32 results about "Probabilistic framework" patented technology

To improve network reliability and management in today's high-speed communication networks, we propose an intelligent system using adaptive statistical approaches. The system learns the normal behavior of the network. Deviations from the norm are detected and the information is combined in the probabilistic framework of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. As demonstrated on real network data, this method can detect abnormal behavior before a fault actually occurs, giving the network management system (human or automated) the ability to avoid a potentially serious problem.

Finite-state systems and methods allow multiple input streams to be parsed and integrated by a single finite-state device. These systems and methods not only address multimodal recognition, but are also able to encode semantics and syntax into a single finite-state device. The finite-state device provides models for recognizing multimodal inputs, such as speech and gesture, and composes the meaning content from the various input streams into a single semantic representation. Compared to conventional multimodal recognition systems, finite-state systems and methods allow for compensation among the various input streams. Finite-state systems and methods allow one input stream to dynamically alter a recognition model used for another input stream, and can reduce the computational complexity of multidimensional multimodal parsing. Finite-state devices provide a well-understood probabilistic framework for combining the probability distributions associated with the various input streams and for selecting among competing multimodal interpretations.

A data processing system generates recommendations for on-line shopping by scoring recommendations matching the customer's cart contents using by assessing and ranking each candidate recommendation by the expected incremental margin associated with the recommendation being issued (as compared to the expected margin associated with the recommendation not being issued) by taking into consideration historical associations, knowledge of the layout of the site, the complexity of the product being sold, the user's session behavior, the quality of the selling point messages, product life cycle, substitutability, demographics and/or other considerations relating to the customer purchase environment. In an illustrative implementation, scoring inputs for each candidate recommendation (such as relevance, exposure, clarity and/or pitch strength) are included in a probabilistic framework (such as a Bayesian network) to score the effectiveness of the candidate recommendation and/or associated selling point messages by comparing a recommendation outcome (e.g., purchase likelihood or expected margin resulting from a given recommendation) against a non-recommendation outcome (e.g., the purchase likelihood or expected margin if no recommendation is issued). In addition, a probabilistic framework may also be used to select a selling point message for inclusion with a selected candidate recommendation by assessing the relative strength of the selling point messages by factoring in a user profile match factor (e.g., the relative likelihood that the customer matches the various user case profiles).

A probabilistic framework for acoustic-phonetic automatic speech recognition organizes a set of phonetic features into a hierarchy consisting of a broad manner feature sub-hierarchy and a fine phonetic feature sub-hierarchy. Each phonetic feature of said hierarchy corresponds to a set of acoustic correlates and each broad manner feature of said broad manner feature sub-hierarchy is further associated with a corresponding set of acoustic landmarks. A pattern recognizer is trained from a knowledge base of phonetic features and corresponding acoustic correlates. Acoustic correlates are extracted from a speech signal and are presented to the pattern recognizer. Acoustic landmarks are identified and located from broad manner classes classified by the pattern recognizer. Fine phonetic features are determined by the pattern recognizer at and around the acoustic landmarks. The determination of fine phonetic features may be constrained by a pronunciation model. The most probable feature bundles corresponding to words and sentences are those that maximize the joint a posteriori probability of the fine phonetic features and corresponding acoustic landmarks. When the hierarchy is organized as a binary tree, binary classifiers such as Support Vector Machines can be used in the pattern classifier and the outputs thereof can be converted probability measures which, in turn may be used in the computation of the aforementioned joint probability of fine phonetic features and corresponding landmarks.

A system and method for correlation of resources with hardware events includes event driven sampling a call chain of functions at to determine when functions of the call chain are active. The call chain is mapped to execution times based upon a probabilistic integration of the functions such that when portions of the call chain are active, resources associated with call chain activity are correlated with hardware events.

A controller receives outputs form a plurality of sensors such as a camera, LIDAR sensor, RADAR sensor, and ultrasound sensor. Sensor outputs corresponding to an object are assigned to a tracklet. Subsequent outputs by any of the sensors corresponding to that object are also assigned to the tracklet. A trajectory of the object is calculated from the sensor outputs assigned to the tracklet, such as by means of Kalman filtering. For each sensor output assigned to the tracklet, a probability is updated, such as using a Bayesian probability update. When the probability meets a threshold condition, the object is determined to be present and an alert is generated or autonomous obstacle avoidance is performed with respect to an expected location of the object.

