A model training method and apparatus for federated learning, a device and a storage medium are provided, which belong to the technical field of machining learning. The method includes: generating an i.sup.th scalar operator based on a (t-1).sup.th round of training data and a t.sup.th round of training data (201); transmitting an i.sup.th fusion operator to a next node device based on the i.sup.th scalar operator (202); determining an i.sup.th second-order gradient descent direction of an i.sup.th sub-model based on an acquired second-order gradient scalar, an i.sup.th model parameter and an i.sup.thfirst-order gradient; and updating the i.sup.th sub-model based on the i.sup.th second-order gradient descent direction to obtain a model parameter of the i.sup.th sub-model during a (t+1).sup.th round of iterative training.

A model training method and apparatus for federated learning, a device and a storage medium are provided, which belong to the technical field of machining learning. The method includes: generating an i.sup.th scalar operator based on a (t-1).sup.th round of training data and a t.sup.th round of training data (201); transmitting an i.sup.th fusion operator to a next node device based on the i.sup.th scalar operator (202); determining an i.sup.th second-order gradient descent direction of an i.sup.th sub-model based on an acquired second-order gradient scalar, an i.sup.th model parameter and an i.sup.thfirst-order gradient; and updating the i.sup.th sub-model based on the i.sup.th second-order gradient descent direction to obtain a model parameter of the i.sup.th sub-model during a (t+1).sup.th round of iterative training.

Disclosed are embodiments for providing batch performance using a stream processor. In one embodiment, a method is disclosed comprising receiving, at a stream processor, an event, the stream processor including a plurality of processing stages; generating, by the stream processor, an augmented event based on the event, the augmented event including at least one additional field not appearing in the event, the additional field generated by an operation selected from the group consisting of a join or dimensional annotation operation; and emitting, by the stream processor, the augmented event to downstream consumer.

Disclosed are embodiments for providing batch performance using a stream processor. In one embodiment, a method is disclosed comprising receiving, at a stream processor, an event, the stream processor including a plurality of processing stages; generating, by the stream processor, an augmented event based on the event, the augmented event including at least one additional field not appearing in the event, the additional field generated by an operation selected from the group consisting of a join or dimensional annotation operation; and emitting, by the stream processor, the augmented event to downstream consumer.

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: executing a greedy incremental batcher loop, which can comprise: selecting a first picklist of one or more picklists; and merging the first picklist with one or more picklists in a first remainder of the one or more picklists when the merging would decrease a cost of the first picklist, wherein the first remainder of the one or more picklists comprises the one or picklists excluding the first picklist; executing a randomized tote local search loop, which can comprise: selecting two random picklists of the one or more picklists, as merged; and swapping totes of the two random picklists of the one or more picklists, as merged, when the swapping would decrease a cost of at least one picklist of the two random picklists; executing an update min trolley loop, which can comprise: selecting a shortest picklist of the one or more picklists, as merged and swapped; and combining the shortest picklist with at least one picklist of the one or more picklists, as merged and swapped, wherein a combination picklist of the shortest picklist and the at least one picklist results in a savings of cost; and facilitating displaying the combination picklist and the one or more picklists, as merged and swapped, to a picker. Other embodiments are disclosed herein.

One or more techniques and/or systems are provided for implementing a pipeline used to generate, train, test, and implement a document scoring model for assigning document scores to documents. Features from various sources are combined to create a joined page level feature set, a joined domain level feature set, and a host level feature set. Numerical features and content features are extracted from ground truth documents and random documents. The numerical features are joined with the joined feature sets to create a set of joined features. The document scoring model is trained using the set of joined features and a training technique. A document is scored with a document score using the document scoring model based upon the content features and the set of joined features with document scores obtained during training.

In an approach for optimization of integer arithmetic expressions implemented as a Boolean logic circuit, a processor converts arithmetic operators in an arithmetic expression into adders. A processor identifies a topological order of the adders. A processor merges the adders based on the topological order into a multi-operand adder. A processor converts the multi-operand adder to a compressor tree and a two-operand adder. A processor performs the arithmetic expression based on the converted multi-operand adder.

In an approach for optimization of integer arithmetic expressions implemented as a Boolean logic circuit, a processor converts arithmetic operators in an arithmetic expression into adders. A processor identifies a topological order of the adders. A processor merges the adders based on the topological order into a multi-operand adder. A processor converts the multi-operand adder to a compressor tree and a two-operand adder. A processor performs the arithmetic expression based on the converted multi-operand adder.

A system includes one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: selecting, by a greedy tote reduction algorithm within an infeasible totes loop, items that have a highest volume restriction and a highest weight restriction for each of a plurality of respective totes; iteratively executing a swap of the items; iteratively searching for candidate solutions until a solution of picklists for the plurality of respective totes containing the items, as swapped, is found; executing a minimum trolley loop algorithm on the solution of picklists to create combined picklists; and displaying to a picker on an interface of a computing device, turn-by-turn directions within a pick walk for the combined picklists. Other embodiments are disclosed herein.

Systems and methods for discovering and/or determining section types for a given document class in a data-driven manner are provided. A modified Bayesian model merging algorithm can be used, along with extending an Analogical Story Merging (ASM) algorithm. The systems and methods can learn the section structure of documents without a pre-existing ontology of sections or time-intensive annotation efforts.