First experience in operating the population of the condition databases for the CMS experiment

Reliable population of the condition databases is critical for the correct operation of the online selection as well as of the offline reconstruction and analysis of data. We will describe here the system put in place in the CMS experiment to populate the database and make condition data promptly available both online for the high-level trigger and offline for reconstruction. The system, designed for high flexibility to cope with very different data sources, uses POOL-ORA technology in order to store data in an object format that best matches the object oriented paradigm for \texttt{C++} programming language used in the CMS offline software. In order to ensure consistency among the various subdetectors, a dedicated package, PopCon (Populator of Condition Objects), is used to store data online. The data are then automatically streamed to the offline database hence immediately accessible offline worldwide. This mechanism was intensively used during 2008 in the test-runs with cosmic rays. The experience of this first months of operation will be discussed in detail.Comment: 15 pages, submitter to JOP, CHEP0

A configuration system for the ATLAS trigger

The ATLAS detector at CERN's Large Hadron Collider will be exposed to proton-proton collisions from beams crossing at 40 MHz that have to be reduced to the few 100 Hz allowed by the storage systems. A three-level trigger system has been designed to achieve this goal. We describe the configuration system under construction for the ATLAS trigger chain. It provides the trigger system with all the parameters required for decision taking and to record its history. The same system configures the event reconstruction, Monte Carlo simulation and data analysis, and provides tools for accessing and manipulating the configuration data in all contexts.Comment: 4 pages, 2 figures, contribution to the Conference on Computing in High Energy and Nuclear Physics (CHEP06), 13.-17. Feb 2006, Mumbai, Indi

On the Collaboration of an Automatic Path-Planner and a Human User for Path-Finding in Virtual Industrial Scenes

This paper describes a global interactive framework enabling an automatic path-planner and a user to collaborate for finding a path in cluttered virtual environments. First, a collaborative architecture including the user and the planner is described. Then, for real time purpose, a motion planner divided into different steps is presented. First, a preliminary workspace discretization is done without time limitations at the beginning of the simulation. Then, using these pre-computed data, a second algorithm finds a collision free path in real time. Once the path is found, an haptic artificial guidance on the path is provided to the user. The user can then influence the planner by not following the path and automatically order a new path research. The performances are measured on tests based on assembly simulation in CAD scenes

Efficient Online Timed Pattern Matching by Automata-Based Skipping

The timed pattern matching problem is an actively studied topic because of its relevance in monitoring of real-time systems. There one is given a log $w$ and a specification $\mathcal{A}$ (given by a timed word and a timed automaton in this paper), and one wishes to return the set of intervals for which the log $w$, when restricted to the interval, satisfies the specification $\mathcal{A}$. In our previous work we presented an efficient timed pattern matching algorithm: it adopts a skipping mechanism inspired by the classic Boyer--Moore (BM) string matching algorithm. In this work we tackle the problem of online timed pattern matching, towards embedded applications where it is vital to process a vast amount of incoming data in a timely manner. Specifically, we start with the Franek-Jennings-Smyth (FJS) string matching algorithm---a recent variant of the BM algorithm---and extend it to timed pattern matching. Our experiments indicate the efficiency of our FJS-type algorithm in online and offline timed pattern matching

Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution

It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes