Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: The present disclosure provides a method for intelligent user localization in a metaverse, including: detecting movements of a wearable head gear configured to present virtual content to a user, and generating sensor data and visual data using an inertial sensor and a camera, respectively, mapping the visual data to a virtual world using an image associated with the visual data to localize the user in the virtual world; providing the visual data and the sensor data to a first Machine Learning (ML) model and a second ML model, respectively; extracting a plurality of key points from the visual data and distinguishing stable key points and dynamic key points; and removing visual impacts corresponding to the visual data having a relatively low weightage, and providing a relatively high weightage to the sensor data processed through the second ML model.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: The disclosure herein describes processing deletion requests using sequencing numbers with change feed updates. When a deletion occurs on the source data store, a deletion notification is created in a change feed on the source server. The deletion notification includes a set of deletion record IDs identifying a set of records to be deleted, a tombstone sequence number (TSN) identifying a sequence of the deletion notification within a set of deletion notifications and/or a deletion sequence number (DSN). The DSN is incremented by one each time a new deletion notification is created. A deletion notification can represent deletion of a single record or a set of records. Each deletion notification is assigned a time-to-live (TTL) value. The deletion notification is deleted at expiration of the TTL. The TSN and the DSN entries are used to determine whether any deletion updates have been missed to prevent silent failures.

Abstract: Certain aspects of the present disclosure provide techniques for intelligent grouping of travel data for review through a user interface. In one embodiment, a method for providing grouped travel data to a user interface of an application, comprises: receiving a plurality of trip records from an application running on a remote device; providing a first subset of the plurality of trip records to a prediction model; providing a second subset of the plurality of trip records to a model training module; receiving labels for each trip record of the first subset of the plurality of trip records from the prediction model; grouping the first subset of the plurality of trip records based on the received labels; and transmitting the grouped first subset of the plurality of trip records to the application running on the remote device.

Abstract: A system and method are provided for managing creation of data objects, for example in a network or big data environment. A method comprises: receiving, at a processor, a data object creation command for creating the data object; comparing, at the processor, at least one data object creation parameter with a stored data object creation standard; and initiating creation of the data object in response to the data object creation parameter matching a corresponding parameter in the stored data object creation standard. The data object creation command can be modified to meet the stored data object creation standard, and to change an associated permission level. Embodiments of the present disclosure encapsulate standards defining how to create a data object, such that it is no longer necessary to have a person to make sure that these standards are followed, and automate management of object creation while ensuring conformity to organizational standards.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: Method and system to extract domain concepts to create domain dictionaries and ontologies comprises collecting a plurality of reference papers and further classifying the collected plurality of reference papers as relevant and irrelevant. Each of the ‘relevant’ reference papers is further processed by the system, during which the system identifies relevant sections from each document and further processes data in the relevant sections to extract required information and also to identify a relationship between different extracted information, which is further used to create domain dictionaries and ontologies.

Abstract: The disclosure herein describes processing deletion requests using sequencing numbers with change feed updates. When a deletion occurs on the source data store, a deletion notification is created in a change feed on the source server. The deletion notification includes a set of deletion record IDs identifying a set of records to be deleted, a tombstone sequence number (TSN) identifying a sequence of the deletion notification within a set of deletion notifications and/or a deletion sequence number (DSN). The DSN is incremented by one each time a new deletion notification is created. A deletion notification can represent deletion of a single record or a set of records. Each deletion notification is assigned a time-to-live (TTL) value. The deletion notification is deleted at expiration of the TTL. The TSN and the DSN entries are used to determine whether any deletion updates have been missed to prevent silent failures.

Abstract: The disclosure herein describes platform-level checkpointing for deep learning (DL) jobs. The checkpointing is performed through capturing two kinds of state data: (i) GPU state (device state), and (ii) CPU state (host state). The GPU state includes GPU data (e.g., model parameters, optimizer state, etc.) that is located in the GPU and GPU context (e.g., the default stream in GPU, various handles created by the libraries such as DNN, Blas, etc.). Only a fraction of the GPU memory is copied because the checkpointing is done in a domain-aware manner. The “active” memory contains useful data like model parameters. To be able to capture the useful data, memory management is controlled to identify which parts of the memory are active. Also, to restore the destination GPU to the same context/state, a mechanism is used to capture such state-changing events on an original GPU and replayed on a destination GPU.

