A combinatorial optimization problem processing device is for associating a combinatorial optimization problem having N elements with an Ising model to process the combinatorial optimization problem. The combinatorial optimization problem processing device includes: a 1×2 Mach-Zehnder optical modulator that receives a polarized clock pulse train; an optical interference circuit that receives polarized clock pulse trains that were modulated by the Mach-Zehnder optical modulator; an optical coupler that couples output of the optical interference circuit with an initialization optical pulse train that creates a neutral state with respect to interactions between the elements; and a modulation signal generator that performs waveform shaping on an electrical signal obtained by photoelectrically converting an output signal of the optical coupler, generates a modulation signal for the Mach-Zehnder optical modulator, and externally outputs a monitor signal that represents a solution to the optimization problem. The optical interference circuit repeatedly allows a predetermined interaction in the Ising model to occur from the neutral state at a period corresponding to the N pulses of the polarized clock pulse train.

An equilibrium state calculation apparatus according to an embodiment is an equilibrium state calculation apparatus for calculating an equilibrium state of a congestion game. The equilibrium state calculation apparatus includes an input unit input with graph information representing a set of strategies represented by a combination of items, in a zero-suppressed binary decision diagram, the strategies used by a player of the congestion game, and a calculation unit that calculates, by using the graph information input through the input unit, equilibrium state information including a proportion of players selecting the strategies in the equilibrium state by a variant of the Frank-Wolfe algorithm.

Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.

Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

A mechanism is provided in a data processing system for corpus quality analysis. The mechanism applies at least one filter to a candidate corpus to determine a degree to which the candidate corpus supplements existing corpora for performing a natural language processing (NLP) operation. Responsive to a determination to add the candidate corpus to the existing corpora based on a result of applying the at least one filter, the mechanism adds the candidate corpus to the existing corpora to form modified corpora. The mechanism performs the NLP operation using the modified corpora.

Apparatuses and systems for a die-integrated aspheric mirror are described herein. One apparatus includes an ion trap die including a number of ion locations and an aspheric mirror integrated with the ion trap die.

A method is described in which a topic similarity score, a product similarity score and an operating system similarity score between an original post and each one of a plurality of previous posts are determined; an overall similarity score of the each one of the plurality of previous posts based on the topic similarity score, the product similarity score and the operating system similarity score is determined; and a recommendation of a top K number of the plurality of previous posts based on the overall similarity score of the each one of the plurality of previous posts is sent to a display device.

A learning apparatus in a digital environment is advantageous to interaction and communication among users who use a knowledge point structure for learning. The learning apparatus in the digital environment constructs a structurized knowledge library by editing knowledge points, tags of the knowledge points and a relationship among the knowledge points; then records a user's mastery degree for the knowledge points on the basis of the tags; and records knowledge learning information and social attribute information thereof for each user, so as to construct a general-class learning record, a single-class learning record and a general knowledge learning system of the user, which can also be used for the user to view the popularity of each tag in the knowledge library.

Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. An improved approach is provided for identifying log files that have undergone a change in status that would require retrieve of its log data, by including a module directly into the operating system that allows the log collection component to be reactively notified of any changes to pertinent log files.