Abstract: An administrative terminal receives designation of generation target data and of a generation destination storage device and identifies data similar to the target data. The terminal calculates a first predicted time expected for transmitting the target data from a storage device holding the target data to the generation destination storage device, and a second predicted time expected to be required for a second transmission process of transmitting the similar data from an object storage service to the generation destination storage device and of transmitting difference data between the target data and the similar data from the storage device holding the target data to the generation destination storage device. If the second predicted time is shorter than the first predicted time, the administrative terminal performs the second transmission process to transmit the similar data and the difference data to the generation destination storage device to generate the generation target data therein.

Abstract: In failover processing, a CPU restores data stored in a first volume to a second volume of a storage system, associates a unique ID of the first volume with the second volume, and stores the unique ID associated therewith in a memory. After the failover processing is completed, the CPU manages an update difference management bitmap indicating an updated content with respect to data stored in the second volume. The CPU transmits, in failback processing, update data updated after the failover processing among the data stored in the second volume to the first volume identified by the unique ID associated with the second volume based on the update difference management bitmap.

Abstract: A computer-implemented method is provided for learning with incomplete data in which some of entries are missing. The method includes acquiring an incomplete set of covariates x including incomplete features {tilde over (x)} and an incomplete pattern m indicating missing entries of the incomplete set of covariates {tilde over (x)}. The method further includes obtaining, by a hardware processor, a predictive distribution p?(y|x) of an outcome y by using the incomplete set of covariates x and a parameter ?, the parameter ? being unknown. A learning of the parameter ? includes performing a maximization by maximizing a stochastically approximated conditional evidence lower bound.

Abstract: A computer-implemented process for training a prediction model for survival analysis includes the following operations. A batch of data is elected from a training dataset representing a plurality of individuals. A curve representing a survival rate of a group of individuals within the batch over a period of time is generated using a non-parametric statistical function and for the batch of data. Individual survival functions for each individual within the batch are estimated using the prediction model. An average survival function is generated from the individual survival functions. A calibration loss is generated using the curve representing the survival rate and the average survival function. Weight of a neural network including the prediction model are updated based upon a total loss including the calibration loss.

Abstract: A method, which trains a neural network to perform an analysis that satisfies average calibration, includes a processor manipulating a data set that includes an outcomes vector and a set of feature vectors, each of which corresponds to one of the outcomes in the outcomes vector. The processor repeatedly: selects a subset of the set of feature vectors; generates a distribution vector for a subset of the outcomes vector that corresponds to the subset of the set of feature vectors; produces a prediction vector by running the neural network on the subset of the set of feature vectors; calculates a Bregman divergence between the distribution vector and a scoring distribution vector of the prediction vector; and updates weights of the neural network based on the Bregman divergence.

Abstract: A computer-implemented method is provided for machine prediction. The method includes forming, by a hardware processor, a Convolutional Dynamic Boltzmann Machine (C-DyBM) by extending a non-convolutional DyBM with a convolutional operation. The method further includes generating, by the hardware processor using the convolution operation of the C-DyBM, a prediction of a future event at time t from a past patch of time-series of observations. The method also includes performing, by the hardware processor, a physical action responsive to the prediction of the future event at time t.

Abstract: A computer-implemented method for updating hidden states in a recurrent neural network (RNN) to predict future data from multivariate time-series data with irregular time intervals is provided including inputting, for each of time steps at observations, observation data at a current time step in the multivariate time-series data to the RNN, for each of the time steps: subdividing a time interval between a previous time step and the current time step by a predetermined number, for each of subdivided time steps calculating a first element of the hidden state at a current subdivided time step using ODE-RNNs, and calculating a second element of the hidden state at the current subdivided time step using the last updated hidden state and a hidden state at the previous time step so that the last updated hidden state is decayed to be close to the hidden state at the previous time step.

