Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

Abstract: An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

Abstract: Various implementations disclosed herein include devices, systems, and methods that track a position of a device within an object-based coordinate system. An example process may include, at a device and prior to movement of an object, acquiring first images of the object, identifying three-dimensional (3D) keypoints on surfaces of the object, and tracking positions of the device in an object-based coordinate system during acquisition of the first images. The process may further include, subsequent to a movement of the object, acquiring second images of the object, identifying the 3D keypoints on surfaces of the object, and tracking positions of the device in the object-based coordinate system during acquisition of the second images based on identifying the 3D keypoints. The process may further include generating a 3D model of the object based on the first and second images and the tracked positions of the device during acquisition of the images.

Abstract: Devices, systems, and methods are disclosed for partial point cloud registration. In some implementations, a method includes obtaining a first set of three-dimensional (3D) points corresponding to an object in a physical environment, the first set of 3D points having locations in a first coordinate system, obtaining a second set of 3D points corresponding to the object in the physical environment, the second set of 3D points having locations in a second coordinate system, predicting, via a machine learning model, locations of the first set of 3D points in the second coordinate system, and determining transform parameters relating the first set of 3D points and the second set of 3D points based on the predicted location of the first set of 3D points in the second coordinate system.

Abstract: In response to receiving a request for an identity key from a first entity, an identity key for the first entity is generated. A first request from the first entity to replicate a set of data is received. The generated identity key for the first entity is added to the metadata of the set of data requested to be replicated. A determination is made whether a replication rule exists for the first entity. In response to determining that a replication rule exists for the first entity, the set of data is replicated according to the replication rule for the first entity.

Abstract: Various implementations disclosed herein include devices, systems, and methods that generates a three-dimensional (3D) model of an object based on images and tracked positions of a device during acquisition of the images. For example, an example process may include acquiring sensor data during movement of the device in a physical environment including an object, the sensor data including images of a physical environment acquired via a camera on the device, identifying the object in at least some of the images, tracking positions of the device during acquisition of the images based on identifying the object in the at least some of the images, the positions identifying positioning of the device with respect to a coordinate system defined based on a position and orientation of the object, and generating a 3D model of the object based on the images and positions of the device during acquisition of the images.

Abstract: A system and method for safety testing a host autonomous vehicle (AV). This method includes: generating a trained machine learning (ML) agent and testing the host AV in an environment that includes one or more background vehicles configured to operate according to the trained ML agent. The ML agent is generated by: (i) obtaining a testing state model having non-safety-critical states and safety-critical states, (ii) editing the testing state model to obtain an edited testing state model that omits data concerning the non-safety-critical states, and (iii) training a ML agent using the edited state testing model so as to generate the trained ML agent.

Abstract: A computer-implemented method comprises initializing a plurality of segment lists. Each segment list of the plurality of segment lists corresponds to a respective one of a plurality of disk drives. Each segment list divides storage space of the respective disk drive into a plurality of segments. The method further comprises, for each of the plurality of disk drives, identifying one or more candidate segments from the plurality of segments; calculating a respective segment distance variance for one or more combinations of identified candidate segments. Each combination of identified candidate segments includes one candidate segment for each of the plurality of disk drives. The method further comprises selecting a combination of the one or more combinations of identified candidate segments having the smallest respective segment distance variance; and storing data on the plurality of disk drives according to the selected combination of identified candidate segments.

Abstract: Various implementations disclosed herein include devices, systems, and methods that generates a three-dimensional (3D) model of an object based on images and tracked positions of a device during acquisition of the images. For example, an example process may include acquiring sensor data during movement of the device in a physical environment including an object, the sensor data including images of a physical environment acquired via a camera on the device, identifying the object in at least some of the images, tracking positions of the device during acquisition of the images based on identifying the object in the at least some of the images, the positions identifying positioning of the device with respect to a coordinate system defined based on a position and orientation of the object, and generating a 3D model of the object based on the images and positions of the device during acquisition of the images.

Abstract: A computer-implemented method comprises initializing a plurality of segment lists. Each segment list of the plurality of segment lists corresponds to a respective one of a plurality of disk drives. Each segment list divides storage space of the respective disk drive into a plurality of segments. The method further comprises, for each of the plurality of disk drives, identifying one or more candidate segments from the plurality of segments; calculating a respective segment distance variance for one or more combinations of identified candidate segments. Each combination of identified candidate segments includes one candidate segment for each of the plurality of disk drives. The method further comprises selecting a combination of the one or more combinations of identified candidate segments having the smallest respective segment distance variance; and storing data on the plurality of disk drives according to the selected combination of identified candidate segments.

Abstract: A bicycle folding mechanism includes a frame, a stem, and a front fork. The frame includes a front tube at an end thereof. The stem is inserted into the front tube. The stem is connected and pivotal relative to the frame. The stem has an end, which is adjacent to the frame, connected with a connecting seat. The front fork includes at least one connecting end and at least one front end on opposite ends. The at least one connecting end is connected and pivotal relative to the connecting seat such that the front fork is pivotal relative to the connecting seat. The front fork is pivotal between a deployed position in which the at least one front end is positioned away from the frame and a folded position in which the at least one front end is positioned adjacent to the frame.

