Abstract: A method includes receiving, by a processor, an input data set. The input data set includes a plurality of features. The method includes determining, by the processor, one or more characteristics of the input data set. The method includes, based on the one or more characteristics, adjusting, by the processor, one or more architectural parameters of an automated model generation process. The automated model generation process is configured to generate a plurality of models using a weighted randomization process. The one or more architectural parameters weight the weighted randomization process to adjust a probability of generation of models having particular architectural features. The method further includes executing, by the processor, the automated model generation process to output a mode, the model including data representative of a neural network.

Abstract: A database engine receives a human-readable database query that includes a subquery, and parses the database query to build an operator tree. The operator tree includes a subtree corresponding to the subquery. The database engine estimates the number of rows that will accessed when the subtree is executed and estimates the fraction of the cardinality of rows that will be filtered out by subsequent operations in the operator tree. In accordance with a determination that the estimated fraction exceeds a first threshold, the database engine inserts a domain constraint into the subtree that restricts rows retrieved by execution of the subtree, thereby forming a modified operator tree. The database engine executes the modified operator tree to form a final result set corresponding to the database query and returns the final result set.

Abstract: A programming model provides a method for type inference in programming operations. Information defining one or more attributes of an operation is received, the information specifying a field including a field name and a field type identifier for each of the attributes. Constraints for the operation are determined at least based on the attributes, wherein the constraints restrict at least one of a type of input for the operation or a type of output for the operation. Information defining an input for the operation is received, and it is determined, based on the constraints and the received information defining the input, the type of output for the operation. The type of output is associated with an output for the operation.

Abstract: Systems and methods for implementing a biomedical innovations datametrics dashboard are presented. The dashboard can utilize data from real-world data sources to inform a simulation model which performs simulations of the biomedical innovation pipeline and related entities and processes. The dashboard can utilize the simulation model as well as feedback solicited from a user to assist in performing multi-objective optimization according to the user's preferences. The dashboard can help policy decision-makers and other stakeholders decide on a combination of policies that best incentivizes private sector biomedical innovation in order to achieve a satisfactory set of outcomes, which may include, but is not limited to, reductions in DALYs, healthcare costs, increases in industry NPV, and improved clinical trial quality.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a Potential Consumer Engine identifies profile data of a target member account accessible in a social network service during a period of time equal in length to a pre-submission time period. The Potential Consumer Engine identifies channel data and assembles feature vector data for the target member account. The Potential Consumer Engine inputs into generalized linear mix model the feature vector data. The Potential Consumer Engine receives predictive output from the generalized linear mix model. The predictive output indicative of a current probability of the target member account joining the freelancer marketplace.

Abstract: A user guided data mining system includes: a database storing a network graph comprising a plurality nodes and edges, node attributes for the nodes, and edge attributes for the edges; and a data mining module configured to: request feedback from a user indicating at least one of the node attributes, visualize the network graph considering the indicated attributes, request feedback from the user indicating a part of the visualized network graph and a class of graph mining algorithm, and rank a plurality of different graph mining algorithms of the indicated class based on the indicated part.

Abstract: A data handling system includes at least two host devices. For a particular search query that is received by a first host device, a preliminary set of search results is therein generated. The first host system maps the query to one or more topics that are representative to the query. The first host system provides topical click data associated with the topic to a machine-learning module located within a second host device that determines a relevancy score of the result utilizing the topical click data. The first host system obtains the relevancy score of the result and re-ranks the order of the result within a set of results based upon the relevancy score.

Abstract: A database operation is performed in a file system residing on a plurality of processing modules. The file system includes a first relation having a plurality of first-relation entries. Each of the plurality of first-relation entries has a first-relation attribute that is of interest in the database operation. A value of a distribution attribute in each of the first-relation entries is set to a unique value selected from among a domain of unique values. The first-relation entries of the first relation are redistributed among the plurality of processing modules based on the first-relation attribute and the distribution attribute. The computational operation is performed to produce a result.

Abstract: Described herein are systems, methods, and techniques by which a processing unit can build an end-to-end dialogue agent model for end-to-end learning of dialogue agents for information access and apply the end-to-end dialogue agent model with soft attention over knowledge base entries to make the dialogue system differentiable. In various examples the processing unit can apply the end-to-end dialogue agent model to a source of input, fill slots for output from the knowledge base entries, induce a posterior distribution over the entities in a knowledge base or induce a posterior distribution of a target of the requesting user over entities from a knowledge base, develop an end-to-end differentiable model of a dialogue agent, use supervised and/or imitation learning to initialize network parameters, calculate a modified version of an episodic algorithm. e.g., the REINFORCE algorithm, for training an end-to-end differentiable model based on user feedback.

