Abstract: This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.

Abstract: In implementations of systems for predicting a terminal event, a computing device implements a termination system to receive input data defining a period of time and a maximum event threshold. This system uses a classification model to generate event scores for a plurality of entity devices. Each of the event scores indicates a probability of an event occurrence for a corresponding entity device within a period of time. The plurality of entity devices are segmented into a first segment and a second segment based on an event score threshold. Entity devices included in the first segment have event scores greater than the event score threshold and entity devices included in the second segment have event scores below the event score threshold. The termination system generates an indication of a probability that a number of event occurrences for the entity devices included in the second segment exceeds the maximum even threshold within the period of time.

Abstract: Techniques for exchanging data segments between data aggregators and data consumers. In an embodiment, a value of an arbitrary data segment selected by a data consumer is computed. In particular, an individual user value is calculated for each user represented in the data segment, wherein the individual user value is a weighted sum (or other function) of the one or more features of the data segment attributable to that user, plus an additive gaussian noise. The overall value of the data segment is the sum of the individual user values. An offer price for the data segment can then be calculated using the overall value. Once a request is received from the consumer to purchase the data segment at the offer price, the data segment can be exchanged between the aggregator and consumer. Thus, a data marketplace or platform for the exchange of data segments is enabled.

Abstract: Disclosed herein, inter alia, are nucleotide analogues, and methods of use thereof, having cleavable orthogonal linkers.

Abstract: An interactive query processing system provides a user interface, including a “chat bot,” capable of receiving natural language queries from users, processing those queries to determine intents and entities, extracting data from applications and portals, synthesizing the data, and displaying the data to the user via the user interface in a useful, easy to understand, and unified format. The user utilizes a singular UI to receive data responsive to queries rapidly instead of manually extracting and synthesizing data from multiple different portals or dashboards. Additionally, the system provides actionable insights without requiring a user to sort or analyze data from multiple sources.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for training and utilizing a generative machine learning model to select one or more treatments for a client device from a set of treatments based on digital characteristics corresponding to the client device. In particular, the disclosed systems can train and apply a variational autoencoder with a task embedding layer that generates estimated effects for treatment combinations. For example, the disclosed systems receive, as input, digital characteristics corresponding to the client device and various treatment combinations. The disclosed systems apply the trained generative machine learning model with the task embedding layer to the digital characteristics to generate effect estimations for the various treatment combinations. Based on the effect estimations for the treatment combinations, the disclosed systems select one or more treatments to provide to the client device.

Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for determining latent components of a metrics time series and identifying anomalous data within the metrics time series based on one or both of spikes/dips and level changes from the latent components satisfying significance thresholds. To identify such latent components, in some cases, the disclosed systems account for a range of value types by intelligently subjecting real values to a latent-component constraint for decomposing the time series and intelligently excluding non-real values from the latent-component constraint. The disclosed systems can further identify significant anomalous data values from latent components of the metrics time series by jointly determining whether one or both of a subseries of a spike-component series and a level change from a level-component series satisfy significance thresholds.

Abstract: One illustrative transistor device disclosed herein includes a gate structure positioned above a semiconductor substrate and first and second overall cavities formed in the semiconductor substrate on opposite sides of the gate structure. In this example, each of the first and second overall cavities comprise a substantially vertically oriented upper epitaxial cavity and a lower insulation cavity, wherein the substantially vertically oriented upper epitaxial cavity extends from an upper surface of the semiconductor substrate to the lower insulation cavity. The transistor also includes an insulation material positioned in at least a portion of the lower insulation cavity of each of the first and second overall cavities and epitaxial semiconductor material positioned in at least the substantially vertically oriented upper epitaxial cavity of each of the first and second overall cavities.

Abstract: Techniques for exchanging data segments between data aggregators and data consumers. In an embodiment, a value of an arbitrary data segment selected by a data consumer is computed. In particular, an individual user value is calculated for each user represented in the data segment, wherein the individual user value is a weighted sum (or other function) of the one or more features of the data segment attributable to that user, plus an additive gaussian noise. The overall value of the data segment is the sum of the individual user values. An offer price for the data segment can then be calculated using the overall value. Once a request is received from the consumer to purchase the data segment at the offer price, the data segment can be exchanged between the aggregator and consumer. Thus, a data marketplace or platform for the exchange of data segments is enabled.

Abstract: Disclosed herein, inter alia, are compounds, compositions, and methods of use thereof in the sequencing of a nucleic acid.

Abstract: Files can be managed to mitigate undesirable reading of files from secondary storage component (SSC) associated with a storage system comprising primary storage component (PSC). File management component (FMC) can determine file identifiers for files stored in SSC and store them in reference files associated with such files. FMC can determine file identifiers for files stored in PSC and store them in a file entry data store. In response to a client request, FMC can determine whether a local file stored in PSC is a copy of an archived file stored in SSC based on whether the respective file identifiers of the archived file and local file or snapshot of the local file match. If there is a suitable match, FMC can read the snapshot of the local file and provide it to client device; if not, FMC can read the archived file and provide it to client device.

