Abstract: An autoencoder includes memory configured to store data including an encode network and a decode network, and processing circuitry coupled to the memory. The processing circuitry is configured to cause the encode network to convert inputted data to a plurality of values and output the plurality of values, batch-normalize values indicated by at least two or more layers of the encode network, out of the output plurality of values, the batch-normalized values having a predetermined average value and a predetermined variance value, quantize each of the batch-normalized values, and cause the decode network to decode each of the quantized values.

Abstract: Methods for producing the amorphous metal oxide semiconductor layer where amorphous metal oxide semiconductor layer is formed by use of a precursor composition containing a metal salt, a primary amide, and a water-based solution. The methodology for producing the amorphous metal oxide semiconductor layer includes applying the precursor composition onto a substrate to form a precursor film, and firing the film at a temperature of 150° C. or higher and lower than 300° C.

Abstract: Provided is a composition for a hole collecting layer of an organic photoelectric conversion element, the composition containing a solvent and an electron transporting substance comprising a polythiophene derivative that includes a repeating unit represented by formula (1) or formula (1?). (In formula (1) and formula (1?), R1 denotes an alkyl group having 1-6 carbon atoms or a fluorine atom. In formula (1), M denotes a hydrogen atom, an alkali metal selected from the group consisting of Li, Na and K, NH(R2)3 or HNC5H5. R2 groups are each independently a hydrogen atom or an optionally substituted alkyl group having 1-6 carbon atoms.

Abstract: This hole collection layer composition for an organic photoelectric conversion elements comprises: a charge-transporting substance formed of a polyaniline derivative represented by formula (1); fluorochemical surfactant; metal oxide nanoparticles; and a solvent. The hole collection layer composition provides a thin film having excellent adhesiveness to an active layer of an organic photoelectric conversion element. {R1-R6 are each independently a hydrogen atom, a halogen atom, a nitro group, a cyano group, a sulfonic acid group, a C1-C20 alkoxy group, a C1-C20 thioalkoxy group, a C1-C20 alkyl group, etc. Meanwhile, one of R1-R4 is a sulfonic acid group and at least one of the remaining R1-R4 is a C1-C20 alkoxy group, a C1-C20 thioalkoxy group, a C1-C20 alkyl group, etc., and m and n are numbers that satisfy 0?m?1, 0?n?1, and m+n=1.

Abstract: Methods for producing the amorphous metal oxide semiconductor layer where amorphous metal oxide semiconductor layer is formed by use of a precursor composition containing a metal salt, a primary amide, and a water-based solution. The methodology for producing the amorphous metal oxide semiconductor layer includes applying the precursor composition onto a substrate to form a precursor film, and firing the film at a temperature of 150° C. or higher and lower than 300° C.

Abstract: The present invention provides a composition for forming a polyvinylidene fluoride film, which contains: a polyvinylidene fluoride; at least one surfactant that is selected from among sulfuric acid-based surfactants, sulfonic acid-based surfactants and quaternary ammonium salt type surfactants; and a solvent.

Abstract: Provided is a charge-transporting composition for a perovskite photoelectric conversion element, the composition containing: a charge-transporting substance made of a conductive polymer; an organic silane compound; and a solvent.

Abstract: A characteristic of a semiconductor device having a back electrode including an Au—Sb alloy is improved. The semiconductor device has a semiconductor substrate and the back electrode including the Au—Sb alloy layer. The back electrode is formed on the semiconductor substrate. The Sb concentration in the Au—Sb alloy layer is equal to or greater than 15 wt %, and equal to or less than 37 wt %. The thickness of the Au—Sb alloy layer is equal to or larger than 20 nm, and equal to or less than 45 nm.

Abstract: An alkali metal amide is dissolved in a cyclic alkylene urea represented by the formula (1) (wherein each of R1 and R2 represents a C1 to C3 alkyl group, and R3 represents a C1 to C4 alkylene group).

Abstract: Provided is a production method for a carbon-based light-emitting material that generates light having a wavelength of 500 to 700 nm when exposed to excitation light having a wavelength of 300 to 600 nm. The production method comprises a step for mixing and heating a starting material containing ascorbic acid, an acid catalyst containing an inorganic acid, and a solvent.

