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: Entrapping immobilization pellets for purifying breeding water in an aquarium to breed aquatic animals, wherein the entrapping immobilization pellets have a phosphorus content of 0.05 mass % or less.

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: 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: 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: The present invention employs data processing systems to handle debt collection by formulation the collections process as a Markov Decision Process with constrained resources, thus making it possible automatically to generate an optimal collections policy with respect to maximizing long-term expected return throughout the course of a collections process, subject to constraints on the available resources possibly in multiple organizations. This is accomplished by coupling data modeling and resource optimization within the constrained Markov Decision Process formulation and generating optimized rules based on constrained reinforcement learning process comprising applied on the basis of past historical data.

Abstract: In an image forming apparatus, an image forming portion forms an image on a rotator. A storage portion stores change characteristics information relevant to correction parameters corresponding to phase points of the rotator. A designating portion sequentially designates the correction parameters based on the change characteristics information. A correcting portion corrects an image forming position on the rotator based on the correction parameter designated by the designating portion. When it is determined, based on a detecting phase point of the rotator detected by a detecting portion, that the current phase of the rotator corresponds to a gradual phase point at which the correction parameter changes at a rate equal to or lower than a predetermined value, the designation by said designating portion is shifted to the correction parameter corresponding to the gradual phase point.

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: An optical scanning device comprises: an oscillating mirror which has a pair of electrodes and a mirror oscillator; a driving unit which applies a wave-like driving signal to the electrodes so as to oscillate the mirror oscillator correspondingly to the driving signal; a detecting unit which detects an oscillation amplitude of the oscillating mirror; an adjusting unit which changes a value of at least one of a duty ratio, a driving frequency, an amplitude, and a bias voltage of the driving signal as a parameter so as to adjust the oscillation amplitude detected by the detecting unit to a target value; and a storing unit which stores the value of the parameter changed by the adjusting unit as a reference value, wherein when the oscillating mirror is activated, the adjusting unit starts the adjusting of the oscillation amplitude with using the driving signal based on the reference value.

Abstract: The present invention employs data processing systems to handle debt collection by formulation the collections process as a Markov Decision Process with constrained resources, thus making it possible automatically to generate an optimal collections policy with respect to maximizing long-term expected return throughout the course of a collections process, subject to constraints on the available resources possibly in multiple organizations. This is accomplished by coupling data modeling and resource optimization within the constrained Markov Decision Process formulation and generating optimized rules based on constrained reinforcement learning process comprising applied on the basis of past historical data.

Abstract: A method (and system) for causal modeling includes modeling a data set using a reverse Bayesian forest.

Abstract: A light scanning device is provided. The light scanning device includes: an oscillating mirror which oscillates rotationally and reflects a light beam to be scanned over a scanning range, the scanning range including a first scanning range and a second scanning range set across a center of the scanning range; a detection unit including a light receiving face, on which the light beam is incident, to detect the light beam; and first and second stationary mirrors which reflect the light beam reflected by the oscillating mirror to the first scanning range and the second scanning range, respectively, to be incident on the light receiving face, wherein an incident pattern of the light beam reflected by the first stationary mirror incident on the light receiving face is different from an incident pattern of the light beam reflected by the second stationary mirror incident on the light receiving face.

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 system and method for correlating business metrics and business transformations includes correlating metrics between a first level and a second level within a business metric hierarchy, and correlating at least one business transform and at least one metric at the first of the plurality of levels.

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 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: In an image-forming apparatus for forming an image using toner contained in a toner cartridge, genuineness of the toner cartridge with or without a memory device is verified. A counter counts a value concerning image formation. A cartridge detector determines the presence or absence of the toner cartridge. A reader is configured to retrieve data from a memory device if any provided in the toner cartridge. A controller is configured to exercise two types of control. If the cartridge detector determines that the toner cartridge is present, the value counted by the counter is greater than a predetermined value, and the reader has failed retrieval of predetermined data, then a first type of control conformable to a toner cartridge other than a genuine toner cartridge is exercised. Otherwise, a second type of control conformable to the genuine toner cartridge is exercised.

Abstract: The present invention relates to a series of techniques for treating a waste material of an asbestos-containing material, from emergency treatment, dismantling, or transportation of an already constructed asbestos-containing material to a heating and melting treatment of the dismantled asbestos-containing waste material. The preset invention provides a method of preventing the exposure of the fibrous dust of the asbestos involving melting an asbestos-containing waste material, into which lithium silicate is impregnated in a melting furnace for treating an asbestos-containing material into harmless. The lithium silicate impregnated into the asbestos-containing waste material forms a gel within the range of temperatures of 500 to 1,100° C., and the formed gel adheres onto the surfaces of asbestos fibers, thereby preventing the exposure of the fibrous dust of the asbestos. Moreover, the melting temperature of asbestos can be reduced to about 1,360° C.

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: 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.