Abstract: A field ready, unsupervised-use ready, method and apparatus for audio fitting a hearing aid is described in a hand held configuration having paired comparisons (hearing selections) stored in and derivable from a memory therein. The paired comparisons are presented one at a time to a user and a preferred selection for each paired comparison is made by a select indicator after the user toggles back and forth between the selections for as many times necessary in determining their preferences. A genetic algorithm converges all the preferences upon a single solution. Crossover and mutation genetic algorithm operators operate on a linear range of indexes representative of parametric values of the pairs. A fully integrated hearing aid having all the above described features incorporated therein is also presented.

Abstract: A system and method for controlling a plant having a minimum phase transfer function P(s) and given an input signal u, the plant having an output y and a plant frequency range comprising a transfer function J(s) comprising the product of a high gain filter J1(s) having a gain k1 sufficient that ? J ? ( ? ) ? > [ 1 + 1 ? ] when |?|??1 and |1+J(?)|>1/M for all ? wherein ?1 is selected to obtain a desired time response, and a low pass filter J2(s) selected such that |1+J(?)|>1/M for all ? and J(s) is strictly proper, wherein ?<1 and M>1 and ? and M are selected to meet a desired sensitivity requirement. An error signal e is calculated comprising the difference between the system input signal u and the plant output signal y, and the error signal modified according to the transfer function C(s)=P?1(s)J1(s)J2(s) and inputting the error signal into the plant. The system and method can be extended to unstable invertible plants.

Abstract: A quantified belief propagation (QBP) algorithm receives as input an existentially quantified boolean formula (QBF) of existentially quantified boolean variables, universally quantified variables, and boolean operators. A tripartite graph is constructed, and includes (i) there-exists nodes that correspond to and represent the existentially quantified variables, (ii) for-all nodes that correspond to and represent the universally quantified variables, and (iii) sub-formula nodes that correspond to and represent sub-formulas of the QBF. A set of boolean values of the existentially quantified variables is found by (i) passing a first message from an arbitrary sub-formula node to an arbitrary for-all node, and (ii) in response, passing a second message from the arbitrary for-all node to the arbitrary sub-formula node.

Abstract: Some examples are directed to determining a frequency of occurrence of a first value in a component of a first plurality of candidate solutions for an optimization problem where the first plurality of candidate solutions has converged on a sub-optimal solution during a computer simulation that tests fitness of the first plurality of candidate solutions. Some examples are further directed to determining a second value that is inversely biased from the frequency of occurrence of the first value. Some examples are further directed to including the second value in a component of at least a portion of a second plurality of candidate solutions, where the component of the at least the portion of the second plurality of candidate solutions corresponds to the component of the first plurality of candidate solutions.

Abstract: The present invention provides methods and apparatus for generating human readable output with a correlation between one or more genetic traits and one or more medicaments.

Abstract: Disclosed herein is an evaluation predicting device including: an estimating section configured to define a plurality of first latent vectors, a plurality of second latent vectors, evaluation values, a plurality of first feature vectors, a plurality of second feature vectors, a first projection matrix, and a second projection matrix, express the first latent vectors and the second latent vectors, and perform Bayesian estimation with the first feature vectors, the second feature vectors, and a known the evaluation value as learning data, and calculate a posterior distribution of a parameter group including the first latent vectors, the second latent vectors, the first projection matrix, and the second projection matrix; and a predicting section configured to calculate a distribution of an unknown the evaluation value on a basis of the posterior distribution of the parameter group.

Abstract: Computer implemented techniques for classifying mental states of individuals are described. The techniques determine sets of words that are associated with multiple groups having different mental status, and a classification model is used to classify one group against another group. Furthermore, by determining points of intersection of words between a first group and second group, words that are statistically predictive terms and that are unique to each group, to provide further predictive features for differentiating the multiple cohorts.

Abstract: An agent-based modeling system for predicting and/or analyzing brain behavior is provided. The system includes a computer processor configured to define nodes and edges that interconnect the nodes. The edges are defined by physiological interactions and/or anatomical connections. The computer processor further defines rules and/or model parameters that define a functional behavior of the nodes and edges. The computer processor assigns the nodes to respective brain regions, and the rules and/or model parameters are defined by observed physiological interaction of the nodes that are functionally and/or structurally connected by said edges of brain regions to thereby provide an agent-based brain model (ABBM) for predicting and/or analyzing brain behavior.

