Abstract: Disclosed herein is a neural network based pre-decoder comprising a permutation embedding engine, a permutation classifier each comprising one or more trained neural networks and a selection unit. The permutation embedding engine is trained to compute a plurality of permutation embedding vectors each for a respective one of a plurality of permutations of a received codeword encoded using an error correction code and transmitted over a transmission channel subject to interference. The permutation classifier is trained to compute a decode score for each of the plurality of permutations expressing its probability to be successfully decoded based on classification of the plurality of permutation embedding vectors coupled with the plurality of permutations. The selection unit is configured to output one or more selected permutations having a highest decode score. One or more decoders may be then applied to recover the encoded codeword by decoding the one or more selected permutations.

Abstract: Embodiments may classify medical images, such as mammograms, using weakly labeled data sets, fully labeled data sets, or a combination of both. For example, a method may comprise receiving a whole medical image, extracting a plurality of image patches from the whole medical image, each image patch including a portion of the whole image, generating a representation of features found in the plurality of image patches, classifying each image patch as including a malignant abnormality, a benign abnormality or not including an abnormality to form a classification for each patch, in parallel, the detection branch computes a malignant distribution over patches and a benign distribution over patches resulting in ranking of patches compare to one another for malignancy, and ranking of patches compare to one another for benign. Patches classification probabilities and ranking are multiplied and summed for malignant and benign, resulting in global malignant probability and global benign probability.

Abstract: Embodiments may classify medical images, such as mammograms, using weakly labeled data sets, fully labeled data sets, or a combination of both. For example, a method may comprise receiving a whole medical image, extracting a plurality of image patches from the whole medical image, each image patch including a portion of the whole image, generating a representation of features found in the plurality of image patches, classifying each image patch as including a malignant abnormality, a benign abnormality or not including an abnormality to form a classification for each patch, in parallel, the detection branch computes a malignant distribution over patches and a benign distribution over patches resulting in ranking of patches compare to one another for malignancy, and ranking of patches compare to one another for benign. Patches classification probabilities and ranking are multiplied and summed for malignant and benign, resulting in global malignant probability and global benign probability.

Abstract: A method of generating a category model for classifying medical images. The method comprises providing a plurality of medical images each categorized as one of a plurality of categorized groups, generating an index of a plurality of visual words according to a distribution of a plurality of local descriptors in each the image, modeling a category model mapping a relation between each visual word and at least one of the categorized groups according to the index, and outputting the category model for facilitating the categorization of an image based on local descriptors thereof.

Abstract: A method of generating a category model for classifying medical images. The method comprises providing a plurality of medical images each categorized as one of a plurality of categorized groups, generating an index of a plurality of visual words according to a distribution of a plurality of local descriptors in each the image, modeling a category model mapping a relation between each visual word and at least one of the categorized groups according to the index, and outputting the category model for facilitating the categorization of an image based on local descriptors thereof.

Abstract: A method of generating a category model for classifying medical images. The method comprises providing a plurality of medical images each categorized as one of a plurality of categorized groups, generating an index of a plurality of visual words according to a distribution of a plurality of local descriptors in each the image, modeling a category model mapping a relation between each visual word and at least one of the categorized groups according to the index, and outputting the category model for facilitating the categorization of an image based on local descriptors thereof.

Abstract: A communication system having a program of machine-readable instructions for solving an ILS problem, tangibly embodied on a computer readable memory and executable by a digital data processor, to perform actions directed toward outputting a set of a-posteriori probability vectors.

Abstract: Methods and systems are provided for defining and creating an automatic file security policy and a semi-automatic method of managing file access control in organizations with multiple diverse access control models and multiple diverse file server protocols. The system monitors access to storage elements within the network. The recorded data traffic is analyzed to assess simultaneous data access groupings and user groupings, which reflect the actual organizational structure. The learned structure is then transformed into a dynamic file security policy, which is constantly adapted to organizational changes over time. The system provides a decision assistance interface for interactive management of the file access control and for tracking abnormal user behavior.

