Abstract: The present invention discloses a continuous process for the synthesis of dimethyl carbonate from methanol and carbon dioxide over a ceria-based mixed metal oxide-silica catalyst formulation in the presence of a dehydrating or water trapping compound (2-Cyanopyridine).

Abstract: Methods, systems, apparatuses, and computer-readable storage mediums are descried for identifying a similarity between queries. An intermediate representation generator receives a set of queries from a repository, each query in the set of queries having generated a corresponding set of data stored in a data store. An intermediate representation is generated for each query, where the intermediate representation is characterized by a feature associated with text specified in the query. A similarity determiner determines similarity scores between pairs of intermediate representations. A pair of intermediate representations with a similarity score above a threshold is identified. An indication is generated that sets of data corresponding to queries corresponding to the intermediate representations are overlapping.

Abstract: This application relates to systems and methods for automatically correcting labels in untrusted data based on a small sample of trusted data in a training database. In some examples, training data may be divided into a trusted dataset and an untrusted dataset using stratified sampling. An adversarial algorithm may be used to reassign labels in the data samples associated with the untrusted data based on a set of features in the data and labels in the trusted dataset. The untrusted dataset with the reassigned labels may then be used to train a machine learning model.

Abstract: Described are examples for generating a model for forecasting time series data. For a timeseries data set, one or more layers can be provided, where each layer in the one or more layers includes, for each timeseries data input in at least a portion of multiple timeseries data inputs, generating, for the timeseries data input, a short range output from a causal convolution process that is based on timeseries data inputs from the timeseries data set that are associated with timestamps within a threshold time before the timestamp of the timeseries data input, and generating, for the timeseries data input, a long range output from a transformer process based on the short range outputs from the causal convolution process for each timeseries data input from at least the portion of the multiple timeseries data inputs that are associated with timestamps before the timestamp of the timeseries data input.

Abstract: Example aspects include techniques for anomaly detection via sparse judgmental samples. These techniques may include generating, via lexical analysis, a plurality of tokens from a textual representation of a machine learning (ML) model and generating, via a parser, based on the plurality of tokens, an abstract syntax tree (AST) corresponding to the ML model. In addition, the techniques may include identifying a data dependency of the ML model based on an AST node within the AST, the AST node corresponding to a data source and the data dependency indicating the ML model depends on the data source. Further, the techniques may include detecting a potential issue associated with the data source, and transmitting, based on the data dependency, an alert notification in response to the potential issue.

Abstract: In various examples, a system can obtain a first time series data set, the first time series data set including a plurality of data elements. Each data element can include value data and corresponding time data. Based on the first time series data set, the system can generate a second data set and a third dataset. The second dataset can indicate one or more data elements with missing value data and the third dataset can include extremeness data. The extremeness data can indicate an extremeness score for each data element of the plurality of data elements. Additionally, based on the first time series data set, the second data set and a third dataset, the system can implement a set of operations that generate a substitute value data for each data element of the one or more data elements that is missing value data.

Abstract: The invention relates to a composition comprising: at least one alkane-sulphonic acid R—SO3H wherein R represents a saturated, linear or branched, hydrocarbon chain comprising from 1 to 4 carbon atoms, which can or cannot be substituted by at least one halogen atom; sulphuric acid; and optionally at least one solvent; of which the proportions are defined in the description. The invention also relates to the use of the composition as a fatty acid esterification catalyst.

Abstract: This application relates to apparatus and methods for automatically predicting values for a future time period based on time series data of a previous time period. In some examples, a computing device employs multiple algorithms or predictions models to determine baseline predictions and bias predictions accounting for both normal and surge-induced events in the future time period. Accuracy of the algorithms and exogenous variables, such as holidays, events, temporal indicators, are leveraged to accurately predict future values. Baseline predictions using baseline algorithms are aggregated with bias predictions associated with surge events to determine final predictions for the future time period without compromising on the accuracy and efficiency of the predictions for both normal and surge-induced events.

Abstract: This application relates to systems and methods for automatically correcting labels in untrusted data based on a small sample of trusted data in a training database. In some examples, training data may be divided into a trusted dataset and an untrusted dataset using stratified sampling. An adversarial algorithm may be used to reassign labels in the data samples associated with the untrusted data based on a set of features in the data and labels in the trusted dataset. The untrusted dataset with the reassigned labels may then be used to train a machine learning model.

Abstract: An on-demand learning system provides an enhanced leaning environment capable of delivering relevant content on virtually any topic to specific learners. The learning system implements technical features that facilitate curation and subject matter validation of many different types of content. The technical architecture of the learning system also supports intelligent matching of learners to subject matter areas, creation of specific subject matter boards, and resilient maintenance of the boards.