Abstract: Provided is a data processing method for clinical decision support system. The data processing method provides an algorithm capable of performing parsing based on an Ontology technique and automatically updating rule database in order to reduce time and labor overloads accompanied by update of the rule database. According to an aspect, the data processing method includes inferring input data having a natural language format based on an Ontology technique to recognize at least one input rule included in the input data; inferring storage data having a natural language format and stored in rule database based on the Ontology technique to recognize at least one storage rule associated with the input rule from the storage data; comparing the input rule to the storage rule using a Self Evolutionary Rule-base algorithm; and updating the storage data stored in the rule database to the input data according to the result of the comparison.

Abstract: Described is a method for particle swarm optimization (PSO) utilizing a random walk process. A plurality of software agents is configured to operate as a cooperative swarm to locate an optimum of an objective function. The method described herein comprises two phases. In a first phase, the plurality of software agents randomly explores the multi-dimensional solution space by undergoing a Brownian motion style random walk process. In a second phase, the velocity and position vectors for each particle are updated probabilistically according to a PSO algorithm. By allowing the particles to undergo a random walk phase, the particles have an increased opportunity to explore their neighborhood, land in the neighborhood of a true optimum, and avoid prematurely converging on a sub-optimum. The present invention improves on what is currently known by increasing the success rate of the PSO algorithm in addition to reducing the required computation.

Abstract: A method for generating switching plans to restore power to out-of-service areas after fault isolation through back feeding. A chromosome architecture is defined to create chromosomes representing candidate post-restoration systems. The chromosomes are evaluated are repeatedly genetically altered until an acceptable solution is identified. The solution identifies a plurality of switching operations that back feed power to the out-of-service areas in the most optimal manner.

Abstract: Embodiments of a system and method for seeding rule-based machine learning models include generating a set of seed rules including one or more rules resulting from one or more previously performed machine learning operations and one or more randomly or pseudo-randomly generated rules. Embodiments may include performing one or more machine learning operations on the set of seed rules to generate a new set of rules for determining whether a pair of information items have a specific relationship. Generating the new set of rules from the seed rules may be faster than generating a set of rules from random data. Embodiments may also include applying the new set of rules to one or more pairs of information items to identify at least one pair of information items as having the specific relationship. For instance, the rules may be applied to identify pairs of information items that are duplicate pairs.

Abstract: Control systems, apparatus, and methods can apply quantum algorithms to control a control object in the presence of uncertainty and/or information risk. A self-organizing controller can include a quantum inference unit that can generate a set of robust control gains for a controller that can meet the control objectives for the particular realization of the control object. In one embodiment, the quantum inference unit can include a quantum correlator configured to generate a plurality of quantum states based on a plurality of controller parameters and a correlation type. In this embodiment, the quantum inference unit can also include a quantum optimizer configured to select the correlation type of the quantum correlator and to select a quantum state from the plurality of the quantum states. The self-organizing controller can control the control object with one or more controller gains that are based on the selected quantum state.

Abstract: A method is disclosed that generates design profiles and a surface therefrom that promote water drainage from soil in an area of interest. Topographical data describing an initial surface of the area of interest are used to form initial profiles. The method generates the design profiles using the initial profiles and desired design parameters such as minimum slope, maximum slope, optimal slope, maximum depth, optimal depth, starting elevation, ending elevation, rules of curvature, and rules of earth balancing. The method generates design surface profiles so that the cut volumes and fill volumes of the soil are at or near balance as much as possible from the available soil in the area of interest.

Abstract: A method or apparatus for changing a performance level of a disk drive from an initial performance level to a target performance level, wherein the disk drive includes a profile table. The method including determining an initial performance level, determining a target performance level, determining constant offset values that cause the performance level of the disk drive to match the target performance level, and programming a constant offset table of the profile table with the constant offset values.

