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 molecular communication system includes a plurality of molecular transmission nanomachines randomly located in a first space, a molecular reception nanomachine and a molecular transmission channel. The molecular reception nanomachine is located in the first space, and receives at least one information molecule representing first data from an l-th molecular transmission nanomachine to obtain the first data based on the at least one information molecule. The l-th molecular transmission nanomachine is l-th nearest to the molecular reception nanomachine. The molecular transmission channel provides a transmission path for the at least one information molecule moving in the first space based on an anomalous diffusion process. The plurality of molecular transmission nanomachines are scattered in the first space according to a stationary Cox process. A process of transmitting the at least one information molecule from the l-th molecular transmission nanomachine to the molecular reception nanomachine is modeled based on a stochastic nanonetwork.

Aspects of the present disclosure are directed to biosensing circuits that correct crosstalk.

A coupled bio-oscillating material is disclosed. The coupled bio-oscillating material comprises at least two cardiac muscle (CM) cell clusters and at least one cardiac fibroblast (CF) cell bridge on a substrate. The at least one CF cell bridge provides electrical conduction between the at least two CM cell clusters. The at least two CM cell clusters oscillate and synchronize at a unique phase ordering between the at least two CM cell clusters. The coupled bio-oscillating material can be used. The coupled bio-oscillating material can be used to create coupled bio-oscillator networks. A method of creating a coupled bio-oscillator network. The coupled bio-oscillator networks can be used for collective computing. A re-programmable bio-oscillatory network is also disclosed. The re-programmable bio-oscillatory network comprises a patterning layer, an enzyme channeling layer, and a pneumatic controlling layer.

A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit that may be connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits may be arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the bioinformatics analysis on the reads of genomic data. Each subset of the hardwired digital logic circuits may be formed in a wired configuration to perform the one or more steps in the bioinformatics analysis.

The method solves several problems to assist the investigation of human activities, by creating a human activities set of objects, by using an advanced mathematic computation algorithm and a time-based n-dimensional space-curves formula F.sub.i algorithm, by using matrices calculus and tensors calculus, by incorporating artificial intelligence analysis in combination with logic and contextual analysis to create a time-based human activities universal processor. The method extracts time-based escalating risk and priority concepts, anomalous understanding and time-based ranking information, generating action to take, identifying present and predicting future object position, motion and behavior. When, this method is loaded as an application on an automotive and a machine, the method will be at home replacing a human.

Software and lasers are used in finishing apparel to produce a desired wear pattern or other design. A technique includes using machine learning to create or extract a laser input file for wear pattern from an existing garment. Machine learning can be by a generative adversarial network, having generative and discriminative neural nets. The generative adversarial network is trained and then used to create a model. This model is used generate the laser input file from an image of the existing garment with the finishing pattern. With this laser input file, a laser can re-create the wear pattern from the existing garment onto a new garment.

New and improved methods and systems for modeling the performance of selected company metrics. Multiple, non-traditional sets of objective data along with mathematical analytical techniques are used to provide transparency and visibility into company performance relating to the particular metrics. Company inflection points and changes in strategy may be identified. The performance of a company and/or the performance of a selected industry or industry sector may be analyzed.

Embodiments include methods performed by a processor of a vehicle for allocating processing resources to concurrently-executing neural networks. The methods may include determining a priority of each of a plurality of neural networks executing on a vehicle processing system based on a contribution of each neural network to overall vehicle safety performance, and allocating computing resources to the plurality of neural networks based on the determined priority of each neural network. In some embodiments, the methods may dynamically adjust hyperparameters of one or more neural networks.

A treatment model that is a first neural network is trained to optimize a treatment loss function based on a treatment variable t using a plurality of observation vectors by regressing t on x.sup.(1),z. The trained treatment model is executed to compute an estimated treatment variable value {circumflex over (t)}.sub.i for each observation vector. An outcome model that is a second neural network is trained to optimize an outcome loss function by regressing y on x.sup.(2) and an estimated treatment variable t. The trained outcome model is executed to compute an estimated first unknown function value {circumflex over (α)}(x.sub.i.sup.(2)) and an estimated second unknown function value {circumflex over (β)}(x.sub.i.sup.(2)) for each observation vector. An influence function value is computed for a parameter of interest using {circumflex over (α)}(x.sub.i.sup.(2)) and {circumflex over (β)}(x.sub.i.sup.(2)). A value is computed for the predefined parameter of interest using the computed influence function value.