Abstract: Methods, systems and apparatus for modeling states of an entity are presented. For example, a method, implemented on a processor device, of modeling one or more states of an entity is presented. The method includes obtaining a training dataset for training a model by applying a stimulus to the entity, forming a set of model parameters, and using the set of model parameters to form the model, such that the model is configured to predict at least one of the one or more states of the entity. At least one model parameter of the set of model parameters changes with time as a result of dependency of the at least one model parameter on the stimulus and as a result of time-dependency of the stimulus. The steps of obtaining the training dataset, forming the set of model parameters and using the set of model parameters are implemented on the processor device.

Abstract: Systems and methods for modeling functional magnetic resonance image datasets using a multivariate auto-regressive model which captures temporal dynamics in the data, and creates a reduced representation of the dataset representative of functional connectivity of voxels with respect to brain activity. Raw spatio-temporal data is processed using a multivariate auto-regressive model, wherein coefficients in the model with high weights are retained as indices that best describe the full spatio-temporal data. When there are a relatively small number of temporal samples of the data, sparse regression techniques are used to build the model. The model coefficients are used to perform data processing functions such as indexing, prediction, and classification.

Abstract: Methods for predicting states of a subject are presented. For example, a method for predicting states of a subject includes obtaining training data comprising a plurality of variables, obtaining training states associated with the training data, and forming a predictive model according to the training data and the training states, the predictive model predictive of the training states. The forming of the predictive model includes extracting one or more hidden components from the training data. The extracting of the one or more hidden components includes regression analysis including determining one or more relationships between the one or more hidden components and the plurality of variables, and determining one or more relationships between the one or more hidden components and the training states. A number of the one or more hidden components is less than a number of the plurality of variables and greater than a number of the training states.

Abstract: Methods for predicting states of a subject are presented. For example, a method for predicting states of a subject includes obtaining training data comprising a plurality of variables, obtaining training states associated with the training data, and forming a predictive model according to the training data and the training states, the predictive model predictive of the training states. The forming of the predictive model includes extracting one or more hidden components from the training data. The extracting of the one or more hidden components includes regression analysis including determining one or more relationships between the one or more hidden components and the plurality of variables, and determining one or more relationships between the one or more hidden components and the training states. A number of the one or more hidden components is less than a number of the plurality of variables and greater than a number of the training states.

Abstract: Methods for predicting states of a subject are presented. For example, a method for predicting states of a subject includes obtaining training data comprising a plurality of variables, obtaining training states associated with the training data, and forming a predictive model according to the training data and the training states, the predictive model predictive of the training states. The forming of the predictive model includes extracting one or more hidden components from the training data. The extracting of the one or more hidden components includes regression analysis including determining one or more relationships between the one or more hidden components and the plurality of variables, and determining one or more relationships between the one or more hidden components and the training states. A number of the one or more hidden components is less than a number of the plurality of variables and greater than a number of the training states.

Abstract: Methods, systems and apparatus for characterizing networks are presented. For example, a method of characterizing a network represented by a plurality of nodes and a plurality of edges is provided. The method may be implemented on a processor device and includes calculating, for example, by the processor device, a passthrough count of at least a portion of the network. The passthrough count includes a count of a number of passthroughs in the at least a portion of the network. A passthrough includes one of the plurality of nodes, a directed edge of the plurality of edges coupled to the one of the plurality of nodes, and another edge of the plurality of edges coupled to the one of the plurality of nodes. At most one of the directed edge and the other edge is directed towards the one of the plurality of nodes. At most one of the directed edge and the other edge is directed away from the one of the plurality of nodes.

Abstract: Methods for predicting states of a subject are presented. For example, a method for predicting states of a subject includes obtaining training data comprising a plurality of variables, obtaining training states associated with the training data, and forming a predictive model according to the training data and the training states, the predictive model predictive of the training states. The forming of the predictive model includes extracting one or more hidden components from the training data. The extracting of the one or more hidden components includes regression analysis including determining one or more relationships between the one or more hidden components and the plurality of variables, and determining one or more relationships between the one or more hidden components and the training states. A number of the one or more hidden components is less than a number of the plurality of variables and greater than a number of the training states.

Abstract: Methods, systems and apparatus for modeling states of an entity are presented. For example, a method, implemented on a processor device, of modeling one or more states of an entity is presented. The method includes obtaining a training dataset for training a model by applying a stimulus to the entity, forming a set of model parameters, and using the set of model parameters to form the model, such that the model is configured to predict at least one of the one or more states of the entity. At least one model parameter of the set of model parameters changes with time as a result of dependency of the at least one model parameter on the stimulus and as a result of time-dependency of the stimulus. The steps of obtaining the training dataset, forming the set of model parameters and using the set of model parameters are implemented on the processor device.

Abstract: Methods, systems and apparatus for characterizing networks are presented. For example, a method of characterizing a network represented by a plurality of nodes and a plurality of edges is provided. The method may be implemented on a processor device and includes calculating, for example, by the processor device, a passthrough count of at least a portion of the network. The passthrough count includes a count of a number of passthroughs in the at least a portion of the network. A passthrough includes one of the plurality of nodes, a directed edge of the plurality of edges coupled to the one of the plurality of nodes, and another edge of the plurality of edges coupled to the one of the plurality of nodes. At most one of the directed edge and the other edge is directed towards the one of the plurality of nodes. At most one of the directed edge and the other edge is directed away from the one of the plurality of nodes.

Abstract: The present invention provides a method for capturing and storing physiological response attributes measured from a user while different stimuli are presented. Each stimulus may be any multimedia object, for example text, picture, or audio/video. The measured physiological response attributes are paired with the input stimulus, and stored conjointly in one or more databases. The physiological response attributes measure an aspect of the user known as emotional valence, and relate to the emotional state of the user, such as angry or sad. The database of physiological responses attributes of multiple users is first established. Then, when the physiological response attributes of a specific user in the future is examined, the system can suggest which objects in the database best correspond. Moreover, the database can be constructed based on the responses of the individual user for their own utilization, and be updated over the course of its continued use.

Abstract: The present invention provides techniques for transmitting at least one signal through an element of a classification system. One or more input signals are received at the element. One or more functional components are extracted from the one or more input signals, and one or more membership components are extracted from the one or more input signals. An output signal is generated from the element comprising a functional component and a membership component that correspond to one or more functional components and membership components from one or more input signals.