Abstract: An example system and process where some operations include determining that a plurality of values satisfy one or more constraint conditions for an optimization problem. The operations further include randomly selecting a set of one or more values from the plurality of values after determining that the plurality of values satisfy the one or more constraint conditions. The operations further include including the set of one or more values in a candidate solution for the optimization problem. The including the set of one or more values in the candidate solution causes the candidate solution to comply with the one or more constraint conditions for the optimization problem prior to running a computer based simulation for the optimization problem.

Abstract: Computer-implemented systems and methods are provided for generating a data model. A variable predictiveness determination is performed on the population of candidate variables. A plurality of variables from the population of candidate variables are selected as a selected set based on the variable predictiveness values. A plurality derived variables are generated based on variables in the rejected set without consideration of any variables in the selected set. One or more derived variables are selected as based on derived variable predictiveness values of the derived variables, and the selected set and the one or more selected derived variables are stored as the model input variables for the data model.

Abstract: The present invention is a biometric security system and method operable to authenticate one or more individuals using physiological signals. The method and system may comprise one of the following modes: instantaneous identity recognition (MR); or continuous identity recognition (CIR). The present invention may include a methodology and framework for biometric recognition using physiological signals and may utilize a machine learning utility. The machine learning utility may be presented and adapted to the needs of different application environments which constitute different application frameworks. The present invention may further incorporate a method and system for continuous authentication using physiological signals and a means of estimating relevant parameters.

Abstract: An example system and process with some operations that include determining a plurality of constraint compliant values and determining that a candidate solution, which was created from two or more of a plurality of candidate solutions via a crossover operation, fails to comply with a constraint condition for an optimization problem, wherein the two or more of the plurality of candidate solutions comply with the constraint condition. In some examples, the operations further include assigning a value from the plurality of constraint compliant values to a constrained variable of the candidate solution in response to the determining that the candidate solution fails to comply with the constraint condition, wherein the value assigned from the plurality of constraint compliant values is randomly selected from the plurality of constraint compliant values, and wherein the at least one constrained variable is constrained to comply with the constraint condition.

Abstract: The disclosure relates to a method of generating a diverse set of variants to screen improved and novel properties within the variant population, a system for creating the diverse set of variants, and the variant peptides.

Abstract: Embodiments relate to systems and methods for generating interpolated data sets converging to optimized results using iterative overlapping inputs. A database can store sets of operational data, such as financial, medical, climate or other information. For given data, a portion of the input data can be known or predetermined, while for a second portion can be unknown and subject to interpolation. The interpolation engine can generate a conformal interpolation function and interpolated input sets that map to a set of target output data. In aspects, to attempt to drive the set of interpolated input data and/or other results to an optimized and/or steady-state value or values, the interpolation engine can generate a set of shifts or alterations to the set of combined input data as that data exists, to create a set of iterative overlapping input data.

Abstract: A policy scheduler scheduling a policy is provided. The policy scheduler receives the policy for a system and information of a current state of the system. The policy scheduler evaluates one or more rules based on the current state of the system and generates a new rule via an evolutionary algorithm based on the information of the current state of the system. The policy scheduler adds the newly generated rule into the one or more rules and schedules the policy based on the one or more rules including the newly generated rule.

Abstract: Methods for performing genetic algorithm-based feature selection are provided herein. In certain embodiments, the methods include steps of applying multiple data splitting patterns to a learning data set to build multiple classifiers to obtain at least one classification result; integrating the at least one classification result from the multiple classifiers to obtain an integrated accuracy result; and outputting the integrated accuracy result to a genetic algorithm as a fitness value for a candidate feature subset, in which genetic algorithm-based feature selection is performed.

Abstract: In one embodiment, a method includes accessing event information associated with an event listing, calculating one or more category probabilities for the event listing based in part on the event information associated with the event listing using one or more categorization models, and associating with the event listing the category corresponding to the category probability for each category probability greater than or equal to a threshold probability.

Abstract: A computer-based method for analyzing the incentives and predicting the future actions of agents is described. The method includes storing, in a computer memory, data relating to previously observed behavior of at least one of an agent of interest and agents that can be assumed to hold similar utilities to the agent of interest, thereby defining an agent class, deriving with a processing device and based on the stored data, a set of unique incentives that agents in the agent class would have to hold for their observed behavior to be considered rational by an observer of the behavior, observing a short sequence of behavior of the agent of interest, inputting the observed behavior sequence to an analyzer, deriving specific goals of the agent driving that behavior instance, and predicting future actions of the agent of interest based on a maximization of reward for the agent of interest and the derived goals, the reward based on the derived incentives.

Abstract: An example system and process with some operations that include determining a plurality of values, wherein assignment of any of the plurality of values to one or more constrained variables of a candidate solution causes the candidate solution to comply with a constraint condition of an optimization problem, wherein the candidate solution complies with the constraint condition prior to being mutated via a computer-based simulation that tests fitness of the candidate solution. The operations further include after the candidate solution is mutated, determining that the candidate solution fails to comply with the constraint condition. The operations further include assigning at least one value, which is randomly selected from the plurality of values, to the one or more constrained variables of the candidate solution.

Abstract: A method for dynamically selecting string analysis algorithms can begin with the training of the dynamic string analysis handler of a string analysis module to effectively handle a subset of string queries having contextual metadata received from a client application in an instructional environment. The effectiveness of the training module can be based upon feedback from the client application. Upon completion of the training, a string analysis algorithm selection policy can be synthesized. The string analysis algorithm selection policy can correlate a context of a string query in the subset to the usage of a string analysis algorithm. When in the operational environment, the dynamic string analysis handler can dynamically handle string queries having contextual metadata received from the client application in accordance with the string analysis algorithm selection policy. The string analysis algorithm to be used for a string query can be dynamically and independently determined.

Abstract: Performance of a manufacturing tool is optimized. Optimization relies on recipe drifting and generation of knowledge that capture relationships among product output metrics and input material measurement(s) and recipe parameters. Optimized recipe parameters are extracted from a basis of learned functions that predict output metrics for a current state of the manufacturing tool and measurements of input material(s). Drifting and learning are related and lead to dynamic optimization of tool performance, which enables optimized output from the manufacturing tool as the operation conditions of the tool changes. Features of recipe drifting and associated learning can be autonomously or externally configured through suitable user interfaces, which also can be drifted to optimize end-user interaction.

Abstract: In accordance with aspects of the disclosure, a system and methods are provided for managing resource allocation by generating a finance-investment plan relative to one or more time intervals while maintaining a cash reserve at a predetermined threshold based on information related to financial activities including asset related activities and liability related activities. The systems and methods may include evaluating accounts receivable patterns for each asset to determine cash surplus ranges within the one or more time intervals, evaluating accounts payable patterns for each liability to determine cash flow gaps within the one or more time intervals, maintaining an asset-to-liability ratio during the one or more time intervals, and generating the finance-investment plan while maintaining the cash reserve at the predetermined threshold within the one or more time intervals based on the asset-to-liability ratio and potential cash flow forecasting schemes for each asset and liability.

Abstract: A system, method and device define ecosystem ontology, architecture, processes, process operands and operand states within a generic hierarchy to simplify navigating system complexity. The invention facilitates identification of key process interdependencies and tradeoff best practices to optimize system workflow, information flow and value flow. It standardizes and aligns architecture, process and maturity models in the prior art.

Abstract: Autonomous biologically based learning tool system(s) and method(s) that the tool system(s) employs for learning and analysis of performance degradation and mismatch are provided. The autonomous biologically based learning tool system includes (a) one or more tool systems that perform a set of specific tasks or processes and generate assets and data related to the assets that characterize the various processes and associated tool performance; (b) an interaction manager that receives and formats the data, and (c) an autonomous learning system based on biological principles of learning. Objectively generated knowledge gleaned from synthetic or production data can be utilized to determine a mathematical relationship among a specific output variable and a set of associated influencing variables. The generated relationship facilitates assessment of performance degradation of a set of tools, and performance mismatch among tools therein.

Abstract: An information extraction system and methods of operating the system are provided. In particular, an information extraction system for performing meta-extraction of named entities of people, organizations, and locations as well as relationships and events from text documents are described herein.

