50results about "Molecular computers" patented technology

A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. Neuronal network and methods for operating neuronal networks comprise a plurality of units, where each unit has a memory and a plurality of doublets, each doublet being connected to a pair of the plurality of units. Execution of unit update rules for the plurality of units is order-independent and execution of doublet event rules for the plurality of doublets is order-independent.

Methods and systems are disclosed herein in which a physical neural network can be configured utilizing nanotechnology. Such a physical neural network can comprise a plurality of molecular conductors (e.g., nanoconductors) which form neural connections between pre-synaptic and post-synaptic components of the physical neural network. Additionally, a learning mechanism can be applied for implementing Hebbian learning via the physical neural network. Such a learning mechanism can utilize a voltage gradient or voltage gradient dependencies to implement Hebbian and / or anti-Hebbian plasticity within the physical neural network. The learning mechanism can also utilize pre-synaptic and post-synaptic frequencies to provide Hebbian and / or anti-Hebbian learning within the physical neural network.

A physical neural network is disclosed, which comprises a liquid state machine. The physical neural network is configured from molecular connections located within a dielectric solvent between pre-synaptic and post-synaptic electrodes thereof, such that the molecular connections are strengthened or weakened according to an application of an electric field or a frequency thereof to provide physical neural network connections thereof. A supervised learning mechanism is associated with the liquid state machine, whereby connections strengths of the molecular connections are determined by pre-synaptic and post-synaptic activity respectively associated with the pre-synaptic and post-synaptic electrodes, wherein the liquid state machine comprises a dynamic fading memory mechanism.

A universal logic gate apparatus is disclosed, which include a plurality of self-assembling chains of nanoparticles having a plurality of resistive connections, wherein the plurality of self-assembling chains of nanoparticles comprise resistive elements. A plasticity mechanism is also provided, which is based on a plasticity rule for creating stable connections from the plurality of self-assembling chains of nanoparticles for use with the universal, reconfigurable logic gate. In addition, the universal logic gate can be configured with a cross-bar architecture, where nanoconnections are formed from a columbic-educed mechanical stress contact.

A physical neural network synapse chip and a method for forming such a synapse chip. The synapse chip can be configured to include an input layer comprising a plurality of input electrodes and an output layer comprising a plurality of output electrodes, such that the output electrodes are located perpendicular to the input electrodes. A gap is generally formed between the input layer and the output layer. A solution can then be provided which is prepared from a plurality of nanoconductors and a dielectric solvent. The solution is located within the gap, such that an electric field is applied across the gap from the input layer to the output layer to form nanoconnections of a physical neural network implemented by the synapse chip. Such a gap can thus be configured as an electrode gap. The input electrodes can be configured as an array of input electrodes, while the output electrodes can be configured as an array of output electrodes.

A physical neural network synapse chip and a method for forming such a synapse chip. The synapse chip can be configured to include an input layer comprising a plurality of input electrodes and an output layer comprising a plurality of output electrodes, such that the output electrodes are located perpendicular to the input electrodes. A gap is generally formed between the input layer and the output layer. A solution can then be provided which is prepared from a plurality of nanoconductors and a dielectric solvent. The solution is located within the gap, such that an electric field is applied across the gap from the input layer to the output layer to form nanoconnections of a physical neural network implemented by the synapse chip. Such a gap can thus be configured as an electrode gap. The input electrodes can be configured as an array of input electrodes, while the output electrodes can be configured as an array of output electrodes.

The present invention is of a molecular entity comprising a polypeptide core and nucleic acid sequences attached thereto which is capable of forming a universal logic gate such as the NAND gate. Specifically, the present invention can be used to device a molecular computing unit which can be used to detect biological markers and administer therapeutic moieties.

A universal logic gate apparatus is disclosed, which include a plurality of self-assembling chains of nanoparticles having a plurality of resistive connections, wherein the plurality of self-assembling chains of nanoparticles comprise resistive elements. A plasticity mechanism is also provided, which is based on a plasticity rule for creating stable connections from the plurality of self-assembling chains of nanoparticles for use with the universal, reconfigurable logic gate. In addition, the universal logic gate can be configured with a cross-bar architecture, where nanoconnections are formed from a columbic-educed mechanical stress contact.

