Tree pattern matching from regular tree expressions

summary:In this work we deal with tree pattern matching over ranked trees, where the pattern set to be matched against is defined by a regular tree expression. We present a new method that uses a tree automaton constructed inductively from a regular tree expression. First we construct a special tree automaton for the regular tree expression of the pattern $E$, which is somehow a generalization of Thompson automaton for strings. Then we run the constructed automaton on the subject tree $t$. The pattern matching algorithm requires an $\mathcal{O}(\vert t\vert\vert E\vert)$ time complexity, where $\vert t\vert$ is the number of nodes of $t$ and $\vert E\vert$ is the size of the regular tree expression $E$. The novelty of this contribution besides the low time complexity is that the set of patterns can be infinite, since we use regular tree expressions to represent patterns

Language and Automata Theory and Applications

International audienc

Fast probing of many Earth models with full waveforms

Seismology is our primary method for inferring the structure and dynamics of Earth’s interior. A region of particular importance is the lowermost mantle near the core-mantle boundary. Detailed information about lowermost mantle seismic structures can be inferred by comparing synthetic seismic waveforms to those observed at Earth’s surface. In this thesis, a forward modelling method is presented which is based on first order single scattering approximations. These include the well-known Born approximation (which involves linearly perturbing the amplitude of the waveform), as well as the Rytov approximation (which involves linearly perturbing the phase of the waveform) and a whole family of intermediate approximations termed Marks approximations. The computation of these first order waveforms is facilitated by time and space dependent waveform sensitivity kernels. Kernels are computed for each source-receiver pair, but they are independent of the perturbed model. Once the kernel is calculated, synthetic seismograms for many models are generated very quickly (≈ 7 s per model). The applicabilities of the Born, Rytov and Marks approximations are tested by comparing to reference solutions computed with a fully numerical method. For small perturbations to the background model, an excellent agreement is found between the first order and reference solutions, providing validation of the method. The first order method breaks down with increasingly large and strong perturbations. The Rytov ap- proximation consistently performs better than Born (the worst) or Marks(intermediate). The method is valid for P, S and converted phases. The first order method is used to generate synthetic P/Pdiff waveforms under the Rytov approximation for 500 models representative of the large low velocity province under Africa. The database of synthetic data is probed in a statistical manner; sets of models which fit certain supposed observations are presented

On-line monitoring of water distribution networks

This thesis is concerned with the development of a computer-based, real-time monitoring scheme which is a prerequisite of any form of on-line control. A new concept, in the field of water distribution systems, of water system state estimation is introduced. Its function is to process redundant, noise-corrupted telemeasurements in order to supply a real-time data base with reliable estimates of the current state and structure of the network. The information provided by the estimator can then be used in a number of on-line programs. In view of the strong nonlinearity of the network equations, two methods of state estimation, which have enhanced numerical stability, are examined in this thesis. The first method uses an augmented matrix formulation of a classical least-squares problem, and the second is based on a least absolute value solution of an over determined set of equations. Two water systems, one of which is a realistic 34-node network, are used to evaluate the performance of the proposed methods .The problem of bad data processing and its extension to the validation of network topology and leakage detection is also examined. It is shown that the method based on least absolute values estimation provides a more immediate indication of erroneous measurements. In addition, this method demonstrates the useful feature of eliminating the effects of gross errors on the final state estimate. The important question of water system observability is then studied. Two original combinatorial methods are proposed to check topological observability. The first one is an indirect technique which searches for a maximum measurement-to-branch matching and then attempts to build a spanning tree of the network graph using only the branches with measurement assignment. The second method is a direct search for an observable spanning tree. A number of systems are used to test both techniques, including a 34-node water supply network and an IEEE 118-bus power system. The problem of minimisation of distributed leakages is solved efficiently using a state estimation technique. Comparison of the head profile achieved for the calculated optimal valve controls with the standard operating conditions for a 25-node network indicates a major reduction of the volume of leakages. In the final part of this thesis a software package, which simulates the real-time operation of a water distribution system, is described. The programs are designed in such a way that by replacing simulated measurements with live telemetry data they can be directly used for. water network monitoring and control

Phenomenology of event shapes at hadron colliders

We present results for matched distributions of a range of dijet event shapes at hadron colliders, combining next-to-leading logarithmic (NLL) accuracy in the resummation exponent, next-to-next-to leading logarithmic (NNLL) accuracy in its expansion and next-to-leading order (NLO) accuracy in a pure alpha_s expansion. This is the first time that such a matching has been carried out for hadronic final-state observables at hadron colliders. We compare our results to Monte Carlo predictions, with and without matching to multi-parton tree-level fixed-order calculations. These studies suggest that hadron-collider event shapes have significant scope for constraining both perturbative and non-perturbative aspects of hadron-collider QCD. The differences between various calculational methods also highlight the limits of relying on simultaneous variations of renormalisation and factorisation scale in making reliable estimates of uncertainties in QCD predictions. We also discuss the sensitivity of event shapes to the topology of multi-jet events, which are expected to appear in many New Physics scenarios.Comment: 70 pages, 25 figures, additional material available from http://www.lpthe.jussieu.fr/~salam/pp-event-shapes

FutureMapping 2: Gaussian Belief Propagation for Spatial AI

We argue the case for Gaussian Belief Propagation (GBP) as a strong algorithmic framework for the distributed, generic and incremental probabilistic estimation we need in Spatial AI as we aim at high performance smart robots and devices which operate within the constraints of real products. Processor hardware is changing rapidly, and GBP has the right character to take advantage of highly distributed processing and storage while estimating global quantities, as well as great flexibility. We present a detailed tutorial on GBP, relating to the standard factor graph formulation used in robotics and computer vision, and give several simulation examples with code which demonstrate its properties