Abstract: Disclosed is a novel passive fingerprinting technique based on DHCP messages to determine the device type and operating system. DHCP implementations are shown to vary among device types and have an effect on DHCP lease durations. To improve network address utilization, without introducing any protocol changes, the present invention provides a new leasing strategy which takes into account device types. This strategy, compared to current approaches, improves the address utilization sixfold without considerably increasing DHCP overhead.

Abstract: An information processing apparatus for generating a similarity determination algorithm determining a similarity between a pair of data.

Abstract: The invention relates to selecting a set of candidate genes from a pool of genes. The method comprising receiving a set of gene data; arranging the set of gene data into a set of clusters with similar profiles by use of a clustering algorithm; and inputting the set of clusters into a genetic algorithm to select a set of candidate genes from the set of clusters. The method thus relates to hybrid between selection by clustering computation and selection by evolutionary computation. This hybrid is also referred to as an evolutionary clustering algorithm (ECA).

Abstract: Methods and system employing the same for optimizing an objective function are provided. The objective function assesses the quality of a candidate solution. One or more variables of an objective function are selected as pivot variables. Each of the variables include one or more candidate values. An upper bound function is generated from the objective function, where the pivot variables are held fixed. For each combination of the candidate values, one or more candidate solutions are searched using the upper bound function. One or more optimal solutions are selected from among the solutions to the searches.

Abstract: The method of predicting gas composition in a multistage separator includes solutions to the regression problem of gas composition prediction that are developed using an ensemble of hybrid computational intelligence (CI) models. Three separate homogeneous and one heterogeneous ensemble of hybrid computational intelligence (EHCI) models are developed using a parallel scheme. The homogeneous models have the same types of CI models used as base learners, and the heterogeneous model has of different types of CI models used as base learners. Various popular CI models, including multi-layer perceptron (MLP), support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), are used as base learners of ensemble models.

Abstract: Provided embodiments include a method, a system, a device, and an article of manufacture. A system for terminating a genetic algorithm (GA), where the GA uses an iterator and generates one best solution per iteration, includes a memory, an iterative processor, and a terminating processor. The memory is provided for storing a plurality of best solutions generated in a plurality of iterations of the GA. One of the best solutions generated in one of the iterations is stored in the memory if the one of the best solutions is better than a previous one of the best solutions generated in a previous one of the iterations. The iterative processor computes a variance of the plurality of the best solutions stored in the memory. The terminating processor terminates the iterator when the variance is less than or equal to a predetermined threshold.

Abstract: A contingency planning system apparatus including a world description subsystem responsive to data representing a description of the world related to a planning problem and configured to instantiate a plurality of world codelets in a codelet workspace. A coderack subsystem is responsive to a slipnet network and is configured to instantiate a plurality of codelets in the codelet workspace each having one or more slots for binding with binding sites of lower level codelets and configured to monitor constraint changes thereto and propagate those changes to any related codelets. A temperature subsystem is configured to detect when temperature in the codelet workspace has reached a predetermined level indicating a satisfactory solution to the planning problem has been achieved.

Abstract: An information processing apparatus is disclosed. The apparatus may include a processing method preparation unit for generating a first processing method. The apparatus may include an evaluator generation unit for generating an evaluator based on a genetic algorithm, using one or more input data sets, each of which may include data and a corresponding evaluation value. The apparatus may include an evaluation unit for calculating, using the evaluator, a first evaluation value using first output data obtained by processing the data using the first processing method. The apparatus may include a processing method update unit for generating a second processing method such that a second evaluation value calculated by the evaluator, using second output data obtained by processing the data using the second processing method, is higher than the first evaluation value. The apparatus may include an output unit that outputs the second output data and the second processing method.

Abstract: Embodiments of a system and method for identifying and correcting marginal false positives in machine learning models may include, based on reference data that includes pairs of information items and labels indicating whether pairs of information items have a specific relationship, generating a first machine learning model for determining whether pairs of information items have that relationship. Embodiments may include identifying one or more false positive pairs (e.g., a pair of information items that the first machine learning model indicates as having the specific relationship and which are labeled within the reference data as not having that relationship). Embodiments may include selecting identified false positive pairs as candidates for correction.

