Building upon Fast Multipole Methods to Detect and Model Organizations

Many models in natural and social sciences are comprised of sets of inter-acting entities whose intensity of interaction decreases with distance. This often leads to structures of interest in these models composed of dense packs of entities. Fast Multipole Methods are a family of methods developed to help with the calculation of a number of computable models such as described above. We propose a method that builds upon FMM to detect and model the dense structures of these systems

A multiagent urban traffic simulation Part I: dealing with the ordinary

We describe in this article a multiagent urban traffic simulation, as we believe individual-based modeling is necessary to encompass the complex influence the actions of an individual vehicle can have on the overall flow of vehicles. We first describe how we build a graph description of the network from purely geometric data, ESRI shapefiles. We then explain how we include traffic related data to this graph. We go on after that with the model of the vehicle agents: origin and destination, driving behavior, multiple lanes, crossroads, and interactions with the other vehicles in day-to-day, ?ordinary? traffic. We conclude with the presentation of the resulting simulation of this model on the Rouen agglomeration

Representation of Interdependencies Between Urban Networks by a Multi-Layer Graph (Short Paper)

The RGC4 (Urban resilience and Crisis Management in a Context of Slow Flood to Slow Kinetics) project aims to develop tools to help manage critical technical networks as part of the management process of crisis in a context of slow kinetic flooding in Paris. This project focuses on cascading models to identify a number of inter-dependencies between networks and to define tools capable of coordinating the actions of managers before and during the crisis. This paper revisits the conceptual and methodological bases of networks approach to study the inter-dependencies between networks. Research that studies the return to service of infrastructure networks often angle it from the perspective of operational research. The article proposes a graph theory perspective based on a multi-layer network approach and shows how to characterize the inter-dependencies between networks at three process levels (macro, meso, micro

A multiagent urban traffic simulation. Part II: dealing with the extraordinary

In Probabilistic Risk Management, risk is characterized by two quantities: the magnitude (or severity) of the adverse consequences that can potentially result from the given activity or action, and by the likelihood of occurrence of the given adverse consequences. But a risk seldom exists in isolation: chain of consequences must be examined, as the outcome of one risk can increase the likelihood of other risks. Systemic theory must complement classic PRM. Indeed these chains are composed of many different elements, all of which may have a critical importance at many different levels. Furthermore, when urban catastrophes are envisioned, space and time constraints are key determinants of the workings and dynamics of these chains of catastrophes: models must include a correct spatial topology of the studied risk. Finally, literature insists on the importance small events can have on the risk on a greater scale: urban risks management models belong to self-organized criticality theory. We chose multiagent systems to incorporate this property in our model: the behavior of an agent can transform the dynamics of important groups of them

Changing Levels of Description in a Fluid Flow Simulation

We describe here our perception of complex systems, of how we feel the different layers of description are important part of a correct complex system simulation. We describe a rough models categorization between rules based and law based, of how these categories handled the levels of descriptions or scales. We then describe our fluid flow simulation, which combines different fineness of grain in a mixed approach of these categories. This simulation is built keeping in mind an ulterior use inside a more general aquatic ecosystem

Logical segmentation for article extraction in digitized old newspapers

Newspapers are documents made of news item and informative articles. They are not meant to be red iteratively: the reader can pick his items in any order he fancies. Ignoring this structural property, most digitized newspaper archives only offer access by issue or at best by page to their content. We have built a digitization workflow that automatically extracts newspaper articles from images, which allows indexing and retrieval of information at the article level. Our back-end system extracts the logical structure of the page to produce the informative units: the articles. Each image is labelled at the pixel level, through a machine learning based method, then the page logical structure is constructed up from there by the detection of structuring entities such as horizontal and vertical separators, titles and text lines. This logical structure is stored in a METS wrapper associated to the ALTO file produced by the system including the OCRed text. Our front-end system provides a web high definition visualisation of images, textual indexing and retrieval facilities, searching and reading at the article level. Articles transcriptions can be collaboratively corrected, which as a consequence allows for better indexing. We are currently testing our system on the archives of the Journal de Rouen, one of France eldest local newspaper. These 250 years of publication amount to 300 000 pages of very variable image quality and layout complexity. Test year 1808 can be consulted at plair.univ-rouen.fr.Comment: ACM Document Engineering, France (2012