Abstract: Each time evolution processing is performed, an information recording unit records increase-decrease information indicating a type of partial program changed in number between pre- and post-evolution individuals and difference between fitness levels calculated from the pre- and post-evolution individuals in a storage unit. A probability setting unit determines, per partial program, a contribution level indicating how much a partial program contributes to increasing a fitness level, based on the information stored in the storage unit within a predetermined period and sets a selection probability per partial program in such a manner that a partial program having a higher contribution level is given a higher selection probability. When the evolution processing is performed, a generation processing unit selects a partial program to be included in the post-evolution individual in mutation processing according to the set selection probabilities.

Abstract: Methods, systems, and non-transitory computer readable medium for automatically characterizing performance of a System-on-Chip (SoC) and/or Network-on-Chip (NoC) with respect to latency and throughput attributes of one or more traffic flows/profiles under varying traffic load conditions. The characterization of performance may involve a plot representative of latency and throughput, depending on the desired implementation.

Abstract: Systems, devices, articles, and methods are illustrated and described herein. A method of operation in a robotic system including a processor, a first device, and a second device involves receiving, by the processor, a training set including a first plurality of positions in a first configuration space that represents physical configurations of the first device, a second plurality of positions in a second configuration space that represents physical configurations of the second device, and information that represents pairs of positions. A representative pair includes a first representative position in the first configuration space and a second representative position in the second configuration space. The method involves creating, by the processor, from the training set, information that represents a map between a first run-time position in the first configuration space, and a second run-time position in the second configuration space, and returning, by the processor, the information that represents the map.

Abstract: The present disclosure describes a system and method for improving the way computing devices execute genetic algorithms. A fuzzy logic controller takes various properties of the genetic algorithm (such as the diversity of the population, the performance history of the algorithm in terms of time-efficiency and/or effectiveness at improving the best fitness function results, and available computing resources) to dynamically manage the parameters of the genetic algorithm. In some embodiments, the fuzzy inference system that provides parameters to the genetic algorithm is itself controlled by another fuzzy inference system.

Abstract: Described is a system for discriminating between individual accounts and organization accounts in a social media network. The system processes data from a social media network comprising a set of user accounts. A directed mention network, a bi-directed mention network, and a set of computer-observable network metrics are generated. A classification model is trained using the set of computer-observable network metrics and a set of ground-truth user accounts. The trained classification model is used to classify each user account in the set of user accounts as an individual account or an organization account, resulting in a set of classified user accounts.

Abstract: A method which does not rely on explicit inverted indices is provided to search for documents in a corpus of documents responsive to a textual search query. The method includes (a) selecting a program that is customized by setting values for a plurality of parameters, the program structured to receive the textual search query as input and to provide as output values indicating the relevance of the documents in the corpus to the search query; (b) training the program using a machine learning technique; and (c) applying the trained program to the textual search query. The program may be based on a structure that is developed based on a genetic programming technique.

Abstract: An automatic program generation system that includes: an input unit that receives inputs of input data, target data, and design requirements for a first program to be generated; a program storage unit that stores a plurality of existing second programs; a program generation device that generates the first program; and an output unit that outputs the first program. The program generation device includes a program analysis unit that analyzes the plurality of second programs to generate a program model, a basic node/constraint generation unit that generates basic nodes and constraints for evolutionary computation based on the generated program model and the design requirements input from the input unit, and an optimization unit that generates the first program by the evolutionary computation based on the basic nodes and the constraints and the input data and the target data input from the input unit.

Abstract: Various embodiments reconstruct haplotypes from genotype data. In one embodiment, a set of progeny genotype data comprising n progenies encoded with m genetic markers is accessed. A first set of parent haplotypes associated with a first parent of the n progenies and a second set of parent haplotypes associated with a second parent of the n progenies are identified based on at least the set of progeny genotype data. A total minimum number of observable crossovers in the n progenies is determined. An agglomerate data structure comprising a collection of sets of haplotype sequences characterizing the n progenies is constructed based on the set of progeny genotype data and the first and second sets of parent haplotypes. Each set of haplotype sequences includes a number of crossovers equal to the total minimum number of observable crossovers in the n progenies.

