Abstract: Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multilevel pattern recognition schemes.

Abstract: The invention integrates software and knowledge engineering with robotics technology to improve robot-to-robot and robot-to-human conversational interface and provide on-the-fly translations of situational requirements into adaptive behavior models and further down to service scenarios for a collaborative robot teams.

Abstract: A method of diagnosing an aircraft by analyzing at least one spectrum obtained from measurements taken on the aircraft, in particular from vibration measurements, in which the spectrum (spectra) to be analyzed is/are input to a calculation module (23) together with at least one first reference spectrum, the calculation module determining a diagnosis value (Diag) as a function of the spectrum (spectra) to be analyzed, of the reference spectrum (spectra), and of parameters determined from at least two reference spectra including the first reference spectrum.

Abstract: To train a machine learning system, a set of different values of one or more photoresist parameters, which characterize behavior of photoresist when the photoresist undergoes processing steps in a wafer application, is obtained. A set of diffraction signals is obtained using the set of different values of the one or more photoresist parameters. The machine learning system is trained using the set of measured diffraction signals as inputs to the machine learning system and the set of different values of the one or more photoresist parameters as expected outputs of the machine learning system.

Abstract: This invention combines the modeling capabilities of causal models, business process models, and dimensional reporting to allow the locating of root causes of problems. The causal models let the user explore the effects of other parts of the system have on the process in question through accessing dimensional reports. By recording and annotating the problem solving flow, and deriving characteristics of the problem solved, future users can be assisted in other problem solving sessions. Ad-hoc reports created to aid in the problem solving can be attached in the causal model and process model for future use.

Abstract: A method for adapting to a shift in document content according to one embodiment of the present invention includes receiving at least one labeled seed document; receiving unlabeled documents; receiving at least one predetermined cost factor; training a transductive classifier using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents; classifying the unlabeled documents having a confidence level above a predefined threshold into a plurality of categories using the classifier; reclassifying at least some of the categorized documents into the categories using the classifier; and outputting identifiers of the categorized documents to at least one of a user, another system, and another process. Methods for separating documents are also presented. Methods for document searching are also presented.

Abstract: An HTM node learns a plurality of groups of sensed input patterns over time based on the frequency of temporal adjacency of the input patterns. An HTM node receives a new sensed input, the HTM node assigns probabilities as to the likelihood that the new sensed input matches each of the plurality of learned groups. The HTM node then combines this probability distribution (may be normalized) with previous state information to assign probabilities as to the likelihood that the new sensed input is part of each of the learned groups of the HTM node. Then, as described above, the distribution over the set of groups learned by the HTM node is passed to a higher level node. This process is repeated at higher level nodes to infer a cause of the newly sensed input.

Abstract: A nondestructive inspection apparatus includes a sensor unit for detecting vibrations transmitted through a test object from a vibration generator and a signal input unit for extracting a target signal from an electric signal outputted from the sensor unit. An amount of characteristics extracting unit is also included for extracting multiple frequency components from the test signal as an amount of characteristics. Further, a decision unit has a competitive learning neural network for determining whether the amount of the characteristics belongs to a category, wherein the competitive learning neural network has been trained by using training samples belong to the category representing an internal state of the test object, wherein distributions of membership degrees of the training samples are set in the decision unit.

Abstract: An embodiment of a method of ordering operations begins with a first step of a computing entity storing a training data set, which includes previously executed operation pairs, attribute data, and relative cost data. In a second step, the computing entity forms a predictor. In a third step, the computing entity applies the predictor to an operation pair of first and second to-be-executed operations to determine a predicted relative cost between the first and second to-be-executed operations. Another embodiment of the method comprises the first step, the second step, an alternative third step, and a fourth step. In the alternative third step, the computing entity applies the predictor to to-be-executed operations in operation pairs to determine a predicted relative cost between first and second operations for each operation pair. In the fourth step, the computing entity employs the operation pairs and the predicted relative costs to sort the to-be-executed operations.