A carbon nanotube of a nanotube device has at least two segments with different characteristics. The segments meet at a junction and a diameter of the carbon nanotube on either side of the junction is about the same. One segment may be doped differently from another segment. One segment may be p doped and another segment n doped. One segment may be doped with a different carrier concentration from another segment. The nanotube device may be used in power semiconductor devices including power diodes and power transistors. These power devices will be very power efficient, wasting significantly less energy than similar manufactured using silicon technology.

The invention provides a training method of a molecular generation model. The method comprises the steps of obtaining basic molecules and target molecules; encoding the basic molecule through an encoding layer to obtain a first graph node feature and a first tree node feature, and encoding the target molecule to obtain a second graph node feature and a second tree node feature; matching the firstgraph node feature with the second graph node feature through the alignment layer to obtain a first similarity feature, and matching the first tree node feature with the second tree node feature to obtain a second similarity feature; generating a graph node feature through a generation layer according to the first similarity feature and the first graph node feature, and generating a tree node feature according to the second similarity feature and the first tree node feature; respectively decoding the graph node characteristics and the tree node characteristics through a decoding layer to obtain prediction molecules; updating model parameters based on the difference between the prediction molecule and the target molecule; and through the obtained model, generating high-attribute molecules with partial structures of basic molecules reserved.

A simple format is disclosed and referred to as Elementary Network Description (END). The format can fully describe a large-scale neuronal model and embodiments of software or hardware engines to simulate such a model efficiently. The architecture of such neuromorphic engines is optimal for high-performance parallel processing of spiking networks with spike-timing dependent plasticity. Neuronal network and methods for operating neuronal networks comprise a plurality of units, where each unit has a memory and a plurality of doublets, each doublet being connected to a pair of the plurality of units. Execution of unit update rules for the plurality of units is order-independent and execution of doublet event rules for the plurality of doublets is order-independent.

A method of implementation of a P-system in membrane computing comprising: placing three mutually immiscible liquids into a container, such that a first of said liquids provides a barrier between the second and third liquids; providing an initial substrate to at least one of said second and third liquids; and applying a transport rule for said first liquid to allow transport of substrate between said second and said third liquid.

An electrical component and material with fractional order impedance, as well as electrical circuits for use in fractional order calculus for automated signal processing are provided. Fractional order methods can be particularly important in solving nonlinear problems, such as performing automatic control, pattern recognition, system characterization, signal processing, and modeling.

A method, system and computer-readable storage medium for performing a counterfactual generation operation. The counterfactual generation operation includes: receiving a subject data point; classifying the data point via a trained classifier, the classifying providing a classified data point; identifying a counterfactual using the classified data point, the counterfactual comprising another datapoint, the another data point being close to the subject data point, the another data point resulting in production of a different outcome when provided to a model when compared to an outcome resulting from the subject data point being provided to the model; and, providing the counterfactual to a destination.

The invention belongs to the technical field of molecular logic gates in the industry of analytical chemistry, and relates to the construction of a label-free, high-sensitivity and high-selectivity miRNA intelligent detection method. A molecular logic gate is constructed based on silver nano-clusters and graphene oxide. According to the invention, the relative fluorescence intensity of silver nano-clusters is used as a judgment basis, when the relative fluorescence intensity is larger than 0.5, 1 is outputted. When the relative fluorescence intensity is less than 0, 0 is outputted. The molecular logic gate comprises an OR logic gate and an INHIBIT logic gate, and can be used for detecting the presence of two different miRNAs (miR-21 and miR-141) in an actual sample respectively. By adopting the logic detection method, the contents of a plurality of target analytes in a complex biological sample can be identified. Therefore, the invention has a potential application value in the aspectsof early diagnosis, multi-element detection, clinical treatment and the like for major diseases.

