Abstract: Embodiments of the invention provide methods and composition for stimulating a hydrocarbon-bearing, heavy oil containing formation, a deep oil reservoir, or a tight oil reservoir, whereby exothermic reactants are utilized to generate in-situ steam and nitrogen gas downhole in the formation or the reservoir as an enhanced oil recovery process. An oil well stimulation method is provided, which includes injecting, into the one of the formation and the reservoir, an aqueous composition including an ammonium containing compound and a nitrite containing compound. The method further includes injecting, into the one of the formation and the reservoir, an activator. The activator initiates a reaction between the ammonium containing compound and the nitrite containing compound, such that the reaction generates steam and nitrogen gas, increasing localized pressure and improving oil mobility, in the one of the formation and the reservoir, thereby enhancing oil recovery from the one of the formation and the reservoir.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for monitoring slug flow in subterranean wells. In one aspect, a method includes at a time instant, transmitting an acoustic signal across a cross-section of a pipeline flowing multiphase fluid including gaseous fluid and liquid fluid, wherein a portion of the acoustic signal is carried through the cross-section of the pipeline by the multiphase fluid and determining, at the time instant, a first quantity of the gaseous fluid and a second quantity of the liquid fluid passing the cross-section of the pipeline based, in part, on an energy of the portion of the acoustic signal carried through the cross-section and at least a portion of a total energy of the transmitted acoustic signal.

Abstract: Disclosed are computer-readable devices, systems and methods for generating a model of a clothed body. The method includes generating a model of an unclothed human body, the model capturing a shape or a pose of the unclothed human body, determining two-dimensional contours associated with the model, and computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body. Based on the two-dimensional contours and the deformations, the method includes generating a first two-dimensional model of the unclothed human body, the first two-dimensional model factoring the deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation and learning an eigen-clothing model using principal component analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing, to yield a second two-dimensional model of a clothed human body.

Abstract: Systems, methods, and computer-readable storage media for simulating realistic clothing. The system generates a clothing deformation model for a clothing type, wherein the clothing deformation model factors a change of clothing shape due to rigid limb rotation, pose-independent body shape, and pose-dependent deformations. Next, the system generates a custom-shaped garment for a given body by mapping, via the clothing deformation model, body shape parameters to clothing shape parameters. The system then automatically dresses the given body with the custom-shaped garment.

Abstract: Disclosed are computer-readable devices, systems and methods for generating a model of a clothed body. The method includes generating a model of an unclothed human body, the model capturing a shape or a pose of the unclothed human body, determining two-dimensional contours associated with the model, and computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body. Based on the two-dimensional contours and the deformations, the method includes generating a first two-dimensional model of the unclothed human body, the first two-dimensional model factoring the deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation and learning an eigen-clothing model using principal component analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing, to yield a second two-dimensional model of a clothed human body.

Abstract: A novel “contour person” (CP) model of the human body is proposed that has the expressive power of a detailed 3D model and the computational benefits of a simple 2D part-based model. The CP model is learned from a 3D model of the human body that captures natural shape and pose variations; the projected contours of this model, along with their segmentation into parts forms the training set. The CP model factors deformations of the body into three components: shape variation, viewpoint change and pose variation. The CP model can be “dressed” with a low-dimensional clothing model, referred to as “dressed contour person” (DCP) model. The clothing is represented as a deformation from the underlying CP representation. This deformation is learned from training examples using principal component analysis to produce so-called eigen-clothing. The coefficients of the eigen-clothing can be used to recognize different categories of clothing on dressed people.

