Abstract: A teaching apparatus and a teaching method that facilitates accurately setting a retainer at a target position, and minimizes troublesome steps in the manufacturing process, are to be provided. The teaching apparatus includes a first member held by the retainer of the carrying apparatus, a second member attached to the wafer boat (mounting rack) so as to oppose the first member held by the retainer when the retainer is set close to the target position, an output unit, and an arithmetic unit. The first member includes a distance sensor that generates a sensor signal representing a distance between the distance sensor and a surface of the second member opposing the first member. On the surface of the second member opposing the first member, a reference mark A is provided. The first member includes an imaging device that shoots an image including the reference mark A, and generates image data.

Abstract: A strategy is described for detecting anomalies in the operation of a data processing environment. The strategy relies on parameter information to detect the anomalies in a detection operation, the parameter information being derived in a training operation. The parameter information is selected such that the detection of anomalies is governed by both a desired degree of sensitivity (determining how inclusive the detection operation is in defining anomalies) and responsiveness (determining how quickly the detection operation reports the anomalies). The detection operation includes specific algorithms for determining undesired trending and spiking in the performance data.

Abstract: Methods and systems are provided for encoding, transmission and decoding of vectorized input data, for example, video or audio data. A convex invariance learning framework is established for processing input data or a given data type. Each input vector is associated with a variable transformation matrix that acts on the vector to invariantly permute the vector elements. Joint invariance and model learning is performed on a training set of invariantly transformed vectors over a constrained space of transformation matrices using maximum likelihood analysis. The maximum likelihood analysis reduces the data volume to a linear subspace volume in which the training data can be modeled by a reduced number of variables. Principal component analysis is used to identify a set of N eigenvectors that span the linear subspace. The set of N eigenvectors is used a basis set to encode input data and to decode compressed data.

Abstract: Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets possesses structural characteristics, locational kernels can be utilized to provide measures of similarity among data points within the dataset. The locational kernels are then combined to generate a decision function, or kernel, that can be used to analyze the dataset. Where an invariance transformation or noise is present, tangent vectors are defined to identify relationships between the invariance or noise and the data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel.

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: One embodiment of the present invention is a method for optimizing a parameter set comprising object parameters, the method comprising the steps of: (a) creating an initial population of a plurality of individual parameter sets, the parameter sets comprising object parameters describing a model, structure, shape, design or process to be optimized and setting the initial population as a current parent population; (b) for each individual parameter set in a parent population mutating the parameters and optionally recombining the parameters to create an offspring population of individual parameter sets, wherein the strength of an individual object parameter mutation is enlarged by a noise contribution to enhance the robustness of the optimization; (c) evaluating a quality of each individual in the offspring population; (d) selecting individuals of the offspring population to be the next parent generation; and (e) repeating steps (b) through (d) until a termination criterion is reached.

Abstract: A context modeling architecture that includes a context representation portion, which adapted to represent context as features, is provided. The features are specifiable at runtime of an application including the context representation portion.

Abstract: The invention provides a novel control method combining fuzzy logic control with gain auto-tuning sliding mode control. A control error signal e(t) from the detection mechanism and an error change signal ce(t) are combined together as a sliding variable s(t)=ce(t)+?e(t) and used as the fuzzy input control variable. Using the control method, 2D fuzzy control rule is simplified into 1D fuzzy control rule through the sliding variable definition for enhancing the efficiency of computation and saving computation resources. Accordingly, both the quick transient response and steady state precision positioning can be achieved. The present method can overcome the disadvantages of current controller, which can not reach the desired transient and steady state responses simultaneously.

Abstract: Described is a signal processing system. The system comprises a signal processing module having signal processing parameters and being configured to receive a plurality of signals. The signal processing module uses the signal processing parameters to output a processed signal, as either a fused signal or a plurality of separate signals. A classification module is included to recognize information encoded in the processed signal to classify the information encoded in the process signal, with the classification having a confidence level. An optimization module is configured, in a feedback loop, to utilize the information encoded in the processed signal to adjust the signal processing parameters to optimize the confidence level of the classification, thereby optimizing an output of the signal processing module.