Abstract: A computer-implemented method for learning a policy for selection of an associative topic, which can be used in a dialog system, is described. The method includes obtaining a policy base that indicates a topic transition from a source topic to a destination topic and a short-term reward for the topic transition, by analyzing data from a corpus. The short-term reward may be defined as probability of associating a positive response. The method also includes calculating an expected long-term reward for the topic transition using the short-term reward for the topic transition with taking into account a discounted reward for a subsequent topic transition. The method further includes generating a policy using the policy base and the expected long-term reward for the topic transition. The policy indicates selection of the destination topic for the source topic as an associative topic for a current topic.

Abstract: A computer-implemented method for learning a policy for selection of an associative topic, which can be used in a dialog system, is described. The method includes obtaining a policy base that indicates a topic transition from a source topic to a destination topic and a short-term reward for the topic transition, by analyzing data from a corpus. The short-term reward may be defined as probability of associating a positive response. The method also includes calculating an expected long-term reward for the topic transition using the short-term reward for the topic transition with taking into account a discounted reward for a subsequent topic transition. The method further includes generating a policy using the policy base and the expected long-term reward for the topic transition. The policy indicates selection of the destination topic for the source topic as an associative topic for a current topic.

Abstract: A computer-implemented method for reconstructing time series data including irregular time intervals and missing values to predict future data from the time series data using a Recurrent Neural Network (RNN) is provided including obtaining irregular time series data X={x1, . . . , xt, . . . , xT} and time interval data ?={?1, . . . , ?t, . . . , ?T}, where xt is a D-dimensional feature vector, T is a total number of observations, ?t is a D-dimensional time interval vector, and a d-th element ?td of ?t represents a time interval from a last observation, replacing missing values in xt with imputed values using an imputation to obtain {tilde over (x)}t, rescaling data of the time interval ?t to obtain rescaled time interval data ?(?t) by calculating ?(?t)=? log(e+ max(0,??t+b?))+b?, where W?, W?, b?, b? are network parameters of a neural network and e is Napier's constant, and multiplying {tilde over (x)}t by ?(?t) to obtain {circumflex over (x)}t as regular time series data for input of the RNN.

Abstract: A computer-implemented method for computing an objective function of discriminative inference with generative models with incomplete data in which some of entries are missing is provided including acquiring an incomplete set of covariates x including incomplete features {tilde over (x)} and an incomplete pattern m indicating missing entries of the incomplete features {tilde over (x)} and computing a predictive distribution p?(y|x) of an outcome y by using the incomplete set of covariates x and a parameter ?, the parameter ? being unknown. Learning of the parameter ? is performed by minimizing an objective function (?):=?ln p?(y|x)=ln p?({tilde over (x)}|m)?ln p?(y,x|m), and the objective function (?) is bounded with a difference between a marginal evidence upper bound MEUBO and a joint evidence lower bound JELBO, where ln p?({tilde over (x)}|m)?MEUBO and ln p?(y,{tilde over (x)}|m)?JELBO.

Abstract: A method is provided for anonymizing statistical data for a secure transfer. The method calculates statistical information for each of the statistical data. The method aggregates the statistical information to calculate a valid range for each of the statistical information. The method removes outlier data based on the valid range for each of the statistical data. The method creates pair lists from each of the statistical data and target data, the pair lists having a respective member from both the statistical data and the target data. The method replaces each respective member of the target data by a random number existing in a range of a corresponding one of a plurality of target data bins. The method swaps each pair in each pair list in a random order using the randomized number, wherein the random number used for swapping is different for different ones of the pair lists.

Abstract: A computer-implemented method is provided for predicting a medical event time. The method includes receiving an electronic health record (EHR) including a plurality of pairs of observation variables and corresponding timestamps. Each of the plurality of pairs include a respective observation variable and a respective corresponding timestamp. The method further includes converting the EHR into a K-dimensional vector representing a cumulative-stay time at a finite number of patient medical states, the patient medical states being determined by values of the observation variables. The method also includes processing, by a hardware processor, the K-dimensional vector using a medical event time prediction model to output a prediction of a medical event time. The medical event time prediction model has been configured through training to receive and process K-dimensional vectors converted from past EHRs to output predicted medical event times.

