Abstract: A learning device 1X mainly includes a first acquisition means 31X, a second acquisition means 32X, and a learning means 34X. The first acquisition means 31X acquires a partial waveform of electrocardiogram data regarding an electrocardiogram of a subject. The second acquisition means 32X acquires an attention interval, which is used as a basis for a diagnosis of a target disease, in a sequential waveform of the electrocardiogram data. The learning means 34X trains a model configured to diagnose the target disease, based on the partial waveform and the attention interval.

Abstract: This continuous cast wire rod contains Cu: 62.0 mass % or greater and 70.0 mass % or less, Sn: 0.3 mass % or greater and 0.9 mass % or less, Zr: 0.0050 mass % or greater and 0.1000 mass % or less, and P: 0.0050 mass % or greater and 0.1000 mass % or less, with a balance being Zn and impurities, and a mass ratio Zr/P of Zr to P is 0.3 or greater.

Abstract: A medium storing comprising instructions for: dividing a text into words; specifying an unknown word indicating a word other than a static word in a static dictionary among words in the text based on the static dictionary associating the static word with a vector; determining whether a unique word corresponding to the unknown word is included in a definition, using the definition defining a relationship between the unique word and a vector generated based on a condition that similar vectors are set for similar unique words; and when the unique word corresponding to the unknown word is included in the definition, assigning a vector corresponding to the unique word to the unknown word; and when the unique word corresponding to the unknown word is not included in the definition, assigning a vector to the unknown word using words arranged before and after the unknown word.

Abstract: A medium storing a program including: classifying vectors of first sentences in a file into each similar vector; generating an inverted index associating a vector of each first sentence with a position of the first sentence on the file; identifying a feature sentence from a second sentences included in a second sentences; specifying similar vectors being vectors similar to a vector of the feature sentence based on the inverted index; specifying, for each similar vector, first transition data indicating transition of vectors at positions before and after the similar vector based on the inverted index; and specifying, from pieces of first transition data obtained by performing the specifying of the first transition data on the similar vectors, transition data similar to second transition data indicating transition of vectors of sentences before and after the feature sentence, to output the transition data as a response of the search query.

Abstract: A non-transitory computer-readable storage medium storing an information processing program that causes at least one computer to execute a process, the process includes calculating a second index indicating a position of an amino acid on a codon file based on a first index indicating positions of a plurality of codons on the codon file with respect to a plurality of codons having different base sequences indicating the same amino acid; identifying positions of amino acid sequences repeatedly expressed in the codon file based on the second index; and specifying each codon sequence corresponding to a position of each amino acid sequence repeatedly expressed in the codon file as a codon sequence having homology.

Abstract: A non-transitory computer-readable recording medium storing an information processing program for causing a computer to perform processing including: executing preprocessing processing that includes calculating vectors for a plurality of subtexts of text information included in a plurality of pieces of history information in which information on a plurality of question sentences and a plurality of response sentences is recorded; executing training processing that includes training a training model based on training data that defines relationships between the vectors of some subtexts and the vectors of other subtexts among the plurality of subtexts; and executing generation processing that includes calculating, when accepting a new question sentence, the vectors of the subtexts by inputting the vectors of the new question sentence to the training model, and generating a response that corresponds to the new question sentence, based on the calculated vectors.

Abstract: A non-transitory computer-readable recording medium having stores therein an information processing program that causes a computer to execute a process including executing training of a trained model on the basis of training data defining relations between vectors corresponding to genomes and vectors respectively corresponding to pluralities of subgenomes composing the genomes and in a case where a genome to be analyzed has been received, calculating vectors of a plurality of subgenomes corresponding to the genome to be analyzed by inputting the genome to be analyzed into the trained model.

Abstract: An information processing apparatus accepts text data. When specifying a word included in the accepted text data, the information processing apparatus generates a code associated with the specified word and generates information that associates the appearance position of the specified word in the text data with the word. The information processing apparatus stores therein the generated code and the information in association with the accepted text data.

Abstract: A non-transitory computer-readable storage medium storing an information processing program that causes at least one computer to execute a process, the process includes training a model based on training data that defines a relationship between a vector that corresponds to a program and a vector that corresponds to each of subprograms that corresponds to the program; and when receiving a first program to be analyzed, acquiring first vectors of first subprograms that corresponds to the first program by inputting the first program to the training model.

Abstract: A non-transitory computer-readable recording medium has stored therein an information processing program that causes a computer to execute a process including, executing training of a trained model based on training data defining relations between vectors corresponding to target compounds and vectors respectively corresponding to plural subcompounds included in synthetic pathways for manufacture of the target compounds and calculating vectors of plural subcompounds corresponding to a target compound to be analyzed by inputting a vector of the target compound to be analyzed into the trained model in a case where the target compound to be analyzed has been received.

Abstract: A method for outputting a search result includes: executing a reception process that includes receiving a search query for target data; executing a candidate item identification process that includes referring to index information associating each of a plurality of items included in the target data with a position of a corresponding one of the items, and identifying a first storage area configured to store an item corresponding to a keyword included in the search query; and executing an addition process that includes when a description included in the corresponding one of the items includes a reference to a different item, referring to the index information, and adding information on a second storage area configured to store the different item to the reference to the different item.

