Abstract: Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively.

Abstract: Entrapping immobilization pellets for purifying breeding water in an aquarium to breed aquatic animals, wherein microorganisms are entrapped and immobilized in an aqueous polymer gel and the entrapping immobilization pellets are colored.

Abstract: Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively.

Abstract: Entrapping immobilization pellets in which microorganisms are entrapped and immobilized, the entrapping immobilization pellets comprising entrapped and immobilized purified water sludge containing nitrifying bacteria.

Abstract: A method of marketing optimization with respect to brand lifetime management formulates a problem of brand equity maximization utilizing Markov Decision Process (MDP) thereby casting brand equity management as a long term regard optimization problem in MDP, The marketing mix is optimized by formulating the mix as actions in MDP and, utilizing historical marketing and transaction data, aspects of the MDP are estimated.

Abstract: A proposed file delete method is a method used by a computer or a file processing device performing a process of outputting to a device driver a directive corresponding to a directive to delete a file satisfying a predetermined condition from an application, and includes: a step of obtaining in primary storage from an actual data area in secondary storage the directory entry information in a directory storing a file to be deleted; a step of adding in the primary storage a delete mark to the file in the obtained directory entry when the file in the obtained directory entry satisfies the predetermined condition; a step of writing a result of the delete mark adding process to a corresponding portion of the actual data area in the secondary storage; and a step of freeing FAT information stored in the secondary storage corresponding to the delete mark added file.

Abstract: Feature importance information available in a predictive model with correlation information among the variables is presented to facilitate more flexible choices of actions by business managers. The displayed feature importance information combines feature importance information available in a predictive model with correlational information among the variables. The displayed feature importance information may be presented as a network structure among the variables as a graph, and regression coefficients of the variables indicated on the corresponding nodes in the graph. To generate the display, a regression engine is called on a set of training data that outputs importance measures for the explanatory variables for predicting the target variable. A graphical model structural learning module is called that outputs a graph on the explanatory variables of the above regression problem representing the correlational structure among them.

Abstract: A process for storing entrapping immobilization pellets in which microorganisms are entrapped and immobilized in an immobilizing material until the entrapping immobilization pellets are used in a treatment tank, the process comprising: storing a large pellet block in water at 15° C. or less or in air at a relative humidity of 90% or more and a temperature of 15° C. or less until the pellet block is cut into the entrapping immobilization pellets and used.

Abstract: Entrapping immobilization pellets in which microorganisms are entrapped and immobilized in an immobilizing agent, the immobilizing agent comprising an attachment prevention filler for preventing attachment of the entrapping immobilization pellets. Pellets can be stored or transported inexpensively and easily without impairing their inherent pellet performance.

Abstract: The present invention produces entrapping immobilization pellets which have high pellet strength and retain high and stable microbial activity (nitrification activity) even if a low concentration of a material involved in immobilization (such as an immobilizing material or polymerization initiator) used. The entrapping immobilization pellets in which microorganisms are entrapped and immobilized in an immobilizing material comprise a filler having a plate-like and/or needle-like crystal structure in the immobilizing material.

Abstract: A large amount of entrapping immobilization pellets with highly stable quality are produced inexpensively by high-speed treatment. There is provided a process for producing entrapping immobilization pellets in which microorganisms are entrapped and immobilized in an immobilizing agent, the process comprising polymerizing a mixture containing the microorganisms and a solution of the immobilizing agent in a forming frame into a gel to prepare a pellet block.

Abstract: The file information write processing method according to the present invention is a file information write processing method wherein a computer executes a process for outputting instruction corresponding to a file information write instruction from an application to a device driver, wherein: searching clusters which are empty areas within an actual data area of a memory unit of the computer, and obtaining the search result; if clusters which are empty areas exist, writing information to overwrite to one or more clusters within the actual data area of the memory unit which is a target of the write instruction from the application, to the clusters which are empty areas; and freeing clusters which were to be overwritten by the information written to the empty area clusters.

Abstract: Methods for multi-class cost-sensitive learning are based on iterative example weighting schemes and solve multi-class cost-sensitive learning problems using a binary classification algorithm. One of the methods works by iteratively applying weighted sampling from an expanded data set, which is obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, using a weighting scheme which gives each labeled example the weight specified as the difference between the average cost on that instance by the averaged hypotheses from the iterations so far and the misclassification cost associated with the label in the labeled example in question. It then calls the component classification algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its (meta) label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively.

Abstract: A general-purpose knowledge finding method for efficient knowledge finding by selectively sampling only data in large information amounts from a database. Learning means 104 causes a lower-order learning algorithm, inputted via an input unit 107, to perform learning on plural partial samples generated by sampling from data stored in a high-speed main memory 120, to obtain plural hypotheses. Data selection means 105 uses the hypotheses to estimate information amounts of respective candidate data read from a large-capacity data storage device 130, and additionally stores only data in large information amounts into the high-speed main memory 120. A control unit 106 repeats the processing a predetermined number of times, and stores obtained final hypotheses. A prediction unit 102 predicts a label value of unknown-labeled data inputted into the input unit 107 by the final hypotheses, and an output unit 101 outputs the predicted value.

Abstract: A metal member includes: a metal base; a first plating layer of nickel and unavoidable impurities, which is formed on the metal base; a second plating layer of nickel, phosphorus and unavoidable impurities, which is formed on the first plating layer; a third plating layer of a gold alloy and unavoidable impurities, which is formed on the second plating layer; and a fourth layer formed on the third plating layer by a sealing process, wherein the first plating layer has a thickness of 0.5 to 2.5 ?m and the second plating layer has a thickness of 0.05 to 0.5 ?m, the sum of the thickness of the first plating layer and the thickness of the second plating layer being in the range of from 0.60 ?m to 2.5 ?m, and the third plating layer having a thickness of not less than 0.05 ?m.

Abstract: Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively.

Abstract: Various embodiments for maintaining security and confidentiality of data and operations within a fraud detection system. Each of these embodiments utilizes a secure architecture in which: (1) access to data is limited to only approved or authorized entities; (2) confidential details in received data can be readily identified and concealed; and (3) confidential details that have become non-confidential can be identified and exposed.

Abstract: To provide a method whereby unauthorized data access by an RFID data processing system is prohibited without any degradation of performance being incurred. An RFID data processing system 30 comprises a CPU 35, a EEPROM 34, communication devices 31 and 32, and power controllers 40 and 41. When an RFID data processing system 30 in the power-ON state that does not have access authorization passes through a portal gate located at the entrance to an unauthorized data access protection area, the portal gate transmits a signal to set ON a tamper bit 44 in the EEPROM 34. When the tamper bit 44 has been set ON, a tamper bit interrupt request signal is output by the EEPROM 34. Upon receiving this signal, the power controllers 40 and 41 power off the RFID data processing system 30.

Abstract: A system and method for sequential decision-making for customer relationship management includes providing customer data including stimulus-response history data, and automatically generating actionable rules based on the customer data. Further, automatically generating actionable rules may include estimating a value function using reinforcement learning.

Abstract: A banner advertisement selecting method is disclosed comprising the steps of (a) estimating the input probability of each attribute and the click rate of each advertisement for each attribute corresponding to an input attribute distribution of the banner advertisement and a click history of which the banner advertisement was clicked, (b) obtaining a display probability of each banner advertisement for each attribute so that the total click rate becomes maximum with conditions such as the desired number of display times of each banner advertisement being satisfied, (c) selecting a banner advertisement according to the display probability, and (d) transforming a restricted objective function maximizing problem obtained at step (b) to a transportation problem and solving the transportation problem.