The invention discloses a computer aided design system for predicting energetic molecules based on machine learning performance, and belongs to the technical field of computer aided design systems. The system comprises: a molecule rapid generation module, which is used for generating all possible molecular structural formulas according to permutation and combination of a molecular mother ring anda substituent group input by a user, and removing a repeated structure; a molecular data set module used for recording reported energetic molecular performance data, and the energetic molecular performance data being mainly used as a training set of the machine learning model training module; a molecular descriptor generation module used for calculating molecular descriptors of molecules generatedby the molecular rapid generation module or molecules in the molecular data set module or molecules input by a user; a machine learning model training module used for training and storing a model byadopting a machine learning algorithm according to the data of the molecular descriptor set; and a performance prediction module used for reading the model and carrying out performance prediction on the molecules generated by the molecule rapid generation module or the molecules input by the user.

A method of machining a cellular core (14) includes mounting the core (14) atop a table (12) in a multi-axis Computerized Numerical Controlled (CNC) machine (10). The machine (10) is operated to self-scan the core (14) and self-recognize individual cells (30) arranged laterally in columns and longitudinally in rows. A machining path (E) is self-generated from the pre-recognized cells (30), and the core (14) is then machined along the self-generated machining path (E).

The present invention relates to computer science, molecular biology and microfluidics technique which provides a DNA molecular computer based on microfluidic chip. The objective is to provide a DNA molecular computer which uses the microfluidic chip as a operation platform, mainly including: using DNA molecules as operation media, using the microfluidic chip as the operation platform of a DNA molecular operator; using DNA molecules as storage media, using the microfluidic chip as the operation platform of a DNA molecular storage; using a electronic computer and a detector as the kernel of a controller; said microfluidic chip includes a DNA molecular computation region and a DNA molecular storage region. The microfluidic chip consists of digestion, ligation, PCR amplification and chip electrophoresis unit connected by microchannels in turn, and carries out the liquid control through micropumps and microvalves.

A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing an impartiality assessment operation via an impartiality assessment engine, the impartiality assessment operation detecting a presence of bias in an outcome of the cognitive computing function; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

The invention provides a high-voltage circuit-breaker mechanical fault diagnosis method based on atomic sparse decomposition. The method comprises the following steps of using a high-voltage circuit-breaker test to simulate mechanical faults of different types; collecting vibration signals, and carrying out atomic sparse decomposition on the vibration signals to obtain an attenuation modal parameter; carrying out data preprocessing on the attenuation modal parameter to obtain characteristic vectors under different mechanical fault types; dividing the characteristic vectors into a training sample and a test sample; taking the training sample as input, and the mechanical fault types as output, establishing a high-voltage circuit-breaker RBF network mechanical fault diagnosis model; trainingthe fault diagnosis model to acquire the trained high-voltage circuit-breaker RBF network mechanical fault diagnosis model based on the atomic sparse decomposition; and inputting the test sample intothe trained fault diagnosis model to judge the mechanical faults, and outputting a diagnosis result. The method has characteristics of high reliability, good accuracy and the like and can be widely applied to the fault diagnosis field.

The invention provides a prediction method of nanoparticle migration and an influence factor analysis method and system thereof, and the method comprises the steps: extracting parameters and result data from a nanoparticle migration experiment in a porous medium, and obtaining training features and target features; preprocessing the data by using a one-hot coding method and a random forest method,and filling missing values while coding category type features; and carrying out data balance by using an SMOTE technology, establishing and training a model in combination with a support classification characteristic gradient elevator, and carrying out regression or classification prediction on indexes representing nanoparticle migration; finally, analyzing the direction and size of the influence of different characteristics on the migration of the nano particles through a salpril accumulation interpretation method. Generalization of prediction is improved while the cost of a nanoparticle migration experiment is saved; the unbalanced data is subjected to data processing, so that the sample data quality and the prediction precision are improved; A model interpretation method is used for characteristic analysis, so that the nanoparticle migration behavior has interpretability.

The invention discloses a convolution acceleration unit designing method based on a chemical reaction network. The method herein includes the following steps: designing N-parallel fast convolution units, also recording function modules which are used; inputting xi and hi in the form of reactant concentration sequence, designing a corresponding reactant; observing the values of logical circuit branches at different moments and corresponding reactants or products; tracking the value of a variant of respective branch; in case of a negative value of a branch, combining modules around the branch, guaranteeing the combination outputs a positive value, transplanting the combination entirely to the chemical reaction network; after tracking the entire circuit, combining all the related chemical reaction, and completing the convolution acceleration unit based on the chemical reaction network. According to the invention, the method herein simplifies and reconstructs a convolution kernel to a fast convolution unit and re-maps the fast convolution unit to the chemical reaction network, realizes high level of concurrent operation and user friendliness, and provides basis for future applications.

