Abstract: This application relates to apparatus and methods for batching orders for delivery. In some examples, a computing device may obtain order data identifying at least a first order and a second order. The computing device may determine whether the first order can be delivered to a first delivery address and the second order can be delivered to a second delivery address within a delivery time window. Based on the determination, the computing device may batch the first order and the second order to generate a batched order. In some examples, the computing device determines if the delivery vehicle can store the items for the first order and the second order to determine whether the orders may be batched. The computing device may generate assignment data identifying an assignment of the batched order to a delivery vehicle, and may transmit the assignment data to another computing device.

Abstract: A system for rush order fulfilment optimization is discussed. The system includes mobile devices that are each associated with a worker and a rush fulfillment engine executed by a computing system which dynamically updates a task queue of each worker upon receipt of a new rush order according to a task completion rate difference between an estimated task completion rate and the current task completion rate of the worker.

Abstract: According to an aspect, a method includes receiving, over a network, a conversation view request to retrieve messages from a conversation graph stored on a messaging platform from a first user account, generating a ranked list of messages for at least a portion of the messages from the conversation graph, identifying, from the ranked list of messages, a message authored by a second user account having an indirect relationship to the first user account in an engagement graph, selecting a social context object for the message based on a type of the indirect relationship, and transmitting, over the network, information to display the message and the social context object, the social context object providing information about the type of the indirect relationship.

Abstract: Providing overload protection may include receiving, from a first NRF of two or more NRFs, a load metric and comparing the load metric to a threshold. Additionally, when the load metric is above the threshold, providing NRF overload protection may include sending a Network Function (NF) Discovery (NFD) message to a second NRF instead of the first NRF and sending a heartbeat signal to the first NRF.

Abstract: A method implements churn prevention using graphs. The method includes receiving clickstream data, which includes an event, of a user session with an application. The method further includes identifying the event as corresponding to a churn user account and mapping the event to a pair of nodes of a graph. The method further includes updating a churn user count of the pair of nodes in response to identifying the event as corresponding to the churn user account. The method further includes identifying an edge of the graph, corresponding to the pair of nodes. The method further includes updating a value of the edge using an active user count and the churn user count presenting an update responsive to the value.

Abstract: A method implements churn prediction using clickstream data. The method includes receiving clickstream data of a user and converting the clickstream data to a token list. The method further includes processing the token list with a first recurrent layer, a second recurrent layer, and an attention layer of a machine learning model to generate a churn risk. The method further includes executing a reactivation action in response to the churn risk.

Abstract: Systems, methods and computer program products for sample efficient training of machine learning models are provided. A process may proceed, starting with an initial set of labeled examples and the initial set of unlabeled examples, to label unlabeled examples in an iterative manner, with the input dataset for a next iteration comprising an augmented set of labeled examples from a current iteration and selected unlabeled examples, until a final set of labeled examples is created. The final set of labeled examples is used to train a machine learning model. Each iteration includes mapping the input dataset to a reduced dimension space and using the reduced dimension space to identify high value examples to label.

Abstract: Systems for high-performance distributed computing. The systems include techniques for managing data and metadata across multiple nodes. A method embodiment commences by storing data at a node using a first storage mechanism that is local to the node. A first set of metadata is configured to identify a storage location for the stored data. The first set of metadata is stored using the same first storage mechanism that is local to the node. For accessing the first set of metadata, a second set of metadata is configured to identify a storage location for the first set of metadata. The second set of metadata is stored using a second storage mechanism that comprises a distributed metadata storage facility that stores metadata across multiple storage locations having at least one of the multiple storage locations that is not local to the node that stores data and metadata using the first storage mechanism.

Abstract: One or more non-transitory computer-readable media can store program instructions that, when executed by one or more processors, cause the one or more processors to perform steps of organizing storage as a set of storage regions, each storage region having a fixed size; and for each storage region, storing a storage allocation structure of the storage region formatted in a first format selected from a format set including at least two formats, determining a change of an allocation feature of the storage region, based on the allocation feature of the storage region, selecting, from the format set, a second format of the storage allocation structure, and reformatting the storage allocation structure in the second format.

Abstract: In some examples, a system may include a memory resource storing instructions, and one or more processors. In various examples the one or more processors may be configured to execute the instructions to receive data indicating that an order has been dropped by a driver of a first computing system. The order may include a set of order parameters that may include a first order parameter and a second order parameter. Additionally, the one or more processors may be configured to execute the instructions to determine a remaining delivery time period based at least a time when the order was dropped and the first order parameter. Moreover, the one or more processors may be configured to execute the instructions to obtain profile data of a set of drivers that includes availability data indicating the corresponding driver is available for assignment.

Abstract: According to an aspect, a messaging system comprising at least one processor and a non-transitory computer-readable medium storing executable instructions that when executed by the at least one processor cause the at least one processor to obtain a system load metric associated with a messaging platform, compute a pruning factor based on the system load metric, rank messages of a conversation graph using a plurality of first signals to form an intermediate ranked list, prune the intermediate rank list according to the pruning factor to obtain a candidate subset of messages, rank the candidate subset of messages using a plurality of second signals to form a ranked list of messages, and transmit, over a network, information to render at least a portion of the ranked list on a client application.