Abstract: In response to receiving a request for an identity key from a first entity, an identity key for the first entity is generated. A first request from the first entity to replicate a set of data is received. The generated identity key for the first entity is added to the metadata of the set of data requested to be replicated. A determination is made whether a replication rule exists for the first entity. In response to determining that a replication rule exists for the first entity, the set of data is replicated according to the replication rule for the first entity.

Abstract: Embodiments of the present disclosure relate to methods, systems, and computer program products for storage management. In one embodiment, a computer-implemented method is disclosed. According to the method, in response to receiving a request related to managing the at least one directory entry in the file system, at least one directory entry in a file system may be determined by a file system in a storage system, where each of the at least one directory entry is represented by digital numbers. An index for each of the at least one directory entry may be managed in the file system by the file system, where an index key for the index comprises the digital numbers themselves. In other embodiments, a computer-implemented system and a computer program product for managing the index are disclosed.

Abstract: Data-locality-aware task scheduling on hyper-converged computing infrastructures is provided. A plurality of data blocks referenced in an input/output (I/O) request are identified. The I/O request is based on scheduling logic that executes within a container that is deployed on a hyper-converged infrastructure. A block-location mapping table is scanned using a data block identifier that is associated with a present data block of the plurality of data blocks. Physical node(s) of the hyper-converged infrastructure that store the present data block are identified. A container-instance mapping table is scanned using one or more physical node identifiers that are associated with the physical node(s) that store the present data block. Container(s) deployed on physical node(s) that store the present data block are identified. The scheduling logic is provided with a list of identifier(s) that are respectively associated with the containers that are deployed on the physical node(s) that store the present data block.

Abstract: Detecting deadlock in a distributed computing environment. Potential deadlocks between resources of nodes in a computing cluster by determining resource reverse pairs of the resources for each transaction from trace or log files using data analytics. The potential deadlocks are identified offline by matching a global or local resource between the nodes in sub-transactions of each transaction as recursively identified from a transaction resource chain.

Abstract: An approach, for fileset based data locality management in Distributed File Systems. A data locality manager receives fileset identifiers and associated block allocation information, for storing in data block locality tables. The data locality manager determines data block locality factors based on the fileset identifiers and the block allocation information, creating a collection of the data block locality factors. The data locality manager stores the collection of the data block locality factors in the data block locality tables. The data locality manager receives the fileset identifiers for determining the collection of the data block locality factors associated to the fileset identifiers and outputs the collection of the data block locality factors.

Abstract: A bicycle folding mechanism includes a frame, a stem, and a front fork. The frame includes a front tube at an end thereof. The stem is inserted into the front tube. The stem is connected and pivotal relative to the frame. The stem has an end, which is adjacent to the frame, connected with a connecting seat. The front fork includes at least one connecting end and at least one front end on opposite ends. The at least one connecting end is connected and pivotal relative to the connecting seat such that the front fork is pivotal relative to the connecting seat. The front fork is pivotal between a deployed position in which the at least one front end is positioned away from the frame and a folded position in which the at least one front end is positioned adjacent to the frame.

Abstract: Embodiments of the present disclosure relate to methods, systems, and computer program products for storage management. In one embodiment, a computer-implemented method is disclosed. According to the method, in response to receiving a request related to managing the at least one directory entry in the file system, at least one directory entry in a file system may be determined by a file system in a storage system, where each of the at least one directory entry is represented by digital numbers. An index for each of the at least one directory entry may be managed in the file system by the file system, where an index key for the index comprises the digital numbers themselves. In other embodiments, a computer-implemented system and a computer program product for managing the index are disclosed.

Abstract: Embodiments for detecting deadlock in a distributed computing environment. Potential deadlocks between resources of nodes in a computing cluster by determining resource reverse pairs of the resources for each transaction from trace or log files using data analytics. The potential deadlocks are identified offline by matching a global or local resource between the nodes in sub-transactions of each transaction as recursively identified from a transaction resource chain.

Abstract: Data-locality-aware task scheduling on hyper-converged computing infrastructures is provided. A plurality of data blocks referenced in an input/output (I/O) request are identified. The I/O request is based on scheduling logic that executes within a container that is deployed on a hyper-converged infrastructure. A block-location mapping table is scanned using a data block identifier that is associated with a present data block of the plurality of data blocks. Physical node(s) of the hyper-converged infrastructure that store the present data block are identified. A container-instance mapping table is scanned using one or more physical node identifiers that are associated with the physical node(s) that store the present data block. Container(s) deployed on physical node(s) that store the present data block are identified. The scheduling logic is provided with a list of identifier(s) that are respectively associated with the containers that are deployed on the physical node(s) that store the present data block.