Artificial intelligence research is failing to produce true intelligence in spite of enormous resources. The reason is that programming is unavoidable for data processing and so there is no way to replace an user. In addition, because of data deluge problem, it is impossible to analyze all data as conventional information. Hardware inspired by prime metric is provided, where a metric of artificial intelligence is built in which unknown random events are linked as a changing geometric shape. All information is converted such that layered geometric shapes clocking in a pattern or event becomes unit of information, not insignificant bits. All complex events are considered as a single point to go ahead on building higher level geometric shapes as part of a time crystal following prime metric.

There are disclosed molecular scale devices for performing logic functions. Devices comprise at least one input molecular unit, at least one output molecular unit, at least one molecular unit for performing logic or memory functions, and a means for effecting charge transport. Devices of the invention are useful for a variety of electronic and optoelectronic applications.

An electronic device for implementing digital functions comprising a first and a second electrode regions, separated by an interposing region comprising a dielectric region, is described. The first and the second electrode regions comprise at least one first electrode and at least one second electrode, respectively, configured to generate in the interposing region an electric field depending on an electric potential difference applied thereto. In the interposing region, a molecular layer is comprised, which is composed of a plurality of molecules, each being capable of assuming one or more states, in a controllable manner, depending on a sensed electric field. The dielectric region has a spatially variable dielectric profile, to determine a respective spatially variable field profile of the sensed electric field at the molecular layer.

Big data, which is too massive to analyze instantly, could cause various problems, including threats to the security by terrorism and intellectual crimes hiding behind its huge volume and complexity. Using topological information as key unit to encode information, every single piece of information is converted into a topology or geometric shape and encoded in a clock without requiring software programming by a human. Each of the converted piece of information itself indicates an event, a decision, etc. that has its own significance / meaning. Geometric shapes integrate within their single decision time and again to find the intricate pattern of any event, including the above-mentioned threats.

An electronic device for implementing digital functions comprising a first and a second electrode regions, separated by an interposing region comprising a dielectric region, is described. The first and the second electrode regions comprise at least one first electrode and at least one second electrode, respectively, configured to generate in the interposing region an electric field depending on an electric potential difference applied thereto. In the interposing region, a molecular layer is comprised, which is composed of a plurality of molecules, each being capable of assuming one or more states, in a controllable manner, depending on a sensed electric field. The dielectric region has a spatially variable dielectric profile, to determine a respective spatially variable field profile of the sensed electric field at the molecular layer.

The invention provides a transformer winding fault diagnosis method based on atomic sparse decomposition. The method comprises the following steps of using a three-phase transformer test to simulate transformer winding faults under different working conditions; collecting vibration signals, and carrying out atomic sparse decomposition on the vibration signals to obtain an attenuation modal parameter; carrying out data preprocessing on the attenuation modal parameter to obtain characteristic vectors of a transformer winding under different fault types; dividing the characteristic vectors into atraining sample and a test sample; taking the training sample as input, and the fault types of the transformer winding as output, establishing a transformer winding GSO-SOM network fault diagnosis model; training the fault diagnosis model to acquire the trained transformer winding GSO-SOM network fault diagnosis model based on the atomic sparse decomposition; and inputting the test sample into the trained fault diagnosis model to judge a transformer winding fault, and outputting a diagnosis result. The method has characteristics of high reliability, good accuracy and the like and can be widely applied to the fault diagnosis field.

The present disclosure relates to a Turing machine having a reactor comprising a reactant solution comprising a reactant; a first chemical species source to provide a selected amount of a first chemical species; a second chemical species source to provide a selected amount of a second chemical species; one or more controllers coupled to control the addition of the first and second chemical species from the first and second chemical species sources responsive to an input; and a sensor positioned to sense changes in the reactant as the controller controls the first and second chemical species sources to add selected amounts of the respective first and second chemical species to the reactor. The controller receives signals corresponding to the state of the reactant and correlates the states of the reactant to a result that is computed as a function of the input.