Abstract: A novel “contour person” (CP) model of the human body is proposed that has the expressive power of a detailed 3D model and the computational benefits of a simple 2D part-based model. The CP model is learned from a 3D model of the human body that captures natural shape and pose variations; the projected contours of this model, along with their segmentation into parts forms the training set. The CP model factors deformations of the body into three components: shape variation, viewpoint change and pose variation. The CP model can be “dressed” with a low-dimensional clothing model, referred to as “dressed contour person” (DCP) model. The clothing is represented as a deformation from the underlying CP representation. This deformation is learned from training examples using principal component analysis to produce so-called eigen-clothing. The coefficients of the eigen-clothing can be used to recognize different categories of clothing on dressed people.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for monitoring slug flow in subterranean wells. In one aspect, a method includes at a time instant, transmitting an acoustic signal across a cross-section of a pipeline flowing multiphase fluid including gaseous fluid and liquid fluid, wherein a portion of the acoustic signal is carried through the cross-section of the pipeline by the multiphase fluid and determining, at the time instant, a first quantity of the gaseous fluid and a second quantity of the liquid fluid passing the cross-section of the pipeline based, in part, on an energy of the portion of the acoustic signal carried through the cross-section and at least a portion of a total energy of the transmitted acoustic signal.

Abstract: A system and method of estimating the body shape of an individual from input data such as images or range maps. The body may appear in one or more poses captured at different times and a consistent body shape is computed for all poses. The body may appear in minimal tight-fitting clothing or in normal clothing wherein the described method produces an estimate of the body shape under the clothing. Clothed or bare regions of the body are detected via image classification and the fitting method is adapted to treat each region differently. Body shapes are represented parametrically and are matched to other bodies based on shape similarity and other features. Standard measurements are extracted using parametric or non-parametric functions of body shape. The system components support many applications in body scanning, advertising, social networking, collaborative filtering and Internet clothing shopping.

Abstract: A novel “contour person” (CP) model of the human body is proposed that has the expressive power of a detailed 3D model and the computational benefits of a simple 20 part-based model. The CP model is learned from a 3D model of the human body that captures natural shape and pose variations. The CP model factors deformations of the body into three components: shape variation, viewpoint change and pose variation. The CP model can be “dressed” with a low-dimensional clothing model. The clothing is represented as a deformation from the underlying CP representation. This deformation is learned from training examples using principal component analysis to produce so-called eigen-clothing. The coefficients of the eigen-clothing can be used to recognize different categories of clothing on dressed people. The parameters of the estimated 20 body can be used to discriminatively predict 3D body shape using a learned mapping approach.

Abstract: A system and method of estimating the body shape of an individual from input data such as images or range maps. The body may appear in one or more poses captured at different times and a consistent body shape is computed for all poses. The body may appear in minimal tight-fitting clothing or in normal clothing wherein the described method produces an estimate of the body shape under the clothing. Clothed or bare regions of the body are detected via image classification and the fitting method is adapted to treat each region differently. Body shapes are represented parametrically and are matched to other bodies based on shape similarity and other features. Standard measurements are extracted using parametric or non-parametric functions of body shape. The system components support many applications in body scanning, advertising, social networking, collaborative filtering and Internet clothing shopping.

Abstract: Systems, methods, and computer-readable storage media for simulating realistic clothing. The system generates a clothing deformation model for a clothing type, wherein the clothing deformation model factors a change of clothing shape due to rigid limb rotation, pose-independent body shape, and pose-dependent deformations. Next, the system generates a custom-shaped garment for a given body by mapping, via the clothing deformation model, body shape parameters to clothing shape parameters. The system then automatically dresses the given body with the custom-shaped garment.

Abstract: Embodiments of the invention provide methods and composition for stimulating a hydrocarbon-bearing, heavy oil containing formation, a deep oil reservoir, or a tight oil reservoir, whereby exothermic reactants are utilized to generate in-situ steam and nitrogen gas downhole in the formation or the reservoir as an enhanced oil recovery process. An oil well stimulation method is provided, which includes injecting, into the one of the formation and the reservoir, an aqueous composition including an ammonium containing compound and a nitrite containing compound. The method further includes injecting, into the one of the formation and the reservoir, an activator. The activator initiates a reaction between the ammonium containing compound and the nitrite containing compound, such that the reaction generates steam and nitrogen gas, increasing localized pressure and improving oil mobility, in the one of the formation and the reservoir, thereby enhancing oil recovery from the one of the formation and the reservoir.