Abstract: A method of training connections in a memory arrangement includes training a connection between a memory section and a receiver portion of a controller for controlling the memory arrangement before or simultaneously with a training of essentially all other connections between elements of the memory arrangement that are to be trained.

Abstract: A method for data analysis according to various aspects of the present invention generally includes selecting a conclusion from a plurality of conclusions for one or more test data sets by generating discriminators from one or more threshold indicators associated with the conclusion, selecting a portion of the discriminators, ranking the discriminators in a sequence, and applying the sequence of discriminators to one or more test data sets to select the conclusion.

Abstract: The present invention relates to a charged particle beam apparatus which employs a scanning electron microscope for sample inspection and defect review. The present invent provides solution of improving imaging resolution by utilizing a field emission cathode tip with a large tip radius, applying a large accelerating voltage across ground potential between the cathode and anode, positioning the beam limit aperture before condenser lens, utilizing condenser lens excitation current to optimize image resolution, applying a high tube bias to shorten electron travel time, adopting and modifying SORIL objective lens to ameliorate aberration at large field of view and under electric drifting and reduce the urgency of water cooling objective lens while operating material analysis. The present invent provides solution of improving throughput by utilizing fast scanning ability of SORIL and providing a large voltage difference between sample and detectors.

Abstract: A system and integration infrastructure to provide a distributed matrix or neural network of connected real-time decision support modules designed to perform business intelligence evaluations in real time. The system and integration infrastructure provide a network of intelligence superimposed upon any company's existing IT data centers, and cloud computing connections. The system is highly customizable to the unique business model deployed by the client company within the best practices of the client company's industry. Whether or not the client company has integrated their diverse enterprise systems, the elements of the matrix are annealed to the various data sources, transaction logs and client software installations currently deployed. These matrix elements or neurons are designed to house critical operational data, determined by the operational model of the client company to be of critical importance.

Abstract: A method for measuring tastes, which can better simulate the human gustation than known methods, as well as a taste sensor, computer program and an apparatus for measuring tastes, is disclosed. In this method, data processing is carried out by a two-phase radial basis function neural network. That is, by sensors, each of which sensors can quantify at least one component representing, individually or cooperatively, the taste of saltiness, sourness, sweetness, umami or bitterness, to obtain a response value from each sensor, and each of the obtained response values is input to a first phase radial basis function neural network to calculate the concentration of each component from each response value.

Abstract: There is provided a method of generating a pulmonary index value of a patient, which includes receiving two or more measured patient parameters, wherein at least one of the measured parameters originates from a pulmonary sensor; and computing the pulmonary index value based on the two or more measured patient parameters.

Abstract: The present technique provides a system and method for dynamic configuration of medical diagnostic systems using distributable multi-component configuration files having extractable component-specific configuration data. The component-specific configuration data is extractable and processable at each component receiving a broadcast of the distributable multi-component configuration file. If a configuration change is desired in the system or in a particular component, then the change is made via the distributable multi-component configuration file. For example, the foregoing distribution and extraction techniques may be performed during operation of the distributed medical diagnostic system in response to a global or application change, such as different medical diagnostic applications. Accordingly, the present technique provides a flexible and architecture-independent mechanism for configuring components of a distributed medical diagnostic system.

Abstract: Systems, methods, and apparatus are provided using signals from a set of tactile sensors mounted on a surface to determine the three-dimensional morphology (e.g., size, shape, orientation, and/or position) and texture of objects of arbitrary shape. Analytical, numerical, and/or neural network approaches can be used to interpret the sensory data.

Abstract: According to an aspect of the present invention, there is provided with a data division apparatus which divides multi-dimensional data, including: a data input unit which inputs multi-dimensional data; a division plane candidate creator which creates a plurality of division plane candidates for dividing the multi-dimensional data; a data provisional division unit which provisionally divides the multi-dimensional data by using the division plane candidate to generate clusters; a model generator which generates models from the clusters; an evaluation value calculator which calculates an evaluation value on the basis of the generated models and the multi-dimensional data; a division candidate selector which compares evaluation values respectively corresponding to the division plane candidates and selects a division plane candidate having a highest evaluation value; and a data division unit which divides the multi-dimensional data by using the selected division plane candidate.

