Abstract: Logging systems and methods are disclosed to reduce usage of radioisotopic sources. Some embodiments comprise collecting at least one output log of a training well bore from measurements with a radioisotopic source; collecting at least one input log of the training well bore from measurements by a non-radioisotopic logging tool; training a neural network to predict the output log from the at least one input log; collecting at least one input log of a development well bore from measurements by the non-radioisotopic logging tool; and processing the at least one input log of the development well bore to synthesize at least one output log of the development well bore. The output logs may include formation density and neutron porosity logs.

Abstract: A neural model for simulating a scorecard comprises a neural network for transforming one or more inputs into an output. Each input of the neural model has a squashing function applied thereto for simulating a bin of the simulated scorecard. The squashing function includes a control variable for controlling the steepness of the response to the squashing function's input so that during training of the neural model the steepness can be controlled. The output of the neural model represents the score of the simulated scorecard. The neural network is trained to behave like a scorecard by providing plurality of example values to the inputs of the neural network. Each output score produced is compared to an expected score to produce an error value. Each error value is back-propagated to adjust the neural network transformation to reduce the error value. The steepness of each squashing function is controlled using the respective control variable to affect the response of each squashing function.

Abstract: A network architecture of radial basis function neural network system utilizes a blocking layer (4) to exclude successfully mapped neighborhoods from later node influence. A signal is inserted into the system at input nodes (I1, I2, . . . In), which then promulgates to a non-linear layer (2). The non-linear layer (2) comprises a number of non-linear activation function nodes (10). After passing through the non-linear layer (2), the signal passes through the blocking layer (4) that is comprised of either binary signal blocking nodes, or inverted symmetrical Sigmoidal signal blocking nodes (12) that act in a binary fashion. Finally, the signal is weighted by a weighting function (6a, 6b, 6c, 6n), summed at a summer (8) and outputted at (O).

Abstract: A method for making it possible to set the link between all the types of vehicles and roads with the rolling limits on the roadway is described. This setting can be established from the existing road data bases and from the characteristics of the known vehicles. This method is capable of determining the vehicle rolling limits. A device which can be fitted on any vehicle and capable of implementing the method according to the invention is also disclosed.

Abstract: A method for generating an artificial neural network ensemble for determining stimulation design parameters. A population of artificial neural networks is trained to produce one or more output values in response to a plurality of input values. The population of artificial neural networks is optimized to create an optimized population of artificial neural networks. A plurality of ensembles of artificial neural networks is selected from the optimized population of artificial neural networks and optimized using a genetic algorithm having a multi-objective fitness function. The ensemble with the desired prediction accuracy based on the multi-objective fitness function is then selected.

Abstract: The present invention relates to a system and method for intelligent computer-implemented transmittal of data, the system determining and using the best available method of transmission for the data In determining the best available transmission method, the system considers at least one of available transmission methods at a particular site, transmission cost, data type, and data security.

Abstract: Systems and methods for minimizing a resolve trace are provided. The method comprises identifying at least a first clause that won't take part in determining the final result; removing at least a first resolve source associated with the first clause from the resolve trace, wherein the first clause is a disjunction of one or more literals that define the SAT problem; and removing the first resolve source from the resolve trace, in response to said first clause not having any children.

Abstract: Described is a technology by which personal information that comes into a computer system is intelligently managed according to current state data including user presence and/or user attention data. Incoming information is processed against the state data to determine whether corresponding data is to be output, and if so, what output modality or modalities to use. For example, if a user is present and busy, a notification may be blocked or deferred to avoid disturbing the user. Cost analysis may be used to determine the cost of outputting the data. In addition to user state data, the importance of the information, other state data, the cost of converting data to another format for output (e.g., text-to-speech), and/or user preference data, may factor into the decision. The output data may be modified (e.g., audio made louder) based on a current output environment as determined via the state data.

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 method and system for generating one or more Computer-executable procedures simultaneously learns from a collection of procedure instances recorded by different users on a variety of computers aligning multiple traces and using the aligned traces to generalize.

