A super-network comprising a plurality of layers may be generated. Each layer may comprise cells with different structures. A predetermined number of cells from each layer may be selected. A plurality of cells may be generated based on selected cells using a local mutation model, wherein the local mutation model comprises a mutation window for removing redundant edges from each selected cell. Performance of the plurality of cells may be evaluated using a differentiable fitness scoring function. The operations of the generating a plurality of cells using the local mutation model, the evaluating performance of the plurality of cells using the differentiable fitness scoring function and the selecting the subset of cells based on the evaluation results may be iteratively performed until the super-network converges. A search space for each layer may be generated based on a predetermined top number of cells with largest fitness scores after the super-network converges.

Systems and methods include obtaining unused user accounts associated with a cloud application where an unused user account is one where a corresponding user has not accessed the cloud application in a certain period of time; determining a subset of the unused user accounts that are abnormal user accounts, wherein an abnormal user account is one that is anomalous compared to similar users; scoring and ranking the unused and abnormal user accounts; and remediating a set of the ranked unused and abnormal user accounts.

The present invention relates to a dynamic scheduling system and method of a dyeing process using a genetic algorithm, and more particularly, to technology which performs an optimized process corresponding to an ordered work command in a dyeing process by using process scheduling based on a genetic algorithm to increase production efficiency.

Systems and methods for discovering approximations for compilers to apply through genetic programming and deterministic symbolic regression heuristics are provided. A method for discovering approximations for compilers and runtime optimization can include profiling a program to identify performance critical functions, determining appropriate candidates for approximation and developing application and architecture specific approximations through machine learning techniques, genetic programming, and deterministic heuristics. Such approximations can target any optimization goal, with a primary emphasis on parallelism, or can provide a set of Pareto-optimal tradeoffs.

Aspects of the disclosure relate to using synthetic DNA stranding and mutant nucleotide processes to conduct enterprise market volatility predictions. In some embodiments, a computing platform may receive market data from a plurality of lines of business across an enterprise, wherein the market data is received in a raw, uncompressed format. Thereafter, the computing platform may assimilate and preprocess the market data to output vectored market data. The computing platform may perform a synthetic DNA stranding process on the vectored market data to create one or more strands of synthetic DNA market data, and output the one or more strands of synthetic DNA market data to a synthetic DNA client server, wherein the one or more stands of synthetic DNA market data is configured for input in a market volatility prediction model.

Aspects of the disclosure relate to using synthetic DNA stranding and mutant nucleotide processes to conduct enterprise market volatility predictions. In some embodiments, a computing platform may initiate a set of instructions associated with performing an action on a synthetic DNA market data set associated with a plurality of lines of business across an enterprise organization. Thereafter, the computing platform may convert the set of instructions to a mutant nucleotide sequence, and insert the mutant nucleotide sequence into the synthetic DNA market data set. The computing platform may extract, using the mutant nucleotide sequence, target information from the synthetic DNA market data set, and validate the target information to detect one or more anomalies. The computing platform may remove the one or more data anomalies, and subsequently output a validated synthetic DNA market data set to a synthetic DNA client server.

Aspects of the disclosure relate to using synthetic DNA stranding and mutant nucleotide processes to conduct enterprise market volatility predictions. In some embodiments, a computing platform may receive raw market data from a plurality of lines of business of an enterprise organization. Thereafter, the computing platform may preprocess the raw market data to obtain enterprise level market data, execute synthetic DNA stranding of the enterprise level market data to obtain synthetic DNA stranded market data, run the synthetic DNA stranded market data through one or more market volatility models, and compile results from the market volatility models on the synthetic DNA stranded market data. The computing platform may transmit results from the market volatility models on the synthetic DNA stranded market data. The transmitted results may be configured to display a market application interface that includes market volatility forecasting parameters based on results of the market volatility models.

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.

A method of forming a neural network includes specifying layers of neural network neurons. A parameter genome is defined with numerical parameters characterizing connections between neural network neurons in the layers of neural network neurons, where the connections are defined from a neuron in a current layer to neurons in a set of adjacent layers, and where the parameter genome has a unique representation characterized by kilobytes of numerical parameters. Parameter genomes are combined into a connectome characterizing all connections between all neural network neurons in the connectome, where the connectome has in excess of millions of neural network neurons and billions of connections between the neural network neurons.

Methods and systems that provide one or more recommended configurations to planners using large data sets in an efficient manner. These methods and systems provide optimization of objectives using a genetic algorithm that can provide parameter recommendations that optimize one or more objectives in an efficient and timely manner. The methods and systems disclosed herein are flexible enough to satisfy diverse use cases.