Abstract: In one embodiment, a computer system performs a method for verifying the validity or invalidity of a software routine by learning appropriate invariants at each program point. A computer system chooses an abstract domain that is sufficiently precise to express the appropriate invariants. The computer system associates an inconsistency measure with any two abstract elements of the abstract domain. The computer system searches for a set of local invariants configured to optimize a total inconsistency measure which includes a sum of local inconsistency measures. The computer system optimizes the total inconsistency measure for all input/output pairs of the software routine. In one embodiment, the optimization of total inconsistency is achieved by the computer system which repeatedly replaces a locally inconsistent invariant with a new invariant, randomly selected among the possible invariants which are locally less inconsistent with the current invariants at the neighboring program points.

Abstract: Epitope prediction models are described herein. By way of example, a system for predicting epitope information relating to a epitope can include a classification model (e.g., logistic regression model). The trained classification model can illustratively operatively execute one ore logistic functions on received protein data, and incorporate one or more of hidden binary variables and shift variables that when processed represent the identification (e.g., prediction) of one or more desired epitopes. The classification model can be configured to predict the epitope information by processing data including various features of an epitope, MHC, MHC supertype, and Boolean combinations thereof.

Abstract: A system and method facilitating speech detection and/or enhancement utilizing audio/video fusion is provided. The present invention fuses audio and video in a probabilistic generative model that implements cross-model, self-supervised learning, enabling rapid adaptation to audio visual data. The system can learn to detect and enhance speech in noise given only a short (e.g., 30 second) sequence of audio-visual data. In addition, it automatically learns to track the lips as they move around in the video.

Abstract: Cluster models are described herein. By way of example, a system for predicting binding information relating to a binding of a protein and a ligand can include a trained binding model and a prediction component. The trained binding model can include a probability distribution and a hidden variable that represents a cluster of protein sequences, and/or a set of hidden variables representing learned supertypes. The prediction component can be configured to predict the binding information by employing information about the protein's sequence, the ligand's sequence and the trained binding model.

Abstract: Methods, systems, devices and/or storage media for passwords. An exemplary method tiles an image, associates an index with each tile and optionally determines offsets for select tiles. Further, the tiling optionally relies on probability and/or entropy. An exemplary password system includes an image; a grid associated with the image, the grid composed of polygons; an index associated with each polygon; and an offset associated with each polygon wherein password identification relies on one or more indices and one or more offsets.

Abstract: A hierarchical statistical framework for separating mixed data is provided. The approach is a shift from chaining hard-decision modules to an integrated soft-decision approach. The framework facilitates separating, for instance, sequencing data obtained from a mixture of two or more different sequences. The sequencing data can be separated using machine learning techniques to determine the correspondence between the sequencing data and the two or more different sequences.

Abstract: A system and method to facilitate pattern recognition or matching between patterns are disclosed that is substantially invariant to small transformations. A substantially smooth deformation field is applied to a derivative of a first pattern and a resulting deformation component is added to the first pattern to derive a first deformed pattern. An indication of similarity between the first pattern and a second pattern may be determined by minimizing the distance between the first deformed pattern and the second pattern with respect to deformation coefficients associated with each deformed pattern. The foregoing minimization provides a system (e.g., linear) that may be solved with standard methods.

Abstract: A system and method facilitating speech detection and/or enhancement utilizing audio/video fusion is provided. The present invention fuses audio and video in a probabilistic generative model that implements cross-model, self-supervised learning, enabling rapid adaptation to audio visual data. The system can learn to detect and enhance speech in noise given only a short (e.g., 30 second) sequence of audio-visual data. In addition, it automatically learns to track the lips as they move around in the video.

Abstract: Adaptive threading models for predicting an interaction between two or more molecules such as proteins are provided. The adaptive threading models have one or more learnable parameters that can be learned from all or some of the available data. The available data can include data relating to known interactions between the two or more molecules, the composition of the molecules and the geometry of the molecular complex.

Abstract: Shift invariant predictors are described herein. By way of example, a system for predicting binding information relating to a binding of a protein and a ligand can include a trained binding model and a prediction component. The trained binding model can include a hidden variable representing an unknown alignment of the ligand at a binding site of the protein. The prediction component can be configured to predict the binding information by employing information about the protein's sequence, the ligand's sequence and the trained binding model.

