A device for determining the amount or concentration of an analyte in a fluid sample and a flow rate of the fluid sample in a channel is provided. The device includes a chamber including a channel and an opening the channel in fluid communication with the opening. The device further includes a wicking component positioned adjacent to the opening configured to receive an amount of fluid from the channel. The device may further include an analyte sensor positioned on the wicking component, the analyte sensor configured to detect an analyte in fluid in contact with the analyte sensor, wherein the wicking component is configured to contact the amount of fluid with the analyte sensor. Alternatively the device may include at least one pair of electrodes configured to determine a flow rate of the fluid in the channel.

A method and apparatus for training a semantic segmentation model, a computer device, and a storage medium are described herein. The method includes: constructing a training sample set; inputting the training sample set into a deep network model for training; inputting the training sample set into a weight transfer function for training to obtain a bounding box prediction mask parameter; and constructing a semantic segmentation model.

The present invention relates to a neuromimetic network comprising a set of neurons and a set of synapses,

at least one neuron comprising a first stack of superimposed layers, the first stack successive1y comprising:

a first electrode,

a first barrier layer made of an electrically insulating material, and

a second electrode,

the first electrode, the first barrier layer and the second electrode forming a first ferroelectric tunnel junction,

at least one synapse comprising a second stack of superimposed layers, the second stack successive1y comprising:

a third electrode,

a second barrier layer made of an electrically insulating material, and

a fourth electrode,

the third electrode, the second barrier layer and the fourth electrode forming a second ferroelectric tunnel junction.

Systems and methods are described for the coaching of users through successful calibration of a myoelectric prosthetic controller. The systems and methods are comprised of, and/or utilize, hardware and software components to input and analyze electromyography (EMG) based signals in association with movements, and to calibrate and output feedback about the signals. The hardware is further comprised of an apparatus for the detection of EMG signals, a prosthesis, an indicator, and a user interface. The software is further comprised of a user interface, a pattern recognition component, a calibration procedure, and a feedback mechanism. The systems and methods facilitate calibration of a myoelectric controller and provides the user with feedback about the calibration including information of the signal inputs and outputs, and messages about connected hardware and how to optimize signal data.

A superconducting neuromorphic pipelined processor core can be used to build neural networks in hardware by providing the functionality of somas, axons, dendrites and synaptic connections. Each instance of the superconducting neuromorphic pipelined processor core can implement a programmable and scalable model of one or more biological neurons in superconducting hardware that is more efficient and biologically suggestive than existing designs. This core can be used to build a wide variety of large-scale neural networks in hardware. The biologically suggestive operation of the neuron core provides additional capabilities to the network that are difficult to implement in software-based neural networks and would be impractical using room-temperature semiconductor electronics. The superconductive electronics that make up the core enable it to perform more operations per second per watt than is possible in comparable state-of-the-art semiconductor-based designs.

A method for tuning the conductance of a molecular network includes a network of covalently bound molecular units, which are molecular entities assembled so as to form a network that can typically be compared to a finite, imperfect 2D crystal. Each of the molecular entities includes: a branching junction; M branches (M3) branching from said branching junction, where each of the M branches comprises an aliphatic group; and M linkers, each terminating a respective one of the M branches. Each of the M linkers is covalently bound to a linker of another molecular entity of the network. The method involves tuning the electrical conductance of molecular entities of a subset of the molecular entities of the network, in one or several (e.g., parallel or successive) steps.

Various embodiments include methods and devices for transforming a data block into weights for a neural network. Some embodiments may include training a first neural network of a cybernetic engram to reproduce the data block, and replacing the data block in memory with weights used by the first neural network to reproduce the data block.

An interface structure for a biological environment including at least one composite electrical impulse generating layer comprising a matrix phase of a piezo polymer material, a first dispersed phase of piezo nanocrystals, and second dispersed phase of carbon nanotubes, the first and second dispersed phase presented through the matrix phase. The piezo polymer material and piezo nanocrystal convert mechanical motion into electrical impulses and accept electrons to charge the composite impulse generating layer. The carbon nanotubes provide pathways for distribution of the electrical impulses to a surface of the composite impulse generating layer contacting the biological environment. The carbon nanotubes further provide for the delivery of the byproducts of the free radical degradation from the biological environment to both piezo-nanocrystals and piezo-polymer.

Neural networks can be implemented with DNA strand displacement (DSD) circuits. The neural networks are designed and trained in silico taking into account the behavior of DSD circuits. Oligonucleotides comprising DSD circuits are synthesized and combined to form a neural network. In an implementation, the neural network may be a binary neural network in which the output from each neuron is a binary value and the weight of each neuron either maintains the incoming binary value or flips the binary value. Inputs to the neural network are one more oligonucleotides such as synthetic oligonucleotides containing digital data or natural oligonucleotides such as mRNA. Outputs from the neural networks may be oligonucleotides that are read by directly sequencing or oligonucleotides that generate signals such as by release of fluorescent reporters.

A generative adversarial network device and a training method thereof. The generative adversarial network device includes a generator and a discriminator. The generator is configured to generate a first sample according to an input data; the discriminator is coupled to the generator, and is configured to receive the first sample and be trained based on the first sample; the generator includes a first memristor array serving as a first weight array. The generative adversarial network device can omit a process of adding noise to fake samples generated by the generator, thereby saving training time, reducing resource consumption and improving training speed of the generative adversarial network.