Abstract: Methods and systems are provided for defining and creating an automatic file security policy and a semi-automatic method of managing file access control in organizations with multiple diverse access control models and multiple diverse file server protocols. The system monitors access to storage elements within the network. The recorded data traffic is analyzed to assess simultaneous data access groupings and user groupings, which reflect the actual organizational structure. The learned structure is then transformed into a dynamic file security policy, which is constantly adapted to organizational changes over time. The system provides a decision assistance interface for interactive management of the file access control and for tracking abnormal user behavior.

Abstract: A decoding device for receiving a data stream from a data source over a noisy channel, the data being arranged in variable length packets using unequal encoding levels for different parts of the data stream, the decoder having a feedback transmitter for sending feedback data via a feedback channel to said data source to indicate a level of quality of data receipt at said decoder, thereby to provide adaptive error correction and concealment in a data stream transferred over said channel.

Abstract: A system for streaming data and corresponding protective parity bits in packets over a channel, the system comprising a recursive systematic convolutional encoder at a sending end for producing said corresponding protective parity bits and a recursive systematic convolutional decoder at a receiving end for reconstructing data lost in the channel, and a data interleaver at a sending end for interleaving data for said recursive systematic convolutional encoder according to a uniformity criterion to form parity bits therefrom, and a parity bit distributor operable to distribute said parity bits over said packets differentially from corresponding data. The system is useful in enabling real time multimedia data distribution over cellular networks.

Abstract: A method of discriminating between equally probable trellis paths surviving in a trellis decoder to select a single output path. The method comprises: windowing over the trellis decoder to view said surviving paths in successive windows, identifying a predetermined feature in the paths as they appear in the successive windows, and eliminating surviving paths having the predetermined feature to leave a single surviving path to form the single output path.

Abstract: A system is disclosed for stitching a plurality of reconstructions of three-dimensional surface features of at least one object in a scene to relate the reconstructions to a common coordinate system. Each reconstruction is generated from a plurality of two-dimensional images of the scene, and each reconstruction is defined relative to a respective one of a plurality of reconstruction coordinate systems defined relative to one of the images in the scene. The system generates, from at least some of the images, values for translational and rotational components relating a respective reconstruction coordinate system to the common coordinate system. Thereafter, the system uses the values of the translational and rotational components to convert coordinates of the surface features in the respective reconstruction coordinate systems to the common coordinate system.

Abstract: A pattern recognition method of dynamic time warping of two sequences of feature sets onto each other is provided. The method includes the steps of creating a rectangular graph having the two sequences on its two axes, defining a swath of width r, where r is an odd number, centered about a diagonal line connecting the beginning point at the bottom left of the rectangle to the endpoint at the top right of the rectangle and also defining r−1 lines within the swath. The lines defining the swath are parallel to the diagonal line. Each array element k of an r-sized array is associated with a separate array of the r lines within the swath and for each row of the rectangle, the dynamic time warping method recursively generates new path values for each array element k as a function of the previous value of the array element k and of at least one of the current values of the two neighboring array elements k−1 and k+1 of the array element k.

Abstract: A handwritten pattern recognition system for recognizing an input pattern is provided. The system has a plurality of parameter determining units, each determining the value of a desired parameter for an input pattern to be recognized. The system also includes a pattern match determiner which produces match values for each parameter of the input pattern with its corresponding parameter of each reference parameter. The match determiner also produces an overall match value for each reference pattern. A pattern classifier selects the reference pattern whose parameter set is "closest", by some matching criterion, to that of the input pattern.

Abstract: A pattern recognition method of dynamic time warping of two sequences of feature sets onto each other is provided. The method includes the steps of creating a rectangular graph having the two sequences on its two axes, defining a swath of width r, where r is an odd number, centered about a diagonal line connecting the beginning point at the bottom left of the rectangle to the endpoint at the top right of the rectangle and also defining r-1 lines within the swath. The lines defining the swath are parallel to the diagonal line. Each array element k of an r-sized array is associated with a separate array of the r lines within the swath and for each row of the rectangle, the dynamic time warping method recursively generates new path values for each array element k as a function of the previous value of the array element k and of at least one of the current values of the two neighboring array elements k-1 and k+1 of the array element k.