Abstract: A generator is configured to generate a domain-independent representation of an input data sample, an encoder is configured to generate a domain-dependent representation of the input data sample, and a decoder is configured to ensure that a combination of the domain-independent representation and the domain-dependent representation contains sufficient information to reconstruct the input data sample. A discriminator is configured to attempt to determine an originating domain of the domain-independent representation and a classifier is configured to classify the input data sample based on the domain-independent representation of the input data sample.

Abstract: In some embodiments, an electronic device displays a plurality of content items in a messaging conversation. In some embodiments, the electronic device displays user interfaces associated with one or more content items in a messaging conversation.

Abstract: A method, computer system, and a computer program product for training a machine learning model are provided. A first set of labelled training data from a source domain is obtained. A second set of labelled training data from a target domain is obtained. A number of labelled samples of the first set is greater than a number of labelled samples of the second set. The first machine learning model is trained with the first set and the second set and with a discriminator so that the discriminator is unable to distinguish whether a sample is from the first set or from the second set. The first machine learning model is trained with triplet loss regularization using the first set and the second set.

Abstract: A method for reading and writing frame images having variable frame rates and a system therefor are provided. The system provides a video processing system that includes a write end and a read end for accessing a single buffer block. In the method performed in the video processing system, when the read end starts to read an image frame, the read end is controlled to not read data from the buffer block until the data written by the write end to the buffer block reaches a progress threshold; and when it is determined that a portion of the data in the buffer block not yet read by the read end will be overwritten by the data written by the write end, the writing process is terminated until the read end finishes reading the data in the portion in the buffer block.

Abstract: A method for reading and writing frame images with multiple buffer blocks and a system are provided. In the system, when an input-fast mode is switched to an input-low mode, a read end decides whether to read a next buffer block according to a writing progress. If a write end does not change the buffer block to be written, the read end keeps reading the buffer block, and otherwise the read end selects a next buffer block to be read. In an instance that the read end is required to read the same frame image repeatedly, when the input-slow mode is switched to the input-fast mode, the write end decides whether to write a next buffer block according to a reading progress. The write end selects a next buffer block to be written with a next frame image if the read end already reads last buffer block.

Abstract: A method of generating forecasts from time series data includes receiving a set of time series data organized according to a data structure having a plurality of nodes, generating a plurality of base forecasts, including a base forecast for each node, and selecting a sub-set of the plurality of nodes as fixed nodes. The method also includes performing a reconciliation process to generate reconciled forecasts, where the reconciliation process includes reconciling only the base forecasts of non-fixed nodes, and merging the base forecasts of the fixed nodes and the reconciled forecasts of the non-fixed nodes to generate an overall forecast.

Abstract: A prediction system may obtain data, via a network, from devices and process the data, using a first machine learning, to identify a plurality of signals. The prediction system may train a second machine learning model to analyze the plurality of signals to forecast a first forecasted time series and evaluate a first performance of the first forecasted time series. The prediction system may determine that the first performance does not satisfy a performance threshold and may refine the plurality of signals to obtain a refined plurality of signals. The prediction system may train a third machine learning model to analyze the refined plurality of signals to forecast a second forecasted time series and evaluate a second performance of the second forecasted time series. The prediction system may use the refined plurality of signals and the third machine learning model to predict a performance of a third forecasted time series.

Abstract: Aspects of the invention include techniques for decomposing trend and seasonality components in a forecast of parametric time series data. A non-limiting example method includes receiving time series data that includes a plurality of values taken over a first period of time. A forecast is generated using the time series data. The forecast can include one or more predicted values over a second period of time. The forecast is decomposed into N components and 2N coalitions are defined for the N components. A coalition value is determined for each coalition of the 2N coalitions.

Abstract: To automate time series forecasting machine learning pipeline generation, a data allocation size of time series data may be determined based on one or more characteristics of a time series data set. The time series data may be allocated for use by candidate machine learning pipelines based on the data allocation size. Features for the time series data may be determined and cached by the candidate machine learning pipelines. Predictions of each of the candidate machine learning pipelines using at least the one or more features may be evaluated. A ranked list of machine learning pipelines may be automatically generated from the candidate machine learning pipelines for time series forecasting based upon evaluating predictions of each of the one or more candidate machine learning pipelines.

Abstract: Provided herein is an electrically conductive composition capable of sintering. More particularly, the electrically conductive composition comprises sinterable silver particles dispersed in a binder resin, which binder resin is not yet in a fully cured state when the composition is heated to a temperature at which the silver particles start to sinter.

Abstract: Embodiments relate to a system, program product, and method for employing deep learning techniques to fuse data across modalities. A multi-modal data set is received, including a first data set having a first modality and a second data set having a second modality, with the second modality being different from the first modality. The first and second data sets are processed, including encoding the first data set into one or more first vectors, and encoding the second data set into one or more second vectors. The processed multi-modal data set is analyzed, and the encoded features from the first and second modalities are iteratively and asynchronously fused. The fused modalities include combined vectors from the first and second data sets representing correlated temporal behavior. The fused vectors are then returned as output data.

Abstract: In some embodiments, an electronic device displays a plurality of content items in a messaging conversation. In some embodiments, the electronic device displays user interfaces associated with one or more content items in a messaging conversation.

Abstract: In some embodiments, an electronic device presents representations of messaging conversations. In some embodiments, an electronic device indicates which messages are replies to other messages in a messaging conversation. In some embodiments, an electronic device creates and presents links (e.g., rich links) to contacts in a messaging conversation (e.g., “mentions”). In some embodiments, an electronic device presents indications of messages that are replies to other messages. In some embodiments, an electronic device presents options to create a link (e.g., a rich link) to contacts in a messaging conversation (e.g., “mentions”) using a suggested entry user interface element.

Abstract: Selecting a time-series forecasting pipeline by receiving target variable time-series data and exogenous variable time-series data, generating a regular forecasting pipeline comprising a model according to the target variable time-series data, generating an exogenous forecasting pipeline comprising a model according to the target variable time-series data and the exogenous variable time-series data, evaluating the regular forecasting pipeline and the exogenous forecasting pipeline, selecting a pipeline according to the evaluation, and providing the selected pipeline.

Abstract: Providing time-series forecasting by receiving target variable data and exogenous variable data, training a plurality of time-series models according to the target variable data and the exogenous variable data, determining a historical error for each of the plurality of time series models, and providing a time-series forecasting model having a lowest historical error.

Abstract: In some embodiments, an electronic device presents representations of messaging conversations. In some embodiments, an electronic device indicates which messages are replies to other messages in a messaging conversation. In some embodiments, an electronic device creates and presents links (e.g., rich links) to contacts in a messaging conversation (e.g., “mentions”). In some embodiments, an electronic device presents indications of messages that are replies to other messages. In some embodiments, an electronic device presents options to create a link (e.g., a rich link) to contacts in a messaging conversation (e.g., “mentions”) using a suggested entry user interface element.

Abstract: A first set of data associated with assets can be received. An ontology graph can be constructed based on the first set of data. A second set of data associated with the assets can be received, the second set of data having a first frequency of sampling. Based on the second set of data, nodes of the ontology graph representing the assets can be characterized. A third set of data associated with the assets can be received, the third set of data having a second frequency of sampling. The third set of data can include real time data associated with the assets. Based on the third set of data and information associated with the assets represented by the ontology graph, a deep learning neural network can be trained to predict a future state of at least one asset of the assets and discover dynamic mutual impact of the assets.

Abstract: In some embodiments, an electronic device displays a plurality of content items in a messaging conversation. In some embodiments, the electronic device displays user interfaces associated with one or more content items in a messaging conversation.