410 results about "Computational problem" patented technology

A method of encoding knowledge is disclosed, which can be used to automatically detect problems in software application deployments. The method includes accessing a source of knowledge describing a problem known to occur in deployments of a particular software application, and which identifies a plurality of conditions associated with the problem. An encoded representation of the knowledge source is generated according to a predefined knowledge encoding methodology. The encoded representation is adapted to be applied automatically by a computer to analyze data representing a current state of a monitored deployment of the software application to detect whether the conditions and the problem exist therein. In various implementations, the encoded representation of the knowledge can include queries for deployment information, information concerning the relative importance of the conditions to a detection of the problem, and / or logical constructs for computing a confidence value in the existence of the problem and for determining whether to report the problem if some of the conditions are not true. The knowledge source can comprise a text document (such as a knowledge base article), a flowchart of a diagnostic troubleshooting method, and the like. Also disclosed are methods of at least partially automating the encoding process.

Analog processors for solving various computational problems are provided. Such analog processors comprise a plurality of quantum devices, arranged in a lattice, together with a plurality of coupling devices. The analog processors further comprise bias control systems each configured to apply a local effective bias on a corresponding quantum device. A set of coupling devices in the plurality of coupling devices is configured to couple nearest-neighbor quantum devices in the lattice. Another set of coupling devices is configured to couple next-nearest neighbor quantum devices. The analog processors further comprise a plurality of coupling control systems each configured to tune the coupling value of a corresponding coupling device in the plurality of coupling devices to a coupling. Such quantum processors further comprise a set of readout devices each configured to measure the information from a corresponding quantum device in the plurality of quantum devices.

This disclosure includes techniques for using distributed computing over a network to resolve difficult computational problems. Anonymization of the data allows computing devices in the distributed computing system to solve the computational problem without exposing private aspects of the data. Individual computing devices receive instructions and data that correspond to a piece of a larger computational problem. In one implementation, a user may request a webpage from a web server and receive the webpage along with commands in a scripting language that instructs the user's computer to process a piece of the larger computational problem. The web server may assign the piece of the problem to the user's computer based on processing capabilities of the user's computer. Combining solutions received from multiple distributed computing devices and reversing the anonymization process yields a solution to the computational problem.

A method for computing using a quantum system comprising a plurality of superconducting qubits is provided. Quantum system can be in any one of at least two configurations including (i) an initialization Hamiltonian H0 and (ii) a problem Hamiltonian HP. The plurality of superconducting qubits are arranged with respect to one another, with a predetermined number of couplings between respective pairs of superconducting qubits in the plurality of qubits, such that the plurality of superconducting qubits, coupled by the predetermined number of couplings, collectively define a computational problem to be solved. In the method, quantum system is initialized to the initialization Hamiltonian HO. Quantum system is then adiabatically changed until it is described by the ground state of the problem Hamiltonian HP. The quantum state of quantum system is then readout thereby solving the computational problem to be solved.

A method for quantum computing using a quantum system comprising a plurality of qubits is provided. The system can be in any one of at least two configurations at any given time including one characterized by an initialization Hamiltonian H0 and one characterized by a problem Hamiltonian HP. The problem Hamiltonian HP has a final state. Each respective first qubit in the qubits is arranged with respect to a respective second qubit in the qubits such that they define a predetermined coupling strength. The predetermined coupling strengths between the qubits in the plurality of qubits collectively define a computational problem to be solved. In the method, the system is initialized to H0 and is then adiabatically changed until the system is described by the final state of the problem Hamiltonian HP. Then the state of the system is read out by probing an observable of the σX Pauli matrix operator.

A distributed parallel computing system actively takes advantage of problem partitioning to balance the computing load among computing resources continually during processing. Variable problem partitions (VPPs) are initially defined as groups of original problem cells (OPCs). VPPs may be redefined and redistributed during execution, if necessary, to optimize performance based on the actual computing agent parameters and costs observed or reported through self-tests. For example, a good rule for efficient execution of a computing problem may be that the time required to perform a computation sequence (iteration) of all OPCs in a VPP should be comparable to the time required to share results via edge OPCs at the VPP collection perimeters. The rules that yield cost-efficient execution may be saved and re-used to generate initial partitionings for subsequent computing problem execution runs.