Abstract: A system, method and computer program product for identifying an entity and characteristics thereof, including identifying an entity and characteristics thereof, wherein the characteristics include as demographics, and psychographics behavior; and identifying the entity and the characteristics thereof based on patterns of social interactions and relations with other entities.

Abstract: A method of optimizing a plurality of objectives includes the steps of initializing a set of simplices; selecting a simplex from the set of simplices; computing one or more weights based at least in part on the selected simplex; and generating a point on a tradeoff surface by utilizing the one or more weights in a weighted-sum optimization.

Abstract: Methods of analyzing solutions to a failure of a product are provided. A method comprises evaluating the fitness of solutions associated with a plurality of individuals in a population for the failure. Each of the plurality of individuals comprises a dominant genotype and a recessive genotype. Each genotype represents a solution for addressing a potential failure of the product. A fitness function is applied to the dominant genotype of each individual. The fitness function is configured to determine a confidence value for one or more of the solutions based on historical data regarding the solutions that is stored in a database. The method further comprises storing previously encountered genotypes in the recessive genotypes of the individuals and determining a proposed solution for addressing the failure of the product based on the fitness evaluation.

Abstract: Object recognition techniques are disclosed that provide both accuracy and speed. One embodiment of the present invention is an identification system. The system is capable of locating objects in images by searching for local features of an object. The system can operate in real-time. The system is trained from a set of images of an object or objects. The system computes interest points in the training images, and then extracts local image features (tokens) around these interest points. The set of tokens from the training images is then used to build a hierarchical model structure. During identification/detection, the system computes interest points from incoming target images. The system matches tokens around these interest points with the tokens in the hierarchical model. Each successfully matched image token votes for an object hypothesis at a certain scale, location, and orientation in the target image. Object hypotheses that receive insufficient votes are rejected.

Abstract: A method may use a genetic algorithm to varying prediction parameters in forecasting software to obtain optimal predictions is disclosed. The method identifies parameters that can be varied and by modifying the parameters, the predictions of the forecasting software improve. The method uses sample data to train and validate the forecast and the optimal forecasting parameters are determined.

Abstract: A method of monitoring an evolving system, the method including the steps of: obtaining a plurality of sensor data streams relating to outputs from sensors monitoring said system, wherein at least one of said sensors monitors a condition of said system, and wherein at least one of said sensors monitors a causal agent for said condition; iteratively constructing a plurality of functional nests, each functional nest being a functional formed from a combination of selected functionals from a basic set of functionals; determining an output data stream for each functional nest by inputting said sensor data streams into said functional nests; selecting a functional nest from said plurality of functional nests based on said output data streams; and using said selected functional nest to monitor said system.

Abstract: The present invention belongs to the field of functional proteomics and more particularly to the field of protein aggregation. The invention discloses a method for interfering with the function of a target protein and uses a non-naturally, user-designed molecule, designated as interferor, that has a specificity for a target protein and which induces aggregation upon contact with said target protein. The present invention also discloses such interferor molecules and their use in agrobiotech applications.

Abstract: Embodiments of the invention provide a method, computer program and apparatus for processing a computer message, the method comprising: upon receipt of a computer message at a computer, classifying the computer message and assigning it a message cluster identification in dependence thereon; and, utilizing a message template to trans-denotate the message, wherein the message template is selected in dependence on the message cluster identification.

Abstract: The classification problem is commonly encountered when a finite sample of data is leveraged to determine the probabilistic relationship between a category label c and a multivariate coordinate x for an entire population. Solving this problem requires approximating the optimal classifier, a function of x that evaluates the conditional probability p(c|x) in an optimal way. This patent describes a new method, machine, and algorithm named MoRPE. MoRPE can be used to approximate optimal classifiers. MoRPE is a machine learning method for probabilistic classification based on Monotonic Regression of a Polynomial expansion. MoRPE can easily understood in terms of the differences between MoRPE and another commonly understood method known as Fisher's Quadratic Discriminant. MoRPE can approximate an optimal classifier with remarkable and unprecedented precision in common scenarios.