Abstract: In a method for the processing of radiological images for the detection of opacities in a radiography image, an algorithm is proposed for detecting opacities according to their contrasts. To determine a contrast that is not over-estimated in each element, an algorithm is proposed for computing the background intensity by means of a sliding-window filtering. This type of sliding-window filtering computes the set of mean values of grey levels about an immediate environment of a region of elements whose size depends on the size of the opacity to be detected. The algorithm considers a background intensity of the region of elements to be the lowest mean value grey levels among the set of mean values of grey levels.

Abstract: A system and method for processing information in unstructured or structured form, comprising a computer running in a distributed network with one or more data agents. Associations of natural language artifacts may be learned from natural language artifacts in unstructured data sources, and semantic and syntactic relationships may be learned in structured data sources, using grouping based on a criteria of shared features that are dynamically determined without the use of a priori classifications, by employing conditional probability constraints.

Abstract: A method includes retrieving a signal that measures an activity of a cardiac system. A heart beat model is created based on the retrieved signal, and heart beats are detected using the heart beat model. The model can be trained using a machine learning technique and also respiratory cycle phase information can be used in training the model. A related algorithm, apparatus and computer program are also included.

Abstract: A system for identifying radionuclide emissions is described. The system includes at least one processor for processing output signals from a radionuclide detecting device, at least one training algorithm run by the at least one processor for analyzing data derived from at least one set of known sample data from the output signals, at least one classification algorithm derived from the training algorithm for classifying unknown sample data, wherein the at least one training algorithm analyzes the at least one sample data set to derive at least one rule used by said classification algorithm for identifying at least one radionuclide emission detected by the detecting device.

Abstract: A method and system of creating an approximate kernel matrix to train a kernel machine. In one embodiment of the invention, a set of kernel machine training data is partitioned into a set of partitioned training data based on a set of partition parameters. The set of partition parameters includes one or more of, an axis-aligned grid location, or an axis-aligned grid resolution in one embodiment of the invention. A partition matrix that approximates a kernel machine kernel matrix is created from the set of partitioned training data and the partition matrix is used to train a kernel machine in one embodiment of the invention.

Abstract: In a particular embodiment, an apparatus is disclosed that includes at least one controllable energy consuming module and a prediction engine. The prediction engine includes detection logic configured to determine a historical usage pattern of the at least one controllable energy consuming module, prediction logic configured to create a prediction rule that predicts future usage of the at least one controllable energy consuming module based on the historical usage pattern, control logic configured to selectively control an energy level of the at least one controllable energy consuming module based on the prediction rule, and learning logic configured to update the prediction logic in response to detecting usage of the at least one controllable energy consuming module in a manner that differs from the usage predicted by the prediction rule.

Abstract: A method for configuring a machine learning model for use in a motion characterisation process. The method comprises, in a processing system, acquiring user characterisations for respective portions of at least one video sequence and configuring the model using the user characterisations and at least one property associated with the respective portions. Any inconsistency in the user characterisations is then determined, using the model, with an indication of the inconsistency being displayed. This allows the user to select at least one option for addressing the inconsistency, with the model being reconfigured based on the selected at least one option.

Abstract: A method of creating translingual text representations takes in documents in a first language and in a second language and creates a matrix using the words in the documents to represent which words are present in which language. An algorithm is applied to each matrix such that like documents are placed close to each other and unlike documents are moved far from each other.

Abstract: At least one embodiment of the invention addresses the problem of accidental user operation (e.g., dialing) of a mobile communication device such as a mobile telephone. In this regard, embodiments of the invention provide a proactive solution for preventing unintended operation of a mobile device based on various types of input data of the device, most notably sensory data.

