Abstract: One or more embodiments described herein include a computer-implemented method of determining a bounding box for an object in an image. The method includes determining a label for an object in a first image using a first algorithm, and generating a set of images based on the first image, by cropping the first image from a selected direction. The method further includes determining labels for each image in the set using the first algorithm, and removing images from the set such that the remaining images have a label matching the initial label. The method further includes determining a key image for the set, which is the smallest image from the set that has a confidence score exceeding a threshold. Further, the method includes determining a bounding box for the object in the first image based on a perimeter of a portion of the first image that overlaps the key image.

Abstract: A method includes logically organizing, by an object hierarchy processor, data objects in a first hierarchy. A portion of the data objects in the first hierarchy logically includes groupings of other data objects. The object hierarchy processor physically organizes the data objects across two or more types of memory in a second hierarchy. Another portion of the data objects in the second hierarchy physically includes groupings of other data objects. Groupings of the data objects in the second hierarchy are dynamically moved across the two or more types of memory. Levels of access of the data objects are tracked using a data structure that maps groupings of the data objects in the first hierarchy onto metadata information including combined access frequencies of the data objects, and current number of accessors to the data objects, in each grouping of the data objects.

Abstract: One or more embodiments described herein include a computer-implemented method of determining a bounding box for an object in an image. The method includes determining a label for an object in a first image using a first algorithm, and generating a set of images based on the first image, by cropping the first image from a selected direction. The method further includes determining labels for each image in the set using the first algorithm, and removing images from the set such that the remaining images have a label matching the initial label. The method further includes determining a key image for the set, which is the smallest image from the set that has a confidence score exceeding a threshold. Further, the method includes determining a bounding box for the object in the first image based on a perimeter of a portion of the first image that overlaps the key image.

Abstract: A computer-implemented method for data labeling is provided. The computer-implemented method assigns pseudo-labels to unlabeled examples of data using a similarity metric on an embedding space to produce pseudo-labeled examples. A curriculum learning model is trained using the pseudo-labeled examples. The curriculum learning model trained with the pseudo-labeled examples is employed in in a fine-tuning task to enhance classification accuracy of the data.

Abstract: Aspects include a system, computer program production and computer-implemented method for tagging an image. An image classification engine stored in a memory of a computer device generates a plurality of tags for the image and uses the plurality of tags to generate a relevance subgraph for the image. An embedding engine embeds nodes and edges of the relevance subgraph into fixed dimension vectors of a matrix. A neural network stored in the memory determines a feature vector from the image. A processor applies the feature vector to the matrix to generate a context vector for the image. The context vector is used to tag the image.

Abstract: A method includes storing an anchor row vector identification for an anchor row to a local memory. It is determined whether the anchor row vector identification is visible based on isolation requirements. The anchor row vector identification is accessed upon a determination that the anchor row vector identification is visible, and the row vector identification is re-read from the local memory. It is determined whether the anchor row vector identification has not changed since a start of the accessing. Upon a determination that the anchor row vector identification has not changed, read anchor row fields are returned. A first check history is performed on an anchor row history tuple sequence number (TSN) for the anchor row.

Abstract: A method includes setting, by an update processor, a write latch in a first data structure associated with an object. The first data structure is copied to a storage structure. A history tuple sequence number (TSN) of the first data structure is set to point to a TSN of the copied first data structure. The version identifier is set to point to a transaction identification for the object. Data portions are updated for the first data structure. The version identifier is read from the first data structure. It is determined whether the version identifier of the first data structure is visible for a transaction including isolation requirements. If version identifier of the first data structure is visible, the first data structure is accessed and it is determined whether the version identifier of the first data structure changed since starting the transaction.

Abstract: Techniques regarding autonomously facilitating the selection of one or more transfer models to enhance the performance of one or more machine learning tasks are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an assessment component that can assess a similarity metric between a source data set and a sample data set from a target machine learning task. The computer executable components can also comprise an identification component that can identify a pre-trained neural network model associated with the source data set based on the similarity metric to perform the target machine learning task.

Abstract: Methods and apparatus are provided for incremental frequent subgraph mining on dynamic graphs. An exemplary subgraph mining method comprises maintaining a set of embeddings comprising matching embeddings of a given subgraph in an input graph; maintaining a first fringe set of subgraphs comprising subgraphs substantially on a fringe of frequent subgraphs in the input graph that satisfy a predefined support threshold; maintaining a second fringe set of subgraphs comprising subgraphs substantially on a fringe of infrequent subgraphs in the input graph that do not satisfy the predefined support threshold; for an edge addition, checking a support of the subgraphs in the second fringe set based on the set of the embeddings and searching for new embeddings created by the edge addition; and for an edge deletion, removing obsolete embeddings that comprise the deleted edge from the first fringe set based on the set of embeddings.

Abstract: A computer-implemented mitigation and warning method, system, and computer program product including detecting an enclosure temperature via an enclosure temperature probe and activating, via a controller, a thermoelectric device when the enclosure temperature exceeds a predetermined threshold temperature to transfer heat between an enclosure and a phase change material (PCM) to cause the PCM to change phase and cool the enclosure.

