73results about "Biomolecular computers" patented technology

Systems and methods described herein include embodiments for generating a geometrical structure of a binding complex formed between a plurality of macromolecules, comprising: processing an input representation comprising a plurality of representations of the plurality of macromolecules to generate a geometry prior; sampling an initial geometrical structure of the binding complex based on the geometry prior; and processing, using a neural network, the initial geometrical structure to generate the geometrical structure of the binding complex formed by the plurality of macromolecules.

The invention discloses an adaptive distributed network threat detection and response system and method, and the system comprises a data collection module which is used for capturing network traffic in real time and converting the network traffic into structured data; the data analysis module is used for performing parallel analysis on the structured data by adopting a plurality of anomaly detection algorithms; the algorithm fusion module is used for counting the abnormal judgment times of each algorithm on the same sample through a voting mechanism, and outputting a final classification result according to a preset threshold value; the method comprises the following working steps: concurrently capturing network traffic through distributed nodes, analyzing the network traffic into structured data, operating an isolated forest algorithm, a DBSCAN algorithm and a K-means algorithm in parallel, and outputting an anomaly judgment result; and fusing multi-algorithm results and generating final classification through a voting mechanism. The invention provides an efficient, intelligent and distributed network security protection system, which can effectively detect and respond to various network threats, and is particularly suitable for large-scale distributed environments and dynamic network security defense requirements.

The invention discloses a traffic control and guidance system and method based on an ant colony algorithm, belongs to the technical field of road traffic control, and solves the problems that a neural network model in an existing method mainly focuses on respective route conditions of a plurality of bifurcation routes of a bifurcation on a one-way driving road, and the information fusion degree between traffic elements is low. The method comprises the following steps: identifying dynamic characteristic information of vehicles in a road network, pre-constructing a road network optimization model based on an ant colony algorithm combined with deep learning, and analyzing a pheromone spread function cluster solution; according to the invention, the road network optimization model based on the ant colony algorithm and the deep learning is pre-constructed, the collaborative optimization of traffic signal control and vehicle induction is realized, the traffic signal optimization control strategy and the vehicle optimization path strategy are output, so that the signal timing and the induction path can be matched with each other, and the control accuracy is improved. Therefore, the operation efficiency of the traffic system is improved and the accuracy and effectiveness of the guidance strategy are ensured.

A computing architecture is optimized for the reuse of machine learning model. A request is received for a machine learning model recommendation that specifies parameters associated with a desired machine learning model (e.g., schema, etc.). Thereafter, a machine learning-based discovery model, recommends at least one machine learning model (to reuse) based on the parameters specified in the request. Next, data characterizing the recommended at least one machine learning model is provided (e.g., loaded into memory, displayed in a graphical user interface, transmitted to a remote computing system, and / or stored in physical persistence, etc.). In some variations, a graphical user interface can be rendered that allows a client application to select one of the recommended machine learning models and, further optionally, to activate the selected machine learning models. Related apparatus, systems, techniques and articles are also described.

An artificial reality system performs local user adaptation of machine learning models and global improvement of the machine learning models while ensuring data security and privacy. An artificial reality headset uses smart sensors and a system on chip (SoC). The smart sensor extracts features from sensor data, encrypts them and transmits the encrypted features to the SoC of the headset. The SoC provides the encrypted features to a server, for example, a server in a cloud platform. The server updates a base model with collected features and sends the updated base model parameters to headsets. The smart sensors of a headset receiving the updated model parameters, balance the model parameters with locally stored feature data, for example, user calibration features to perform user customization of the machine learning model.

The invention relates to the technical field of electromagnetic metasurfaces and bionic computing, and discloses a bionic information processing method and system based on an electromagnetic metasurface. The method comprises the following steps: S1, mapping DNA sequence information into a multi-dimensional coding state of the bionic metasurface information processing unit in an electromagnetic adjustable parameter space; s2, realizing co-evolution of the multi-dimensional coding state in space, frequency, phase and amplitude dimensions by dynamically regulating and controlling electromagnetic response parameters of the bionic metasurface information processing unit; and S3, using propagation and interference of electromagnetic waves in the diffraction neural network and a near-field coupling effect between the bionic metasurface information processing units to complete copying, logical operation and information read-write operation of the DNA sequence information. The functions of DNA coding, copying, logic calculation, information reading and writing and the like are uniformly mapped into the programmable electromagnetic metasurface platform, and cooperative processing of information in the aspects of space, electromagnetism and algorithm is achieved.