Abstract: Capacitor structures with pitch-matched capacitor unit cells are described. In an embodiment, the capacitor unit cells are formed by interdigitated finger electrodes. The finger electrodes may be pitch-matched in multiple metal layers within a capacitor unit cell, and the finger electrodes may be pitch-matched among an array of capacitor unit cells. Additionally, border unit cells may be pitch-matched with the capacitor unit cells.

Abstract: An Artificial Intelligence (AI) based resource identification system enables identifying available resources in response to receiving a request for resources. The request attributes are mapped to the attributes of the resources in a resource pool. Matching index scores are calculated and resources that match the request can be selected based on the matching index scores. If no resources are available suitable alternate resources with lower matching index scores are suggested so that a user has the choice to filter this alternate resources based on a threshold score. Particular suggestions to change one or more of the request attributes can be provided based on analysis of the mapping of the request and the resource attributes which enables the resource identification system to suggest changes to one or more of the skill attributes, level attributes, time attributes or location attributes of the request.

Abstract: Capacitor structures with pitch-matched capacitor unit cells are described. In an embodiment, the capacitor unit cells are formed by interdigitated finger electrodes. The finger electrodes may be pitch-matched in multiple metal layers within a capacitor unit cell, and the finger electrodes may be pitch-matched among an array of capacitor unit cells. Additionally, border unit cells may be pitch-matched with the capacitor unit cells.

Abstract: Capacitor structures with pitch-matched capacitor unit cells are described. In an embodiment, the capacitor unit cells are formed by interdigitated finger electrodes. The finger electrodes may be pitch-matched in multiple metal layers within a capacitor unit cell, and the finger electrodes may be pitch-matched among an array of capacitor unit cells. Additionally, border unit cells may be pitch-matched with the capacitor unit cells.

Abstract: An Artificial Intelligence (AI) based resource identification system enables identifying available resources in response to receiving a request for resources. The request attributes are mapped to the attributes of the resources in a resource pool. Matching index scores are calculated and resources that match the request can be selected based on the matching index scores. If no resources are available suitable alternate resources with lower matching index scores are suggested so that a user has the choice to filter this alternate resources based on a threshold score. Particular suggestions to change one or more of the request attributes can be provided based on analysis of the mapping of the request and the resource attributes which enables the resource identification system to suggest changes to one or more of the skill attributes, level attributes, time attributes or location attributes of the request.

Abstract: Capacitor structures with pitch-matched capacitor unit cells are described. In an embodiment, the capacitor unit cells are formed by interdigitated finger electrodes. The finger electrodes may be pitch-matched in multiple metal layers within a capacitor unit cell, and the finger electrodes may be pitch-matched among an array of capacitor unit cells. Additionally, border unit cells may be pitch-matched with the capacitor unit cells.

Abstract: The invention relates to a composition comprising; at least one alkane-sulphonic acid R—SO3H wherein R represents a saturated, linear or branched, hydrocarbon chain comprising from 1 to 4 carbon atoms, which can or cannot be substituted by at least one halogen atom; sulphuric acid; and optionally at least one solvent; of which the proportions are defined in the description. The invention also relates to the use of the composition as a fatty acid esterification catalyst.

Abstract: This disclosure describes, generally, methods and systems for implementing memory overcommit of virtual machines. The method includes establishing a plurality of virtual machines on a physical machine, broadcasting, from each of the plurality of virtual machines to a central scheduler, resource usage requirements, and then based at least in part on the resource usage requirements broadcasted from each of the plurality of virtual machines, determining a resource requirements schedule for each of the plurality of virtual machines. The method further includes receiving at least one resource request from at least one of the plurality of virtual machines, based on the resource requirements schedule, un-assigning resources from at least one of the plurality of virtual machines, and assigning the un-assigned resources to the at least one of the plurality of virtual machines which initiated the resource request.

Abstract: Capacitor structures with pitch-matched capacitor unit cells are described. In an embodiment, the capacitor unit cells are formed by interdigitated finger electrodes. The finger electrodes may be pitch-matched in multiple metal layers within a capacitor unit cell, and the finger electrodes may be pitch-matched among an array of capacitor unit cells. Additionally, border unit cells may be pitch-matched with the capacitor unit cells.

Abstract: An on-demand learning system provides an enhanced leaning environment capable of delivering relevant content on virtually any topic to specific learners. The learning system implements technical features that facilitate curation and subject matter validation of many different types of content. The technical architecture of the learning system also supports intelligent matching of learners to subject matter areas, creation of specific subject matter boards, and resilient maintenance of the boards.