Abstract: In a noise removing apparatus, a data acquisition unit acquires sets of odor data measured using a sensor with respect to a plurality of objects, each set of odor data representing features of an odor of an object by respective rates of a plurality of odor molecules. A noise component extraction unit extract a noise component using a set of odor data. A noise removing unit removes the noise component from each set of odor data to be processed.

Abstract: An information processing device is configured to include an acquisition unit, a determination unit, an instruction unit, an instruction unit, and output unit. The acquisition unit is configured to acquire measurement target and measurement environment information, and measurement environment information that a measurer can measure with an odor sensor. The determination unit is configured to determine a measurement target that the measurer should be instructed to measure, based on the measurement target and measurement environment information and the measurement environment information that can be measured, the instruction is configured to instruct the measurer to measure the determined measurement target. The output unit configured to output a reward to the measurer after the acquisition means acquires odor data of the determined measurement target.

Abstract: In a data generation apparatus, an acquisition unit acquires original data which are odor data measured in a specific environment. A generation unit performs a linear transformation with respect to the original data, and generates augmented data which are odor data in an environment where temperature and humidity are different from those in the specific environment.

Abstract: In a noise removing apparatus, a data acquisition unit acquires sets of odor data measured using a sensor with respect to a plurality of objects, each set of odor data representing features of an odor of an object by respective rates of a plurality of odor molecules. A noise component extraction unit extract a noise component using a set of odor data. A noise removing unit removes the noise component from each set of odor data to be processed.

Abstract: A training data generation device includes a label candidate generation unit, a reception unit, and a training data generation uni. The acquisition unit is configured to acquire smell data and information pertaining to the smell data. The label candidate generation unit which generates label candidates on the basis of the information pertaining to the smell data; an output unit which outputs the generated label candidates. The reception unit is configured to receive selection of a label from the output label candidates. The training data generation unit which generates training data from the selected label and the smell data.

Abstract: A learning apparatus acquires learning data in which odor data of each object and a label representing the object in a label space expressing features of odors are associated with each other, and learns, based on odor data, a model for predicting a label of the odor data in the label space, by using the learning data. In a data processing apparatus for processing odor data, an acquisition unit acquires odor data from an outside. A prediction unit predicts a label of the acquired odor data in the label space by using the model in which a relationship between sets of odor data and labels in the label space expressing the features of the odors is learned.

Abstract: To mitigate degradation in the accuracy of a prediction model by re-learning the prediction model with consideration given to the characteristics of a detection value of a sensor. This prediction model re-learning device comprises: a calculation unit that, on the basis of data related to smell detection by a sensor, calculates an index for determining whether or not to re-learn a prediction model for smell; and a re-learning unit that re-learns the prediction model in cases where the calculated index satisfies a predetermined condition.

Abstract: A selection unit (11) in a learning device (10) inputs a plurality of “learning candidate data units.” The plurality of learning candidate data units are respectively related to a plurality of subjects including a plurality of cancer patients and a plurality of non-cancer patients. Further, each learning candidate data unit at least includes a “urine odor data unit” and a “cancer label.” Then, from the plurality of input learning candidate data units, the selection unit (11) selects part of the plurality of learning candidate data units as a “learning target data set,” based on a “selection rule.” By using the learning target data set selected by the selection unit (11), a determination model formation unit (12) forms a “determination model” for determining which of urine of a cancer patient and urine of a non-cancer patient a determination target urine odor data unit is related to.

Abstract: An information processing apparatus according to the present invention divides a period in which performance data at a business facility as a prediction target is present into a plurality of partial periods. The information processing apparatus performs prediction processing using each of a plurality of prediction models for a second partial period which is a partial period other than a first partial period including a start time of a predetermined period, and compares the result of the process with the performance data in a partial period as a target of the prediction processing. The information processing apparatus decides a prediction model to be used for sales prediction for a period subsequent to the predetermined period on the basis of the result of the comparison.

Abstract: Provided an area-specific environment management system, which manages an environment in an individual one of a plurality of areas, the system including: a biological information sensor that acquires biological information about at least one of an individual and a group who belongs to an individual one of the areas; an environmental information sensor that acquires environmental information about an individual one of the areas; an arousal level estimation section that estimates an arousal level with respect to the biological information by using an arousal level estimation model; and an environment provision section that provides an environment to an individual one of the areas based on the arousal level and the environmental information, wherein the arousal level includes, as a state to which the arousal level belongs, a state from sleepy until awakened; a wakeful state; and an excessively wakeful state, and wherein the environment provision section includes an individual control mode corresponding to a stat

Abstract: Matrix element calculation carried out efficiently without the overhead of communication between a host computer and processor elements even in parallel calculation utilizing a low-cost communication device and multiple processor elements having memories of a small capacity. In a method for calculating molecular orbitals, for example, all elements F(I, J) of a Fock matrix are calculated where an outermost loop is a loop associated with combinations (RT) of contracted shell R and contracted shell T which satisfy relationships R≦Nshell and T≦R. A second loop is a loop associated with contracted shell S, and a third loop is a loop associated with contracted shell U. Alternatively, the second loop is a loop associated with the contracted shell U, and the third loop is a loop associated with the contracted shell S. The value of S ranges from 1 to R, and the value of U ranges from 1 to R.

Abstract: There is provided a parallel computer and a parallel computing method which allows high precision parallel calculation to be executed without requiring a hardware scale while maintaining high calculation speed. A system is constructed by connecting a host processor with a plurality of special purpose processors via buses. The host processor carries out the operation in a format of double-precision floating-point and the special purpose processor carries out the operation in an internal format of floating-point. The special purpose processor comprises an input data converting section for converting from the double-precision to the internal format and an output data converting section for converting from the internal format to the double-precision. Because the sign part and the exponent part can use data in common in the data before and after the conversion, only the mantissa part is converted by a specific procedure.

Abstract: To obtain the sufficiently precise result of floating-point accumulation even if the quantity of computation is enormous, a floating-point accumulator according to the present invention is constituted as follows:When two floating-point data are stored in any of shift registers, the two data are respectively output to BUS0 and BUS1 via one connected to the shift register of buffers. The two output data are input to an adder via BUS0 and BUS1 and output as added result data after adding the floating-point numbers. The above added result data is returned to each input of the shift registers via BUSW and a multiplexer and written into the shift register corresponding to the addition of the higher level by one of the shift register holding floating-point data before addition. The floating-point numbers are accumulated by repeating the above operation.

Abstract: Electron repulsion integrals are classified according to atomic nucleus coordinates, etc., coefficients are generated and are stored in a data memory, multiplication with addition operation is executed according to a product sum procedure of auxiliary integrals of recursive order 1 or less, and the result is stored in the data memory. Next, density matrix element is stored in the data memory, a multiplication with addition operation procedure of an electron repulsion integral of recursive order 2 not containing any procedure of recursive order 1 or less is generated, and an instruction memory is updated. Multiplication with addition operation is executed while data is read from the data memory, and the result is stored in the data memory. At the termination of the product sum procedure, calculation of electron repulsion integral gRstu is complete and the Fock matrix element value is updated.