In order to provide a non-factoid question answering system with improved precision, the question answering system (160) includes: a candidate retrieving unit (222), responsive to a question, extracting answer candidates from a corpus storage (178); a feature vector generating unit (232) for generating features from combinations of a question with each of the answer candidates; SVMs (176) trained to calculate a score of how correct a combination of the question with an answer candidate is, upon receiving the feature vector therefor; and an answer ranker unit (234) outputting the answer candidate with the highest calculated score as the answer. The features are generated on the basis of the results of morphological analysis and parsing of the question, a phrase in the question evaluated as being positive or negative as well as its polarity, and the semantic classes of nouns in the features.

A computer implemented process is provided for fast and accurate evaluation of the performance characteristics of the periodic-review (s,S) inventory policy with complete back ordering. This policy has an underlying stochastic process that is a renewal process. The method provides a novel computer implementation of a fast and accurate way to compute approximations of the renewal function. In order to overcome the computational problems in evaluating renewal functions numerically, an approximation scheme has been devised whereby the renewal function of the truncated normal distribution can be characterized by two parameters: (1) its coefficient of variation, and (2) the point at which the function needs to be evaluated. This approximation is derived in two stages. In the first stage, a class of rational polynomial approximations are developed to the renewal function, called Pad+E,acu e+EE approximants. In the second stage, polynomial expressions are derived for each coefficient of the Pad+E,acu e+EE approximants in terms of the coefficient of variation of the distribution.

A benefit task system implements a policy for allocating resources to yield some benefit. The method implemented may be applied to a variety of problems, and the benefit may be either tangible (e.g., profit) or intangible (e.g., customer satisfaction). In one example, the method is applied to server allocation in a Web site server “farm” given full information regarding future loads to maximize profits for the Web hosting service provider. In another example, the method is applied to the allocation of telephone help in a way to improve customer satisfaction. In yet another example, the method is applied to distributed computing problem where the resources to be allocated are general purpose computers connected in a network and used to solve computationally intensive problems. Solution of the Web server “farm” problem is based on information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach which reduces the Web server farm problem to a minimum-cost network flow problem, which can be solved in polynomial time. Similar solutions are applicable to other resource allocation problems.

The initial partitioning of a distributed computing problem can be critical, and is often a source of tedium for the user. A method is provided that automatically segments the problem into fixed sized collections of original program cells (OPCs) based on the complexity of the problem specified, and the combination of computing agents of various caliber available for the overall job. The OPCs that are on the edge of a collection can communicate with OPCs on the edges of neighboring collections, and are indexed separately from OPCs that are within the ‘core’ or inner non-edge portion of a collection. Consequently, core OPCs can iterate independently of whether any communication occurs between collections and groups of collections (VPPs). All OPCs on an edge have common dependencies on remote information (i.e., their neighbors are all on the same edge of a neighboring collection).

Systems, devices, and methods for using an analog processor to solve computational problems. A digital processor is configured to track computational problem processing requests received from a plurality of different users, and to track at least one of a status and a processing cost for each of the computational problem processing requests. An analog processor, for example a quantum processor, is operable to assist in producing one or more solutions to computational problems identified by the computational problem processing requests via a physical evolution.

Solving computational problems may include generating a logic circuit representation of the computational problem, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using a quantum processor. Output(s) of the logic circuit representation may be clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that corresponds to the clamped output(s). The representation may be of a Boolean logic circuit. The discrete optimization problem may be composed of a set of miniature optimization problems, where each miniature optimization problem encodes a respective logic gate from the logic circuit representation. A quantum processor may include multiple sets of qubits, each set coupled to respective annealing signal lines such that dynamic evolution of each set of qubits is controlled independently from the dynamic evolutions of the other sets of qubits.

Determining semantically equivalent text or questions using hybrid representations based on neural network learning. Weighted bag-of-words and convolutional neural networks (CNN) based distributed vector representations of questions or text may be generated to compute the semantic similarity between questions or text. Weighted bag-of-words and CNN based distributed vector representations may be jointly used to compute the semantic similarity. A pair-wise ranking loss function trains neural network. In one embodiment, the parameters of the system are trained by minimizing a pair-wise ranking loss function over a training set using stochastic gradient descent (SGD).