Abstract: A computer-based self-learning system for managing a price in a retail environment, including: an interface element for at least one specially programmed general-purpose computer for receiving an input related to initiation of a transaction between a customer and a first business entity; a memory unit for the at least one specially programmed general-purpose computer for storing an artificial intelligence program (AIP) and a history of at least one previous transaction between the customer and the first business entity; and a processor for the at least one specially programmed general-purpose computer for: determining, using the AIP, the input, and the history, a price for the good or service to optimize revenue for the first business entity or profitability of the first business entity. The interface element is for receiving a request for the price, and the processor is for transmitting, using the interface element, the price for display.

Abstract: A semantic conversion system (1900) includes a self-learning tool (1902). The self-learning tool (1902) receives input files from legacy data systems (1904). The self-learning tool (1902) includes a conversion processor (1914) that can calculate probabilities associated with candidate conversion terms so as to select an appropriate conversion term. The self-learning tool (1902) provides a fully attributed and normalized data set (1908).

Abstract: A data search apparatus includes an obtaining unit that obtains a content and a first metadata corresponding to the content and including at least a search key indicating an object of the content; a feature amount computing unit that computes a feature amount indicating a feature of the content from the obtained content; a learning-data storing unit that stores a learning-data that correspondingly includes the first metadata corresponding to each of the obtained content and the computed feature amount; a learning-data reconstructing unit that reconstructs the learning-data by generating a second metadata from the first metadata included in the learning-data stored in the learning-data storing unit so that the second metadata includes all search keys in the first metadata of all the learning-data, and by replacing the first metadata by the second metadata in learning-data; and a model generating unit that generates a model from the learning-data, the model being a coefficient matrix indicating a relation betwe

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: Mechanisms model, detect, and predict user behavior as a user navigates the Web. In one embodiment, mechanisms model user behavior using predictive models, such as discrete Markov processes, where the user's behavior transitions between a finite number of states. The user's behavior state may not be directly observable (e.g., a user does not proactively indicate what behavior state he is in). Thus, the behavior state of a user is usually only indirectly observable. Mechanisms use predictive models, such as hidden Markov models, to predict the transitions in the user's behavior states.

Abstract: A method includes defining a reference model of a system having a plurality of terms for modeling data associated with the system. A reference fit error metric is generated for the reference model. A set of evaluation models each having one term different than the reference model is generated. An evaluation fit error metric for each of the evaluation models is generated. The reference model is replaced with a selected evaluation model responsive to the selected evaluation model having an evaluation fit error metric less than the reference fit error metric. The model evaluation is repeated until no evaluation model has an evaluation fit error metric less than the reference fit error metric. The reference model is trained using the data associated with the system, and the trained reference model is employed to determine at least one characteristic of the system.

Abstract: Techniques are described herein for determining a distractibility measure for an item to be displayed on a display. The distractibility measure for an item is determined based on the individual distractibility measures for one or more of: the static distraction of the item, the onset response of the item, the optic-flow motion of the item, and the change in velocity of objects in the item. Each individual distractibility measure can be further multiplied by a weighting factor which affects the composition of the distractibility measure for the item. The distractibility measure for the item can be further based on the size of the item, how far away the item is from a primary content on the display, and the distractibility measure of the primary content on the display. The distractibility measure for the item can be compared to a maximum level of distractibility for automatically determining whether the item should be displayed on the display.

Abstract: An information processing apparatus, capable of identifying vital sign information. A learning unit identifies an emotion/state of the user by comparing the generated feature vectors with feature vectors accumulated in a storage unit. Information indicating the resultant identified emotion/state of the user is output to an output unit via an output controller.

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 method determines temporal patterns in data sequences. A hierarchical tree of nodes is constructed. Each node in the tree is associated with a composite hidden Markov model, in which the composite hidden Markov model has one independent path for each child node of a parent node of the hierarchical tree. The composite hidden Markov models are trained using training data sequences. The composite hidden Markov models associated with the nodes of the hierarchical tree are decomposed into a single final composite Markov model. The single final composite hidden Markov model can then be employed for determining temporal patterns in unknown data sequences.

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: In a rule induction method, an overbroad candidate rule is selected for categorizing a node to be categorized. The candidate rule is specialized by: (i) adding a rule node corresponding to a node level of structured training examples; (ii) including in a rule node a rule pertaining to an attribute of at least one node of the corresponding node level to produce a specialized candidate rule; and (iii) evaluating the specialized candidate rule respective to the structured training examples.

Abstract: An adaptive system modeling method is provided. A system model is generated by using data corresponding to an input features set selected by using a baseline significance signature of the system. A superset of the input features and other features also is selected by using the baseline significance signature. Data collected from the system corresponding to the superset is maintained online. A new significance signature of the system is periodically or intermittently determined by performing a discriminant analysis using the online superset data, and is used to detect an evolutionary change in the system.

Abstract: A computer implemented method, system and program product for automatic fault classification. A set of abnormal data can be automatically grouped based on sensor contribution to a prediction error. A principal component analysis (PCA) model of normal behavior can then be applied to a set of newly generated data, in response to automatically grouping the set of abnormal data based on the sensor contribution to the prediction error. Data points can then be identified, which are indicative of abnormal behavior. Such an identification step can occur in response to applying the principal component analysis mode of normal behavior to the set of newly generated data in order to cluster and classify the data points in order to automatically classify one or more faults thereof. The data points are automatically clustered, in order to identify a set of similar events, in response to identifying the data points indicative of abnormal behavior.

Abstract: A neural network comprising a plurality of neurons in which any one of the plurality of neurons is able to associate with itself or another neuron in the plurality of neurons via active connections to a further neuron in the plurality of neurons.

Abstract: A new architecture overcomes the limitation of conventional robotic technologies. Named the Self Organizing Model (“SOM”) it includes a method that allows systems to learn, grow and continually evolve without outside control. This technology enables Artificial Life, one aspect of which is robotic “life.” If this system is compared to a real living thing, the hardware is like the body and the potential instinct and habits and related data are like the DNA. The hardware includes memory which contains the instinct and related data. Algorithms and organizations are provided so that the hardware forms an adapting and evolving brain that senses the environment and formulates actions to improve the survival of the Artificial Life according to predetermined rules. The organism can learn and become more complex all without complex software programs that attempt to anticipate all possible situations.

Abstract: A method of training a neural network to perform decoding of a time-varying signal comprising a sequence of input symbols, which is coded by a coder such that each coded output symbol depends on more than one input symbol, characterized by repetitively: providing a plurality of successive input symbols to the neural network and to the coder, comparing the network outputs with the input signals; and adapting the network parameters to reduce the differences therebetween.

Abstract: An information processing apparatus includes a first learning unit adapted to learn a first SOM (self-organization map), based on a first parameter extracted from an observed value, a winner node determination unit adapted to determine a winner node on the first SOM, a searching unit adapted to search for a generation node on a second SOM having highest connection strength with the winner node, a parameter generation unit adapted to generate a second parameter from the generation node, a modification unit adapted to modify the second parameter generated from the generation node, a first connection weight modification unit adapted to modify the connection weight when end condition is satisfied, a second connection weight modification unit adapted to modify the connection weight depending on evaluation made by a user, and a second learning unit adapted to learn the second SOM based on the second parameter obtained when the end condition is satisfied.

Abstract: When a patient enters a medical situation, healthcare professionals can use various amounts of information in evaluating the situation. However, different information can be beneficial dependent on the medical situation. Moreover, personnel can historically use specific information types regardless of the situation. An artificial neuron network is employed to pre-fetch information that personnel likely will want prior to a request from the personnel. In addition, the artificial neuron network can be trained based on results of presented information.

Abstract: A system, method and computer program for developing artificial intelligence through the generational evolution of one or more genomes. Each genome includes a set of functions. The method includes creating one or more cortices, operating the one or more cortices to perform one or more specified tasks, calculating a fitness score for each cortex based on its ability to perform the specified tasks, and selecting one or more of the cortices based on the respective fitness scores. Each cortex includes a plurality of cortical units. Each cortical unit includes a set of functions. Each cortical unit is created from the one or more genomes.

Abstract: A method for operating a computer as a support vector machine (SVM) in order to define a decision surface separating two opposing classes of a training set of vectors. The method involves associating a distance parameter with each vector of the SVM's training set. The distance parameter indicates a distance from its associated vector, being in a first class, to the opposite class. A number of approaches to calculating distance parameters are provided. For example, a distance parameter may be calculated as the average of the distances from its associated vector to each of the vectors in the opposite class. The method further involves determining a linearly independent set of support vectors from the training set such that the sum of the distances associated with the linearly independent support vectors is minimized.

