Abstract: An apparatus for implementing a computing system to predict preferences includes at least one processor device operatively coupled to a memory. The at least one processor device is configured to calculate a parameter relating to a density of a prior distribution at each sample of a set of samples associated with the prior distribution. The at least one parameter including a distance from each sample to at least one neighboring sample. The at least one processor device is further configured to estimate, for the plurality of samples, at least one differential entropy of at least one posterior distribution associated with at least one observation based on the parameter relating to the density of the prior distribution at each sample and the likelihood of observation for each sample. The estimation is performed without sampling the at least one posterior distribution to reduce consumption of resources of the computing system.

Abstract: An apparatus for estimating a characteristic of a posterior distribution includes a sampling section configured to generate a plurality of samples from a prior distribution, an obtaining section configured to obtain, for each sample among the plurality of samples, a likelihood of observation given the sample, a calculation section configured to calculate a parameter relating to a density at each sample in the prior distribution, and an estimation section configured to estimate, for the plurality of samples, a characteristic of the posterior distribution based on the parameter relating to the density at each sample and the likelihood of observation for each sample.

Abstract: A method, a computer system, and/or a computer program product are disclosed. One computer-implemented method for building a hash table includes dividing a hash table into plural blocks; and dividing each block into plural sub-blocks. A certain sub-block uses a first pattern of association between a key and a location for storing the key. Another sub-block which belongs to the same block having the certain sub-block uses a second pattern which is different from the first pattern. The method may further include building a hash table by using memory blocks in a Field Programmable Gate Array.

Abstract: An analysis device which analyzes a system that inputs input data including a plurality of input parameters and outputs output data, including an acquisition unit that acquires learning data including a plurality of sets of the input data and the output data, and a learning processing unit that learns, based on the acquired learning data, the amount of difference of output data corresponding to a difference between input parameters of two pieces of input data, an analysis method using the analysis device, and a program used in the analysis device are provided.

Abstract: An analysis device which analyzes a system that inputs input data including a plurality of input parameters and outputs output data, including an acquisition unit that acquires learning data including a plurality of sets of the input data and the output data, and a learning processing unit that learns, based on the acquired learning data, the amount of difference of output data corresponding to a difference between input parameters of two pieces of input data, an analysis method using the analysis device, and a program used in the analysis device are provided.

Abstract: A method, a computer system, and/or a computer program product are disclosed. One computer-implemented method for building a hash table includes dividing a hash table into plural blocks; and dividing each block into plural sub-blocks. A certain sub-block uses a first pattern of association between a key and a location for storing the key. Another sub-block which belongs to the same block having the certain sub-block uses a second pattern which is different from the first pattern. The method may further include building a hash table by using memory blocks in a Field Programmable Gate Array.

Abstract: An apparatus for estimating a characteristic of a posterior distribution includes a sampling section configured to generate a plurality of samples from a prior distribution, an obtaining section configured to obtain, for each sample among the plurality of samples, a likelihood of observation given the sample, a calculation section configured to calculate a parameter relating to a density at each sample in the prior distribution, and an estimation section configured to estimate, for the plurality of samples, a characteristic of the posterior distribution based on the parameter relating to the density at each sample and the likelihood of observation for each sample.

Abstract: A method for inferring a location where each textual message was posted by a test user. The method includes partitioning an area into plural sub-areas, associating each of textual messages posted by training users with a sub-area where each of the textual message was posted, extracting a keyword from the textual message associated with each sub-area, constructing a feature vector of each sub-area, based on the extracted keyword, computing a transition probability for each sub-area by time-series of location information associated with the textual messages posted by the training user, the transition probability being a probability of transition from a target sub-area to other sub-areas, computing plural scores of each location, using the feature vector, where each textual message was posted by the test user, and computing, based on the plural scores and the transition probability, time-series of locations where each textual message was posted by the test user.

Abstract: Provided are a method, an apparatus and a computer program for calculating a risk assessment value for an event sequence, which are capable of calculating the risk assessment value of each even sequence by calculating a totally ordered set on the basis of a partially ordered set indicating the event sequence. The risk assessment value of an event sequence that is a partially ordered set indicating some events of an event group of M kinds of events (M is a finite natural number) in a time series. The partially ordered set is converted into an approximate totally ordered set, and an M-dimensional feature vector is calculated based on the totally ordered set obtained by the conversion. A projection matrix for calculating the risk assessment value is calculated using the calculated M-dimensional feature vector.

Abstract: An analysis device which analyzes a system that inputs input data including a plurality of input parameters and outputs output data, including an acquisition unit that acquires learning data including a plurality of sets of the input data and the output data, and a learning processing unit that learns, based on the acquired learning data, the amount of difference of output data corresponding to a difference between input parameters of two pieces of input data, an analysis method using the analysis device, and a program used in the analysis device are provided.