Abstract: In general, the invention is directed to a technique for selection of parameter configurations for a neurostimulator using neural networks. The technique may be employed by a programming device to allow a clinician to select parameter configurations, and then program an implantable neurostimulator to deliver therapy using the selected parameter configurations. The parameter configurations may include one or more of a variety of parameters, such as electrode configurations defining electrode combinations and polarities for an electrode set implanted in a patient. The electrode set may be carried by one or more implanted leads that are electrically coupled to the neurostimulator. In operation, the programming device executes a parameter configuration search algorithm to guide the clinician in the selection of parameter configurations. The search algorithm relies on a neural network that identifies potential optimum parameter configurations.

Abstract: In one aspect, the invention is based on a process that combines information present in a joint distribution of the predictor variables and the variable (or variables) to be predicted. This information may be captured in the form of a table or other like data structure that includes a set of vectors (referred to as a “TAB”). The process uses the information in the TAB in conjunction with one or more rules. In one embodiment, a set of different rules are applied to the TAB to determine which rule in the set produces the most accurate predictions. The RULE that produces the most accurate predictions is then used in conjunction with observed information to make predictions.

Abstract: A method, system, and computer program product provides automated determination of the size of the sample that is to be used in training a neural network data mining model that is large enough to properly train the neural network data mining model, yet is no larger than is necessary.

Abstract: An information processing apparatus includes a higher node having a plurality of weak discriminators that have learned a learning sample of a first label and a learning sample of a second label that has a predetermined coordinate relationship with the learning sample of the first label, a first lower node having a plurality of weak discriminators that have learned the learning sample of the first label based on the discrimination result of the higher node and a second lower node that has no weak discriminator.

Abstract: A configurable scanner (1), adapted for contactless measurement of the depth and perimeter of a wound on a target body part (9), has a scan head (4), and a processor (3) for controlling a scanning procedure and analyzing the results. The scan head is translated along a substantially semicircular path (7) having a configurable radial distance from an imaginary axis, such that the imaginary axis is approximately coincident with an axis of the target (9). The scan head (4) projects a contour line having a calibrated length onto the target surface, and the processor (3) stores an image of the projected contour line captured by an image capturing device (11). The processor (3) analyzes a series of captured images to determine the coordinates in three axes of the projected contour line, creates therefrom a 3D model of the region of interest, and determines a depth and perimeter of the wound from the 3D model.

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: A system and method is disclosed which integrates a machine learning solution into a large scale, distributed transaction processing system using a supporting architecture comprising a combination of computer hardware and software. Methods of using a system comprising such supporting architecture provide application designers access to the functionality included in a machine learning solution, but might also provide additional functionality not supported by the machine learning solution itself.

Abstract: A method for simulating the entire superset of potential valid keyword regular expression requests constructed during an Internet browser search and converting the result sets into Environmental Bitmap data to enable efficient and accurate searching without requiring Browser Engine supercomputer cluster searching capabilities.

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 (l0-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, transductive feature selection and single feature using margin-based ranking. 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.

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: The invention is directed to a method for training at least one learning-capable system comprising the steps of providing a predetermined training data set corresponding to a predetermined number of subjects comprising a predetermined input data set and a predetermined outcome data set, augmenting the input data set and/or the outcome data set, and training each learning-capable system using the augmented input data set and/or the augmented outcome data set.

Abstract: The invention relates to a method of controlling a lighting system with multiple controllable light sources 3a, 3b and a system therefor. According to a first aspect, influence data of the lighting system are obtained, which data represent the effect of one or more of the light sources 3a, 3b on the illumination of one or more sections of an illuminated environment. In an optimization method, sets of control commands are continuously determined, a predicted light distribution for these control commands is determined from the influence data, and a colorimetric difference between the predicted light distribution and a target light distribution is determined. A plurality of adjustment steps are performed to minimize the colorimetric difference. According to a second aspect, a neural network is trained with the influence data and a set of control commands for controlling the lighting system is determined with the use of the neural network.

