Abstract: In a method and apparatus for analyzing multivariable data sets, a general computerized platform is provided for evaluating the relationship between large number of measurements of sets of variables characterizing components of complex states of a system under induced stimulation or controlled conditions. The linked responses of variables and their temporal relations tell about the network of interactions and their hierarchy. Processing of data sets by a simple neural network gives a matrix of weight parameters, that allow to identify fingerprints of complex states characterized by patterns of measured variable and estimate the interactions between the components characterized by the measured variables. The results are provided numerically and by color-coded presentation indicating dominating relations between variables and strongly responding variables.

Abstract: A system and method for implementing an indirect controller for a plant. A plant can be provided with both a direct controller and an indirect controller with a system model or a committee of system models. When the system model has sufficient integrity to satisfy the plant requirements, i.e., when the system model has been sufficiently trained, the indirect controller with the system model is automatically enabled to replace the direct controller. When the performance falls, the direct controller can automatically assume operation of the plant, preferably maintaining operation in a control region suitable for generating additional training data for the system model. Alternatively, the system model incorporates a committee of models. Various types of sources for errors in the committee of models can be detected and used to implement strategies to improve the quality of the committee.

Abstract: A method for strategy independent optimization of a multi-objective function of a portfolio containing at least one investment is disclosed. The method involves the use of genetic algorithms to arrive at function optimization. A suite of strategies is provided enabling the user to select a strategy and optimize a function. Real world data is drawn from exchanges and is utilized for replication. The invention also discloses a novel combination of apparatus for carrying out the method of invention, typically, using parallel processing.

Abstract: A method is provided for a virtual sensor system. The method may include starting at least one established virtual sensor process model indicative of interrelationships between a plurality of input parameters and a plurality of output parameters and retrieving calibration data associated with the virtual sensor process model. The method may also include obtaining a set of values of the plurality of input parameters and calculating corresponding values of the plurality of output parameters simultaneously based upon the set of values of the plurality of input parameters and the virtual sensor process model. Further, the method may include determining whether the set of values of input parameters are qualified for the virtual sensor process model to generate the values of the plurality of output parameters with desired accuracy based on the calibration data.

Abstract: The invention relates to a method of detecting and identifying a defect or an adjustment error of a rotorcraft rotor using an artificial neural network (ANN), the rotor having a plurality of blades and a plurality of adjustment members associated with each blade; the network (ANN) is a supervised competitive learning network (SSON, SCLN, SSOM) having an input to which vibration spectral data measured on the rotorcraft is applied, the network outputting data representative of which rotor blade presents a defect or an adjustment error or data representative of no defect, and where appropriate data representative of the type of defect that has been detected.

Abstract: Techniques for detecting temporal process variation and for managing and predicting performance of automatic classifiers applied to such processes using performance estimates based on temporal ordering of the samples are presented.

Abstract: A neural network module is provided. It comprises an input layer comprising one or more input nodes configured to receive gene expression data. It also has a rule base layer comprising one or more rule nodes and an output layer comprising one or more output nodes configured to output one or more conditions. It also comprises an adaptive component configured to extract one or more rules from the rule base layer representing relationships between the gene expression data and the one or more conditions. Methods and systems using the module are disclosed as well as specific profiles utilising the system.

Abstract: A method for using a neural network to deconvolute the effects due to surface topography from the effects due to the other physical property being measured in a scanning probe microscopy (SPM) or atomic force microscopy (AFM) image. In the case of a thermal SPM, the SPM probe is scanned across the surface of a sample having known uniform thermal properties, measuring both the surface topography and thermal properties of the sample. The data thus collected forms a training data set. Several training data sets can be collected, preferably on samples having different surface topographies. A neural network is applied to the training data sets, such that the neural network learns how to deconvolute the effects dues to surface topography from the effects dues to the variations in thermal properties of a sample.

Abstract: A method for identifying a potential fault in a system includes obtaining a set of training data. A first kernel is selected from a library of two or more kernels and the first kernel is added to a regression network. A next kernel is selected from the library of two or more kernels and the next kernel is added to the regression network. The regression network is refined. A potential fault is identified in the system using the refined regression network.

Abstract: Computer method and apparatus determines content type of contents of a subject Web page. A predefined set of potential content types is first provided. For each potential content type, there are one or more tests having test results that enable quantitative evaluation of the contents of the subject Web page. A respective probability of each potential content type being detected in some contents of the subject Web page is determined. A Bayesian network combines the test results to provide indications of the types of contents detected on the subject Web page. A confidence level per detected content type is also provided. A database stores the determined probabilities and confidence levels, and thus provides a cross reference between Web pages and respective content types of contents found on the Web pages.

