A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

Disclosed is a method for diagnosing a neurodegenerative disease in a subject. The method comprises obtaining from the subject a sample comprising at least one live blood cell, and optionally isolating at least one live blood cell from the sample. The method further comprises generating one or more multispectral or hyperspectral images of the at least one cell, and analysing spectral characteristics of autofluorescence from the at least one cell. Also disclosed is a system configured to aid in the detection or diagnosis of a neurodegenerative disease. Also disclosed is a method for selecting a subject for treatment for a neurodegenerative disease. Also disclosed is a method for monitoring the response of a subject to a therapeutic treatment for a neurodegenerative disease. Also disclosed is a protocol for monitoring the efficacy of a therapeutic treatment for a neurodegenerative disease.

In a method for parameterising at least one device (30), at least one environmental value (31) of the device is determined via at least one sensor and/or an automation component. It is checked whether parameters are allocated to the at least one environmental value in a parameter database (32). If parameters are allocated to the at least one environmental value (31) in the parameter database (32), the device (30) is parameterised with parameters from the parameter database (32). If no parameters are allocated to the at least one environmental value (31) in the parameter database (32), the device (30) is parameterised with new parameters. The new parameters are then allocated (54) to the at least one environmental value (31) in the parameter database (32).

A system for typing biological cells includes a tunable Fabry-Perot etalon, and imaging sensor, and a processor. The imaging sensor acquires one or more images of one or more biological cells from light transmitted through the tunable Fabry-Perot etalon. Each image represents signal associated with one or more wavelengths transmitted through the tunable Fabry-Perot etalon. The processor is configured to determine a type of each of the one or more biological cells. Determining the type uses a machine learning algorithm and is based at least in part on one or more of an image segmentation, a patch extraction, a feature extraction, a feature compression, a deep feature extraction, a feature fusion, a feature classification, and a prediction map reconstruction.

Techniques described herein relate to methods and systems for determining data asset criticality. Such techniques may include making a first determination that a plurality of data asset attributes are part of a causal attribute set; calculating a SHapeley Additive explanation (SHAP) value for each of the plurality of data asset attributes in the causal attribute set; and performing a weighted mean calculation using the SHAP values for each of the plurality of data asset attributes and a corresponding attribute value for each of the plurality of data asset attributes of a data asset to obtain a criticality score for the data asset.

Systems and methods to adapt a digital application environment based on psychological attributes of individual users are disclosed. Exemplary implementations may: store, in electronic storage, information associated with the individual users; obtain application usage information from client computing platforms associated with users; obtain stated information provided by the users; determine, based on the sets of answers, sets of psychological parameter values for the individual users; identify, based on the sets of psychological parameter values for the individual users, clusters of users that have similar sets of psychological parameter values; determine adaptions to the digital application environments provided by the client computing platforms for the individual users based on the clusters; and transmit the adaptations to the client computing platforms for implementation.

A feature extraction method of fruit spectrum includes taking a vector of each wavelength point in spectrum of samples as source data, and acquiring a sorting of all vectors by processing the source data by SPA; according to the sorting of the vectors, acquiring distribution points of each sample on a coordinate system; acquiring classification results of the samples by destructive analysis, and acquiring a number of first sample categories; acquiring a first Euclidean distance between the first sample categories; according to a sorting of the wavelength points, acquiring distribution points of each sample on the coordinate system; acquiring a number of second sample categories; acquiring a second Euclidean distance between the second sample categories; determining whether the first Euclidean distance is less than the second Euclidean distance; determine a (M+2)-th vector to be valid or invalid based on a comparison result.

An approach is provided in which the approach generates anomaly score variables using multiple unsupervised models based on a set of data records. The approach normalizes the anomaly score variables into multiple normalized variables, and constructs at least one interaction based on a first one of the normalized variables and a second one of the normalized variables. The first normalized variable corresponds to a first one of the anomaly score variables and the second normalized variable corresponds to a second one of the anomaly score variables. The approach detects a set of anomalies based on the at least one interaction and transmits the set of anomalies to a user.

A method for training a model. The method may include forming a data set from one or more measurements of core samples, selecting one or more parameters from the data set, inputting the one or more parameters into a kernel estimation function, determining a kernel density estimation from the kernel estimation function based at least in part on the one or more parameters, and selecting an input value based at least in part on the kernel density estimation. The method may further include creating a corresponding synthetic target value based at least in part on the input value, augmenting the data set with the corresponding synthetic target value and input value to form a synthetic data set, and training a petrophysical interpretation machine learning model from the data set and the synthetic data set.

Systems and methods for processing and using sensor data. The methods comprise: obtaining semantic labels assigned to data points; performing a supervised machine learning algorithm and an unsupervised machine learning algorithm to respectively generate a first confidence score and a second confidence score for each semantic label of said semantic labels, the first and second confidence scores each representing a degree of confidence that the semantic label is correctly assigned to a respective one of the data points; generating a final confidence score for each said semantic label based on the first and second confidence scores; selecting subsets of the data points based on the final confidence scores; and aggregating the data points of the subsets to produce an aggregate set of data points.