Abstract: The present disclosure generally relates to semiconductor structures and, more particularly, to dual trench isolation structures and methods of manufacture. The structure includes: a doped well region in a substrate; a dual trench isolation region within the doped well region, the dual trench isolation region comprising a first isolation region of a first depth and a second isolation region of a second depth, different than the first depth; and a gate structure on the substrate and extending over a portion of the dual trench isolation region.

Abstract: One illustrative transistor device disclosed herein includes a gate structure positioned above a semiconductor substrate and first and second overall cavities formed in the semiconductor substrate on opposite sides of the gate structure. In this example, each of the first and second overall cavities comprise a substantially vertically oriented upper epitaxial cavity and a lower insulation cavity, wherein the substantially vertically oriented upper epitaxial cavity extends from an upper surface of the semiconductor substrate to the lower insulation cavity. The transistor also includes an insulation material positioned in at least a portion of the lower insulation cavity of each of the first and second overall cavities and epitaxial semiconductor material positioned in at least the substantially vertically oriented upper epitaxial cavity of each of the first and second overall cavities.

Abstract: Connection of clients to nodes associated with a storage system can be managed during non-disruptive upgrades (NDUs) of software or firmware of nodes. During NDU, connection manager component (CMC) manages connections of clients to nodes based on a node pool policy to determine nodes to consider for connection for a client request and a connection policy. CMC can determine whether to reduce a set of nodes to a subset of nodes having a particular upgrade status based on a threshold value associated with the particular upgrade status, which can be one of upgraded or non-upgraded. If threshold is satisfied for the set, CMC can reduce the set to the subset of nodes, and CMC can determine a node of the subset based on the connection policy. If threshold is not satisfied, the set is not reduced, and CMC determines a node from the set based on the connection policy.

Abstract: A method for generating stylistic feature prescriptions to align a body of text with one or more target goals includes receiving, at a stylistic feature model, a body of text, where the body of text is selected by a user via a graphical user interface (GUI). The stylistic feature model identifies stylistic features from the body of text and populates a stylistic feature vector with the stylistic features. A trained de-confounded prediction model receives the stylistic feature vector. The trained de-confounded prediction model using the stylistic feature vector generates a prediction value for each of one or more target goals, compares the prediction value for each of the one or more target goals to a target value for each of the one or more target goals and outputs, for display on the GUI, one or more stylistic feature prescriptions to the body of text based on results of the comparing.

Abstract: The present application discloses novel and inventive methods, apparatuses, and the required molecules to detect compounds (analytes) utilizing antibodies or other protein-binding molecules and nanopore-detectable tags using specially designed nanopores, coupled with ionic current readout, to provide a single molecule electronic detection solution, offering unique opportunities for multiplexing and quantitation.

Abstract: In various implementations, a method includes receiving a set of time series data that corresponds to a metric. A seasonal pattern is extracted from the set of time series data and the extracted seasonal pattern is filtered from the set of time series data. A predictive model is generated from the filtered set of data. The extracted seasonal pattern is filtered from another set of time series data where the second set of time series data corresponds to the metric. The filtered second set of time series data is compared to the predictive model. An alert is generated to a user for a value within the filtered second set of time series data which falls outside of the predictive model.

Abstract: A method for generating stylistic feature prescriptions to align a body of text with one or more target goals includes receiving, at a stylistic feature model, a body of text, where the body of text is selected by a user via a graphical user interface (GUI). The stylistic feature model identifies stylistic features from the body of text and populates a stylistic feature vector with the stylistic features. A trained de-confounded prediction model receives the stylistic feature vector. The trained de-confounded prediction model using the stylistic feature vector generates a prediction value for each of one or more target goals, compares the prediction value for each of the one or more target goals to a target value for each of the one or more target goals and outputs, for display on the GUI, one or more stylistic feature prescriptions to the body of text based on results of the comparing.

Abstract: The invention provides various orthogonal nucleotide analogues and methods for using combinations of said various orthogonal nucleotide analogues for sequencing by synthesis.

Abstract: A system for querying a federated data store includes a metadata knowledge graph describing the contents and relationships among one or more underlying data stores, an interactive user interface receiving requests from a data consumer, a predefined constrainable query (‘nodegroup’) store containing predefined constrainable queries that define data subsets of interest across one or more of the underlying data repositories, a knowledge-driven querying layer generating and executing queries against the federated data store and merging responsive results, a scalable analytic execution layer receiving the search results from the federated data store and applying machine learning/artificial intelligence techniques to analyze the results, and a user interface presenting visualizations of raw or analyzed results to the consumer. A method and a non-transitory computer-readable medium are also disclosed.