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Fradkin and Seymour (J Comb Theory Ser B 110:19–46, 2015) defined the class of digraphs of bounded independence number as a generalization of the class of tournaments. They argued that the class of digraphs of bounded independence number is structured enough to be exploited algorithmically. In this paper, we further strengthen this belief by showing that several cut problems that admit sub-exponential time parameterized algorithms (a trait uncommon to parameterized algorithms) on tournaments, including Directed Feedback Arc Set, Directed Cutwidth and Optimal Linear Arrangement, also admit such algorithms on digraphs of bounded independence number. Towards this, we rely on the generic approach of Fomin and Pilipczuk (in: Proceedings of the Algorithms—ESA 2013—21st Annual European Symposium, Sophia Antipolis, France, September 2–4, 2013, pp. 505–516, 2013), where to get the desired algorithms, it is enough to bound the number of k-cuts in digraphs of bounded independence number by a sub-exponential FPT function (Fomin and Pilipczuk bounded the number of k-cuts in transitive tournaments). Specifically, our main technical contribution is a combinatorial result that proves that the yes-instances of the problems (defined above) have a sub-exponential number of k-cuts. We prove this bound by using a combination of chromatic coding, inductive reasoning and exploiting the structural properties of these digraphs. [ABSTRACT FROM AUTHOR]

This paper proposes a standard documentary framework called Databook for Data Science projects. This proposal is the result of five years of action-research on multiple projects in several sectors of activity in France and a confrontation of standard theoretical processes of Data Science, such as CRISP_DM, with the reality of the field. The minimalist and flexible structure of the Databook prototype, described and illustrated in this paper, has revealed its operationality on more than a hundred projects and has been recognised by various stakeholders as an excellent facilitator of Human Data Mediation, especially for multi-skilled projects. Beyond its proven benefits for project efficiency, this framework, conceived as a frontier object, can be applied more broadly to data project portfolio management and data value, governance and quality. By surpassing the computational aspect of the models, the Databook is an answer to the issues of interpretability and auditability of algorithms. [ABSTRACT FROM AUTHOR]

• The snake optimization algorithm (SOA) and the Improved snake optimization algorithm (ISOA) are proposed to extract the parameters for photovoltaic cells. • The performance of SOA and ISOA is compared with the ones of the best algorithms from specialized literature. • ISOA proved their performance in comparison with the algorithms considered for the three photovoltaic cells analyzed. The accurate extraction of photovoltaic cells and panels parameters is very important in order to precisely forecast their generated energy, to rapidly identify the maximum power point, both for the manufacturer in order to verify the quality control during the production process, and for the researchers to improve their quality, efficiency and also study the photovoltaic cells degradation. This paper is the first to propose the usage of the Snake optimization metaheuristic algorithms to extract the parameters of three photovoltaic cells: monocrystalline commercial silicon, amorphous silicon, and RTC France. The Snake algorithms are used for the one diode and two diode models to extract five and seven parameters respectively. The root mean square error statistical test has been performed in order to prove the algorithm performance. The comparison with the algorithms published in specialized literature reveals that the Snake algorithm outperforms several others, but is not the best. An improved version is developed and the results show that it gives better results than, or at least the same as the best ones. The improvement in root mean square error, when the improved snake algorithm is used, is 16% in the case of the monocrystalline silicon photovoltaic cell for the two diode model, 5.3% in the case of the amorphous silicon photovoltaic cell for the two diode model, and it is slightly better for the RTC France photovoltaic cell, from 0.002 to 0.11% when the best algorithms are considered. The Snake algorithms give very good results, especially the improved one, even for a small population size and a limited number of iterations. Thus, for the improved snake algorithm, the iterations number and the population size are 100 and 80, in the case of the monocrystalline photovoltaic cells, for one diode model, and 200 and 40 respectively for two diode model, 100 and 120 in the case of the amorphous silicon photovoltaic cell for one diode model and 120 and 120 respectively for two diode model, 150 and 100 in the case of RTC photovoltaic cell for one diode model and 150 and 80 respectively for two diode model. This reduces the computational time for the extraction of the photovoltaic cell parameters. The computational time for the improved snake algorithm is half of the necessary one for one of the best algorithms, the hybrid successive discretization algorithm. Therefore, the snake optimization algorithm, especially the improved one is one of the best algorithms that can be used for the parameters extraction of the photovoltaic cells. [ABSTRACT FROM AUTHOR]

Districting problems are of high importance in many different fields. Multiple criteria models seem a more adequate representation of districting problems in real-world situations. Real-life decision situations are by their very nature multidimensional. This paper deals with the problem of partitioning a territory into “homogeneous” zones. Each zone is composed of a set of elementary territorial units. A district map is formed by partitioning the set of elementary units into connected zones without inclusions. When multiple criteria are considered, the problem of enumerating all the efficient solutions for such a model is known as being NP-hard, which is why we decided to avoid using exact methods to solve large-size instances. In this paper, we propose a new method to approximate the Pareto front based on an evolutionary algorithm with local search. The algorithm presents a new solution representation and the crossover/mutation operators. Its main features are the following: it deals with multiple criteria; it allows to solve large-size instances in a reasonable CPU time and generates high quality solutions. The algorithm was applied to a real-world problem, that of the Paris region public transportation. Results will be used for a discussion about the reform of its current pricing system. [ABSTRACT FROM AUTHOR]

