Abstract: Provided is a system and method for detecting a SQL command from a natural language input using neural networks which works even when the SQL command has not been seen before by the neural networks. In one example, the method may include storing a candidate set comprising structured query language (SQL) templates paired with respective text values, reducing, via a first predictive network, the candidate set into a subset of candidates based on a natural language input and the text values included in the candidate set, selecting, via a second predictive network, an SQL template from among the subset of candidates based on the natural language input and text values included in the subset of candidates, and determining a SQL command that corresponds to the natural language input based on the selected SQL template and content from the natural language input.

Abstract: A method includes defining a set of context types; defining a set of source types, each comprising context types; defining, for each source type, and for each context type included in the events from data sources having the source type, a context definition comprising a set of fields, in events from the data sources, that are associated with the context type; receiving a query comprising a first field value and a time period; retrieving a plurality of events that include the first field value and the time period; for each retrieved event, and for each context definition defined for a source type and a context type of a data source from which the retrieved event originated, determining field values of fields in the set of fields of the context definition; aggregating, for each context type, determined field values from the events; and generating an output.

Abstract: A computerized-method for scaling automatic deployment of a machine-learning detection model in a cloud-based managed analytics service by knowledge sharing to overcome an imbalanced dataset learning problem. The computerized-method includes: sending the received data to machine-learning models to synthesize patterns of the received data to yield a differential privacy data; maintaining in the database the differential privacy data of one or more on-prem cloud-based managed analytics services to generate a consortium shared synthetic data lake; operating phases of machine-learning detection model based on the received data and data in the database to create a packaged model. The data in the database is aggregated and used during the operating phases of the machine-learning detection model to create a packaged model for other on-prem cloud-based managed analytics services, thus overcoming imbalanced dataset learning thereof, and after the packaged model is created it is automatically deployed on-prem.

Abstract: Methods and systems for identifying areas of interest in an image and management of images are disclosed. To manage identification of areas of interest in an image, subject matter expert driven processes may be used to identify the areas of interest. The identified areas of interest may be used to maintain a database usable to guide subsequent use of the images. The database may associate image segments of the images with various landmarks and/or area of interest. The associations may be used to limit the quantity of image segments read from storage during subsequent use of the images.

Abstract: An inference system applies a machine-learning transformer model to a batch of requests with variable input length or variable target length or variable internal state length by selectively batching a subset of operations in the transformer model but processing requests in the batch individually for a subset of operations in the transformer model. In one embodiment, the operation to be processed individually is an attention operation of an encoder or a decoder of the transformer model. By selective batching, the inference system can allow batching operations to be performed for a batch of requests with variable input or target length or internal state length to utilize the parallel computation capabilities of hardware accelerators while preventing unnecessary computations that occur for workarounds that restrain the data of a batch of requests to a same length.

Abstract: To reduce a calculation processing load as a whole while realizing a small amount of data loss for at least one of compression and decompression. For each of a plurality of pieces of data, a storage system determines a compression operation scale of the data based on a feature of the data, executes a lossy compression operation according to the determined compression operation scale to covert the data into encoded data, and stores the encoded data or compressed data thereof into a storage device.

Abstract: The present invention refers to the field of transactional data management. The invention provides a transactional semantic concurrency control system for managing a database, wherein the system comprises a data store engine which is adapted to manage the database comprising value rows, and each value row comprising a primary key uniquely identifying each row, at least one semantic column and a commit timestamp; wherein the data store engine is further adapted to manage operation rows, which are rows comprising a primary key, at least one operation to be applied over at least one semantic column of a value row identified by the primary key, and a commit timestamp; store the value rows and the operation rows; generate an operation row when it receives an operation that inserts, updates, upserts, and/or deletes a row of the database with the commit timestamp of the operation; and apply the operations of at least one operation row when a trigger condition is satisfied.

Abstract: An inference system applies a machine-learning transformer model to a batch of requests with variable input length or variable target length or variable internal sate length by selectively batching a subset of operations in the transformer model but processing requests in the batch individually for a subset of operations in the transformer model. In one embodiment, the operation to be processed individually is an attention operation of an encoder or a decoder of the transformer model. By selective batching, the inference system can allow batching operations to be performed for a batch of requests with variable input or target length or internal state length to utilize the parallel computation capabilities of hardware accelerators while preventing unnecessary computations that occur for workarounds that restrain the data of a batch of requests to a same length.

Abstract: According to principles described herein, a system applies Active Learning methodology to multiple models simultaneously. The system includes a means to distribute the sample selection algorithm across large pools of unlabeled data and a automatic model training deployed on hardware matched to the model type that scales to large volumes of data without consuming all resources.

Abstract: A gas turbine meter that is miniaturized based on its design is disclosed. While the size of the gas turbine meter is small, it performs all the functions of prior gas turbine meters and has a large rangeability over a very large range of pressure. The turbine meter includes a body which is bilateral or symmetrical, permitting the turbine meter to be installed in either orientation in a flow line. Diffusers are included with the turbine meter that maintain the rotor of the turbine meter in position and prevent dust from entering into bearings which connect the rotor with the diffusers. The rotor of the turbine meter optimally has twelve flat vanes, each having blades angled at 45.degree. from the plane of the blank in which it was made. Close clearance is maintained between the blades and the interior of the meter. Slots are formed at the lower end of the rotor, thereby forming the shanks of the vanes. The slots are optimally sized to increase stiffness.

Abstract: A method and apparatus for transporting solid particulate material and calculating mass flow rate of the material through a rotary feeder having helical pockets is provided. At least one radiation source and two or more detectors are positioned in a manner whereby radiation flux is directed through, and attenuated by particulate material contained in a helical pocket. The amount of attenuation is measured at least when the pocket is filled with material and when the pocket has discharged most of the material. The difference in the amount of flux detected at the filled and discharged positions is used to determine the mass flow rate of the particulate material.