Analog processors for solving various computational problems are provided. Such analog processors comprise a plurality of quantum devices, for instance qubits, arranged in a lattice, together with a plurality of coupling devices. The analog processors further comprise bias control systems each configured to apply a local effective bias on a corresponding quantum device. A set of coupling devices in the plurality of coupling devices is configured to couple nearest-neighbor quantum devices in the lattice. Another set of coupling devices is configured to couple next-nearest neighbor quantum devices. The analog processors further comprise a plurality of coupling control systems each configured to tune the coupling value of a corresponding coupling device in the plurality of coupling devices to a coupling. Such quantum processors further comprise a set of readout devices each configured to measure the information from a corresponding quantum device in the plurality of quantum devices.

Quantum and digital processors are employed together to solve computational problems. The quantum processor may be configured with a problem via a problem Hamiltonian and operated to perform adiabatic quantum computation and / or quantum annealing on the problem Hamiltonian to return a first solution to the problem that is in the neighborhood of the global minimum of the problem Hamiltonian. The digital processor may then be used to refine the first solution to the problem by casting the first solution to the problem as a starting point for a classical optimization algorithm. The classical optimization algorithm may return a second solution to the problem that corresponds to a lower energy state in the neighborhood of the global minimum, such as a ground state of the problem Hamiltonian. The quantum processor may include a superconducting quantum processor implementing superconducting flux qubits.

A device, system, and method for solving a plurality of sub-problems representing a main problem pertaining to an environment. An abstract graph corresponding to each of the sub-problems may be constructed in a computing system for each of the sub-problems. At least a first abstract graph of the abstract graphs may be resolved, for example, by decomposing one or more nodes of the first abstract graph. At least one of the one or more nodes of the first abstract graph may be associated with one or more nodes of others of the abstract graphs. At least one of the abstract graphs may be resolved using information obtained by the association. Other embodiments are described and claimed.

A benefit task system implements a policy for allocating resources to yield some benefit. The method implemented may be applied to a variety of problems, and the benefit may be either tangible (e.g., profit) or intangible (e.g., customer satisfaction). In one example, the method is applied to server allocation in a Web site server "farm" given full information regarding future loads to maximize profits for the Web hosting service provider. In another example, the method is applied to the allocation of telephone help in a way to improve customer satisfaction. In yet another example, the method is applied to distributed computing problem where the resources to be allocated are general purpose computers connected in a network and used to solve computationally intensive problems. Solution of the Web server "farm" problem is based on information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach which reduces the Web server farm problem to a minimum-cost network flow problem, which can be solved in polynomial time. Similar solutions are applicable to other resource allocation problems.

Optimal Decision Feedback Equalizer (DFE) coefficients are determined from a channel estimate by casting the DFE coefficient problem as a standard recursive least squares (RLS) problem and solving the RLS problem. In one embodiment, a fast recursive method, e.g., fast transversal filter (FTF) technique, is used to compute the Kalman gain of the RLS problem, which is then directly used to compute MIMO Feed Forward Equalizer (FFE) coefficients. The FBE coefficients are computed by convolving the FFE coefficients with the channel impulse response. Complexity of a conventional FTF algorithm may be reduced to one third of its original complexity by selecting a DFE delay to force the FTF algorithm to use a lower triangular matrix. The length of the DFE may be selected to minimize the tap energy in the FBE coefficients or to ensure that the tap energy in the FBE coefficients meets a threshold.

The initial partitioning of a distributed computing problem can be critical, and is often a source of tedium for the user. A method is provided that automatically segments the problem into fixed sized collections of original program cells (OPCs) based on the complexity of the problem specified, and the combination of computing agents of various caliber available for the overall job. The OPCs that are on the edge of a collection can communicate with OPCs on the edges of neighboring collections, and are indexed separately from OPCs that are within the ‘core’ or inner non-edge portion of a collection. Consequently, core OPCs can iterate independently of whether any communication occurs between collections and groups of collections (VPPs). All OPCs on an edge have common dependencies on remote information (i.e., their neighbors are all on the same edge of a neighboring collection).

Analog processors for solving various computational problems are provided. Such analog processors comprise a plurality of quantum devices, for instance qubits, arranged in a lattice, together with a plurality of coupling devices. The analog processors further comprise bias control systems each configured to apply a local effective bias on a corresponding quantum device. A set of coupling devices in the plurality of coupling devices is configured to couple nearest-neighbor quantum devices in the lattice. Another set of coupling devices is configured to couple next-nearest neighbor quantum devices. The analog processors further comprise a plurality of coupling control systems each configured to tune the coupling value of a corresponding coupling device in the plurality of coupling devices to a coupling. Such quantum processors further comprise a set of readout devices each configured to measure the information from a corresponding quantum device in the plurality of quantum devices.