Abstract: A system for obtaining diagnostic information, such as evidence about a mechanism, within an algorithmic framework, including filtering and aggregating the information through, for instance, a stochastic process. The output may be an overall belief value relative to a presence of an item such as, for example, a fault in the mechanism.

Abstract: A set of objects having facts is established. Facts of objects having positions in an order are identified. Some facts explicitly describe the positions in the linear order, while are facts do not explicitly describe the positions. The facts are presented in the order on a linear graph, such as a timeline. Facts of the objects describing geographic positions are presented on a map.

Abstract: A character programming and control system and method for programming and controlling a virtual character during a social simulation, a character programming method for programming the actions of a virtual character, and a system and method for developing social skills of a user.

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

Abstract: A document management apparatus according to the invention is aimed at easily processing, managing and reusing newly taken image data in accordance with user's needs. The apparatus includes: a document area analyzing unit; a text information analyzing unit; a text information semantic analysis unit; a managing unit; an integrated information presenting unit; and a user-designated semantic setting unit capable of allowing the user to change the semantics of the text information presented by the integrated information presenting unit and to set the changed semantics.

Abstract: The present disclosure includes a system and method for learning (or discovering and extracting) business knowledge from a collection of source code. The collection of source code is abstracted to generate an abstracted data stream, which is then transformed to an Extensible Markup Language (XML) format. The transformed data in XML format can be further converted to target formats or processed to satisfy different needs such as software system documentation, migration, impact analysis and security analysis. The disclosure also includes an implementation and operation for a pattern abstraction engine configured to receive an input data stream and format it for abstraction into a standard format using a pattern matching mechanism. The disclosure also includes an implementation and operation for a contextual pattern decoder engine configured to extract knowledge attributes and contextual taxonomy from classified blocks of an input data stream.

Abstract: Multiple thread functionality in a general purpose set theoretic processor (GPSTP) is implemented by addition of a thread memory for processing multiple interleaved data input streams to enable state save-and-restore functionality. The thread memory is functionally distributed among three parts of the GPSTP that change state during execution. The system structure minimizes the number of bits required to be saved and restored, and cell structures are configured implement the multi-thread functionality.

Abstract: A hybrid causal framework applies properties of probabilistic models, such as Bayesian belief networks, to causal logic models, such as fault trees and event sequence diagrams. The probabilistic model establishes a joint probability distribution of causal relationships between events and conditions in the logic models. The probability of the events and conditions are found by propagating probabilities from the probabilistic model through the logic models.

Abstract: First and second applications having disparate concrete configuration models are integrated into a unified abstract model wherein configuration elements and associated element values are used to control configurable functions of the first and second application programs, respectively, and wherein the second configuration element has a similar configuration function as the first configuration element; using a first equivalence definition reflecting a functional relationship between the first application configuration element and an abstract model configuration element having the similar configuration function, wherein the abstract model configuration is application-independent and platform-independent; using a second equivalence definition reflecting a functional relationship between the second application program configuration element and the abstract model configuration element; and producing and associating a second configuration element value with the second application configuration element wherein the se

Abstract: A method of selecting and presenting content based on learned user preferences is provided. The method includes receiving search input from the user for identifying desired content items and receiving content selection actions from the user. The method further includes analyzing the date, day, and time of content selection actions by the user and analyzing descriptive terms associated with the selected content items to learn a periodicity of user selections of similar content items. In response to subsequent searches by the user, the method calls for selecting and ordering a collection of content items for presentation to the user based on comparing the user's search input to descriptive terms associated with content items and based on the learned periodicities of the user.

Abstract: Disclosed is a method and system for the use of virtual robots (Bots) to assist in the answering of inquiries from persons over the Internet, telephone, wireless, or other form of electrical, digital, or electronic communication. Such method and system consists of (1) a Bot processor consisting of (a) a connection interface capable of receiving, directly or indirectly, inputs from a user in natural or machine language, which the Bot processor then responds to in natural or machine language, and (b) an engine capable of recognizing an input and providing a response; and (2) a service desk consisting of (a) a human being, (b) a connection interface that connects directly or indirectly to the Bot processor and to the front end, and (c) a display where the human being can see, in humanly comprehensible form, information transmitted by the user and/or the Bot processor.