Abstract: A power saving method for a mobile device is disclosed. Multiple user samples are generated. One behavior vector for each of the user samples is calculated. A neural network system is trained using the user samples and the corresponding behavior vectors. Multiple user events are collected. The user events are transformed to multiple behavior samples using a weighting transformation function. The behavior samples are classified into behavior sample groups. The behavior sample group comprising the most behavior samples is obtained. The behavior vector for the behavior sample group comprising the most behavior samples is calculated. The neural network system is trained using the behavior sample group comprising the most behavior samples and the corresponding behavior vector.

Abstract: For sequentially input data string, the outliner and the change point are detected through calculation of the outlier score and the change point score by combining a time-series model learning device to learn the generation mechanism of the read data series as the time-series statistic model, a score calculator to calculate the outlier score of each data based on the time-series model parameter and the input data, a moving average calculator to calculate the moving average of the outlier score, a time-series model learning device to learn the generation mechanism of the moving average series as the time-series statistic model and the above score calculator that further calculates the outlier score of the moving average based on the moving average of the outlier score and outputs the result as the change point score of the original data.

Abstract: A learning apparatus for learning time series data in a link model including a plurality of input time series pattern storage networks and a plurality of output time series pattern storage networks with nodes of the input time series pattern storage networks linked to nodes of the output time series pattern storage networks, includes a learning unit for updating in a self-organizing manner each of the plurality of input time series pattern storage networks and updating in a self-organizing manner each of the plurality of output time series pattern storage networks and a link relationship updating unit for updating a link relationship between each node of the output time series pattern storage network and an input winner node, and updating a link relationship between each node of the input time series pattern storage network and an output winner node.

Abstract: A scheduling system and method for moving plural objects through a multipath system described as a freight railway scheduling system. The scheduling system utilizes a cost reactive resource scheduler to minimize resource exception while at the same time minimizing the global costs associated with the solution. The achievable movement plan can be used to assist in the control of, or to automatically control, the movement of trains through the system.

Abstract: A scheduling system and method for moving plural objects through a multipath system described as a freight railway scheduling system. The scheduling system utilizes a cost reactive resource scheduler to minimize resource exception while at the same time minimizing the global costs associated with the solution. The achievable movement plan can be used to assist in the control of, or to automatically control, the movement of trains through the system.

Abstract: Hyperlinking or associating documents to other documents based on the names of people in the documents has become more desirable. Although there is an automated system for installing such hyperlinks into judicial opinions, the system is not generally applicable to other types of names and documents, nor well suited to determine hyperlinks for names that might refer to two or more similarly named persons. Accordingly, the inventor devised systems, methods, and software that facilitate hyperlinking names in documents, regardless of type. One exemplary system includes a descriptor module and a linking module. The descriptor module develops descriptive patterns for selecting co-occurent document information that is useful in recognizing associations between names and professional classes.

Abstract: There are provided methods and systems for inferring a user's interests from user-generated tags of web-based content. In accordance with the invention, user-generated tags from viewing web-based content are collected over a predetermined period of time. A subset of distinct or unique tags is identified from among the collected tags. A z-score is calculated for each identified distinct tag, where the z-score is a measure of the statistical significance of the tag. The subset of distinct tags is then sorted based on their corresponding z-score. All distinct tags having a corresponding z-score lower than a predetermined threshold are rejected and the remaining distinct tags, having a corresponding z-score higher than the threshold are used to infer a user's interest.

Abstract: A computer system as a computer system of AI of a cyborg or an android based on a natural language. The computer system includes the hardware devices 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, the sensors groups 1, 2, 3, 4, 5, 6, the interfaces 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, the senses input receiver 13, the senses output transmitter 14, the database 23, the cyborg-interpreter 26. The natural language which the computer system uses with its working method is interpreted by the computer system in a object-oriented way. The objects are no objects of a computer language. The computer system uses the references for another language.

Abstract: A constrained non-linear approximator for empirical process control is disclosed. The approximator constrains the behavior of the derivative of a subject empirical model without adversely affecting the ability of the model to represent generic non-linear relationships. There are three stages to developing the constrained non-linear approximator. The first stage is the specification of the general shape of the gain trajectory or base non-linear function which is specified graphically, algebraically or generically and is used as the basis for transfer functions used in the second stage. The second stage of the invention is the interconnection of the transfer functions to allow non-linear approximation. The final stage of the invention is the constrained optimization of the model coefficients such that the general shape of the input/output mappings (and their corresponding derivatives) are conserved.