A method for quantum computing using a quantum system comprising a plurality of qubits is provided. The system can be in any one of at least two configurations at any given time including one characterized by an initialization Hamiltonian HO and one characterized by a problem Hamiltonian HP. The problem Hamiltonian HP has a final state. Each respective first qubit in the qubits is arranged with respect to a respective second qubit in the qubits such that they define a predetermined coupling strength. The predetermined coupling strengths between the qubits in the plurality of qubits collectively define a computational problem to be solved. In the method, the system is initialized to HO and is then adiabatically changed until the system is described by the final state of the problem Hamiltonian HP. Then the state of the system is read out by probing an observable of the σX Pauli matrix operator.

Solving computational problems may include generating a logic circuit representation of the computational problem, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using a quantum processor. Output(s) of the logic circuit representation may be clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that corresponds to the clamped output(s). The representation may be of a Boolean logic circuit. The discrete optimization problem may be composed of a set of miniature optimization problems, where each miniature optimization problem encodes a respective logic gate from the logic circuit representation. A quantum processor may include multiple sets of qubits, each set coupled to respective annealing signal lines such that dynamic evolution of each set of qubits is controlled independently from the dynamic evolutions of the other sets of qubits.

The invention relates to a multi-gas-source steam pipe network computing system of a hydraulic thermal-coupling simulation model, belonging to the technical field of energy pipe network simulation calculation. The system provided by the invention comprises a relation database, a data acquisition module, a data result display module and a pipe network simulation calculation module, wherein the data acquisition module comprises a real-time database and a data acquisition subsystem; the data result display module comprises a data input submodule and a calculation result display submodule, and the pipe network simulation calculation module comprises a coupling simulation calculation submodule and a calculation result correction submodule. The system provides a modeling reference for multi-gas-source calculation through describing the topological structure of the pipe network by using a graph theory method, is coupled with hydraulic and thermodynamic calculation models, solves the modules by using a finite element method, and can trigger result correction when the pipe network environment is changed, and the data result display module can be displayed in a view control fitting of the pipe network in a visualization mode. The invention has the advantages of improving the calculation accuracy of state parameters and solving the calculation problems of the multi-gas-source pipe network model.

The invention provides a distributed system node processing task managing method which includes the steps of receiving an external task request, storing a task in a task buffer of a management node, when some computational node is connected and asks for the task, sending the task to the computational node, and receiving task results when the task processing results are sent by the computational node. By means of the distributed system node processing task method, automation of the whole task processing process is achieved through simple configuration, the management cost is reduced, and in-time finding and in-time processing on the computational node with faults are facilitated.

Analog processors for solving various computational problems are provided. Such analog processors comprise a plurality of quantum devices, arranged in a lattice, together with a plurality of coupling devices. The analog processors further comprise bias control systems each configured to apply a local effective bias on a corresponding quantum device. A set of coupling devices in the plurality of coupling devices is configured to couple nearest-neighbor quantum devices in the lattice. Another set of coupling devices is configured to couple next-nearest neighbor quantum devices. The analog processors further comprise a plurality of coupling control systems each configured to tune the coupling value of a corresponding coupling device in the plurality of coupling devices to a coupling. Such quantum processors further comprise a set of readout devices each configured to measure the information from a corresponding quantum device in the plurality of quantum devices.

This invention discloses a carrying guidance system in the route optimum domain, which comprises the following steps: establishing a biparametric path unit standard dynamic file; considering the path rational constrained and joint failure constrained; using the method of heuristics weight to establish the optional path set off line; improving A* heuristics function to increase the efficiency of optional path set which satisfies the customer rational constrained and joint failure constrained; Off-line optional path set coded storing and backtrack on line; screening of on-line dynamic path, supplementary searching and multiple path issue.

Methods and apparatus for selecting and prioritizing execution threads for consideration of resource allocation include eliminating threads for consideration from all the running execution threads: if they have no available entries in their associated reorder buffers, or if they have exceeded their threshold for entry allocations in the issue window, or if they have exceeded their threshold for register allocations in some register file and if that register file also has an insufficient number of available registers to satisfy the requirements of the other running execution threads. Issue window thresholds may be dynamically computed by dividing the current number of entries by the number of threads under consideration. Register thresholds may also be dynamically computed and associated with a thread and a register file. Execution threads remaining under consideration can be prioritized according to how many combined entries the thread occupies in the resource allocation stage and the issue window.