This special section is devoted to a selection of journal versions of papers that appeared originally in the Proceedings of the 8th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), which took place in Grenoble, France in April 2002 as a constituent event of the European joint conferences on Theory and Practice of Software (ETAPS). All papers are relevant to the field of systems validation. The first three papers advance and extend model-checking techniques, the fourth presents algorithms for run-time verification, and the last paper is about animation and test generation for formal system specifications. [ABSTRACT FROM AUTHOR]

The monitoring of land cover requires that stable land cover classes be distinguished from changes over time. Within this paper, a postclassification method is presented that provides land cover change information, based on a time series of land cover maps. The method applies a kernel filter to sequential land cover maps. Under some basic assumptions, it shows robustness against classification errors. Despite seasonality, land cover changes often occur at a low temporal frequency (e.g., maximum once every 5–10 years). If land cover maps are available more frequently, some of the information will become redundant (oversampling). The proposed method uses this redundancy for tolerating (nonsystematic) misclassifications. In order to demonstrate the benefits and limitations of the proposed method, analytical expressions have been derived. When compared to a simple postclassification comparison, one of the key strengths of the proposed approach is that it is able to improve both the overall and user's accuracy of change, while also maintaining the same level of producer's accuracy. As a case study, MODerate Resolution Imaging Spectroradiometer remote sensing data from 2006-2010 were classified into forest (F)/nonforest (NF) at pan-European scale. Promising results were obtained for detecting forest loss due to natural disasters. Quality was assessed using burnt area maps in southern Europe and a forest damage report after a windstorm in France. Results indicated a considerable reduction of change detection errors, confirming the theoretical results. [ABSTRACT FROM PUBLISHER]

Abstract: Multipurpose detectors are used widely in high energy physics experiments. They work on the same set of fundamental operating principles although specialized in their design goals and detection features. The Large Hadron Collider (LHC) has four major experiments exploiting these techniques, and in the future the International Linear Collider (ILC) will employ the detectors that are being designed presently and will utilize the particle-flow algorithms in order to obtain the desired dijet mass resolution. A complete understanding of the above mentioned detectors, their designs and the particle detection mechanisms can enable the prediction of the most desirable attributes of an ideal particle-detector design. In this introductory paper, we highlight the techniques used for radiation detection particularly at LHC, and the articles that follow in this volume. [Copyright &y& Elsevier]

Summary: This paper proposes a method for using major flash flood events occurred at ungauged catchments to reduce the uncertainties in estimating regional flood quantiles. The approach is based on standard regionalization methods assuming that the flood peak distribution rescaled by a site-dependent index flood is uniform within a homogeneous region. A likelihood formulation and a Bayesian Markov Chain Monte Carlo (MCMC) algorithm are used to infer the parameter values of the regional distributions. This statistical inference technique has been selected for its rigorousness – various hypotheses are explicitly formulated in the likelihood function, its flexibility as for the type of data that can be treated, and its ability to compute accurate estimates of the confidence intervals for the adjusted parameters and for the corresponding flood quantiles. The proposed method is applied to two data sets from Slovakia and the South of France that consist of series of annual peak discharges at gauged sites and estimated peak discharges of extreme flash flood events that have occurred at ungauged sites. The results suggest that the confidence intervals of the quantiles can be significantly narrowed down provided that the set of ungauged extremes is the result of a comprehensive sampling over the selected region. This remains valid, even if the uncertainties in the estimated ungauged extreme discharges are considered. The flood quantiles estimated by the proposed method are also consistent with the results of site specific flood frequency studies based on historic and paleoflood information. [ABSTRACT FROM AUTHOR]

Abstract: The meanders of a baroclinic coastal current in the Northwestern Mediterranean Sea have already been reported in the literature. These meanders can be surrounded by vortices. Such vortices have been observed in the western part of the Gulf of Lions but the location and the mechanism of their formation are poorly documented. In this paper, we use the current measurements of a one-year experiment, which was conducted in the eastern part of the Gulf of Lions to detect and characterize the vortex activity. A vortex detection algorithm based on few velocity data was developed. Current measurements were available at the sea surface (HF radars) and in the water column from 50 to 140m depth (four current meter moorings). SST images and hydrologic data were also used. Results focus on observations that are coherent 50m and at the surface. Vortices are anticyclonic, of submesoscale size and present maximal velocities of 30–50cm/s. The drift speed of the vortices is comparable to but less than the velocity of the Northern Current. These observations enable to estimate the minimum vortex occurrence in this area. The presence of vortex structures is strongly correlated with a specific sequence of wind patterns. [Copyright &y& Elsevier]

