Abstract: Described herein is a system that facilitates assigning indications of usefulness to query suggestions. The system includes a query suggestion generator component that receives a query and generates a query suggestion based at least in part upon the received query. A model component outputs an indication of usefulness with respect to the query suggestion, wherein the model component is a machine-learned model of user behavior with respect to query suggestions.

Abstract: A method of producing a model for use in predicting time to an event includes obtaining multi-dimensional, non-linear vectors of information indicative of status of multiple test subjects, at least one of the vectors being right-censored, lacking an indication of a time of occurrence of the event with respect to the corresponding test subject, and performing regression using the vectors of information to produce a kernel-based model to provide an output value related to a prediction of time to the event based upon at least some of the information contained in the vectors of information, where for each vector comprising right-censored data, a censored-data penalty function is used to affect the regression, the censored-data penalty function being different than a non-censored-data penalty function used for each vector comprising non-censored data.

Abstract: Techniques enable the reduction of bandwidth requirements for peer-to-peer gaming architectures. In some embodiments, these techniques allow differentiation among players to decide which players should receive continuous updates and which should receive periodic updates. For those gaming systems receiving periodic updates, guided artificial intelligence is employed to simulate activity of a game object based on guidance provided by the periodic updates. Conversely, for those gaming systems receiving continuous updates, the continuous updates may be employed to update the activity of the game object rather than simulating the activity.

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: Techniques for using motion recognizers are described. The motion recognizers are created or generated in advance by trained users. The motion recognizers are then loaded into a processing unit that receives motion signals from one or more motion sensitive devices being manipulated by one or more end users to control one or more objects in a virtual environment. Depending on implementation, the virtual environment may represent a remote scene or a video game, where objects in the virtual environment can be controlled by the users to perform desired actions or moves.

Abstract: Disclosed herein is an information-processing apparatus for constructing an information analysis processing configuration to be applied to information analysis processing in an observation domain including an uncertainty. The information-processing apparatus includes a data processing unit for: taking a Partially Observable Markov Decision Process as a basic configuration; taking each of elements included in every information space defined in the Partially Observable Markov Decision Process as a unit; analyzing relations between the elements; and constructing an Factored Partially Observable Markov Decision Process, which is a Partially Observable Markov Decision Process including the relations between the elements, on the basis of results of the analysis.

Abstract: A method for designing a fixed cutter drill bit, includes simulating the fixed cutter drill bit drilling in an earth formation, determining radial and circumferential components of imbalance forces on the drill bit and a Beta angle between the radial and circumferential components of the imbalance forces during a period of simulated drilling, and adjusting a value of at least one design parameter for the fixed cutter drill bit at least based upon the Beta angle. To facilitate drill bit design, the Beta angel can be displayed to a drill bit designer. To improve performance, the method can include repeating the simulating, determining, and adjusting to change a simulated performance of the fixed cutter drill bit. A drill bit may be made according to the design resulting from the method.

Abstract: A passive macromodel for lossy, dispersive multiconductor transmission lines uses a multiplicative approximation of the matrix exponential known as the Lie product. The circuit implementation of the macromodel is a cascade of elementary cells, each cell being the combination of a pure delay element and a lumped circuit representing the transmission line losses. Compared with passive rational macromodeling, the Lie product macromodel is capable of efficiently simulating long, low-loss multiconductor transmission lines while preserving passivity. This result is combined with transmission line theory to derive a time-domain error criterion for the Lie product macromodel. This criterion is used to determine the minimum number of cells needed in the macromodel to assure that the magnitude of the time-domain error is less than a given engineering tolerance.

Abstract: A method for determining a probability of a hidden variable from an observed variable in a Dynamic Bayesian Network is presented. The method includes identifying the network based on predetermined criteria, determining a number of hidden variables in a time slice of the network, determining a number of the time slices of the network, and determining the probability of the hidden variable from the observed variable in less than exponential time with respect to the number of hidden variables.

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: 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: 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: This invention relates to a method of determining stability of unstable equilibrium point (UEP) computed by using BCU method, comprising selecting UEP computed by using BCU method, obtaining a test vector Xtest for the selected UEP, say XUEP using the following equation: Xtest=Xspost+0.99(XUEP?Xspost) where Xspost is the SEP, and checking boundary condition of XUEP by simulating system trajectory of post-fault original system starting from Xtest.

Abstract: The overall operating efficiency of processing objects, which are placed in a selected one of several available queues for subsequent processing at respective processing sites, is improved using a model relating the total time for processing to time of waiting in queue and processing. A deicing decision support tool uses the model for estimating completion times for deicing aircraft.

Abstract: A dynamic rule classifier for mining a data stream includes at least one window for viewing data contained in the data stream and a set of rules for mining the data. Rules are added and the set of rules are updated by algorithms when an drift in a concept within the data occurs, causing unacceptable drops in classification accuracy. The dynamic rule classifier is also implemented as a method and a computer program product.

Abstract: We set out a graphical model for describing probability distributions over labeled partitions of an undirected graph which are conditioned on observed data. We show how to efficiently perform exact inference in these models, by exploiting the structure of the graph and adapting the sum-product and max-product algorithms. The method can be used for partitioning and labeling hand-drawn ink fragments, image data, speech data and natural language data amongst other types of data elements. A significant performance increase is obtained by labeling and partitioning simultaneously. It is also possible to partition without labeling.

Abstract: Searching multimedia information which allows determining preferences based on very little amounts of data. The preferences are nonparametrically determined. Each preference is quantized into one of a plurality of bins. By doing the quantization, the distances between positive and negative samples are increased. The quantization amount may change depending on the number of samples which are used. The quantization can be used in a support vector machine or the like.

Abstract: Generating motion recognizers from example motions, without substantial programming, without limitation to any fixed set of well-known gestures, and without limitation to motions that occur substantially in a plane, or are substantially predefined in scope. From example motions for each class of motion to be recognized, a system automatically generates motion recognizers using machine learning techniques. Those motion recognizers can be incorporated into an end-user application, with the effect that when a user of the application supplies a motion, those motion recognizers will recognize the motion as an example of one of the known classes of motion. Motion recognizers can be incorporated into an end-user application; tuned to improve recognition rates for subsequent motions to allow end-users to add new example motions.

Abstract: A system and method to facilitate automatic identification of event classification errors in a network are described. Session data containing events logged by a user entity over a network in a predetermined period of time is retrieved from one or more event logs. Each event is further parsed to generate one or more event units. A frequency parameter of co-occurrence of the event units within each event of the session data is further determined and at least one session distance is determined among the events based on the frequency parameter. At least one classification distance is further retrieved from a data storage module, such as, for example, a database or a datastore, the one or more classification distances representing a relation between the events and corresponding classification categories within the database or datastore. Each session distance is further compared to each retrieved classification distance to ascertain event classification errors within the database or datastore.