A control method based on an adaptive neural network model for dissolved oxygen of an aeration system includes: obtaining related water quality monitoring data of a sewage treatment plant, and performing data preprocessing on the related water quality monitoring data; performing principal component analysis on the preprocessed related water quality monitoring data and a dissolved oxygen concentration of the aeration system through a principal component analysis method, and determining a water quality parameter with a highest rate of contribution to a principal component; taking the water quality parameter with the highest rate of contribution to the principal component, and predicting a dissolved oxygen concentration of the aeration system; and optimizing a dissolved oxygen predictive value obtained by means of the adaptive neural network model to obtain an optimal regulation value, and performing online regulation on a fuzzy control system of the adaptive neural network model.

The technology disclosed relates to a variant pathogenicity classifier. The variant pathogenicity classifier comprises memory and runtime logic. The memory stores (i) a reference amino acid sequence of a protein, (ii) an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide, and (iii) a protein contact map of the protein. The runtime logic has access to the memory, and is configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map as input to a first neural network, and to cause the first neural network to generate a pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map.

A control method based on an adaptive neural network model for dissolved oxygen of an aeration system includes: obtaining related water quality monitoring data of a sewage treatment plant, and performing data preprocessing on the related water quality monitoring data; performing principal component analysis on the preprocessed related water quality monitoring data and a dissolved oxygen concentration of the aeration system through a principal component analysis method, and determining a water quality parameter with a highest rate of contribution to a principal component; taking the water quality parameter with the highest rate of contribution to the principal component, and predicting a dissolved oxygen concentration of the aeration system; and optimizing a dissolved oxygen predictive value obtained by means of the adaptive neural network model to obtain an optimal regulation value, and performing online regulation on a fuzzy control system of the adaptive neural network model.

Aspects described herein relate to a centralized computing system that interacts with a plurality of data centers, each having an edge server. Each edge server obtains sensor information from a plurality of sensors and processes the sensor information to detect an imminent shutdown and sends emergency data to a centralized processing entity when detected. In order to make a decision, the edge server processes the sensor data based on dynamic sensor thresholds and dynamic prioritizer data by syncing with the centralized computing system. Because of the short time duration to report emergency data before an imminent complete shutdown, an edge server may utilize a quantum data pipeline and quantum data storage as a key medium for all data transfer in a normal condition and at the time of emergency for internally transporting processed sensor data and providing the emergency data to the centralized processing entity.

Aspects described herein relate to a centralized computing system that interacts with a plurality of data centers, each having an edge server. Each edge server obtains sensor information from a plurality of sensors and processes the sensor information to detect an imminent shutdown and sends emergency data to a centralized processing entity when detected. In order to make a decision, the edge server processes the sensor data based on dynamic sensor thresholds and dynamic prioritizer data by syncing with the centralized computing system. Because of the short time duration to report emergency data before an imminent complete shutdown, an edge server may utilize a quantum data pipeline and quantum data storage as a key medium for all data transfer in a normal condition and at the time of emergency for internally transporting processed sensor data and providing the emergency data to the centralized processing entity.

Systems and methods for generating motion forecast data for actors with respect to an autonomous vehicle and training a machine learned model for the same are disclosed. The computing system can include an object detection model and a graph neural network including a plurality of nodes and a plurality of edges. The computing system can be configured to input sensor data into the object detection model; receive object detection data describing the location of the plurality of the actors relative to the autonomous vehicle as an output of the object detection model; input the object detection data into the graph neural network; iteratively update a plurality of node states respectively associated with the plurality of nodes; and receive, as an output of the graph neural network, the motion forecast data with respect to the plurality of actors.

A parallel recursive neural network, including: a memory configured to store data and processing instructions; and a parallel computer processor configured to: receive a set of input values; apply a recursive layer function individually on each of the set of input values in parallel to produce a set of hidden states; apply a reduction function on pairs of adjacent hidden states in the set of hidden states in parallel to produce a new set of hidden states; and repeat applying the reduction function of pairs of adjacent states in the new set of hidden states in parallel until a single output hidden state results.

A separate quantization method of forming a combination of 4-bit and 8-bit data of a neural network is disclosed. When a training data set and a validation data set exist, a calibration manner is used to determine a threshold for activations of each of a plurality of layers of a neural network model, so as to determine how many of the activations to perform 8-bit quantization. In a process of weight quantization, the weights of each layer are allocated to 4-bit weights and 8-bit weights according to a predetermined ratio, so as to make the neural network model have a reduced size and a combination of 4-bit and 8-bit weights.

In an approach for reasoning with real-valued propositional logic, a processor receives a set of propositional logic formulae, a set of intervals representing upper and lower bounds on truth values of a set of atomic propositions in the set of propositional logic formulae, and a query. A processor generates a logical neural network based on the set of propositional logic formulae and the set of intervals representing upper and lower bounds on truth values. A processor generates a credal network with a same structure of the logical neural network. A processor runs probabilistic inference on the credal network to compute a conditional probability based on the query. A processor outputs the conditional probability as an answer to the query.

A platform for training deep neural networks using push-to-corner preprocessing and adversarial training. A training engine adds a preprocessing layer before the input data is fed into a deep neural network at the input layer, for pushing the input data further to the corner of its domain.