A method and system learn functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

A federated learning method, an electronic device, and a storage medium, which relate to a field of artificial intelligence, in particular to fields of distributed data processing and deep learning. The method includes: determining, for each task in a current learning period, a set of target devices corresponding to the task according to respective scheduling information of a plurality of candidate devices corresponding to the task based on a scheduling policy, the scheduling policy enables a time cost information and a device fairness evaluation information of completing the task in the current learning period to meet a predetermined condition; transmitting a global model corresponding to each task to a set of target devices corresponding to the task; and updating the corresponding global model based on trained models in response to receiving the trained models from the corresponding set of target devices.

Methods for controlled segregation of blocks of information encoded in the sequence of a biopolymer, such as nucleic acids and polypeptides, with rapid retrieval based on multiply addressing nanostructured data have been developed. In some embodiments, sequence controlled polymer memory objects include data-encoded biopolymers of any length or form encapsulated by natural or synthetic polymers and including one or more address tags. The sequence address labels are used to associate or select memory objects for sequencing read-out, enabling organization and access of distinct memory objects or subsets of memory objects using Boolean logic. In some embodiments, a memory object is a single-stranded nucleic acid scaffold strand encoding bit stream information that is folded into a nucleic acid nanostructure of arbitrary geometry, including one or more sequence address labels. Methods for controlled degradation of biopolymer-encoded blocks of information in the memory objects are also developed.

Using nucleotide architectures to very closely and precisely place chromophores on a nucleic acid template to form dye aggregates that produce quantum coherent excitons, biexcitons, and triexcitons upon excitement to create excitonic quantum wires, switching, and gates that would then form the basis of quantum computation. Creating the various excitons and controlling the timing of the excitons would be performed using light of the corresponding wavelength and polarization to stimulate the corresponding chromophores.

This invention deals generally with DNA-based microprocessors. In an exemplary embodiment of the invention, a DNA lattice or grid with photoreceptors forms a microprocessor and is configured to perform the functions of a series of logic gates. An input signal is supplied to the DNA lattice by shining a light signal on the lattice. The lattice performs the functions of the series of logic gates that are placed on the lattice. The lattice, in turn, supplies an augmented light output signal, which is decoded to reflect the processing by the DNA-based microprocessor.

A method includes obtaining, for each type of nucleotide, a probability density function, the probability density functions being obtained from measurements of current drops produced during at least one passage of at least one sequence of reference nucleotides through a nanopore sequencer; obtaining measurements of current drops produced when the sequence of nucleotides to be decoded passes through the nanopore sequencer; calculating, for each measurement value considered and for each type of nucleotide of the B types of nucleotides, a piece of reliability information based on the probability density function obtained for the type of nucleotide considered; obtaining a decoded value identifying a type of nucleotide from the B types of DNA nucleotides, by applying a soft decoding algorithm with an error correction code to the current drop measurement and to the B pieces of reliability information obtained for the considered measurement value.

The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

Amino acid sequences of antibodies can be generated using a generative adversarial network that includes a first generating component that generates amino acid sequences of antibody light chains and a second generating component that generates amino acid sequences of antibody heavy chains. Amino acid sequences of antibodies call be produced by combining the respective amino acid sequences produced by the first generating component and the second generating component. The training of the first generating component and the second generating component can proceed at different rates. Additionally, the antibody amino acids produced by combining amino acid sequences front the first generating component and the second generating component may be evaluated according to complentarity-determining regions of the antibody amino acid sequences. Training datasets may be produced using amino acid sequences that correspond to antibodies have particular binding affinities with respect to molecules, such as binding affinity with major histocompatibility complex (MHC) molecules.

Amino acid sequences of antibodies can be generated using a generative adversarial network that includes a first generating component that generates amino acid sequences of antibody light chains and a second generating component that generates amino acid sequences of antibody heavy chains. Amino acid sequences of antibodies call be produced by combining the respective amino acid sequences produced by the first generating component and the second generating component. The training of the first generating component and the second generating component can proceed at different rates. Additionally, the antibody amino acids produced by combining amino acid sequences front the first generating component and the second generating component may be evaluated according to complentarity-determining regions of the antibody amino acid sequences. Training datasets may be produced using amino acid sequences that correspond to antibodies have particular binding affinities with respect to molecules, such as binding affinity with major histocompatibility complex (MHC) molecules.