The invention discloses an industrial control network risk monitoring method based on immune recognition, and relates to the technical field of network security risk monitoring, and the method comprises the steps: obtaining multi-source heterogeneous industrial control protocol flow data and a known data set which are collected in real time; preprocessing the data to obtain a standardized antigen vector; according to the standardized antigen vector, carrying out mixed training on a preset depth auto-encoder and a graph neural network model based on an immune algorithm to obtain a trained depth auto-encoder-graph neural network model and an initial detector population; inputting the standardized antigen vector into a lightweight model to obtain a compressed antigen vector; and detecting the compressed antigen vector based on the initial detector population to obtain an antigen vector for triggering an alarm. According to the method, the definition and expandability of original protocol semantics are improved, continuous coverage of unknown attacks is ensured, and the deployment and updating process of the model is optimized.

The governed interaction interface maintains an identity-preserving semantic-state for a knowledge entity and updates it only through validated semantic evidence generated autonomously by execution mechanisms. Semantic intentions describe semantic needs and are transformed into capability-requests containing no operational commands. Execution mechanisms—including environmental devices, perception models, symbolic analyzers, virtual actors, and software agents—interpret capability-requests independently through tool-side semantic interpreters and may act or decline to act. The system does not observe or evaluate tool behavior and receives only semantic evidence describing semantic meaning of any resulting effect. The governance engine evaluates each evidence fragment independently under identity, coherence, lineage, evidentiary sufficiency, deviation constraints, and contextual compatibility. Semantic-state transitions occur only when validated evidence satisfies governance constraints. The system performs no prediction, optimization, control computation, multimodal fusion, or supervisory coordination, remaining fully separated from device-level behavior.

The invention discloses a cellular automaton-based land utilization and urban element collaborative simulation method. The method comprises the following steps of: calculating the development probability of each element; the method comprises the following steps of: constructing a cellular automata (CA) model of each element; space collaborative simulation calculation; and evaluating the result precision. According to the method, the urban land use change simulation precision can be effectively improved, the land use change in reality can be better simulated, the simulation precision of population and economic elements can be improved, and the interaction and synergistic effect between different urban elements are effectively revealed. By means of multi-element collaborative simulation, small changes of urban development elements can be caught more finely, and the method has good perception ability for residential land, industrial land, commercial land, public management land and the like and has important significance for simulating urban collaborative development and reasonably planning urban layout.

The invention discloses an energy storage capacity regulation and control method and system of a power generation system based on complementation of light, wind and electricity, relates to the field of energy storage capacity regulation and control, and ensures that the energy storage capacity influence parameters of various types and energy storage capacity regulation and control strategies are not influenced in the power generation process of the current power generation system by constructing a mapping equation between the energy storage capacity influence parameters and the energy storage capacity regulation and control strategies. Real-time energy storage capacity influence parameters are acquired at intervals, so that a corresponding optimal energy storage capacity regulation and control strategy is mapped; besides, the time interval for acquiring the real-time energy storage capacity influence parameter is adjusted by acquiring the percentage data of the time period in which the power generation capacity of the current power generation system in a period cannot meet the electric quantity required by the electric load in the current energy storage capacity regulation and control strategy adjustment evaluation period; and it is ensured that the percentage data of the time period of the electric quantity required by the electric load in the current energy storage capacity regulation and control strategy adjustment evaluation period meets the requirement.

Disclosed are metalloproteins (e.g, vanadyl-substituted metalloproteins), such as metal-substituted rubredoxins including vanadyl-substituted rubredoxins. These metalloproteins can be used in quantum computing applications, for example, as quantum bits (qubits) or quantum dits (qudits).

