Certain aspects of the present disclosure are generally directed to monitoring different decoding candidates assuming different encoding schemes. For example, certain aspects of the present disclosure are directed to a method for wireless communication. The method generally includes determining a first encoding scheme used to encode first downlink control information (DCI) and a second encoding scheme used to encode second DCI, and monitoring one or more first decoding candidates for the first DCI based on the first encoding scheme and one or more second decoding candidate for the second DCI based on the second encoding scheme.

A material structure analysis scheme for using machine learning to predict a general structure of an arbitrary material is provided. One aspect of the present disclosure relates to a material structure analysis method, including acquiring, by one or more processors, structural data representing a structure of a material and spectral data representing a spectrum of a material, inputting, by the one or more processors, the structural data to a first neural network to acquire a structural feature from the first neural network, inputting, by the one or more processors, the spectral data to a second neural network to acquire a spectral feature from the second neural network, and determining, by the one or more processors, a degree of coincidence between the material corresponding to the structural data and the material corresponding to the spectral data based on the structural feature and the spectral feature.

A method is performed by a computer for a preprocessing for calculating binding free energy between a first substance and a second substance. The method includes: obtaining a binding structure of the first substance and the second substance under a condition where the second substance is constrained such that a binding state of the second substance to the first substance is maintained in a predetermined state; and then, based on the obtained binding structure, obtaining the binding structure under a condition where the second substance is not constrained.

In one example, a solver engine may execute a reverse calculation to determine a recipe ingredient set based on a goal descriptor describing a ceramic glaze. A descriptor interface of the solver engine may receive a goal descriptor describing a ceramic glaze. A model applicator of the solver engine may apply a glaze process model to the goal descriptor. The model applicator may automatically reverse calculate a glaze recipe describing a recipe ingredient set to produce the ceramic glaze described by the goal descriptor. A glaze mixing machine interface may direct a glaze mixing machine to mix the recipe ingredient set to produce the ceramic glaze.

Presented are new, earth-abundant lithium superionic conductors, Li.sub.3Y(PS.sub.4).sub.2 and Ll.sub.5PS.sub.4CI.sub.2, that emerged from a comprehensive screening of the LiPS and Li-M-PS chemical spaces. Both candidates are derived from the relatively unexplored quaternary silver thiophosphates. One key enabler of this discovery is the development of a first-of-its-kind high-throughput first principles screening approach that can exclude candidates unlikely to satisfy the stringent Li+ conductivity requirements using a minimum of computational resources. Both candidates are predicted to be synthesizable, and are electronically insulating. Systems and methods according to present principles enable new, all-solid-state rechargeable lithium-ion batteries.

The present invention relates to computing system and method for determining a safety index of a product. The method includes: retrieving, by the computing system, a plurality of toxicity values of the product from a toxicity database, wherein the toxicity values correspond to a plurality types of the product respectively; retrieving, by the computing system, a plurality of weight values of the product from a weight database, wherein the weight values correspond to the types of the product respectively; and determining, by the computing system, the safety index of the product according to the toxicity values and the weight values.

A method, system and computer program are provided for determining the porosity of a flexible porous structure when it is subjected to deformation. The method performs the following steps by processing representative data of the flexible porous structure: a) generates a first function (F.sub.s) defining how the flexible porous structure changes shape when it is subjected to deformation; b) generates a second function (F.sub.p) defining how a covered surface of the flexible porous structure changes when it is subjected to changes in shape, wherein the second function (F.sub.p) is directly linked with porosity of the flexible porous structure; c) obtains reference porosity values of a reference region (CU-R) of the flexible porous structure in a reference configuration via the first function (F.sub.s); and d) calculates the porosity of at least one deformed region (CU-D) of the flexible porous structure, from said reference porosity values and from the second function (F.sub.p).

The invention is directed to a method for elucidating the three-dimensional structure of compounds by X-ray diffraction (X-ray SCD) characterized in that the compound is co-analyte crystallized with tetraaryladamantanes according to general formula I Wherein R and R are identical or different residues selected from the group consisting of OR1, SR1, NHR1, NR1R2, F, Cl, Br or I and R1, R2 stand for identical or different, substituted on not substituted aliphatic or aromatic residues having 1 to 25 carbon atoms and the the three-dimensional structure of the compound is obtained by X-ray diffraction (X-ray SCD). ##STR00001##

A method used in making a product, wherein a characteristic of the product is at least in part determined by values of parameters used in making the product, the method including the steps of: (a) applying a machine-based transfer learning process to prior result data, the application of the transfer learning process resulting in the generation of predictive data; (b) selecting one or more parameter values to be used in making the product based on the generated predictive data; (c) making the whole or a part of the product using the selected one or more parameter values.

Protein confidence values are calculated in proteomic analysis. A protein database is searched for proteins matching peptides found from mass spectrometry of a sample producing a set of proteins and a corresponding set of peptides. Peptide confidence values for the set of peptides are determined. Protein confidence values are calculated for the set of proteins based on the peptide confidence values. A protein is selected from the set of proteins with a largest protein confidence value, the largest protein confidence value is saved for the protein, the protein is removed from the set of proteins, and one or more peptides corresponding to the protein are removed from the set of peptides. Protein confidence values are recalculated for the set of proteins based on the peptide confidence values and an effect of removing the one or more peptides from the set of peptides.