Linear Support Vector Machines for Error Correction in Optical Data Transmission

Reduction of bit error rates in optical transmission systems is an important task that is difficult to achieve. As speeds increase, the difficulty in reducing bit error rates also increases. Channels have differing characteristics, which may change over time, and any error correction employed must be capable of operating at extremely high speeds. In this paper, a linear support vector machine is used to classify large-scale data sets of simulated optical transmission data in order to demonstrate their effectiveness at reducing bit error rates and their adaptability to the specifics of each channel. For the classification, LIBLINEAR is used, which is related to the popular LIBSVM classifier. It is found that is possible to reduce the error rate on a very noisy channel to about 3 bits in a thousand. This is done by a linear separator that can be built in hardware and can operate at the high speed required of an operationally useful decode

Derivative expansion and gauge independence of the false vacuum decay rate in various gauges

In theories with radiative symmetry breaking, the calculation of the false vacuum decay rate requires the inclusion of higher-order terms in the derivative expansion of the effective action. I show here that, in the case of covariant gauges, the presence of infrared singularities forbids the consistent calculation by keeping the lowest-order terms. The situation is remedied, however, in the case of $R_{\xi}$ gauges. Using the Nielsen identities I show that the final result is gauge independent for generic values of the gauge parameter $v$ that are not anomalously small.Comment: Some comments and references adde

3D Human Body Model Acquisition from Multiple Views

We present a novel motion-based approach for the part determination and shape estimation of a human’s body parts. The novelty of the technique is that neither a prior model of the human body is employed nor prior body part segmentation is assumed. We present a Human Body Part Identification Strategy (HBPIS) that recovers all the body parts of a moving human based on the spatiotemporal analysis of its deforming silhouette. We formalize the process of simultaneous part determination, and 2D shape estimation by employing the Supervisory Control Theory of Discrete Event Systems. In addition, in order to acquire the 3D shape of the body parts, we present a new algorithm which selectively integrates the (segmented by the HBPIS) apparent contours, from three mutually orthogonal views. The effectiveness of the approach is demonstrated through a series of experiments, where a subject performs a set of movements according to a protocol that reveals the structure of the human body

Active Motion-Based Segmentation of Human Body Outlines

We present an integrated approach towards the segmentation and shape estimation of human body outlines. Initially, we assume that the human body consists of a single part, and we fit a deformable model to the given data using our physics-based shape and motion estimation framework. As an actor attains different postures, new protrusions emerge on the outline. We model these changes in the shape using a new representation scheme consisting of a parametric composition of deformable models. This representation allows us to identify the underlying human parts that gradually become visible, by monitoring the evolution of shape and motion parameters of the composed models. Based on these parameters, their joint locations are identified. Our algorithm is applied iteratively over subsequent frames until all moving parts are identified. We demonstrate our technique in a series of experiments with very encouraging results

Active Part-Decomposition, Shape and Motion Estimation of Articulated Objects: A Physics-Based Approach

We present a novel, robust, integrated approach to segmentation shape and motion estimation of articulated objects. Initially, we assume the object consists of a single part, and we fit a deformable model to the given data using our physics-based framework. As the object attains new postures, we decide based on certain criteria if and when to replace the initial model with two new models. These criteria are based on the model\u27s state and the given data. We then fit the models to the data using a novel algorithm for assigning forces from the data to the two models, which allows partial overlap between them and determination of joint location. This approach is applied iteratively until all the object\u27s moving parts are identified. Furthermore, we define new global deformations and we demonstrate our technique in a series of experiments, where Kalman filtering is employed to account for noise and occlusion

Plasmon interactions in the quark-gluon plasma

Yang-Mills theory at finite temperature is rewritten as a theory of plasmons which provides a Hamiltonian framework for perturbation theory with resummation of hard thermal loops.Comment: 12 pages, LaTeX, minor typos corrected, discussion adde

Model-based Analysis of Cardiac Motion from Tagged MRI Data

We develop a new method for analyzing the motion of the left ventricle (LV) of a heart from tagged MRI data. Our technique is based on the development of a new class of physics-based deformable models whose parameters are functions allowing the definition of new parameterized primitives and parameterized deformations. These parameter functions improve the accuracy of shape description through the use of a few intuitive parameters such as functional twisting. Furthermore, these parameters require no complex post-processing in order to be used by a physician. Using a physics-based approach, we convert these geometric models into deformable models that deform due to forces exerted from the datapoints and conform to the given dataset. We present experiments involving the extraction of shape and motion of the LV from MRI-SPAMM data based on a few parameter functions. Furthermore, by plotting the variations over time of the extracted model parameters from normal and abnormal heart data we are able to characterize quantitatively their differences