The invention provides an intelligent terminal outdoor cooperation method and system based on an ant colony algorithm. The method comprises the following steps: acquiring outdoor navigation data, processing the outdoor navigation data into navigation data fragments, and distributing the navigation data fragments to a plurality of intelligent terminals; screening out a main relay node; when entering the target area, determining the intelligent terminal as main navigation equipment, and controlling the gas intelligent terminal to enter a low-power-consumption mode; summarizing the navigation data fragments stored in the intelligent terminals through the main relay node, forwarding the summarized navigation data fragments to the main navigation equipment, and splicing to generate complete navigation data of the target area; and the main navigation equipment obtains and generates navigation information and synchronizes the navigation information to each intelligent terminal through the main relay node. Through pheromone dynamic iteration of the ant colony algorithm and a distributed decision-making mechanism, efficient cooperation of multiple intelligent terminals on division storage and group collaborative splicing is realized, and on the premise of guaranteeing complete navigation data splicing precision and real-time navigation reliability, the overall energy consumption is remarkably reduced, and the equipment endurance is prolonged.

Systems and techniques are provided for making genetically engineered ion channels (ICs) with bioluminescent resonance energy transfer (BRET) complexes and using such ICs for efficient readout of neural activity and output of biological neuronal networks.SOLUTION: In one implementation, the disclosed technology includes identifying a target location in the IC for expression of a target protein including a donor tag protein and an acceptor tag protein, and modifying the genome of the neuronal cell at a portion associated with the target location in the IC. The technique further includes causing the neuronal cell to express the target protein in the IC according to the modified genome. In the first (second) state of the IC, the donor tag protein is at a first (second) distance from the acceptor tag protein that is related to the absence (presence) of energy transfer between the donor tag protein and the acceptor tag protein.SELECTED DRAWING: Figure 3A-3C

Aspects herein describe a new approach to providing an interpretable solution for a domain-specific problem using artificial intelligence. Problems of a particular domain, such as biological problems, are identified by a computing unit. The computing unit determines an initial subset of features including various features for observable characteristics of the problem. The computing unit optimizes the subset of initial features to improve the accuracy of the subset in generating a solution to the problem. The computing unit trains one or more predictive artificial intelligence models based on the optimized subset to output a solution to the problem. The computing unit then uses the trained predictive model to output solutions to the one or more additional domain-specific problems. The computing unit may additionally provide an interpretable solution for a problem of a particular field by outputting a representation of steps performed by the predictive model to output the solution.

Techniques for identifying types of cells present in a biological sample using flow cytometry performed using a panel of markers and multiple machine learning models. The techniques include: obtaining flow cytometry data for the biological sample, the biological sample previously-obtained from a subject and comprising a plurality of cells, the flow cytometry data including flow cytometry measurements obtained during respective flow cytometry events, the flow cytometry events including a subset of events corresponding to cells in the biological sample being measured by the flow cytometry platform; and identifying types of cells of the plurality of cells using the multiple machine learning models to obtain a respective plurality of cell types.

The invention discloses an emotion memory generation system based on a memristor, and belongs to the field of integrated circuit design. The system comprises a brain emotion learning model and a hippocampus model. The brain emotion learning model comprises a thalamus module, a sensory cortex module, an orbital frontal cortex module and an amygdala kernel module; the hippocampus model comprises a DG module, a CA3 module and a CA1 module; under the stimulation of the emotional signals, the thalamus module firstly preprocesses the emotional signals, then the sensory cortex module and the orbital frontal lobe module integrate and process the preprocessed emotional signals, and the processed signals are respectively transmitted to the CA3 module and the amygdala kernel module to generate output signals representing emotions; under the stimulation of a learning signal, the DG module firstly preprocesses the learning signal, then the CA3 region is responsible for receiving the learning signal and generating an output signal representing short-term memory, and the output signal is transmitted to the CA1 region; and after receiving the short-term memory signal, the CA1 area converts short-term memory into long-term memory and stores the long-term memory.

The invention discloses a carbon dioxide-starch biosynthesis intelligent regulation and control chip system based on multi-mode AI. The system comprises a photonic crystal biosensor array used for monitoring physical and chemical parameters such as a pH value, a dissolved oxygen concentration and a carbon dioxide concentration in a reaction process in real time; the 16 * NPU array is used for efficiently calculating and processing the data of the multi-modal sensor and predicting the reaction dynamic state in real time; the 4 * Cortex-M7 treatment core is used for controlling the reaction process in real time and adjusting reactor conditions; and a neural symbol system-based multi-modal reaction regulation and control algorithm which is used for optimizing reaction dynamics and realizing cooperative regulation of 2000 + reaction parameters. All parameters in the reaction process are optimized through real-time multi-mode data monitoring (including enzyme activity, reaction temperature, pH value and the like). The enzyme activity can be accurately adjusted, so that the purity of the final product is remarkably improved, and the purity of the starch can be stabilized at 99% or above, while the purity of the starch prepared by the traditional method is only 89.7%.

