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

Abstract: A method, apparatus, and non-transitory computer readable medium for data analytics are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include monitoring online activity corresponding to a plurality of users; receiving aggregate marketing data for a marketing activity; identifying online activity data for a time period corresponding to the marketing activity based on the monitoring; generating a regression model based on the aggregate marketing data and the online activity data using Bayesian regression, wherein the regression model represents a relationship between the marketing activity and the online activity, comprises a time effect coefficient, and is based on a prior distribution of the time effect coefficient that decays to zero as time increases; and estimating a treatment effect for the marketing activity on the online activity based on the regression model, wherein the treatment effect comprises a rate of effect decay.

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: This invention provides nucleoside polyphosphate analogues each of which comprises a tag comprising a plurality of Raman-scattering moieties; compounds comprising said nucleoside polyphosphate analogs. This invention also provides nucleotide polymerases with one or more attached and/or conjugated noble metal nanoparticles, wherein the noble metal nanoparticles are surface-enhanced Raman spectroscopy (SERS) substrates thereby creating a region of enhanced sensitivity for surface enhanced Raman spectroscopy (SERS) within or adjacent to the polymerase. This invention also provides a surface with regions of enhanced sensitivity for surface enhanced Raman spectroscopy comprising interspersed rough or nanostructured noble metal surface. This invention also provides methods for determining the sequence of a single stranded DNA or RNA polynucleotide using one or more of nucleoside polyphosphate analogues, polymerase with noble metal nanoparticles, and surface with noble metal.

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: Techniques of forecasting web metrics involve generating, prior to the end of a period of time, a probability of a metric taking on an anomalous value, e.g., a value indicative of an anomaly with respect to web traffic, at the end of the period based on previous values of the metric. Such a probability is based on a distribution of predicted values of the metric at some previous period of time. For example, a web server may use actual values of the number of bounces collected at hourly intervals in the middle of a day to predict a number of bounces at the end of the current day. Further, the web server may also compute a confidence interval to determine whether a predicted end-of-day number of bounces may be considered anomalous. The width of the confidence interval indicates the probability that a predicted end-of-day number of bounces has an anomalous value.

Abstract: This disclosure involves allocating content-delivery resources to electronic content-delivery channels based on attribution models accuracy. For instance, a simulation is executed that involves simulating user exposures, times between user exposures, and user responses. The simulation is performed based on parameters associated with simulating user exposures to electronic content-delivery channels and user responses to the user exposures. An accuracy of a channel attribution model when estimating an attribution of an electronic content-delivery channel to a user response is evaluated based on the simulation. A channel attribution model is selected based on the evaluation. An attribution of the electronic content-delivery channel is determined by applying the selected channel attribution model to actual user exposures and actual user responses.

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: 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: 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: 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.