Abstract: A first request may be received to update a first set of values. The first set of values may be stored at a first location within a first data page of a database. The first location may be read-only. In response to the receiving of the first request, a first set of records may be inserted into a second data page. The first set of records may include the update of the first set of values. In response to the inserting, a forward pointer may be stored in the first data page that points to the first set of records on the second data page. One or more committed values may be identified on the second data page. In response to the identifying, the one or more committed values may be merged from the second data page to a third data page.

Abstract: Methods and a system are provided. A method includes maintaining an in-page log for records in each of a plurality of data pages of a multi-version database. The method further includes adding record update information to the in-page log when a corresponding one of the records is deleted or updated. The method also includes consulting the in-page log for a recently updated one of the records or a recently deleted one of the records to determine a record status thereof. The method additionally includes spilling, by a processor-based overflow manager, to overflow pages when the in-page log is full. The data pages include any of row-oriented data pages and column-oriented data pages.

Abstract: Methods and a system are provided. A method includes maintaining an in-page log for records in each of a plurality of data pages of a multi-version database. The method further includes adding record update information to the in-page log when a corresponding one of the records is deleted or updated. The method also includes consulting the in-page log for a recently updated one of the records or a recently deleted one of the records to determine a record status thereof. The method additionally includes spilling, by a processor-based overflow manager, to overflow pages when the in-page log is full. The data pages include any of row-oriented data pages and column-oriented data pages.

Abstract: A method, information processing system, and computer program storage product optimize the placement of database objects on a multiplicity of storage devices. A set of database objects are placed on a first storage device in a multiplicity of storage devices. Each storage device comprises differing characteristics. A query workload is run on the set of database objects that have been placed on the first storage device. Profiling information associated with the query workload that is running is collected. A subset of database objects is selected from the set of the database objects to be stored on a second storage device. The subset of database objects is stored on the second storage device and all remaining database objects in the set of database objects on the first storage device.

Abstract: A method includes the following steps. One or more records are accessed from a database memory bypassing a database access mechanism of a database system. Data representing access frequency of the one or more records are collected. The collected access frequency data for the one or more records are maintained. The access frequency data for the one or more records are aggregated until the access frequency reaches a threshold value. The aggregated access frequency data are asynchronously reported for the one or more records to the database system.

Abstract: A first request may be received to update a first set of values. The first set of values may be stored at a first location within a first data page of a database. The first location may be read-only. In response to the receiving of the first request, a first set of records may be inserted into a second data page. The first set of records may include the update of the first set of values. In response to the inserting, a forward pointer may be stored in the first data page that points to the first set of records on the second data page. One or more committed values may be identified on the second data page. In response to the identifying, the one or more committed values may be merged from the second data page to a third data page.

Abstract: Staging data record changes from a faster storage medium to a slower storage medium using data query rewriting is provided. In response to receiving a data query corresponding to a particular data record, it is determined whether the data query is one of a transactional data query or an analytical data query. In response to determining that the data query is a transactional data query, the transactional data query is rewritten to apply transactional delta changes to the particular data record on a storage-class memory of a computer. In response to determining that the data query is an analytical data query, the analytical data query is rewritten to select and reconcile each data record corresponding to the particular data record stored on the storage-class memory with the particular data record stored on a persistent data storage device of the computer.

Abstract: Staging data record changes from a faster storage medium to a slower storage medium using data query rewriting is provided. In response to receiving a data query corresponding to a particular data record, it is determined whether the data query is one of a transactional data query or an analytical data query. In response to determining that the data query is a transactional data query, the transactional data query is rewritten to apply transactional delta changes to the particular data record on a storage-class memory of a computer. In response to determining that the data query is an analytical data query, the analytical data query is rewritten to select and reconcile each data record corresponding to the particular data record stored on the storage-class memory with the particular data record stored on a persistent data storage device of the computer.

Abstract: Methods and apparatus are provided for incremental frequent subgraph mining on dynamic graphs are provided. An exemplary subgraph mining method comprises maintaining a set of embeddings comprising matching embeddings of a given subgraph in an input graph; maintaining a first fringe set of subgraphs comprising subgraphs substantially on a fringe of frequent subgraphs in the input graph that satisfy a predefined support threshold; maintaining a second fringe set of subgraphs comprising subgraphs substantially on a fringe of infrequent subgraphs in the input graph that do not satisfy the predefined support threshold; for an edge addition, checking a support of the subgraphs in the second fringe set based on the set of the embeddings and searching for new embeddings created by the edge addition; and for an edge deletion, removing obsolete embeddings that comprise the deleted edge from the first fringe set based on the set of embeddings.

Abstract: A first request may be received to update a first set of values. The first set of values may be stored at a first location within a first data page of a database. The first location may be read-only. In response to the receiving of the first request, a first set of records may be inserted into a second data page. The first set of records may include the update of the first set of values. In response to the inserting, a forward pointer may be stored in the first data page that points to the first set of records on the second data page. One or more committed values may be identified on the second data page. In response to the identifying, the one or more committed values may be merged from the second data page to a third data page.