Abstract: Horizontally-scalable systems and methods for scheduling and optimizing deliveries are described herein. At least one scheduler is configured to receive a request to schedule a delivery for an origination location. The request includes a desired time slot. The request is compared to a persistent delivery snapshot for the origination location to determine availability of the desired time slot. An interim delivery snapshot including the requested delivery is generated when the persistent delivery snapshot has an available time slot corresponding to the desired time slot. At least one optimizer is configured to receive the interim delivery snapshot and generate an updated persistent delivery snapshot by applying an optimization process to the interim delivery snapshot.

Abstract: Various embodiments described herein relate to an asset discovery engine with a deep vulnerabilities scanner with respect to assets in an industrial network. In an embodiment, a request to perform an asset vulnerability assessment of one or more assets within a network is received, the request comprising an asset descriptor describing the one or more assets. In response to the request, aggregated asset property data associated with the one or more assets is obtained based on the asset descriptor. Furthermore, the asset vulnerability assessment is performed based on the aggregated asset property data and asset vulnerability signature data stored in an asset vulnerability signature repository. In response to determining that the asset vulnerability assessment satisfies a defined criterion, one or more actions associated with the network are performed.

Abstract: Various embodiments set forth techniques for transactional allocation and deallocation of blocks in a block store. A first technique includes sending a first request that causes a non-persistent allocation of a block. The first technique also includes adding a first entry in a log recording the allocation as tentative, sending a second request that causes persistence of the allocation, and adding a second entry in a log recording the allocation as finalized. A second technique includes adding a first entry in a log recording a deallocation of a block, sending a first request that causes the deallocation of the block and causes the block to be unavailable for reallocation in a non-persistent manner, adding a second entry in the log recording that the deallocation is finalized, and sending a second request that causes the block to be made available for reallocation.

Abstract: This disclosure relates to high-performance computing, and more particularly to techniques for kernel-assisted device polling of user-space devices. A common kernel-based polling mechanism is provided for concurrently handling both kernel-based polling for kernel-space devices such as network interfaces (e.g., network NICs) and kernel-based polling for user-space devices such as remote direct memory access devices (e.g., RDMA NICs). Embodiments perform kernel-based polling on a first device that has a corresponding device driver in an operating system kernel. Using the same polling mechanism, the kernel-based polling is performed on a second device, the second device being a user-space device wherein the kernel-based polling on the second device is configured by creating a second device file descriptor that is not associated with a corresponding device driver in the operating system kernel.

Abstract: A technique replicates an index of an operations log (oplog) from a primary node to a secondary node of a cluster in the event of failure. The oplog functions as a staging area to coalesce random write operations directed to a virtual disk (vdisk) stored on a backend storage tier. The oplog temporarily caches write data as well as metadata describing the write data. The metadata includes descriptors to the write data corresponding to offset ranges of the vdisk and are used to identify ranges of write data for the vdisk that are cached in the oplog. To facilitate fast lookup operations of whether write data is cached in the oplog, an oplog index provides a state of the latest data for offset ranges of the vdisk that enables fast failover of metadata used to construct the oplog index in memory without downtime or significant metadata replay.

Abstract: Data transformation and data quality checking is provided by reading data from a source datastore and storing the data into memory, performing in-memory processing of the data stored in memory, where the data is maintained in-memory for performance of the in-memory processing thereof, and where the in-memory processing includes performing one or more transformations on the data stored in memory, in which the data stored in memory is transformed and stored back into the memory and applying one or more data quality rules to the data stored in-memory, and based on performing the in-memory processing of the data stored and maintained in memory for the in-memory processing, loading to a target datastore at least some of the data processed by the in-memory processing.

Abstract: Various embodiments set forth techniques for free space management in a block store. The techniques include receiving a request to allocate one or more blocks in a block store, accessing a sparse hierarchical data structure to identify an allocator page identifying a region of a backing store having a greatest number of free blocks, and allocating the one or more blocks.

Abstract: Disclosed is a method for generating high resolution point cloud data for electro-anatomical mapping comprising receiving sparsely measured point cloud data having a plurality of data points. Surface mesh data comprising mesh points defining triangles on a myocardial surface is generated. The point cloud data is mapped to the surface mesh data. For each point of the surface mesh data that cannot be mapped to the point cloud data because there is a missing data point in point cloud data, an interpolation operation is performed based on the point cloud data within the neighbourhood of the point to generate a value for the missing data point. The interpolation operation is repeated N times. For every repetition, a difference between the value for the missing data point generated from the current iteration and the value for the missing data point generated from the immediately preceding iteration is compared, until the difference is below a threshold.

Abstract: One or more non-transitory computer-readable media can store program instructions that, when executed by one or more processors, cause the one or more processors to perform steps of organizing storage as a set of storage regions, each storage region having a fixed size; and for each storage region, storing a storage allocation structure of the storage region formatted in a first format selected from a format set including at least two formats, determining a change of an allocation feature of the storage region, based on the allocation feature of the storage region, selecting, from the format set, a second format of the storage allocation structure, and reformatting the storage allocation structure in the second format.