The invention belongs to the technical field of vehicle insurance assessment, and discloses a vehicle operation risk assessment method and device, computer equipment and a storage medium. An accident record of a to-be-evaluated vehicle and driving behavior characteristic data of a driver are acquired, and the driving behavior characteristic data comprise bad driving behavior frequency, night driving duration, fatigue driving duration, load data, speed data and dangerous driving behavior frequency; determining a plurality of risk evaluation parameters based on an accident record and the driving behavior characteristic data; determining the risk level of each risk evaluation parameter according to a preset risk level division standard; and inputting the risk evaluation parameters and the corresponding risk levels into a risk evaluation model to obtain an estimated operation risk value of the to-be-evaluated vehicle, the risk evaluation model being constructed based on a dendritic cell algorithm. And the operation risk of the heavy-duty truck can be estimated more accurately.

To provide a circuit and a method for computing a neural network inference in hardware.SOLUTION: An architecture 300 including a matrix computation unit, includes a two-dimensional systolic array 306 including a plurality of cells, and a weight fetcher interface. For each of a plurality of neural network layers, the weight fetcher interface sends a plurality of weight inputs to cells along a first dimension of the two-dimensional systolic array for the neural network layer. For each of the plurality of neural network layers, a plurality of weight sequencers, each of which is coupled to a distinct cell along the first dimension of the two-dimensional systolic array, shift, for the neural network layer, the plurality of weight inputs to cells along the second dimension of the two-dimensional systolic array over a plurality of clock cycles. The cells in the two-dimensional systolic array each compute a product of an activation input and a respective weight input using a multiplication circuit.SELECTED DRAWING: Figure 3

Provided herein are novel Class 2 Type II and Type V CRISPR-Cas RNA-guided endonucleases such as Cas9 and Cas12 endonucleases and systems comprising the same. Methods of making and methods of using the same are also provided. Exemplary methods of use include modifying target DNA and detecting targeted DNA, which can be useful for therapeutic and diagnostic applications. Some diagnostic applications can take advantage of the collateral nuclease activity of the enzyme bound to the target sequence.

The present invention is directed to a single input multiplex decision expression system, and a method of using same, such as for expressing a reporter protein.

The technology disclosed relates to a neural network-based variant pathogenicity classifier for indicating a likelihood that a variant amino acid is benign or pathogenic. The disclosure particularly relates to a computer-implemented method comprising inputting, into a neural network based variant pathogenicity classifier trained to classify a variant amino acid sequence as benign or pathogenic a reference amino acid sequence for a protein; a variant amino acid sequence for the protein comprising a variant amino acid; and a conservation profile representing conservation of amino acids in a reference protein sequence across aligned protein sequences of other species; and generating, as an output of the neural network-based variant pathogenicity classifier, a pathogenicity score indicating a likelihood that the variant amino acid is benign or pathogenic.

A wearable includes a sensor to generate sensor data and a processor. The sensor includes compute resources configured to update a machine learning model based on feature data extracted from the sensor data. The processor is configured to receive the encrypted data associated with the machine learning model from the sensor and transmit the encrypted data to a server. The parameters of a base machine learning model are received from the server and the parameters are transmitted to the sensor. The sensor is configured to locally update the received base machine learning model based on the feature data.

The invention discloses a multi-source heterogeneous data fusion scientific and technological achievement dynamic valuation method, and relates to the technical field of scientific research evaluation, and the method comprises the following steps: S1, data collection: obtaining subjective cognition data of experts on scientific and technological achievements through brain-computer interface equipment, technical application scene data and market terminal feedback data generated by Internet of Things equipment are collected in real time in combination with an edge computing node, and distributed preprocessing and encrypted transmission of the data are achieved through federal edge learning; and S2, data cleaning and feature extraction: encoding data into a molecular sequence, and performing data de-duplication and abnormal value detection by using DNA calculation parallel processing characteristics. Through the combination of the brain-computer interface equipment, the edge computing node and federal edge learning, a real-time acquisition and preprocessing system of multi-modal heterogeneous data is constructed, and expert subjective cognitive data, technical application scene data of Internet of Things equipment and market terminal feedback data can be effectively integrated.

