask. A plurality of segmentation masks, each including a group of segments, is obtained for a geographic area. For each segmentation mask, a subgroup of segments is filtered from the group of segments of the segmentation mask based on areas of the subgroup of segments. Pairs of segments of the filtered subgroup of segments are matched to form matched segment groups in the geographic area. Representative segments of the geographic area are selected from the matched segment groups to generate a refined segmentation mask including the selected representative segments. A segment-specific action is performed on at least one of the selected representative segments of the generated refined segmentation mask of the geographic area. In some examples, the segment-specific action is for precision farming in agriculture domain.

Abstract: A model that includes a generative network mapping a latent vector to a feature vector, wherein weights in the generative network are modelled as probabilistic distributions, is trained on observed data points with incomplete observations of the feature vector to learn values of the weights. From a plurality of potential next features to observe, a target feature is selected that maximizes a measure of expected reduction in uncertainty in the distribution of the weights given the observed data points so far. A target data point comprising at least the target feature is then requested, and the generative network is further trained based on the target data point received in response to the request.

Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.

Abstract: Independent refresh rates may be enabled for multiple monitors. Any displays that are presenting content in a full screen mode are refreshed at a rate set by the application presenting the content. All other devices are refreshed at a rate that is determined by a window manager based on various factors including the type of application(s) executing, the size of the applications presenting content, the location of the applications presenting content, hardware configurations, and so forth.

Abstract: Systems and methods for characterizing and forecasting evolving query workloads. The method includes receiving a query, the received query including a parameter value and an arrival time; identifying the query as a recurrent query; extracting a query template from the received query by parsing the received query; based at least on the identifying, generating a feature vector for the received query, the feature vector generated based on the extracted template and the parameter value; and forecasting a future query based on the generated feature vector by applying a neural network.

Abstract: The disclosure herein describes training a document recommendation model using loss data generated from a linear score difference vector. A training data entry is provided including a query and a set of candidate documents. A document recommendation model generates a set of document prediction scores indicative of a likelihood that the candidate documents are responses to the query and a pairwise score difference matrix is generated using the set of document prediction scores. The pairwise score difference matrix is transformed into a score difference vector using a correct document vector that indicates a correct document among the set of candidate documents. Loss data of the document recommendation model is generated using the score difference vector and the document recommendation model is adjusted using the calculated loss data. Training the document recommendation model based on the linear score difference vector reduces resource usage when compared to training with a difference matrix.

Abstract: A source enclave of a source application includes: at least one process thread; a respective at least one process stack memory; a heap memory; and a thread context area. An interrupt is sent to the source enclave which causes the process thread to exit. A migrator thread is sent to the source enclave to save to an external memory, using a migrator stack memory, the thread context area, the at least one process stack memory, and the heap memory, but not the migrator stack memory. A destination enclave is instantiated at a destination application. An initiator thread is sent to the destination enclave to clone, using an initiator stack memory, the state of the source enclave from the external memory.

Abstract: An experiment design is determined for a plurality of physical experiment entities, based on a training loss that is dependent on a critic function and an action parameter individually associated with each physical experiment entity, with the aim of increasing (e.g., optimizing) information gain with respect to a plurality of test entities. The training loss encodes a predicted information gain between a predicted experiment outcome and a predicted test quantity. The predicted experiment outcome associated therewith is sampled from a joint probability distribution based on an entity context. A numerical output is computed using the critic function applied to the predicted experiment outcome and the predicted test quantity.

Abstract: A method for streaming videos with internally-variable frame quality is implemented via a computing system including a processor. The method includes accessing a video including and evenly sampling the video frames. The method includes, for each sampled video frame, analyzing the video frame using a CNN model to determine whether the video frame includes high-relevance region(s). The method also includes, for each sampled video frame including high-relevance region(s), extracting coordinates of the high-relevance region(s) using the CNN model, and for each sampled video frame including high-relevance region(s) and each intervening video frame between the sampled video frame and a next sampled video frame, setting a minimum frame quality for the extracted coordinates.

Abstract: An imaging apparatus comprises two actuators, such as an autofocus actuator and optical image stabilizer. The actuators are nested, wherein the outer actuator is suspended from the device body and the inner actuator is suspended from the outer actuator. A suspension element may be a flexure bearing, allowing a flat actuator design.

Abstract: A click-to-script service enables developers of big-data job scripts to quickly see the underlying script operations from optimized execution plans. Once a big-data job is received, the disclosed examples compile it and generate tokens that are associated with each operation of the big-data job. These tokens include may include the file name of the job, the line number of the operation, and/or an Abstract Syntax Tree (AST) node for the given operations. An original execution plan is optimized into an optimized execution plan, and the tokens for the original operations of the job script are assigned to the optimized operations of the optimized execution plan. The optimized execution plan is graphically displayed in an interactive manner such that users may view the optimized execution plan and click on its optimized operations to find the original operations of the job script.

Abstract: A computer-implemented method for performing a document search action includes receiving a search request identifying a sample document. A plurality of terms occurring within the sample document is identified. Each term is scored based on a global term frequency of that term within a collection of documents. Each term is then weight based on proximity, within the sample document, of that term to at least one of the other terms of the first plurality of terms. A document search query is created to include a proximity-limiting clause restricting results to include documents that have a highest-weighted term of the first plurality of terms within a threshold distance of another term of the first plurality of terms. The document search query is performed, thereby resulting in identification of a first resulting document.