Abstract: A method for determining an optimum policy by using a Markov decision process in which T subspaces each have at least one state having a cyclic structure includes identifying, with a processor, subspaces that are part of a state space; selecting a t-th (t is a natural number, t?T) subspace among the identified subspaces; computing a probability of, and an expected value of a cost of, reaching from one or more states in the selected t-th subspace to one or more states in the t-th subspace in a following cycle; and recursively computing a value and an expected value of a cost based on the computed probability and expected value of the cost, in a sequential manner starting from a (t?1)-th subspace.

Abstract: A method for determining an optimum policy by using a Markov decision process in which T subspaces each have at least one state having a cyclic structure includes identifying, with a processor, subspaces that are part of a state space; selecting a t-th (t is a natural number, t?T) subspace among the identified subspaces; computing a probability of, and an expected value of a cost of, reaching from one or more states in the selected t-th subspace to one or more states in the t-th subspace in a following cycle; and recursively computing a value and an expected value of a cost based on the computed probability and expected value of the cost, in a sequential manner starting from a (t?1)-th subspace.

Abstract: Provided are a method, an apparatus and a computer program for calculating a risk assessment value for an event sequence, which are capable of calculating the risk assessment value of each even sequence by calculating a totally ordered set on the basis of a partially ordered set indicating the event sequence. The risk assessment value of an event sequence that is a partially ordered set indicating some events of an event group of M kinds of events (M is a finite natural number) in a time series. The partially ordered set is converted into an approximate totally ordered set, and an M-dimensional feature vector is calculated based on the totally ordered set obtained by the conversion. A projection matrix for calculating the risk assessment value is calculated using the calculated M-dimensional feature vector.

Abstract: A computer-executable method for determining an allocation configuration for allocating virtual machines to physical machines. The method includes the steps of determining a predicted peak usage amount of physical resources for each time interval for individual clusters wherein the clusters include a plurality of virtual machines; setting a constraint that ensures the total predicted peak usage amount of the physical resource predicted for the first physical machine does not exceed the physical resource amount for the first physical machine; and deriving an allocation configuration by calculating, in accordance with the constraint, a solution to an optimization problem for minimizing, as an objective function, the sum total of the physical resource amounts of the plurality of physical machines to which the virtual machines are allocated.

Abstract: An information processing apparatus having a prediction section that determines the predicted peak usage amount of physical resources for each time interval for individual clusters each including a plurality of virtual machines having the same function; a setting section sets a constraint that ensures that, for individual combinations of a first physical machine, a second physical machine, and a time interval, the total predicted peak usage amount of a physical resource predicted for the first physical machine if the second physical machine stops during the time interval does not exceed a physical resource amount prepared for the first physical machine; and an allocation-configuration deriving section derives an allocation configuration by calculating, in accordance with the constraint, a solution to an optimization problem for minimizing, as an objective function, the sum total of the physical resource amounts of all of physical machines to which the virtual machines are allocated.

Abstract: A method for determining an optimum policy by using a Markov decision process in which T subspaces each have at least one state having a cyclic structure includes identifying, with a processor, subspaces that are part of a state space; selecting a t-th (t is a natural number, t?T) subspace among the identified subspaces; computing a probability of, and an expected value of a cost of, reaching from one or more states in the selected t-th subspace to one or more states in the t-th subspace in a following cycle; and recursively computing a value and an expected value of a cost based on the computed probability and expected value of the cost, in a sequential manner starting from a (t ?1)-th subspace.

Abstract: A method for determining an optimum policy by using a Markov decision process in which T subspaces each have at least one state having a cyclic structure includes identifying, with a processor, subspaces that are part of a state space; selecting a t-th (t is a natural number, t?T) subspace among the identified subspaces; computing a probability of, and an expected value of a cost of, reaching from one or more states in the selected t-th subspace to one or more states in the t-th subspace in a following cycle; and recursively computing a value and an expected value of a cost based on the computed probability and expected value of the cost, in a sequential manner starting from a (t?1)-th subspace.

Abstract: A computer-executable method for determining an allocation configuration for allocating virtual machines to physical machines. The method includes the steps of determining a predicted peak usage amount of physical resources for each time interval for individual clusters wherein the clusters include a plurality of virtual machines; setting a constraint that ensures the total predicted peak usage amount of the physical resource predicted for the first physical machine does not exceed the physical resource amount for the first physical machine; and deriving an allocation configuration by calculating, in accordance with the constraint, a solution to an optimization problem for minimizing, as an objective function, the sum total of the physical resource amounts of the plurality of physical machines to which the virtual machines are allocated.

Abstract: An information processing apparatus having a prediction section that determines the predicted peak usage amount of physical resources for each time interval for individual clusters each including a plurality of virtual machines having the same function; a setting section sets a constraint that ensures that, for individual combinations of a first physical machine, a second physical machine, and a time interval, the total predicted peak usage amount of a physical resource predicted for the first physical machine if the second physical machine stops during the time interval does not exceed a physical resource amount prepared for the first physical machine; and an allocation-configuration deriving section derives an allocation configuration by calculating, in accordance with the constraint, a solution to an optimization problem for minimizing, as an objective function, the sum total of the physical resource amounts of all of physical machines to which the virtual machines are allocated.