Abstract: Dynamic timers are determined using machine learning. The timers are used to control the amount of time that new data transaction requests wait before being processed by a data transaction processing system. The timers are adjusted based on changing conditions within the data transaction processing system. The dynamic timers may be determined using machine learning inference based on feature values calculated as a result of the changing conditions.

Abstract: Dynamic timers are determined using machine learning. The timers are used to control the amount of time that new data transaction requests wait before being processed by a data transaction processing system. The timers are adjusted based on changing conditions within the data transaction processing system. The dynamic timers may be determined using machine learning inference based on feature values calculated as a result of the changing conditions.

Abstract: Dynamic timers are determined using machine learning. The timers are used to control the amount of time that new data transaction requests wait before being processed by a data transaction processing system. The timers are adjusted based on changing conditions within the data transaction processing system. The dynamic timers may be determined using machine learning inference based on feature values calculated as a result of the changing conditions.

Abstract: Dynamic timers are determined using machine learning. The timers are used to control the amount of time that new data transaction requests wait before being processed by a data transaction processing system. The timers are adjusted based on changing conditions within the data transaction processing system. The dynamic timers may be determined using machine learning inference based on feature values calculated as a result of the changing conditions.

Abstract: A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

Abstract: A computer system includes a transceiver that receives over a data communications network different types of input data from multiple source nodes and a processing system that defines for each of multiple data categories, a set of groups of data objects for the data category based on the different types of input data. Predictive machine learning model(s) predict a selection score for each group of data objects in the set of groups of data objects for the data category for a predetermined time period. Control machine learning model(s) determine how many data objects are permitted for each group of data objects based on the selection score. Decision-making machine learning model(s) prioritize the permitted data objects based on one or more predetermined priority criteria. Subsequent activities of the computer system are monitored to calculate performance metrics for each group of data objects and for data objects actually selected during the predetermined time period.

Abstract: A continuous casting apparatus includes a first belt carried by a first upstream pulley and a first downstream pulley, a second belt carried by a second upstream pulley and a second downstream pulley, and a mold region defined by a first mold support section arranged behind the first belt and a second mold support section arranged behind the second belt. The first mold support section supports the first belt and defines a shape of the first belt in the mold region and the second mold support section supports the second belt and defines a shape of the second belt in the mold region. At least one of the first mold support section and the second mold support section includes a transition portion and a generally planar portion downstream from the transition portion. The transition portion has a variable radius configured to receive molten metal from a metal feeding device.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

Abstract: The present invention provides, in various embodiments, a miniature movable-beam laser objective configured to fit within the very small dimensions of a standard objective. This small, portable movable-laser source allows the beam to be directed at a computer-generated target or at the spot of a focused target-designator beam.

Abstract: Apparatus for determining an agricultural condition in an agricultural environment, the apparatus including one or more processing devices configured to acquire spectral data by measuring sample radiation at least one of reflected from and transmitted through an agricultural sample obtained from the agricultural environment, use the spectral data and at least one computational model to determine an agricultural condition, the computational model embodying relationships between the spectral data and different agricultural conditions and use the agricultural condition to determine an indicator indicative of at least one of: the agricultural condition and an intervention to improve the agricultural condition.

Abstract: The described technology relates to systems and techniques for accessing a database by dynamically choosing an index from a plurality of indexes that includes at least one learned index and at least one non-learned index. The availability of learned and non-learned indexes for accessing the same database provides for flexibility in accessing the database, and the dynamic selection between learned indexes and non-learned indexes provide for choosing the index based on the underlying data in the database and the characteristics of the query. Certain example embodiments provide a learned model that accepts a set of features associated with the query as input, and outputs a set of evaluated weights for respective features, which are then processed according to a set of rules to predict the most efficient index to be used.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: The described technology relates to systems and techniques for accessing a database by dynamically choosing an index from a plurality of indexes that includes at least one learned index and at least one non-learned index. The availability of learned and non-learned indexes for accessing the same database provides for flexibility in accessing the database, and the dynamic selection between learned indexes and non-learned indexes provide for choosing the index based on the underlying data in the database and the characteristics of the query. Certain example embodiments provide a learned model that accepts a set of features associated with the query as input, and outputs a set of evaluated weights for respective features, which are then processed according to a set of rules to predict the most efficient index to be used.

Abstract: A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

Abstract: Natural language processing techniques provide sentence level analysis on one or more topics that are associated with keywords. Indirect learning is used to expand the understanding of the keywords and associated topics. Semantic similarity is used on a sentence-level to assess whether a given sentence relates or mentions a particular topic. In some examples, additional keywords are suggested using filtering techniques in connection with graph embedding-based entity linking techniques.

Abstract: A computer system includes a transceiver that receives over a data communications network different types of input data and multiple data transaction objects from multiple source nodes. A pre-processor processes the different types of input data and the data transaction objects to generate an input data structure. Based on the input data structure, one or more predictive machine learning models is trained and used to predict a probability of execution of each of the data transaction objects at a future execution time. Output data messages are then generated for transmission by the transceiver over the data communications network indicating the probability of execution for at least one of the data transaction objects at the future execution time.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

Abstract: The present disclosure relates to compounds of Formula (I) which are multimeric forms of a monomeric binding peptide linearly bonded to PEG moieties to form the multimers and their use in treating or preventing coronavirus infections and Acute Respiratory Distress Syndrome