Abstract: A system for reconstructing haplotypes from genotype data includes a memory, a processor, and a reconstruction module. The reconstruction module is configured to access a set of progeny genotype data including n progenies encoded with m genetic markers. A first set of parent haplotypes associated with a first parent of the n progenies and a second set of parent haplotypes associated with a second parent of the n progenies are identified based on at least the set of progeny genotype data. An agglomerate data structure including a collection of sets of haplotype sequences characterizing the n progenies is constructed based on the set of progeny genotype data and the first and second sets of parent haplotypes. Each set of haplotype sequences includes a number of crossovers equal to a total minimum number of observable crossovers in the n progenies.

Abstract: Described herein is a mechanism to adapt a machine learning model used in a language understanding model that has been trained using a first set of user input having a first set of features to effectively operate using user input having a second set of features. Losses are defined based on the first set of features, the second set of features or features common to both the first set and second set. The losses comprise one or more of a source side tagging loss, a reconstruction loss, an adversarial domain classification loss, a non-adversarial domain classification loss, an orthogonality loss, and target side tagging loss. The losses are jointly minimized using a gradient descent method and the resulting coefficients are used to retrain the machine learning model.

Abstract: The present invention relates to a radiotherapy planning system (100) for determining a solution (101) corresponding to a fluence profile. The invention proposes to use a Pareto frontier navigator (140) to select the best plan from a set of various auto-planned solutions. An interactive graphical user interface (400) is provided to the planner to navigate among convex combinations of auto-planned solutions. This proposed Pareto plan navigation can be considered as a further optional refinement process, which can be applied to find the best plan in those cases where auto-generated solutions are not fully satisfying the planner's requirements. The navigation tool (400) moves locally through a set of auto-generated plans and can potentially simplify the planner's decision making process and reduce the whole planning time on complex clinical cases from several hours to minutes.

Abstract: Approaches for optimizing network demand forecasting models and network topology using hyperparameter selection are provided. An approach includes defining a pool of features that are usable in models that predict demand of network resources, wherein the pool of features includes at least one historical forecasting feature and at least one event forecasting feature. The approach also includes generating, using a computer device, an optimal model using a subset of features selected from the pool of features. The approach further includes predicting future demand on a network using the optimal model. The approach additionally includes allocating resources in the network based on the predicted future demand.

Abstract: A method for reducing energy costs at a facility includes identifying candidate production entities at the facility having a high potential for energy intensity variation reduction, determining potential cost savings associated with reducing energy intensity variation in the candidate production entities, identifying a root cause for energy intensity variation in the candidate production entities, and causing an action to be performed to mitigate the root cause.

Abstract: Roughly described, the technology disclosed provides a so-called machine learned conversion optimization (MLCO) system that uses evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Website funnels with a single webpage or multiple webpages are represented as genomes. Genomes identify different dimensions and dimension values of the funnels. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well. Each webpage is tested only to the extent that it is possible to decide whether it is promising, i.e., whether it should serve as a parent for the next generation, or should be discarded.

Abstract: A system for outputting an action signal to a controlled system is provided. The system includes a memory storing individuals to be deployed to a production environment as an actor, wherein each of the individuals has a rule associated therewith for asserting an action, and the actor includes one or more individuals, is associated with the controlled system and is configured to transmit an intermediate action signal for asserting the action. The system includes a management server configured to receive the intermediate action signal, select, from a set of available operations, a selected operation to perform with respect to the intermediate action signal, and the set of available operations including allowance and a blocking of the intermediate action signal. Further, in response to the selected operation being the allowance, transmitting the intermediate action signal, and in response to the selected operation being the blocking, blocking the intermediate action signal.

Abstract: A computer implemented method for determining acceptability of an unverified survey item can include outputting interactive displays comprising a survey. The survey can include a verified quantitative item for measuring respondent positions along a continuum for a first trait, and the verified quantitative item can include a prompt and a plurality of quantitative response choices. The survey can include an unverified non-quantitative item for measuring respondent positions along the continuum for the first trait, and the unverified non-quantitative item can include a prompt and a plurality of non-quantitative response choices. A plurality of responses to the verified quantitative item and the plurality of unverified non-quantitative items can be received from a plurality of users.