Abstract: A novel “contour person” (CP) model of the human body is proposed that has the expressive power of a detailed 3D model and the computational benefits of a simple 20 part-based model. The CP model is learned from a 3D model of the human body that captures natural shape and pose variations. The CP model factors deformations of the body into three components: shape variation, viewpoint change and pose variation. The CP model can be “dressed” with a low-dimensional clothing model. The clothing is represented as a deformation from the underlying CP representation. This deformation is learned from training examples using principal component analysis to produce so-called eigen-clothing. The coefficients of the eigen-clothing can be used to recognize different categories of clothing on dressed people. The parameters of the estimated 20 body can be used to discriminatively predict 3D body shape using a learned mapping approach.

Abstract: Disclosed herein are systems and methods for providing power to a load. Systems according to the present embodiment may include an electrical generator to generate electrical power and a battery to store electrical power. The present disclosure may be applied to electrical power generators having variable outputs, and may be utilized to provide a more constant electrical output by drawing power from the electrical generator and the battery as necessary to satisfy the power requirements of the electrical load. The system draws power from the electrical power generator and the battery to satisfy the power requirements of the load. Based on a mode of operation, the system may draw power only from the battery, only from the electrical power generator, or from both the electrical power generator and the battery alternately.

Abstract: A system and method of estimating the body shape of an individual from input data such as images or range maps. The body may appear in one or more poses captured at different times and a consistent body shape is computed for all poses. The body may appear in minimal tight-fitting clothing or in normal clothing wherein the described method produces an estimate of the body shape under the clothing. Clothed or bare regions of the body are detected via image classification and the fitting method is adapted to treat each region differently. Body shapes are represented parametrically and are matched to other bodies based on shape similarity and other features. Standard measurements are extracted using parametric or non-parametric functions of body shape. The system components support many applications in body scanning, advertising, social networking, collaborative filtering and Internet clothing shopping.

Abstract: A visual motion analysis method that uses multiple layered global motion models to both detect and reliably track an arbitrary number of moving objects appearing in image sequences. Each global model includes a background layer and one or more foreground “polybones”, each foreground polybone including a parametric shape model, an appearance model, and a motion model describing an associated moving object. Each polybone includes an exclusive spatial support region and a probabilistic boundary region, and is assigned an explicit depth ordering. Multiple global models having different numbers of layers, depth orderings, motions, etc., corresponding to detected objects are generated, refined using, for example, an EM algorithm, and then ranked/compared. Initial guesses for the model parameters are drawn from a proposal distribution over the set of potential (likely) models.

Abstract: A method and system to decode neural activity in the motor cortex to infer at least the position and velocity of a subject's hand from neural spiking activity of some number of nerve cells. In one embodiment the method includes simultaneously recording electrical activity of the nerve cells in the primary motor cortex to obtain neural data; and modeling the encoding and decoding of the neural data using a Kalman filter, where a measurement model assumes a cell firing rate to be a stochastic linear function of at least the position and velocity of the hand, and where the measurement model is learned from training data in conjunction with a system model that encodes a manner in which the hand moves.

Abstract: A visual motion analysis method that uses multiple layered global motion models to both detect and reliably track an arbitrary number of moving objects appearing in image sequences. Each global model includes a background layer and one or more foreground “polybones”, each foreground polybone including a parametric shape model, an appearance model, and a motion model describing an associated moving object. Each polybone includes an exclusive spatial support region and a probabilistic boundary region, and is assigned an explicit depth ordering. Multiple global models having different numbers of layers, depth orderings, motions, etc., corresponding to detected objects are generated, refined using, for example, an EM algorithm, and then ranked/compared. Initial guesses for the model parameters are drawn from a proposal distribution over the set of potential (likely) models.

Abstract: A method and apparatus analyzes and annotates a technical talk typically illustrated with overhead slides, wherein the slides are recorded in a video sequence. The video sequence is condensed and digested into key video frames adaptable for annotation to time and audio sequence. The system comprises a recorder for recording a technical talk as a sequential set of video image frames. A stabilizing processor segregates the video image frames into a plurality of associated subsets each corresponding to a distinct slide displayed at the talk and for median filtering of the subsets for generating a key frame representative of each of the subsets. A comparator compares the key frame with the associated subsets to identify differences between the key frame and the associates subset which comprise nuisances and affordances. A gesture recognizer locates, tracks and recognizes gestures occurring in the subset as gesture affordances and identifies a gesture video frame representative of the gesture affordance.