Massive population evacuation in an urban context

International audienceIncreasing urban sprawl all over the world leads to an increase of vulnerabilities, as greater numbers of goods and people are exposed to hazards, both natural (flood, quakes, fires, tsunami, epidemic) and industrial (factories, plants). Population evacuation features amongst the tools political managers can use to mitigate these risks. When evacuations are decided, managers must strike a fine balance between displacing all the people needed to avoid injuries and fatalities, and not evacuating people that would finally not be struck by the hazard. Evacuated populations convoys must furthermore not lead to a sub-crisis where they become new vulnerabilities, or through disorder and density prevent the management of other vulnerabilities. There is regretfully a scarcity of tools to help risks manager make this kind of decision. We present here our works on such tools. Most risk management policies focus on a planar, continuous conception of space. Seveso Directives for example use different radius around the hazard center, a plant for example, to find which housings and business will be affected at which degree. We described in our presentation for UrbanNet 2013 how this approach was found lacking for handling road networks, both as vulnerabilities of the direct hazard, and as the means for a successful crisis management. In order to overcome this obstacle we proposed a city-wide agent based simulation called MOSAIIC to model the car traffic, both in normal and extraordinary situations. In MOSAIIC each driver is capable of strategical, tactical and operational planning and decision making. They have a list of destinations they try to reach, and choose a path to get there using their knowledge of the network. They accelerate, brake, change ways depending on their surroundings and personality. They choose alternative solutions if trapped in traffic jams of when facing a road networks altered from their initial knowledge. In this follow-up article we would like to discuss the data and its analysis we used to calibrate and validate simulations built with MOSAIIC to study theoretical all-car evacuation of the city of Rouen. Furthermore, since MOSAIIC, we started a new project, ESCAPE (Exploring by Simulation Cities Awareness on Population Evacuation), which aims at simulating massive evacuation

Detecting Articles in a Digitized Finnish Historical Newspaper Collection 1771–1929: Early Results Using the PIVAJ Software

This paper describes first large scale article detection and extraction efforts on the Finnish Digi newspaper material of the National Library of Finland (NLF) using data of one newspaper, Uusi Suometar 1869-1898 . The historical digital newspaper archive environment of the NLF is based on commercial docWorks software. The software is capable of article detection and extraction, but our material does not seem to behave well in the system in t his respect. Therefore, we have been in search of an alternative article segmentation system and have now focused our efforts on the PIVAJ machine learning based platform developed at the LITIS laborator y of University of Rouen Normandy. As training and evaluation data for PIVAJ we chose one newspaper, Uusi Suometar. We established a data set that contains 56 issues of the newspaper from years 1869 1898 with 4 pages each, i.e. 224 pages in total. Given the selected set of 56 issues, our first data annotation and experiment phase consisted of annotating a subset of 28 issues (112 pages) and conducting preliminary experiments. After the preliminary annotation and annotation of the first 28 issues accordingly. Subsequently, we annotated the remaining 28 issues . We then divided the annotated set in to training and evaluation set s of 168 and 56 pages. We trained PIVAJ successfully and evaluate d the results using the layout evaluation software developed by PRImA research laboratory of University of Salford. The results of our experiments show that PIVAJ achieves success rates of 67.9, 76.1, and 92.2 for the whole data set of 56 pages with three different evaluation scenarios introduced in [6]. On the whole, the results seem reasonable considering the varying layouts of the different issues of Uusi Suometar along the time scale of the data.Peer reviewe