Abstract: This invention relates to an information processing device and method that enable generation of an unlearned new pattern. Data xt corresponding to a predetermined time series pattern is inputted to an input layer (11) of a recurrent neural network (1), and a prediction value x*t+1 is acquired from an output layer 13. A difference between teacher data xt+1 and the prediction value x*t+1 is learned by a back propagation method, and a weighting coefficient of an intermediate layer 12 is set at a predetermined value. After the recurrent neural network is caused to learn plural time series patterns, a parameter having a different value from the value in learning is inputted to parametric bias nodes (11-2), and an unlearned time series pattern corresponding to the parameter is generated from the output layer (13). This invention can be applied to a robot.

Abstract: A data mining system and method are provided. The system includes at least one client and a service broker configured to include an interface to receive a consultation request from the client. The service broker forwards the consultation request to a Neugent to invoke a consultation of the Neugent, and forwards to the client a result object returned by the Neugent.

Abstract: In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (lo-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score and transductive feature selection. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection. (FIG.

Abstract: Method and apparatus for training a system model with gain constraints. A method is disclosed for training a steady-state model, the model having an input and an output and a mapping layer for mapping the input to the output through a stored representation of a system. A training data set is provided having a set of input data u(t) and target output data y(t) representative of the operation of a system. The model is trained with a predetermined training algorithm which is constrained to maintain the sensitivity of the output with respect to the input substantially within user defined constraint bounds by iteratively minimizing an objective function as a function of a data objective and a constraint objective. The data objective has a data fitting learning rate and the constraint objective has constraint learning rate that are varied as a function of the values of the data objective and the constraint objective after selective iterative steps.

Abstract: A system that can integrate activities across machines and/or devices is disclosed. The innovation can be used in an “asynchronous” manner that enables a user to move or direct a set of activities and/or tasks within an activity from one device to another. Further, the system can facilitate adapting user interface factors with respect to a particular device such that a user can “synchronously” utilize all devices for the given activities and/or tasks. In other words, the “synchronous” scenario enables a user to share activity information between devices when simultaneously utilizing multiple devices in accordance with a particular activity.

Abstract: A system that can identify, create, update and/or process a workflow based upon a current, past or future activity is disclosed. A ‘workflow’ can be defined as an activity flow that includes interaction with, or assignment of work to, people, devices, or services by a single individual or a group of individuals. Once a workflow is determined in accordance with the innovation, the system can inform other users or groups that are performing, or intend to perform, a similar or like activity. In establishing the workflow, the innovation can operate in an ad hoc or authored manner. As well, the system can employ a combination of either ad hoc or authored mechanisms in establishment of the workflow.

Abstract: This invention relates to using artificial intelligence for analyzing real-life collected data from an operation system, modeling the collected data to identify characteristics of events, analyzing the models to conclude an optimal solution for maximizing the performance of the operation system.

Abstract: A method and system for automatically producing data representative of a modified head shape from data representative of a deformed head is provided. The method includes a step of processing captured data for the deformed head utilizing Principal Component Analysis (PCA) to generate PCA data representative of the deformed head. The method also includes the steps of providing the PCA data as input to a neural network; and utilizing the neural network to process the PCA data to provide data representative of a corresponding modified head shape.

Abstract: A crystal lookup table used to define a matching relationship between a signal position of a detected event in a PET scanner and a corresponding detector pixel location is generated using a neural network-based algorithm, and is implemented by a FPGA.

Abstract: Disclosed is a method for training a transductive support vector machine. The support vector machine is trained based on labeled training data and unlabeled test data. A non-convex objective function which optimizes a hyperplane classifier for classifying the unlabeled test data is decomposed into a convex function and a concave function. A local approximation of the concave function at a hyperplane is calculated, and the approximation of the concave function is combined with the convex function such that the result is a convex problem. The convex problem is then solved to determine an updated hyperplane. This method is performed iteratively until the solution converges.

Abstract: Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively.

Abstract: Described are systems and methods for determining the gross weight of an aircraft. A flight regime is determined based on one or more inputs. A neural net is selected based on a flight regime. The neural net inputs may include derived values. A first estimate of the gross weight is produced by the selected neural net. The first estimate is used, along with other inputs, with a Kalman filter to produce a final gross weight estimate. The Kalman filter blends or fuses together its inputs to produce the final gross weight estimate.

Abstract: A method for organizing processors to perform artificial neural network tasks is provided. The method provides a computer executable methodology for organizing processors in a self-organizing, data driven, learning hardware with local interconnections. A training data is processed substantially in parallel by the locally interconnected processors. The local processors determine local interconnections between the processors based on the training data. The local processors then determine, substantially in parallel, transformation functions and/or entropy based thresholds for the processors based on the training data.

