Methods and systems for security, authentication, tagging, and tracking using nucleic acid (e.g., deoxyribonucleic acid) molecules encoding information. Unique nucleic acid molecules are efficiently produced from pre-fabricated fragments to quickly produce libraries of nucleic acid molecules encoding encrypted or randomized information. Physical objects or artifacts can be tagged with libraries to authenticate the objects, grant access to secured assets or locations, or track the objects or entities. Chemical methods can be applied to verify authenticity, decrypt, or decode information stored in the libraries.

Embodiments relate to a neural processor that includes one or more neural engine circuits and planar engine circuits. The neural engine circuits can perform convolution operations of input data with one or more kernels to generate outputs. The planar engine circuit is coupled to the plurality of neural engine circuits. A planar engine circuit can be configured to multiple modes. In a reduction mode, the planar engine circuit may process values arranged in one or more dimensions of input to generate a reduced value. The reduced values across multiple input data may be accumulated. The planar engine circuit may program a filter circuit as a reduction tree to gradually reduce the data into a reduced value. The reduction operation reduces the size of one or more dimensions of a tensor.

Methods, apparatuses, and systems for in-or near-memory processing are described. Bits of a first number may be stored on a number of memory elements, wherein each memory element of the number of memory elements intersects a bit line and a word line of a number of word lines. A number of signals corresponding to bits of a second number may be driven on the number of word lines to generate a number of output signals. A value equal to a product of the first number and the second number may be generated based on the number of output signals.

A system and method of 3-D image segmentation of brain images includes obtaining a 3-D MRI image, an employee phase including performing search cycles of generating solutions in a neighborhood, taking into account (a) movement of a bee's current location toward a mean value of a positive direction of a global best location and a positive direction of its own best location, (b) movement of the bee's current location toward the mean value of the positive direction of its own best location and a negative direction of the global best location, and (c) a random number, calculating a fitness value for the solutions based on membership values of pixels and distances between the pixels to cluster centers of pixels until search ends. Image segmentation of the image is performed using centers of clusters.

Aspects of the disclosure relate to fragmenting payment transactions. Based a virtual assistant establishing an active session with a voice activated device, a computing platform may receive, from the virtual assistant, a request initiated at the voice activated device for a payment transaction. The computing platform may retrieve user identifying information associated with fragmenting payment transactions for determining whether to fragment the payment transaction between a plurality of user accounts. In determining to fragment the payment transaction, the computing platform may generate allocation information indicating an amount of funds to be transferred from each of the plurality of user accounts. The computing platform may cause the voice activated device to prompt the user to confirm the allocation information. The computing platform may transfer funds to a recipient account in accordance with the allocation information and send a notification indicating completed processing of the fragmented payment transaction.

The present disclosure discloses a workflow scheduling method and system based on a multi-target particle swarm algorithm, and a storage medium. The method comprises the following steps that first, the difference between the frequency reduction characteristic and the execution time of each server in a cluster is considered; a multi-target comprehensive evaluation model covering workflow execution overhead, execution time and cluster load balance is constructed on the basis of a traditional model; second, a multi-target particle swarm algorithm is provided for workflow scheduling, and an efficient solving method is provided. The method alleviates the defects of premature convergence and low species diversity of the particle swarm algorithm, reduces the execution overhead and execution time of the workflow on the cluster server, and better balances the load of the cluster server.

A system, method and computer program product, which given in input a question in natural language format, delivers personalized insights related to the answer. Personalized insights are selected among candidate insights mined from the data and ranked based on closeness to (mined) user-preference, relevance to the question, and surprise factor. Two core components include: Question analysis and meaningful insight look up and Multi-dimensional insight ranking. The Question analysis and meaningful insights lookup module performs a semantic analysis of the questions and, uses techniques including “templates” to build new questions which could uncover insights from the data. The Multi-dimensional insight ranking module takes in input a list of insights returned from Question analysis and meaningful insights lookup and rank such insights based on such factors as: relevance to the query, surprise factor, and user preferences.

A system for feature relevance visualization optimization is provided. The system comprises a controller configured for modifying placement of features in a relevance visualization. The controller is further configured to: receive interaction data comprising one or more features positioned in the relevance visualization, wherein the one or more features are defined and measurable properties of the interaction data; construct a logical grouping of the one or more features based on a type of each of the one or more features, wherein similar features are collocated in the relevance visualization; construct a machine learning-based grouping of the one or more features based on relevance calculations for the one or more features; combine the logical grouping and the machine learning-based grouping to generate a combined feature placement, wherein the one or more features are repositioned in the relevance visualization; and output the relevance visualization having the combined feature placement.

A system for feature relevance visualization optimization is provided. The system comprises a controller configured for modifying placement of features in a relevance visualization. The controller is further configured to: receive interaction data comprising one or more features positioned in the relevance visualization, wherein the one or more features are defined and measurable properties of the interaction data; construct a logical grouping of the one or more features based on a type of each of the one or more features, wherein similar features are collocated in the relevance visualization; construct a machine learning-based grouping of the one or more features based on relevance calculations for the one or more features; combine the logical grouping and the machine learning-based grouping to generate a combined feature placement, wherein the one or more features are repositioned in the relevance visualization; and output the relevance visualization having the combined feature placement.

Provided herein are novel Class 2 Type II and Type V CRISPR-Cas RNA-guided endonucleases, e.g. Cas9 and Cas12 endonucleases, and systems comprising the same. Provided also are methods of making, and methods of use thereof. Exemplary methods of use include modifying target DNAs and detecting targeting DNAs, useful for therapeutic and diagnostic applications. Some of the diagnostic applications may utilise the collateral nuclease activity of an enzyme bound to a target sequence.