Charm and Charmonium Spectroscopy at B-factories

Since a few years, charm and charmonium spectroscopy has revived, both from experimental and theoretical point of views. Many new states have been discovered triggering numerous theoretical publications. The B-factories with their large enriched charm sample have played a leading role on the experimental side with the observation and study of most of the new states. Other experiments such as CLEO and CDF have also contributed. Classical hadron spectroscopy predicted some of these new states, but not all of them. Therefore a lot of effort have been spent in order to understand the nature of the later. We are summarizing here the most recent and important results in hadron spectroscopy, including strange-charm mesons, charm baryons and charmonium and charmonium-like states

Constraints on Light Dark Matter From Core-Collapse Supernovae

We show that light ($\simeq$ 1 -- 30 MeV) dark matter particles can play a significant role in core-collapse supernovae, if they have relatively large annihilation and scattering cross sections, as compared to neutrinos. We find that if such particles are lighter than $\simeq$ 10 MeV and reproduce the observed dark matter relic density, supernovae would cool on a much longer time scale and would emit neutrinos with significantly smaller energies than in the standard scenario, in disagreement with observations. This constraint may be avoided, however, in certain situations for which the neutrino--dark matter scattering cross sections remain comparatively small.Comment: 4 pages, 1 figur

Randomness versus non-determinism in distributed computing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1995.Includes bibliographical references (p. 209-214) and index.by Alain Isaac Saias.Ph.D

Proving Program Invariance and Termination by Parametric Abstraction, Lagrangian Relaxation and Semidefinite Programming Patrick Cousot

In order to verify semialgebraic programs, we automatize the Floyd/Naur/Hoare proof method. The main task is to automatically infer valid invariants and rank functions. First we express the program semantics in polynomial form. Then the unknown rank function and invariants are abstracted in parametric form. The implication in the Floyd/Naur/Hoare verification conditions is handled by abstraction into numerical constraints by Lagrangian relaxation. The remaining universal quantification is handled by semidefinite programming relaxation. Finally the parameters are computed using semidefinite programming solvers. This new approach exploits the recent progress in the numerical resolution of linear or bilinear matrix inequalities by semidefinite programming using e#cient polynomial primal/dual interior point methods generalizing those well-known in linear programming to convex optimization. The framework is applied to invariance and termination proof of sequential, nondeterministic, concurrent, and fair parallel imperative polynomial programs and can easily be extended to other safety and liveness properties

Counting overlap-free binary words

Aword on a nite alphabet A is said to be overlap-free if it contains no factor of the form xuxux, where x is a letter and u a (possibly empty) word. In this paper we study the number un of overlap-free binary words of length n, whichisknown to be bounded by a polynomial in n. First,we describe a bijection between the set of overlap-free words and a rational language. This yields recurrence relations for un, which allow to compute un in logarithmic time. Then, we prove that the numbers =supf r j n r = O (un) g and = inf f r j un = O (n r) g are distinct, and we give an upper bound for and a lower bound for. Finally, we compute an asymptotically tight bound to the number of overlap-free words of length less than n.

Recognizing 3D Objects by Generating Random Actions

This paper presents a formal model of an active recognition system that can be programmed by learning. At each time step the system decides between producing an action to generate new data and stopping to issue the name of the object observed. The actions can be directed either towards the external environment or towards the internal perceptual system of the agent. The decision strategy is based on a quantitative evaluation of the system learning experience. The problem studied is the recognition of chess pieces using a moving camera and a multiscale feature detector. The recognition is difficult because the objects are complex -- neither polyhedral nor smooth -- and rather similar between classes, especially in certain view configurations. The system uses the information obtained by observing internal state transitions when the camera is moved or when the feature detector scale is changed. A simulation of the agent and the environment is used for experimental measures of the model per..

Tree Structured Non-linear Signal Modeling and Prediction

We develop a non-parametric method of nonlinear prediction based on adaptive partitioning of the phase space associated with the process. The partitioning method is implemented with a recursive tree-structured vector quantization algorithm which successively re nes the partition by binary splitting where the splitting threshold is determined by a penalized maximum entropy criterion. A complexity penalty is derived and applied to protect against high statistical variability of the predictor structure. We establish an important relation between our tree-structured model for the process and generalized non-linear thresholded AR model (ART). We illustrate our method for two cases where classical linear prediction is ine ective: a chaotic "doublescroll" signal measured at the output of a Chua-type electronic circuit, and a simulated second order ART model. 1