Abstract: Adaptive threading models for predicting an interaction between two or more molecules such as proteins are provided. The adaptive threading models have one or more learnable parameters that can be learned from all or some of the available data. The available data can include data relating to known interactions between the two or more molecules, the composition of the molecules and the geometry of the molecular complex.

Abstract: Adaptive threading models for predicting an interaction between two or more molecules such as proteins are provided. The adaptive threading models have one or more learnable parameters that can be learned from all or some of the available data. The available data can include data relating to known interactions between the two or more molecules, the composition of the molecules and the geometry of the molecular complex.

Abstract: Methods, systems, devices and/or storage media for passwords. An exemplary method tiles an image, associates an index with each tile and optionally determines offsets for select tiles. Further, the tiling optionally relies on probability and/or entropy. An exemplary password system includes an image; a grid associated with the image, the grid composed of polygons; an index associated with each polygon; and an offset associated with each polygon wherein password identification relies on one or more indices and one or more offsets.

Abstract: The subject matter described herein facilitates monetizing a database of unclean health-related data collected on a large-scale and pertaining to a non-selected population. At least one pattern can be automatically ascertained from the unclean health-related data at least in part by applying a statistical technique, a data mining technique and/or a machine-learning technique to the database. The use of the database can be tracked and fees determined accordingly.

Abstract: A tool for providing health and/or wellness services is described herein. Not necessarily clean or unclean data about a plurality of self-selected or non-selected or unselected subjects is received. The data can be aggregated and mined at least in part by employing a statistical algorithm, a data-mining algorithm and/or a machine-learning algorithm. The data can be further employed to provide health and/or wellness services to participants.

Abstract: A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, images or frames in a video sequence are represented as collections of flat moving objects that change their appearance and shape over time, and can occlude each other over time. A statistical generative model is defined for generating such visual data where parameters such as appearance bit maps and noise, shape bit-maps and variability in shape, etc., are known. Further, when unknown, these parameters are estimated from visual data without prior pre-processing by using a maximization algorithm. By parameter estimation and inference in the model, visual data is segmented into components which facilitates sophisticated applications in video or image editing, such as, for example, object removal or insertion, tracking and visual surveillance, video browsing, photo organization, video compositing, etc.

Abstract: The methods/systems described herein facilitate large-scale data collection and aggregation. One exemplary system that facilitates large-scale reporting of health-related data comprises a data collection component, a database and an aggregation component. The data collection component can collect health-related data on a large-scale from a non-selected population. The database can store at least some of the health-related data. The aggregation component can facilitate automatically ascertaining at least one pattern from the health-related data at least in part by applying one or more statistical, data-mining or machine-learning techniques to the database.

Abstract: A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, images or frames in a video sequence are represented as collections of flat moving objects that change their appearance and shape over time, and can occlude each other over time. A statistical generative model is defined for generating such visual data where parameters such as appearance bit maps and noise, shape bit-maps and variability in shape, etc., are known. Further, when unknown, these parameters are estimated from visual data without prior pre-processing by using a maximization algorithm. By parameter estimation and inference in the model, visual data is segmented into components which facilitates sophisticated applications in video or image editing, such as, for example, object removal or insertion, tracking and visual surveillance, video browsing, photo organization, video compositing, etc.

Abstract: A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, images or frames in a video sequence are represented as collections of flat moving objects that change their appearance and shape over time, and can occlude each other over time. A statistical generative model is defined for generating such visual data where parameters such as appearance bit maps and noise, shape bit-maps and variability in shape, etc., are known. Further, when unknown, these parameters are estimated from visual data without prior pre-processing by using a maximization algorithm. By parameter estimation and inference in the model, visual data is segmented into components which facilitates sophisticated applications in video or image editing, such as, for example, object removal or insertion, tracking and visual surveillance, video browsing, photo organization, video compositing, etc.

Abstract: A user interface (UI) for adaptive video fast forward provides a novel fully adaptive content-based UI for allowing user interaction with an image sequence or video relative to a user identified query sample. This query sample is drawn either from an image sequence being searched or from another image sequence entirely. The user interaction offered by the UI includes providing a user with computationally efficient searching, browsing and retrieval of one or more objects, frames or sequences of interest in video or image sequences, as well as automatic content-based variable-speed playback based on a computed similarity to the query sample. In addition, the UI also provides the capability to search for image frames or sequences that are dissimilar to the query sample, thereby allowing the user to quickly locate unusual or different activity within an image sequence.