Abstract: The disclosure herein describes processing deletion requests using sequencing numbers with change feed updates. When a deletion occurs on the source data store, a deletion notification is created in a change feed on the source server. The deletion notification includes a set of deletion record IDs identifying a set of records to be deleted, a tombstone sequence number (TSN) identifying a sequence of the deletion notification within a set of deletion notifications and/or a deletion sequence number (DSN). The DSN is incremented by one each time a new deletion notification is created. A deletion notification can represent deletion of a single record or a set of records. Each deletion notification is assigned a time-to-live (TTL) value. The deletion notification is deleted at expiration of the TTL. The TSN and the DSN entries are used to determine whether any deletion updates have been missed to prevent silent failures.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: Aspects of the present disclosure provide techniques for classifying a trip. Embodiments include receiving, from a plurality of users, a plurality of historical trip records. Each of the plurality of historical trip records may comprise one or more historical trip attributes and historical classification information. Embodiments include training a predictive model, using the plurality of historical trip records, to classify trips based on trip records. Training the predictive model may comprise determining a plurality of hot spots based on the historical trip records, each of the plurality of hot spots comprising a region encompassing one or more locations, and associating, in the predictive model, the plurality of hot spots with historical classification information. Embodiments include receiving, from a user, a new trip record comprising a plurality of trip attributes related to a trip and using the predictive model to predict a classification for the trip based on the trip record.

Abstract: The disclosure herein describes processing deletion requests using sequencing numbers with change feed updates. When a deletion occurs on the source data store, a deletion notification is created in a change feed on the source server. The deletion notification includes a set of deletion record IDs identifying a set of records to be deleted, a tombstone sequence number (TSN) identifying a sequence of the deletion notification within a set of deletion notifications and/or a deletion sequence number (DSN). The DSN is incremented by one each time a new deletion notification is created. A deletion notification can represent deletion of a single record or a set of records. Each deletion notification is assigned a time-to-live (TTL) value. The deletion notification is deleted at expiration of the TTL. The TSN and the DSN entries are used to determine whether any deletion updates have been missed to prevent silent failures.

Abstract: A system and method are provided for managing creation of data objects, for example in a network or big data environment. A method comprises: receiving, at a processor, a data object creation command for creating the data object; comparing, at the processor, at least one data object creation parameter with a stored data object creation standard; and initiating creation of the data object in response to the data object creation parameter matching a corresponding parameter in the stored data object creation standard. The data object creation command can be modified to meet the stored data object creation standard, and to change an associated permission level. Embodiments of the present disclosure encapsulate standards defining how to create a data object, such that it is no longer necessary to have a person to make sure that these standards are followed, and automate management of object creation while ensuring conformity to organizational standards.

Abstract: A system and method are provided for coordinating data ingestion and workflow. In an implementation, the method includes: obtaining, at a processor, a plurality of data ingestion jobs; identifying, based on a stored batching factor, a subset of the plurality of data ingestion jobs to be grouped together; performing batch processing of the subset of data ingestion jobs together in a single shell action; and creating a workflow schedule based on the single shell action comprising the batched data ingestion jobs. The present disclosure advantageously provides batch processing of data ingestion jobs themselves, in contrast to existing approaches which may use data ingestion jobs to perform batch processing on underlying data. The data ingestion jobs can be Sqoop jobs, or in other formats or using other approaches such as through Kafka, Flume or Spark.

Abstract: A timing fixing logic section may select a timing path from among a plurality of timing paths. For the selected timing path, multiple nets along the path may be traversed. For a particular net, multiple metal layers may be traversed. For a particular metal layer, multiple shapes that are associated with the particular net may be traversed. A timing fixing logic section may examine space that is nearby each of the shapes, and identify unused space. The timing fixing logic section may add an extension metal section to the shape. In addition, the timing fixing logic section may identify an existing via of a first type, and select an alternate via of a second type having a resistance that is higher or lower than the existing via. The existing via may be replaced with the alternate via. Accordingly, hold and setup timing of a circuit may be improved.

Abstract: A distributed key-value database management system may provide transactions having a high degree of conformance to ACID properties. A data element may be maintained as a versioned list, where each entry may comprise a timestamp and a value indicative of a corresponding version of the data element. The timestamp may be based at least in part on a vended time value. Timestamps may be monotonically increasing and unique across all entries in a distributed system. Conflicting updates to a data element may be detected directly, prior to the completion of involved transactions.

Abstract: A system includes a plurality of servers, a control plane to determine a first partition of a plurality of devices and to determine a subset of the plurality of servers to assign as candidate servers for the first partition, and a common data store comprising a first stream and a second stream. The control plane is to store, in the first stream, a first message indicating the first partition, the candidate servers, the second stream, and a first message tag, the candidate servers elect a primary server of the first partition from the candidate servers using the first stream, and the elected primary server inserts read and write updates associated with the plurality of devices of the first partition into the second stream.