The present invention discloses a method for rapidly discovering a phenotype related gene based on a probabilistic framework and a resequencing technology, and establishes a method based on the probabilistic framework. The method comprises: modeling a forward genetics study process based on a genome resequencing technology, estimating, by calculating four indexes, an effect of a design of each step in a study process on overall availability of the study, thereby guiding optimization of a current experimental scheme and analytical method, and achieving the purpose of rapidly discovering a gene possibly associated with a particular phenotype by using samples as few as possible. According to the method provided by the present invention, the estimation, according to four indexes, of the overall availability of the forward genetics study process based on the genome resequencing technology is firstly proposed, and a method success rate and a non-Mendelian phenotype significance are two indexes proposed creatively, and have a significant value on guiding the optimization of an overall study process.

The invention discloses an image segmentation method based on a local region homogeneity manifold constrained MRF model. On the basis of a PairwiseMRF model, an image segmentation model based on a local region MRF model is constructed on an extended neighborhood of MRF nodes, and the prior distribution of a local region effectively avoids the interference of noise or texture mutations; and meanwhile, on the basis of a manifold learning theory, a manifold regularization term under a probabilistic framework is established, a local region probability distribution is used to effectively describe the local spatial geometric structure prior with complex natural images, and the local spatial geometric structure described by this manifold learning is introduced into the local region MRF segmentation model. Experiments have proved that compared with the prior art, the method of the invention not only avoids the local region prior oversmoothing penalty, but also effectively maintains the local geometric structure information of the image segmentation.

Generally, techniques for analyzing foreground-segmented images are disclosed. The techniques allow clusters to be determined from the foreground-segmented images. New clusters may be added, old clusters removed, and current clusters tracked. A probabilistic framework is used for the analysis of the present invention. A method is disclosed that estimates cluster parameters for one or more clusters determined from an image comprising segmented areas, and evaluates the cluster or clusters in order to determine whether to modify the cluster or clusters. These steps are generally performed until one or more convergence criteria are met. Additionally, clusters can be added, removed, or split during this process. In another aspect of the invention, clusters are tracked during a series of images, and predictions of cluster movements are made.

The invention discloses a semantic segmentation method under a small sample based on variational prototype reasoning, and belongs to the field of computer vision semantic segmentation. The invention discloses a semantic segmentation method under a small sample based on variational prototype reasoning. According to the invention, variational prototype reasoning is proposed for the first time; semantic segmentation under a small sample is brought into a probability framework; in the probability framework, prototype representation is not vector representation of a fixed numerical value, but distribution, distribution of a hidden space in variational reasoning is used for representing distribution of the prototype, and the generalization ability of the whole prototype is improved under the condition of a small sample so as to adapt to uncertainty represented by the small sample; moreover, an objective function of variational prototype reasoning suitable for image semantic segmentation under a probability framework is proposed for the first time, a semantic segmentation process under a small sample is assisted, and multiple tests prove that a very good segmentation effect is also obtained under the condition of utilizing a single support set image.

A probabilistic framework for compiler optimization with multithread power-gating controls includes scheduling all thread fragments of a multithread computer code with the estimated execution time, logging all time stamps of events, and sorting and unifying the logged time stamps. Time slices are constructed using adjacent time stamps of each thread fragment. A power-gating time having a component turned off for each time slice is determined. Power-gateable windows that reduce energy consumption of the time slice is determined according to the power-gating time. The compiler inserts predicated power-gating instructions at locations corresponding to the selected power-gateable windows into the power-gateable computer code.

Probabilistic models may be used in deterministic workflows for robotic process automation (RPA). Machine learning (ML) introduces a probabilistic framework, where the result is uncertain, and thus the steps are also uncertain. Deterministic workflows may be mixed with probabilistic workflows, or probabilistic activities may be inserted into the deterministic workflows in order to create more dynamic workflows. When an error is detected by the data drift detector, the concept drift detector, or both, the supervisory system may be used to monitor the ML model and issue an alarm, disable the RPA robot, bypass the RPA robot, or roll back to a previous version of the ML model.