Abstract: A method and a system for scheduling parallel machines based on hybrid shuffled frog leaping algorithm and variable neighborhood search algorithm are provided to solve collaborative production and processing of jobs on a plurality of unrelated batch processing machines. The jobs are distributed to machines based on the normal processing time and deterioration situation of the jobs on different machines and are arranged. An effective multi-machine heuristic rule is designed according to the structural properties of an optimal solution for the single-machine problem, and the improved rule is applied to the improved shuffled frog leaping algorithm to solve this problem. The improvement strategy for the traditional shuffled frog leaping algorithm is to improve the local search procedure of the traditional frog leaping algorithm by introducing the variable neighborhood search algorithm. The convergence rate and optimization capacity of the original algorithm are thus improved.

Abstract: A method and associated systems for using machine-learning methods to perform linear regression on a DNA-computing platform. One or more processors generate and initialize beta coefficients of a system of linear equations. These initial values are encoded into nucleobase chains that are then padded to a standard length. The chains are allowed to bind with complementary template chains in a DNA-computing reaction, and the resulting DNA molecules are decoded to reveal the relative the relative likelihood of each chain to bind. The initial values of the beta coefficients are weighted proportionally to these likelihoods, and the process is repeated iteratively until the beta coefficients converge to optimal values.

Abstract: Described are systems, methods and computer-program product for replacing a prior input/output (I/O) module and terminal board with a universal I/O device by providing software based instructions and configuration settings for the installer. The method includes provisions for new wiring changes or harnesses as well as preset adapters, converting prior device configuration settings from an I/O module, pack, and/or terminal board to new configuration settings for a programmable I/O device, generating a wiring chart for any wiring changes based on the settings, and displaying the information for the installer and/or programmer's use. This allows a universal I/O device capable of each channel having different operating modes to replace one or more of a mixture of several types of dedicated I/O modules.

Abstract: A method for establishing communication and for transmitting data between sensor units in a rail vehicle uses a communication network which has multiple network nodes. An advantageous communication network and/or an advantageous data transmission can be achieved if the sensor units independently form an ad hoc network with a network topology for transmitting data in the rail vehicle, configure the network, and change the network topology over the course of the data transmission and/or if the communication network is an ad hoc network and the network nodes are sensor units. A rail vehicle including a communication network which has multiple network nodes is also provided.

Abstract: A computer implemented method can include selecting a first flow rate model for a well, providing reservoir data to the first flow rate model, providing production history data to the first flow rate model, computing a solution to the first flow rate model and comparing the solution to production history data. A method can include implementing dual, triple or quad porosity models of a reservoir and history matching a model against actual well production data. A method can include comparing one or more models and determining whether a parameter has a unique solution. A system can include a computer readable medium having instructions stored thereon that, when executed by a processor, cause the processor to perform one or more methods.

Abstract: Methods, systems and apparatus for constructing and programming quantum annealing hardware. In one aspect, a method includes deriving data characterizing an energy spectrum of a Hamiltonian Htotal that characterizes quantum states of a quantum processor, wherein the quantum processor is controllable such that the Hamiltonian Htotal evolves from an initial Hamiltonian Hi to a problem Hamiltonian Hp comprising an energy spectrum that encodes a solution to an optimization problem, the deriving being based on Hi and Hp at a time that Htotal has the energy spectrum; estimating an average phonon energy of a bath in which the quantum processor is located; and determining, based on the derived data and the calculated average phonon energy, an additional Hamiltonian HQG that when combined with Htotal limits evolution of the quantum state of the quantum processor into the ground state of Hp when Htotal evolves to Hp without diagonalizing Hp.

Abstract: Methods for constructing classifiers for binary classification of quantitative brain electrical activity data is described. The classifier building methods are based on the application of one or more evolutionary algorithms. In one embodiment, the evolutionary algorithm used is a genetic algorithm. In another embodiment, the evolutionary algorithm used is a modified Random Mutation Hill Climbing algorithm. In yet another embodiment, a combination of a genetic algorithm and a modified Random Mutation Hill Climbing algorithm is used for building a classifier. The classifier building methods are fully automated, and are adapted to generate classifiers (for example, Linear Discriminant Functions) with high sensitivity, specificity and classification accuracy.