Abstract: A computer-implemented method is provided for ranking features within a large dataset containing a large number of features according to each feature's ability to separate data into classes. For each feature, a support vector machine separates the dataset into two classes and determines the margins between extremal points in the two classes. The margins for all of the features are compared and the features are ranked based upon the size of the margin, with the highest ranked features corresponding to the largest margins. A subset of features for classifying the dataset is selected from a group of the highest ranked features. In one embodiment, the method is used to identify the best genes for disease prediction and diagnosis using gene expression data from micro-arrays.

Abstract: Described herein are systems and methods for normalizing data without the use of external controls. Also described herein are systems and methods for analyzing cluster data, such as genotyping data, using an artificial neural network.

Abstract: A multiphase flow meter used in conjunction with an electrical submersible pump system in a well bore includes sensors to determine and transmit well bore pressure measurements, including tubing and down hole pressure measurements. The multiphase flow meter also includes at least one artificial neural network device to be used for outputting flow characteristics of the well bore. The artificial neural network device is trained to output tubing and downhole flow characteristics responsive to multiphase-flow pressure gradient calculations and pump and reservoir models, combined with standard down-hole pressure, tubing surface pressure readings, and the frequency applied to the electrical submersible pump motor.

Abstract: A system, method, and computer program product for observing and modifying activity in an actor processor are presented. An observer module is provided for observing a physical property of an actor processor. The observer module comprises a property-observing sensor for detecting and sampling a physical property of the actor processor and for generating an observation signal based on the physical property. The observer module further comprises an observer processor coupled with the property-observing sensor for receiving the observation signal, the observer processor operative to generate an observer output signal based on the observation signal. The observer module permits the observer processor to monitor the actor processor in a manner that isolates an instruction set of the observer processor from direct manipulation by means of an instruction set of the actor processor. Observer processors may be used in a recursive manner to provide a completely-coupled observation module.

Abstract: A method for modeling material behavior includes using empirical three dimensional non-uniform stress and strain data to train a self-organizing computational model such as a neural network. A laboratory device for measuring non-uniform stress and strain data from material includes an enclosure with an inclusion in it. As the enclosure is compressed, the inclusion induces a non-uniform state of stress and strain. A field testing device includes a body having a moveable section. When the body is inserted in a material and the moveable section moved, a non-uniform state of stress and strain can be characterized.

Abstract: Systems and methods for determining semantically related terms using an active learning framework such as Transductive Experimental Design are disclosed. Generally, to enhance a keyword suggestion tool, an active learning module trains a model to predict whether a term is relevant to a user. The model is then used to present the user with terms that have been determined to be relevant based on the model so that an online advertisement service provider may more efficiently provide a user with terms that are semantically related to a seed set.

Abstract: Method and device to collect multiplex data simultaneously in analyte detection and analyze the data by experimentally trained software (machine-learning) is disclosed. Various ways (magnetic particles and microcoils) are disclosed to collect multiple reporter (tag) signals. Multiplex detection can increase the biomolecule analysis efficiency by using small sample size and saving assay reagents and time. Machine learning and data analysis schemes are also disclosed. Multiple affinity binding partners, each labeled by a unique reporter, are contacted with a sample and a single spectrum is taken to detect multiple reporter signals. The spectrum is deconvoluted by experimentally trained software to identify multiple analytes.

Abstract: A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating.

Abstract: A system and techniques are disclosed to provide a multi-agent software environment. The system provides several service modules that may be used by software programs to accomplish specific tasks. In one illustrative example, a first program module includes instructions to call core service software modules. The exemplary system further includes a second program module including instructions to call one of multiple intelligent service software modules. In some examples, an intelligent service software module includes program instructions that when executed perform an intelligent service function that includes execution of an intelligent engine. In some examples, the intelligent engine is callable by at least two of the intelligent service software modules.

Abstract: A media item recommendation rating system and method. A recommendation rating for media items is established and dynamically updated in response to media items being recommended to other users. A recommendation server or other device receives a report of a media item recommendation and updates a recommendation rating in response. The recommendation rating may also be updated based on how often a recommended media item is used or played. Thus, a media item's recommendation rating is affected by events relating to its recommendation, as opposed simple play-based ratings that are updated on any play action regardless of whether related to a recommendation or not. Simple play-based ratings do not distinguish between ordinary usages or plays and those resulting from recommendations. Recommendation of a media item to another user may be a better indicator of the user's likeability or popularity of a given media item, since a recommendation is an endorsement by another.