Abstract: A computer-implemented method, a computer program product, and a computer system for tracking progression of chronic conditions. A computer acquires trajectories of estimated glomerular filtration rates of respective patients. The computer determines a number of trajectory parts in each of the trajectories. The computer generates, in each of response vectors of the trajectories, subsets corresponding to respective ones of the trajectory parts. The computer replaces responses in the response vectors with the subsets. The computer determine, in the respective ones of the trajectory parts, numbers of patient groups. The computer calculates probabilities of the respective patients belonging to respective ones of the patient groups, based on the subsets. The computer clusters the respective patients into the respective ones of the patient groups, based on the probabilities. Information of clustering the patient groups is used to identify risk groups for renal functions.

Abstract: A computer-implemented method, a computer program product, and a computer system for using a time-window based attention long short-term memory (TW-LSTM) network to analyze sequential data with time irregularity. A computer splits elapsed time into a predetermined number of time windows. The computer calculates average values of previous cell states in respective ones of the time windows and sets the average values as aggregated cell states for the respective ones of the time windows. The computer generates attention weights for the respective ones of the time windows. The computer calculates a new previous cell state, based on the aggregated cell states and the attention weights for the respective ones of the time windows. The computer updates a current cell state, based on the new previous cell state.

Abstract: A method for implementing artificial intelligence agents to perform machine learning tasks using predictive analytics to leverage ensemble policies for maximizing long-term returns includes obtaining a set of inputs including a set of ensemble policies and a meta-policy parameter, selecting an action for execution within the system environment using a meta-policy function determined based in part on the set of ensemble policies and the meta-policy function parameter, causing the artificial intelligence agent to execute the selected action within the system environment, and updating the meta-policy function parameter based on the execution of the selected action.

Abstract: A method for selecting an action, includes reading, into a memory, a Partially Observed Markov Decision Process (POMDP) model, the POMDP model having top-k action IDs for each belief state, the top-k action IDs maximizing expected long-term cumulative rewards in each time-step, and k being an integer of two or more, in the execution-time process of the POMDP model, detecting a situation where an action identified by the best action ID among the top-k action IDs for a current belief state is unable to be selected due to a constraint, and selecting and executing an action identified by the second best action ID among the top-k action IDs for the current belief state in response to a detection of the situation. The top-k action IDs may be top-k alpha vectors, each of the top-k alpha vectors having an associated action, or identifiers of top-k actions associated with alpha vectors.

Abstract: A method is provided for rule creation that includes receiving (i) a MDP model with a set of states, a set of actions, and a set of transition probabilities, (ii) a policy that corresponds to rules for a rule engine, and (iii) a set of candidate states that can be added to the set of states. The method includes transforming the MDP model to include a reward function using an inverse reinforcement learning process on the MDP model and on the policy. The method includes finding a state from the candidate states, and generating a refined MDP model with the reward function by updating the transition probabilities related to the state. The method includes obtaining an optimal policy for the refined MDP model with the reward function, based on the reward policy, the state, and the updated probabilities. The method includes updating the rule engine based on the optimal policy.

Abstract: Methods and systems for classifying tabular data include clustering columns from one or more input tables into column groups. The column groups are processed using a neural network that has a set of input layers, each input layer accepting a respective one column group from the column groups as input, to generate a classification output. A classification task is performed on the one or more input tables using the classification output.

Abstract: A computer-implemented method is presented for building a topic model to discover topics in a collection of documents generated by a plurality of users. The method includes extracting conversations from the collection of documents, dividing the extracted conversations into a plurality of segments, generating a topic distribution for each of the plurality of segments based on the extracted conversations and a first pre-defined prior probability distribution, and generating continuous value constructs for each of the topic distributions based on an external corpus and a second pre-defined prior probability distribution, wherein similarity is defined between the continuous value constructs.