Disclosed herein are methods, systems, computer-implemented methods, and computer-accessible media for modifying and / or redesigning peptide-centric chimeric antigen receptors (PC-CARs) against novel peptide antigens.

The invention provides a facade transformation module material combination optimization method based on a genetic algorithm and a physarum polycephalum algorithm. The method comprises the following steps of: 1, dividing material categories; 2, establishing a genetic algorithm model; 3, on the basis of the obtained core cavity distribution scheme, generating a path in the core cavity distribution frame based on a physarum polycephalum algorithm to form the solid material part and other cavity parts so as to provide support for a core filling material and fill reserved spaces for other filling materials; and fourthly, combined assembly is conducted. Advanced materials including a phase change material, an air bag and the like and a heat preservation material are deeply combined with an intelligent algorithm, integrated design of the outer wall transformation module is achieved, the requirements for the multiple aspects of the heat preservation effect, the material utilization rate, the construction cost and the like are met, and remarkable application and popularization value is achieved.

The invention discloses an elastic load balancing and tuning method and system, and belongs to the technical field of communication, and the method comprises the steps: determining the load difference of an edge server and the total time delay of a terminal task through constructing a terminal equipment-edge server two-layer system model; constructing an optimization function and setting a constraint condition by taking minimization of the weighted sum of the two as a target; and finally, solving through a two-stage bionic algorithm, training a plurality of initial deep neural networks by means of a multi-group intelligent algorithm, optimizing through a genetic algorithm to obtain an optimal model, and inputting current scene parameters to obtain a final equalization strategy. According to the method, the problem of rapid load redistribution in an abnormal scene is solved, the search efficiency and the generalization ability are considered, the system efficiency and the load balance degree are improved, the energy consumption is reduced, overload or idling of the server is avoided, and the performance is superior to that of a traditional algorithm.

The invention discloses a cooperative control power distribution network harmonic suppression method of a multi-agent tissue enzyme numerical membrane system, and belongs to the technical field of electric energy quality treatment. The method comprises the following steps: sampling current at a bus node, extracting dominant subharmonic current by using sliding window discrete Fourier transform, and calculating the distortion rate of each subharmonic; setting a multi-agent communication connection topology, and adopting a two-way communication connection mode between adjacent regional controllers; using distributed consensus iteration to identify the maximum harmonic source position; distributed calculation is realized by establishing a tissue enzyme numerical membrane system, and then a multi-agent tissue enzyme numerical membrane system model is established to cooperatively control and suppress harmonic waves by using the residual capacity of a photovoltaic grid-connected inverter. The invention provides a novel method for realizing harmonic suppression of the power distribution network through distributed control, the state convergence rate of the system can be effectively improved, and the method can adjust the communication of the controller, reduce the transmission of data and save the calculation and resources of the system.

The invention relates to a strategy determination method and electronic equipment, which are used for improving the strategy determination accuracy. Comprising the following steps: inputting a to-be-processed pulse signal into a decision model; wherein the decision model comprises two first modules and a Purkinje cell layer, and any one of the first modules comprises a moss fiber layer and a granular cell layer; for any first module, inputting a to-be-processed pulse signal to the granular cell layer through the moss fiber layer; performing high-dimensional expansion processing and sparse processing on the pulse signal to be processed by using the granular cell layer to obtain an intermediate pulse signal; the Purkinje cell layer is used for prolonging the time scale of instantaneous input stimulation and gradually accumulating stimulation on intermediate pulse signals obtained by the two first modules respectively, and working memory is realized by self-discharge of two groups of Purkinje neurons in the Purkinje cell layer. And strong mutual inhibition between the two groups of Purkinje neurons is utilized to complete a win-win eating process, and a target decision is obtained.

A lipid nanotablet according to the present disclosure includes: a supported lipid bilayer having a plurality of nanoparticles integrated in nanoparticle units; an immobile nano-receptor including at least one first surface molecule from among the plurality of nanoparticles and coupled to the surface of the nano-receptor, and a mobile nano-floater including at least one second surface molecule from among the plurality of nanoparticles coupled to the surface of the nano-floater. Interaction between the nano-receptor and the nano-floater is controlled according to the result of a reaction to an input by the at least one first surface molecule and the at least one second surface molecule, and the lipid nanotablet provides a logic result on the basis of the interaction.