Abstract: Automated drive assisting systems, methods, and programs acquire an expected travel route for a vehicle that includes an automated drive section in which automated drive control for the vehicle is permitted, and execute the automated drive control for the vehicle such that the vehicle travels along the expected travel route in the automated drive section which is included in the expected travel route. The systems, methods, and programs make a changeover from the automated drive control to manual drive based on a drive operation by a user in the case where there is a branch point that meets a predetermined condition ahead in a travel direction of the vehicle which travels through the automated drive control in the automated drive section, the changeover being made before the branch point.

Abstract: A method for extending the lifetime of a resistive change memory includes generating data and hash candidates by shuffling bit positions of write data with the hash candidates in response to a write request for the resistive change memory, calculating Hamming distances of the generated data and hash candidates from stored data and a stored hash, matching stuck data at a predetermined bit in the resistive change memory with the generated data and hash candidates when the stuck data is at the predetermined bit, and excluding mismatched data and hash candidates that are mismatched with the stuck data among the generated data and hash candidates, finding a data and hash candidate with the shortest Hamming distance among the matched data and hash candidates, and choosing the found data and hash candidate as an encoded data and hash, and storing the encoded data and hash in the resistive change memory.

Abstract: A setting unit sets a selection probability for each of a plurality of parameter values selectively configurable in a parameter-variable program amongst a plurality of partial programs, based on a relationship between the parameter values and an amount of characteristic obtained from at least an input image included in learning data. When having selected the parameter-variable program as a partial program to be newly assigned to a target position for a mutation in order to evolve an individual by introducing the mutation, a selection processing unit selects, amongst the parameter values, one parameter value according to the selection probabilities respectively associated with the parameter values, and incorporates the parameter-variable program with the selected parameter value set therein into the individual.

Abstract: Techniques are provided for determining labels associated with first and second candidate code and whether those labels are compatible based at least in part on the labels. When the first candidate code and the second candidate code are compatible, third candidate code based is determined on the those two. When the third candidate code meets exit criteria the third candidate code is provided as evolved code. Some embodiments also include causing execution of the evolved code.

Abstract: A method for generating an engineering database using lookup tables includes creating multiple table sets for generating the engineering database, inputting multiple pieces of control information for controlling respective ones of the multiple datasets, and inputting multiple lookup tables corresponding to each of the multiple pieces of control information.

Abstract: We disclose transconductor-capacitor classical dynamical systems that emulate quantum dynamical systems and quantum-inspired systems by composing them with 1) capacitors that represent ? termed Planck capacitors; 2) a ‘quantum admittance’ element, which can be emulated efficiently via coupled transconductors; 3) an emulated ‘quantum transadmittance element’ that can couple emulated quantum admittances to each other; and 4) an emulated ‘quantum transadmittance mixer element’ that can couple emulated quantum admittances to each other under the control of an input. We describe a ‘Quantum Cochlea’, a biologically-inspired quantum traveling-wave system with coupled emulated quantum two-state systems for efficient spectrum analysis that uses all of these parts.

Abstract: A picture ranking method and a terminal comprises acquiring pictures stored in a terminal, detecting whether the pictures are first-type pictures, where a first-type picture refers to a picture including a human face, and when the pictures are first-type pictures, ranking the pictures according to a social relation model, or when the pictures are not first-type pictures, ranking the pictures according to a preset rule.

Abstract: Technologies for optimized selection of content for delivery to a user that both optimizes the expected return from the delivery of the content to the user and that enables exploration of delivery of new content to users are disclosed. Content is selected for delivery to a user based on an exploitation score that defines an estimate of the feedback expected from the delivery of the content to the user and an exploration score that varies inversely with the number of times that the content has been transmitted to all users. The use of the exploration score enables the exploration of delivery of new content to users. The content might be delivered via e-mail messages, a web site, or using another mechanism.

Abstract: A clustering system selects a parameter set from a plurality of parameter sets associated with a dataset to generate a hyper-optimized cluster of the dataset. Different parameter sets are generated by varying the parameter values based on the Genetic Algorithm or a particle swarming algorithm. A parameter set having a high Fitness score is selected from the different parameter sets and a clustered solution produced using the selected parameter set having a maximum total score is used to produce actionable items.

Abstract: Described are techniques to enable computers to efficiently determine if they should run a program based on an immediate (i.e., real-time, etc.) analysis of the program. Such an approach leverages highly trained ensemble machine learning algorithms to create a real-time discernment on a combination of static and dynamic features collected from the program, the computer's current environment, and external factors. Related apparatus, systems, techniques and articles are also described.