Abstract: There is described a method for computer-assisted processing of measured values detected in a sensor network, with the sensor network comprising a plurality of sensor nodes, which each feature one or more sensors for detection of the measured values, with the measured values of a number of adjacent sensor nodes being known in a sensor node. A multi-area neural network will be mapped onto a corresponding sensor network by the inventive method, which creates the opportunity, with the aid of the information from adjacent sensors, even with incorrect or failed measurements of a sensor node, of guaranteeing detection of a global situation at the location of the sensor node. A sensor network operated with such a method is in such cases more robust against the failure of a few sensors, since a corresponding measured value can be estimated in a suitable way, so that the measurement not available can be replaced by the estimated measured value.

Abstract: An information processing system having neuron-like signal processors that are interconnected by synapse-like processing junctions that simulates and extends capabilities of biological neural networks. The information processing systems uses integrate-and-fire neurons and Temporally Asymmetric Hebbian learning (spike timing-dependent learning) to adapt the synaptic strengths. The synaptic strengths of each neuron are guaranteed to become optimal during the course of learning either for estimating the parameters of a dynamic system (system identification) or for computing the first principal component. This neural network is well-suited for hardware implementations, since the learning rule for the synaptic strengths only requires computing either spike-time differences or correlations. Such hardware implementation may be used for predicting and recognizing audiovisual information or for improving cortical processing by a prosthetic device.

Abstract: This folding, compact document cover is an apparatus that practically and conveniently protects a specially folded U.S. Geological Survey (USGS) topographic map or virtually any other large-format document with its French-folded binding and easy to handle protective element that is capable of allowing the reader to flip between quadrants without the hassle of continued folding and refolding or rolling and unrolling.

Abstract: A health assessor for assessing health of a target element within a multi-element system includes multiple sensors, each being operatively coupled to the target element to produce measures of the target element. The health assessor also includes measure collectors, each of which collects a measure from one of the sensor. In addition, the health assessor includes evaluators. Each evaluator evaluates at least a subset of all the measures collected by the measure collectors in accordance with (1) a predefined evaluation definition for the respective evaluator and (2) at least a subset of all historical measures to provide an assessment. A probabilistic reasoning network is coupled to the evaluators to receive the assessment from each of the evaluators and to combine all the assessments in accordance with a pre-configured reasoning definition so as to provide an overall health assessment of the target element. A health assessment system including the health assessor is also described.

Abstract: The present invention relates to the field of collective intelligence. More specifically, to the collaborative acquisition of knowledge and the relationships among said knowledge and the application of acquired knowledge and relationships to solving problems. The present invention presents an interface to a community of users that will create nodes and relationships in an artificial neural network and then weight each node and relationship through votes from one or more users.

Abstract: Split machine learning systems can be used to generate an output for an input. When the input is received, a determination is made as to whether the input is within a first, second, or third range of values. If the input is within the first range, the output is generated using a first machine learning system. If the input is within the second range, the output is generated using a second machine learning system. If the input is within the third range, the output is generated using the first and second machine learning systems.

Abstract: A method is provided for a virtual sensor system. The method may include obtaining data records associated with a plurality of input parameters and at least one output parameter and adjusting values of the input parameters based on autocorrelation of respective input parameters. The method may also include reconfiguring the input parameters based on cross-correlation of respective input parameters relative to the output parameter and establishing a first virtual sensor process model indicative of interrelationships between the adjusted and reconfigured input parameters and the output parameter.

Abstract: A neural network is trained with input data. The neural network is used to rescale the input data. Errors for the rescaled values are determined, and neighborhoods of the errors are used adjust connection weights of the neural network.

Abstract: The present invention relates to a system and methodology to facilitate multiattribute adjustments and control associated with messages and other communications and informational items that are directed to a user via automated systems. An interface, specification language, and controls are provided for defining a plurality of variously configured groups that may attempt to communicate respective items. Controls include the specification of priorities and preferences as well as the modification of priorities and preferences that have been learned from training sets via machine learning methods. The system provides both a means for assessing parameters used in the control of messaging and communications and for the inspection and modification of parameters that have been learned autonomously.