Isospin Considerations in Correlations of Pions and BB mesons

The correlations between a $B$ meson and a pion produced nearby in phase space should respect isospin reflection symmetry $I_3 \to -I_3$. Thus, one generally expects similar $\pi^+ B^0$ and $\pi^- B^+$ correlations (non-exotic channels), and similar $\pi^- B^0$ and $\pi^+ B^+$ correlations (exotic channels). Exceptions include (a) fragmentation processes involving exchange of quarks with the producing system, (b) misidentification of charged kaons as charged pions, and (c) effects of decay products of the associated $\overline{B}$. All of these can affect the apparent signal for correlations of charged $B$ mesons with charged hadrons. The identification of the flavor of neutral $B$ mesons through the decay $B^0 \to K^{*0} J/\psi$ requires good particle identification in order that the decay $K^{*0} \to K^+ \pi^-$ not be mistaken for $\overline{K}^{*0} \to K^- \pi^+$, in which case the correlations of neutral $B$ mesons with hadrons can be underestimated.Comment: LaTeX EPSF file; 8 uuencoded figures to be submitted separatel

c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Bayesian Object Localisation in Images

Abstract. A Bayesian approach to intensity-based object localisation is presented that employs a learned probabilistic model of image filter-bank output, applied via Monte Carlo methods, to escape the inefficiency of exhaustive search. An adequate probabilistic account of image data requires intensities both in the foreground (i.e. over the object), and in the background, to be modelled. Some previous approaches to object localisation by Monte Carlo methods have used models which, we claim, do not fully address the issue of the statistical independence of image intensities. It is addressed here by applying to each image a bank of filters whose outputs are approximately statistically independent. Distributions of the responses of individual filters, over foreground and background, are learned from training data. These distributions are then used to define a joint distribution for the output of the filter bank, conditioned on object configuration, and this serves as an observation likelihood for use in probabilistic inference about localisation. The effectiveness of probabilistic object localisation in image clutter, using Bayesian Localisation, is illustrated. Because it is a Monte Carlo method, it produces not simply a single estimate of object configuration, but an entire sample from the posterior distribution for the configuration. This makes sequential inference of configuration possible. Two examples are illustrated here: coarse to fine scale inference, and propagation of configuration estimates over time, in image sequences. Keywords: vision, object location, Monte Carlo, filter-bank, statistical independenc

Enhanced CP Violation with B→KD0(D‾0)B\to K D^0 (\overline D^0) Modes and Extraction of the CKM Angle gamma

The Gronau-London-Wyler (GLW) method extracts the CKM angle $\gamma$ by measuring $B^\pm$ decay rates involving $D^0/\overline D^0$ mesons. Since that method necessitates the interference between two amplitudes that are significantly different in magnitude, the resulting asymmetries tend to be small. CP violation can be greatly enhanced for decays to final states that are common to both D^0 and $\overline D^0$ and that are not CP eigenstates. In particular, large asymmetries are possible for final states f such that $D^0\to f$ is doubly Cabibbo suppressed while $\overline D^0\to f$ is Cabibbo allowed. The measurement of interference effects in two such modes allows the extraction of $\gamma$ without prior knowledge of $Br(B^-\to K^- \overline D^0)$, which may be difficult to determine due to backgrounds.Comment: 12 pages, LaTeX, no figure

Leaf segmentation and tracking using probabilistic parametric active contours

Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is generally a linear combination of a data fit term and a regularization term. This energy function can be adjusted to exploit the intrinsic object and image features. This can be done by changing the weighting parameters of the data fit and regularization term. There is, however, no rule to set these parameters optimally for a given application. This results in trial and error parameter estimation. In this paper, we propose a new active contour framework defined using probability theory. With this new technique there is no need for ad hoc parameter setting, since it uses probability distributions, which can be learned from a given training dataset

Adaptive Feature Selection for Object Tracking with Particle Filter

International audienceObject tracking is an important topic in the field of computer vision. Commonly used color-based trackers are based on a fixed set of color features such as RGB or HSV and, as a result, fail to adapt to changing illumination conditions and background clutter. These drawbacks can be overcome to an extent by using an adaptive framework which selects for each frame of a sequence the features that best discriminate the object from the background. In this paper, we use such an adaptive feature selection method embedded into a particle filter mechanism and show that our tracking method is robust to lighting changes and background distractions. Different experiments also show that the proposed method outperform other approaches

Identifying Highly Connected Counties Compensates for Resource Limitations when Evaluating National Spread of an Invasive Pathogen

Surveying invasive species can be highly resource intensive, yet near-real-time evaluations of invasion progress are important resources for management planning. In the case of the soybean rust invasion of the United States, a linked monitoring, prediction, and communication network saved U.S. soybean growers approximately $200 M/yr. Modeling of future movement of the pathogen (Phakopsora pachyrhizi) was based on data about current disease locations from an extensive network of sentinel plots. We developed a dynamic network model for U.S. soybean rust epidemics, with counties as nodes and link weights a function of host hectarage and wind speed and direction. We used the network model to compare four strategies for selecting an optimal subset of sentinel plots, listed here in order of increasing performance: random selection, zonal selection (based on more heavily weighting regions nearer the south, where the pathogen overwinters), frequency-based selection (based on how frequently the county had been infected in the past), and frequency-based selection weighted by the node strength of the sentinel plot in the network model. When dynamic network properties such as node strength are characterized for invasive species, this information can be used to reduce the resources necessary to survey and predict invasion progress

Adaptative road lanes detection and classification

Proceeding of: 8th International Conference, ACIVS 2006, Antwerp, Belgium, September 18-21, 2006This paper presents a Road Detection and Classification algorithm for Driver Assistance Systems (DAS), which tracks several road lanes and identifies the type of lane boundaries. The algorithm uses an edge filter to extract the longitudinal road markings to which a straight lane model is fitted. Next, the type of right and left lane boundaries (continuous, broken or merge line) is identified using a Fourier analysis. Adjacent lanes are searched when broken or merge lines are detected. Although the knowledge of the line type is essential for a robust DAS, it has been seldom considered in previous works. This knowledge helps to guide the search for other lanes, and it is the basis to identify the type of road (one-way, two-way or freeway), as well as to tell the difference between allowed and forbidden maneuvers, such as crossing a continuous line.Publicad

Improved Methods for Observing CP Violation in B+/- --> K+/- D0 and Measuring the CKM Phase gamma

Various methods are discussed for obtaining the CKM angle gamma through the interference of the charged B-meson decay channels B- -> K- D0 and B- -> K- D0-bar where the D0 and D0-bar decay to common final states. It is found that choosing final states which are not CP eigenstates can lead to large direct CP violation which can give significant bounds on gamma without any theoretical assumptions. If two or more modes are studied, gamma may be extracted with a precision on the order of +/-15 degrees given about 10^8 B-mesons. We also discuss the case of three body decays of the D0 where additional information may be obtained from the distribution of the D0 decay products and consider the impact of D-D-bar oscillations.Comment: 51 pages 8 figures, typo in equation 33 correcte