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.

Abstract: Techniques regarding multivariate time series data analysis are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that generates a machine learning model that discovers a dependency between multivariate time series data using an attention mechanism controlled by an uncertainty measure.

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 display includes a system on chip (SoC), a plurality of driver integrated circuits (ICs), and a plurality of groups of light-emitting diodes (LEDs). The SoC is arranged to generate a plurality of backlight control signals according to an input image data. The plurality of driver ICs are coupled to the SoC, and arranged to receive the plurality of backlight control signals, respectively, to generate a plurality of driving signals. The plurality of groups of LEDs are coupled to the plurality of driver ICs, respectively, wherein the plurality of driving signals are arranged to control brightness of the plurality of groups of LEDs, respectively.

Abstract: Techniques regarding one or more automated machine learning processes that analyze time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a time series analysis component that selects a machine learning pipeline for meta transfer learning on time series data by sequentially allocating subsets of training data from the time series data amongst a plurality of machine learning pipeline candidates.

Abstract: The present invention relates to methods of selecting, screening, engineering, making and modifying antibodies that have improved bioavailability upon subcutaneous administration to a human. Antibodies and variant antibodies with improved bioavailability upon subcutaneous administration to a human are also described.

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.