Abstract: An autonomous classification device which enables the creation of autonomous classifiers that are easy to deploy, adapt and optimize in the environment in which they are used. The classifier is autonomous in that it can perform three functions that define autonomic systems: automatically configure itself in an environment, optimize its performance using the environment and mechanisms for performance, and continually adapting to improve performance and heal itself in a changing environment.

Abstract: Techniques described herein create an accurate active-learning model that takes into account a sample selection bias of elements, such as images, selected for labeling by a user. These techniques select a first set of elements for labeling. Once a user labels these elements, the techniques calculate a sample selection bias of the selected elements and train a model that takes into account the sample selection bias. The techniques then select a second set of elements based, in part, on a sample selection bias of the elements. Again, once a user labels the second set of elements the techniques train the model while taking into account the calculated sample selection bias. Once the trained model satisfies a predefined stop condition, the techniques use the trained model to predict labels for the remaining unlabeled elements.

Abstract: A data processing device for processing time-sequence data includes a learning unit for performing self-organizing learning of a SOM (self-organization map) making up a hierarchical SOM in which a plurality of SOMs are connected so as to construct a hierarchical structure, using, as SOM input data which is input to the SOM, a time-sequence of node information representing a winning node of a lower-order SOM which is at a lower hierarchical level from the SOM.

Abstract: In an information processing apparatus, such as a robot that discriminates human faces, nodes are hierarchically arranged in a tree structure. Each of the nodes has a number of weak classifiers. Each terminal node learns face images associated with one label. An upper node learns learning samples of all labels learned by lower nodes. When a window image to be classified is input, discrimination is performed sequentially from upper nodes to lower nodes. When it is determined that the window image does not correspond to a human face, discrimination by lower nodes is not performed, and discrimination proceeds to sibling nodes.

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: This invention relates to a system (100) and method for selecting a signal processing program to be executed by a signal processor in a hearing aid. The system (100) comprises a single layer of neurons (200) each having one or more neighbours and being arranged in a memory device (106), wherein each neuron comprises a neuron vector referring to a specific acoustic situation and a neuron label referring to signal processing program associated with said specific acoustic situation. The system (100) further comprises a sensor adapted to detect an external acoustic situation and to define a sensor vector associated with said external acoustic situation, and wherein a processor (102) calculates a vector difference between said sensor vector and each of the neuron vectors, identifies that neuron providing the smallest vector difference, and selects a signal processing program referred to by a neuron label of that neuron.

Abstract: A brain-based device (BBD) for moving in a real-world environment has sensors that provide data about the environment, actuators to move the BBD, and a hybrid controller which includes a neural controller having a simulated nervous system being a model of selected areas of the human brain and a non-neural controller based on a computational algorithmic network. The neural controller and non-neural controller interact with one another to control movement of the BBD.

Abstract: A method for effecting a dialogue with an emulated brain. The method includes the step of receiving a query in the form of a semantic string. The semantic string is then parsed into basic concepts of the query. The basic concepts are then clumped into a clump concept. If the clump concept constitutes part of a dialogue, then the dialogue thread is activated by determining the context of the clump concept and assessing a potential reply from a group of weighted replies, which expected replies are weighted based on the parsed concepts produced in the step of parsing. The heaviest weighted one of the expected replies is selected and the weight of the selected reply after it is selected is downgraded. The selected reply is then generated for output in a sentence structure.

Abstract: The invention relates to a method and means for performing a diagnosis of a medical condition and, in particular, an allergy associated with the upper respiratory tract, using an artificial neural network.

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: Various embodiments for intelligent dump suppression in a computing environment following an error are provided. A plurality of historical information is considered in view of a current alert level to generate an output decision. The current alert level is one of an available plurality of alert levels configurable by a user. The current alert level is selectable by the user for a predetermined data collection restrictiveness. Data capture is performed according to the output decision.