Abstract: Provided is a charge-transporting composition that contains: a charge-transporting substance comprising a polythiophene derivative represented by formula (1); a fluorine-based surfactant; metal oxide nanoparticles; and a solvent. (R1 and R2 are each independently a hydrogen atom, an alkoxy group having 1-40 carbon atoms, —O—[Z—O]p—Re, a sulfonic acid group, or the like, or R1 and R2 bond to each other to form —O—Y—O—. Y is an alkylene group having 1-40 carbon atoms, which may contain an ether bond and which may be substituted with a sulfonic acid group. Z is an alkylene group having 1-40 carbon atoms, which may be substituted with a halogen atom. p is 1 or more, and Re is a hydrogen atom, an alkyl group having a 1-40 carbon atoms, or the like.

Abstract: This hole collection layer composition for an organic photoelectric conversion elements comprises: a charge-transporting substance formed of a polyaniline derivative represented by formula (1); fluorochemical surfactant; metal oxide nanoparticles; and a solvent. The hole collection layer composition provides a thin film having excellent adhesiveness to an active layer of an organic photoelectric conversion element. {R1-R6 are each independently a hydrogen atom, a halogen atom, a nitro group, a cyano group, a sulfonic acid group, a C1-C20 alkoxy group, a C1-C20 thioalkoxy group, a C1-C20 alkyl group, etc. Meanwhile, one of R1-R4 is a sulfonic acid group and at least one of the remaining R1-R4 is a C1-C20 alkoxy group, a C1-C20 thioalkoxy group, a C1-C20 alkyl group, etc., and m and n are numbers that satisfy 0?m?1, 0?n?1, and m+n=1.

Abstract: An amorphous metal oxide semiconductor layer is formed by use of a precursor composition containing a metal salt, a primary amide, and a water-based solution. The amorphous metal oxide semiconductor layer produced via a method that includes applying the precursor composition onto a substrate to form a precursor film, and firing the film at a temperature of 150° C. or higher and lower than 300° C.

Abstract: The invention provides a precursor composition for forming an amorphous metal oxide semiconductor layer, containing a metal salt, a primary amide, and a water-based solution. An amorphous metal oxide semiconductor layer is formed by use of the composition.

Abstract: A characteristic of a semiconductor device having a back electrode including an Au—Sb alloy is improved. The semiconductor device has a semiconductor substrate and the back electrode including the Au—Sb alloy layer. The back electrode is formed on the semiconductor substrate. The Sb concentration in the Au—Sb alloy layer is equal to or greater than 15 wt %, and equal to or less than 37 wt %. The thickness of the Au—Sb alloy layer is equal to or larger than 20 nm, and equal to or less than 45 nm.

Abstract: A training apparatus, for training a network, including a first network and a second network, configured to infer a feature of an input graph, includes memory and a processor. The processor is configured to: merge, by the first network, first hidden vectors of first nodes of the input graph and a second hidden vector of a second node coupled to each of the first nodes, based on the first hidden vectors, the second hidden vector, and information on coupling between the first nodes. The processor is further configured to update the first hidden vectors and the second hidden vector, based on a result of the merging; extract, from the second network, the feature of the input graph, based on the updated first hidden vectors and the updated second hidden vector; calculate a loss of the feature of the input graph; and update the first network or the second network.

Abstract: To provide a coating fluid for a gate insulating film, which can be baked at a low temperature of at most 180C; a gate insulating film having excellent solvent resistance and further having good characteristics in e.g. specific resistance or semiconductor mobility; and an organic transistor employing the gate insulating film. A coating fluid for a gate insulating film, which comprises a polyimide obtainable by cyclodehydration of a polyamic acid having repeating units of a specific structure, a gate insulating film employing the coating fluid, and the organic transistor employing the gate insulating film.

Abstract: An autoencoder includes memory configured to store data including an encode network and a decode network, and processing circuitry coupled to the memory. The processing circuitry is configured to cause the encode network to convert inputted data to a plurality of values and output the plurality of values, batch-normalize values indicated by at least two or more layers of the encode network, out of the output plurality of values, the batch-normalized values having a predetermined average value and a predetermined variance value, quantize each of the batch-normalized values, and cause the decode network to decode each of the quantized values.

Abstract: A method for producing a polymer of the present invention includes the following steps (a) and (b): step (a): producing a polymer in the presence of an acid or base catalyst; and step (b): contacting a solution containing the polymer obtained in step (a) to a mixed resin of an anion-exchange resin and a cation-exchange resin.

Abstract: A data prediction apparatus includes a memory and processing circuitry coupled to the memory configured to (1) receive the target data on which to make inference, (2) extract a neighborhood data group that is a set of data points in supervised data that are similar to the target data, (3) generate a local model by performing local and regularization learning using the neighborhood data group, and (4) make inference on the target data by using the local model.