Abstract: A non-transitory computer-readable recording medium has stored therein an information processing program that causes a computer to execute a process including separating input text into a plurality of words, by performing a morpheme analysis on the input text identifying, from among the plurality of words contained in the input text, a polysemous word and a semantic division of the polysemous word, based on a co-occurrence rate table defining co-occurrence rates between polysemous words distinguished according to semantic divisions thereof and categories of words and based on the plurality of words contained in the input text, and assigning a vector corresponding to the semantic division of the polysemous word, to the polysemous word contained in the input text.

Abstract: A determining method includes acquiring first direction information, when a first captured image captured by an imaging device is acquired, by referring to a storage storing a plurality of pieces of direction information indicating a plurality of directions associating with respective shapes of a plurality of contours of an object according to a plurality of directions of the object, the first direction information associated with a shape of a contour that corresponds to a shape of a contour of the subject included in the acquired first captured image among the shapes of the contours, and acquiring second direction information, when a second captured image newly captured by the imaging device is acquired, by referring to the storage to acquire second direction information associated with a shape of a contour that corresponds to a contour of the subject included in acquired second captured image among the shapes of contours.

Abstract: A wire rod of a Cu—Zn—Si based alloy obtained by up-drawing continuous casting is provided; the amount of Cu is within a range of 75.0 mass% or more and 76.9 mass% or less, the amount of Si is within a range of 2.6 mass% or more and 3.1 mass% or less, the amount of Zr is within a range of 0.003 mass% or more and 0.20 mass% or less, the amount of P is within a range of 0.02 mass% or more and 0.15 mass% or less, the balance is composed of Zn and inevitable impurities, and the number density of a Zr—P compound containing Zr and P is within a range of 1500 pieces/mm2 or more and 7000 pieces/mm2 or less.

Abstract: A computer-readable storage medium storing a program for causing a computer to perform processing including: obtaining pieces of segmented genome data being genome information of a specific individual; generating pieces of segmented codon data obtained by encoding each of the pieces of segmented genome data in a codon unit based on a table in which a codon is associated with a code; identifying, based on reference codon data obtained by encoding reference genome data to be a reference in the codon unit and each of the pieces of segmented codon data, a type and a position of an appearance of gene mutation different from the code in the reference codon data among the codes in the pieces of segmented codon data; and generating a gene mutation inverted index in which the gene mutation and the type and position of the appearance of the gene mutation are associated with each other.

Abstract: A non-transitory computer-readable storage medium storing an information processing program for causing a computer to perform processing including: calculating vectors of a plurality of pieces of space-specific basic information defined in a plurality of spaces by performing Poincare Embeddings on the plurality of pieces of basic information, based on a common concept table that classifies the plurality of pieces of basic information with a common concept and calculate a vector of structural information with a granularity larger than the basic information, based on the vectors of the plurality of pieces of basic information; and generating an inverted index that defines a relationship between a position of the basic information in a file that corresponds to the same space and the vector of the basic information and a relationship between a position of the structural information in the file and the vector of the structural information.

Abstract: A computer-readable storage medium storing a program for causing a computer to perform processing including: dividing a sequence indicating a rational formula of a compound, into a character string of a minimum unit of the sequence and a branch symbol indicating a branched portion of the compound; generating a first coded sequence by using a group dictionary indicating a relationship between the sequence and the compression code, the generating including assigning a compression code to the character string of the minimum unit, and assigning the compression code according to a type of the branched portion to the branch symbol; and generating a second coded sequence by using a primary structure dictionary indicating a relationship between a group primary structure of the sequence and the compression code, the generating of the second coded sequence including encoding the compression code in the first coded sequence in units of the group primary structure.

Abstract: A storage medium storing a program for causing a computer to perform processing including: performing a morphological analysis on a source code to divide the source code into a plurality of reserved words and a plurality of variables; performing, based on a static dictionary defining a relationship between a reserved word and a static code, assigning of the static code corresponding to the reserved word to the reserved word and assigning of a dynamic code to the variable, to thereby generate a compressed code array; registering the variable, the dynamic code assigned to the variable, and an attribute of the variable; calculating a vector of the source code by assigning a predetermined vector to the static code in the array and assigning a vector to the dynamic code in the array by embedding the dynamic code in a vector space based on the attribute corresponding to the dynamic code.

Abstract: An information generation method includes: receiving dictionary data used for morpheme analysis; and generating index information indicating relative positions of characters of each character included in a word registered in the dictionary data, a character at a head of the word, and a character at an end of the word based on the received dictionary data, by a processor.

Abstract: An apparatus generates an index including positions of morphemes included in a target text data and semantic attributes between the morphemes corresponding to the positions. The apparatus gives information including positions of morphemes included in an input query and semantic attributes between the morphemes corresponding to the positions to the query, and executes a retrieval on the target text data, based on the information given to the query and the index.