Abstract: A plausible neural network (PLANN) is an artificial neural network with weight connection given by mutual information, which has the capability of inference and learning, and yet retains many characteristics of a biological neural network. The learning algorithm is based on statistical estimation, which is faster than the gradient decent approach currently used. The network after training becomes a fuzzy/belief network; the inference and weight are exchangeable, and as a result, knowledge extraction becomes simple. PLANN performs associative memory, supervised, semi-supervised, unsupervised learning and function/relation approximation in a single network architecture. This network architecture can easily be implemented by analog VLSI circuit design.

Abstract: An interactive design system includes a design application that creates and models a geometry of an object. A programming language application defines engineering rules that may be associated with the object. The programming language application associates the geometry of the object with the engineering rules such that any change made to the geometry is automatically reflected in the engineering rules and any change made to the engineering rules is reflected in the geometry. The programming language application may also generate one or more knowledge features that can be used to verify that associated parameter constraints have not been violated as a result of a geometry or engineering rule change before applying the desired changes.

Abstract: A method and system for capturing emotional preference of human subjects, generating machine-readable emotional code and using the code to optimize computerized searching and matching operations between entities is disclosed. The entity can be a human user, a product, or a service. The emotional code can thus be a universal language expressing human emotion that communicates among entities. After understanding the sending parties' emotional profile, the receiving party can adapt its operation to achieve more optimum results.

Abstract: In a method for detecting the modes of a dynamic system with a large number of modes that each have a set ? (t) of characteristic system parameters, a time series of at least one system variable x(t) is subjected to modeling, for example switch segmentation, so that in each time segment of a predetermined minimum length a predetermined prediction model, for example a neural network, for a system mode is detected for each system variable x(t), whereby modeling of the time series is followed by drift segmentation in which, in each time segment in which there is transition of the system from a first system mode to a second system mode, a series of mixed prediction models is detected produced by linear, paired superimposition of the prediction models of the two system modes.

Abstract: Dynamically filtering and classifying messages, as good messages, bulk periodicals, or spam. A regular expression recognizer, and pre-trained neural networks. The neural networks distinguish “likely good” from “likely spam,” and also operate at a more discriminating level to distinguish among the three categories above. A dynamic whitelist and blacklist; sending addresses are collected when the number of their messages indicates the sender is good or a spammer. A dynamically selected set of regular expressions input to the neural networks.

Abstract: In an electronic communication system, relevance levels of an incoming or outgoing message for presenting it to an interlocutor is measured without having to actually interact with the interlocutor, by a method comprising the steps of extracting from the message, a flow of digital signals pertaining to transmission/reception context features, to content of the message and/or to other interlocutors with the interlocutor; weighting probabilistically the digital signals by means of indicators of relative and interrelated frequencies of occurrences of the same digital signals extracted from previous messages; from the results of the above steps, auto-generating a Bayesian network that allows the interlocutor to obtain a probabilistic prediction on the attractiveness of sent/received signals or messages, or find most probably interested interlocutors for a given information or message, each node of the Bayesian network being associated with a signal.

Abstract: A method and apparatus that generates an estimate of a property of a batch process uses a non-parametric model to generate a plurality of rate of reaction estimates associated with the batch process. Each rate of reaction estimate may correspond, for example, to a particular time during the batch process. The plurality of rate of reaction estimates are then integrated to generate an estimate of a property of the batch at the particular time.

Abstract: An annotating system aids a user in mapping a large number of queries to tasks to obtain training data for training a search component. The annotating system includes a query log containing a large quantity of queries which have previously been submitted to a search engine. A task list containing a plurality of possible tasks is stored. A machine learning component processes the query log data and the task list data. For each of a plurality of query entries corresponding to the query log, the machine learning component suggests a best guess task for potential query-to-task mapping as a function of the training data. A graphical user interface generating component is configured to display the plurality of query entries in the query log in a manner which associates each of the displayed plurality of query entries with its corresponding suggested best guess task.

Abstract: One embodiment of the present invention provides a system that performs high-level parallelization of large scale quadratic-problem (QP) optimization. During operation, the system receives a training dataset comprised of a number of data vectors. The system first determines to what extent each data vector violates conditions associated with a current support vector machine (SVM). The system then sorts the data vectors based on each data vector's degree of violation. Next, the system partitions the sorted data vectors into a number of prioritized subsets, wherein the subset with the highest priority contains the largest number of violators with the highest degree of violation. The system subsequently solves in parallel a QP optimization problem for each subset based on the subset's priority. The system then constructs a new SVM to replace the current SVM based on the QP optimization solution for each subset.