Abstract: Technologies for analyzing a Uniform Resource Locator (URL) include a multi-stage URL analysis system. The multi-stage URL analysis system analyzes the URL using a multi-stage analysis. In the first stage, the multi-stage URL analysis system analyzes the URL using an ensemble lexical analysis. In the second stage, the multi-stage URL analysis system analyzes the URL based on third-party detection results. In the third stage, the multi-stage URL analysis system analyzes the URL based on metadata related to the URL. The multi-stage URL analysis system advances the stages of analysis if a malicious classification score determined by each stage does not satisfy a confidence threshold. The URL may also be selected for additional rigorous analysis using selection criteria not used in by the analysis stages.

Abstract: Embodiments are directed towards collecting, aggregating and indexing unique and non-unique user data from a plurality of users. The result for a query of this indexed aggregation of user data is provided in a plurality of sub-sets of aggregated user data. Each subset of aggregated user data corresponds to a particular portion of the plurality of users. Also, each of these particular portions of the users is set at least large enough to provide general anonymity for the individual users. User data may be collected by one or more user data suppliers and provided to a user data aggregator. In some embodiments, user data may be collected as unique user data, non-unique user data, or any combination thereof. In some embodiments, user data may be aggregated by zip code, expanded zip code, and/or one or more attributes.

Abstract: We disclose transconductor-capacitor classical dynamical systems that emulate quantum dynamical systems and quantum-inspired systems by composing them with 1) capacitors that represent ? termed Planck capacitors; 2) a ‘quantum admittance’ element, which can be emulated efficiently via coupled transconductors; 3) an emulated ‘quantum transadmittance element’ that can couple emulated quantum admittances to each other; and 4) an emulated ‘quantum transadmittance mixer element’ that can couple emulated quantum admittances to each other under the control of an input. We describe a ‘Quantum Cochlea’, a biologically-inspired quantum traveling-wave system with coupled emulated quantum two-state systems for efficient spectrum analysis that uses all of these parts.

Abstract: A network topology system comprises a plurality of nodes, each of the plurality of nodes having a set of connection rules which is built by the steps of: generating a series of prime number differences; generating a series of communication strategy numbers; extracting as many terms as the number of connecting nodes from a recursive sequences to serve as an index series; generating a series of connection strategy numbers by extracting the Nth terms from the series of communication strategy numbers, wherein N stands for each number of the index series; and generating a series of connecting nodes numbers by calculating the sum of each odd number and each term of the series of connection strategy numbers so as to build the connection rules for each odd-numbered node to connect the nodes numbered in corresponding with the numbers of the connecting nodes number series.

Abstract: In one embodiment, a device in a network identifies a set of network entities. The device determines characteristics of the network entities. The device assigns each of the set of network entities to one or more hyperedges of a hypergraph based on the characteristics. The device applies a security policy to a particular one of the network entities based on the one or more hyperedges of the hypergraph to which the particular network entity is assigned.

Abstract: Among the embodiments disclosed herein are variants of the quantum approximate optimization algorithm with different parametrization. In particular embodiments, a different objective is used: rather than looking for a state which approximately solves an optimization problem, embodiments of the disclosed technology find a quantum algorithm that will produce a state with high overlap with the optimal state (given an instance, for example, of MAX-2-SAT). In certain embodiments, a machine learning approach is used in which a “training set” of problems is selected and the parameters optimized to produce large overlap for this training set. The problem was then tested on a larger problem set. When tested on the full set, the parameters that were found produced significantly larger overlap than optimized annealing times. Testing on other random instances (e.g., from 20 to 28 bits) continued to show improvement over annealing, with the improvement being most notable on the hardest problems.

Abstract: The present invention discloses a method and a device for displaying process information, which belongs to the field of computer technologies. The method may include: scanning a memory of a device for running processes; categorizing and ranking the scanned running processes according to a preset process display format; and displaying each process category after categorization and running processes within each process category, and displaying information related to the process category and the running processes within the process category; and/or displaying ranked running processes, and displaying related information corresponding to the ranked running processes.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a user interface automation tool. The user interface automation tool executes a script to perform automation functions on user interface controls in a user interface of an application. Responsive to automation of a given user interface control failing, the user interface automation tool identifies a candidate user interface control that is the same as a user interface control expected in the script using a machine learning model. The user interface automation tool corrects the script to refer to the candidate user interface control to form a corrected script. The user interface automation tool performs a user interface function on the candidate user interface control according the corrected script.