Abstract: In a method and apparatus for analyzing multivariable data sets, a general computerized platform is provided for evaluating the relationship between large number of measurements of sets of variables characterizing components of complex states of a system under induced stimulation or controlled conditions. The linked responses of variables and their temporal relations tell about the network of interactions and their hierarchy. Processing of data sets by a simple neural network gives a matrix of weight parameters, that allow to identify fingerprints of complex states characterized by patterns of measured variable and estimate the interactions between the components characterized by the measured variables. The results are provided numerically and by color-coded presentation indicating dominating relations between variables and strongly responding variables.

Abstract: Apparatus, and an associated method, for selecting operational parameters pursuant to which a radio communication system is operable. The operational parameters form, for instance, adaptive switching thresholds used in an adaptive modulation scheme. Separate learning controllers are configured to operate independently and cooperatively to select the adaptive switching thresholds, or other operational parameters. Iterative operation of the learning controllers causes the values to converge to optimal values.

Abstract: Method and apparatus for training a system model with gain constraints. A method is disclosed for training a steady-state model having an input and an output and a mapping layer for mapping the input to the output, the model comprising a stored representation of a plant or process, and including a linear portion and a non-linear portion, where the non-linear portion includes a function. Input is received to the model, and predicted output computed corresponding to attribute(s) of the plant or process. The predicted output is stored, and is usable to manage the plant or process. The model is trained to optimize a specified objective function subject to one or more constraints, e.g., via a non-linear programming (NLP) optimizer, the constraints including, hard constraint(s) comprising strict limitations on the training in optimizing the objective function, and/or soft constraint(s) comprising a weighted penalty function included in the objective function.

Abstract: A method and apparatus for retraining skew compensation in an interface is presented. In one embodiment, a retraining interval is determined, and counters in the transmitting agent and receiving agent count up until the retraining interval is reached. A tracking unit used to select one of several interpolated clocks may then be powered up, and a special retraining phit may be sent across the interface. During the retraining process, the transfer of flits into and out of the flow-control mechanism may be inhibited. When the retraining process is finished, the tracking unit may be powered down.

Abstract: A novel apparatus and method for controlling a system with multiple observable variables is disclosed. The apparatus and method disclosed use elements of the bottom-up and top-down strategies of artificial intelligence to provide a control system that is able to learn without a training set of information, and that has an learning process that can handle any amount of input data, i.e., cannot become saturated. The control system disclosed is capable of learning and controlling non linear dynamical systems. The control system is also capable of adding additional observable variables or subtracting existing observable variables to determine the state of the plant or system being controlled.

Abstract: The present invention relates to a system and methodology providing layered probabilistic representations for sensing, learning, and inference from multiple sensory streams at multiple levels of temporal granularity and abstraction. The methods facilitate robustness to subtle changes in environment and enable model adaptation with minimal retraining. An architecture of Layered Hidden Markov Models (LHMMs) can be employed having parameters learned from stream data and at different periods of time, wherein inferences can be determined relating to context and activity from perceptual signals.

Abstract: The invention describes a modification of FIFO hardware to allow improved use of FIFOs for burst reading from or writing to a processor direct memory access unit via either an expansion bus or an external memory interface using FIFO flag initiated bursts. The hardware and FIFO signal modifications make the FIFO-DMA interface immune to deadlock conditions and generation of spurious interrupt events in the process of initiating burst transfers. The FIFO function is modified to synchronize the frame transfer on the digital signal processor even if the digital signal processor lacks this functionality. By delaying the programmable flag assertions within the FIFO until after the current burst is complete the DSP-FIFO interface may be made immune to deadlock conditions and generation of spurious events.

Abstract: The vehicle control system having an adaptive controller is provided that accomplishes unsupervised learning such that no prior extensive training is needed for every situation. The inventive controller system is based on a neural network evolved with genetic algorithm. The genetic algorithm will determine the parameters of the neurons, the connections between the neurons and the associated weights to yield the best results. The genetic algorithm evaluates current candidate structures for accomplishing the desired result and develops new candidate structures by reproducing prior candidate structures with modification that replaces the least fit former candidate structures until the system is well satisfied. The vehicle control system is well satisfied when the desired result is met or some failure condition is triggered for vehicle control system whereby the action is never repeated.

Abstract: Databases and a system for operating on such databases are described in which data representative of first head shapes and corresponding modified head shapes and corresponding cranial remodeling devices are provided.