Background: Many different simulation frameworks, in different topics, need to treat realistic datasets to initialize and calibrate the system. A precise reproduction of initial states is extremely important to obtain reliable forecast from the model. Methodology/Principal Findings: This paper proposes an algorithm to create an artificial population where individuals are described by their age, and are gathered in households respecting a variety of statistical constraints (distribution of household types, sizes, age of household head, difference of age between partners and among parents and children). Such a population is often the initial state of microsimulation or (agent) individual-based models. To get a realistic distribution of households is often very important, because this distribution has an impact on the demographic evolution. Usual techniques from microsimulation approach cross different sources of aggregated data for generating individuals. In our case the number of combinations of different households (types, sizes, age of participants) makes it computationally difficult to use directly such methods. Hence we developed a specific algorithm to make the problem more easily tractable. Conclusions/Significance: We generate the populations of two pilot municipalities in Auvergne region (France) to illustrate the approach. The generated populations show a good agreement with the available statistical datasets (not used for the generation) and are obtained in a reasonable computational time. [ABSTRACT FROM AUTHOR]

This paper presents a study undertaken in preparation of the work leading up to the assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the land surface model (LSM) Interaction Soil Biosphere Atmosphere (ISBA) at Météo-France. This study consists of an intercomparison experiment of different space-borne platforms providing surface soil moisture information [Advanced Microwave Scanning Radiometer for Earth Observing (AMSR-E) and European Remote Sensing Satellite Scatterometer (ERS-Scat)] with the reanalysis soil moisture predictions over France from the model suite of Système d'analyse fournissant des renseignements atmosphériques à la neige (SAFRAN), ISBA, and coupled model (MODCOU; SIM) of Météo-France for the years of 2003–05. Both modeled and remotely sensed data are initially validated against in situ observations obtained at the experimental soil moisture monitoring site Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) in southwestern France. Two different AMSR-E soil moisture products are compared in the course of this study—the official AMSR-E product from the National Snow and Ice Data Center (NSIDC) and a new product developed at the Vrije Universiteit Amsterdam and NASA (VUA–NASA)—which were obtained using two different retrieval algorithms. This allows for an additional assessment of the different algorithms while using identical brightness temperature datasets. This study shows that a good correlation generally exists between AMSR-E (VUA–NASA), ERS-Scat, and SIM for low altitudes and low-to-moderate vegetation covers (1.5–3 kgm-2 vegetation water content), with a reduction in the correlation in mountainous regions. It also shows that the AMSR-E (NSIDC) soil moisture product has significant differences when compared to the other datasets. [ABSTRACT FROM AUTHOR]

Data warehouses store aggregated data issued from different sources to meet users' analysis needs for decision support. The nature of the work of users implies that their requirements are often changing and do not reach a final state. Therefore, a data warehouse cannot be designed in one step, usually it evolves over the time. In this paper, we propose a user-driven approach that enables a data warehouse schema update. It consists in integrating the users' knowledge in the data warehouse modeling to allow new analysis possibilities. More precisely, we consider the specific users' knowledge, which defines new aggregated data, under the form of "if-then" rules that we call aggregation rules. These rules are used to dynamically create new granularity levels in dimension hierarchies, following an automatic and concurrent way. Our approach is composed of four phases: (1) users' knowledge acquisition, (2) knowledge integration, (3) data warehouse schema update, and (4) on-line analysis. To support our approach, we define a Rule-based Data Warehouse (R-DW) model composed of two parts: one "fixed" part and one "evolving" part. The fixed part corresponds to the initial data warehouse schema, whose purpose is to provide an answer to global analysis needs. The evolving part is defined by means of aggregation rules, which allow personalized analyses. To validate our approach, we developed a prototype called WEDriK (data Warehouse Evolution Driven by Knowledge), in which the R-DW model is implemented within the Oracle 10g DBMS. We also present how to achieve our approach by proposing a model dedicated to the management of the data warehouse schema evolution and the updates' algorithms. Furthermore, we applied our approach on banking data of the French bank LCL-Le Crédit Lyonnais and we illustrate our purpose with the LCL case study. [ABSTRACT FROM AUTHOR]

The Large Hadron Collider at CERN offers unprecedented challenges to the design and construction of detectors and trigger/data acquisition systems. For ATLAS, a three level trigger system has been developed to extract interesting physics signatures with a 106 rate reduction. To accomplish this, components of physics analysis traditionally deferred to off-line physics analysis must be embedded within the on-line trigger system. For the Muon trigger, the specific off-line algorithms MOORE (Muon Object Oriented REconstruction) and MuId (Muon Identification) have been adopted so far for the on-line use, imposing an operation in a Bayesian-like environment where only specific hypotheses must be validated. After a short review of the ATLAS trigger, the paper shows the general strategy of the Muon Identification and Selection accessing the full event data, or being seeded from results derived at a previous stage of the trigger chain. [ABSTRACT FROM AUTHOR]

The Event Filter (EF) selection stage is a fundamental component of the ATLAS Trigger and Data Acquisition architecture. Its primary function is the reduction of data flow and rate to values acceptable by the mass storage operations and by the subsequent offline data reconstruction and analysis steps. The computing instrument of the EF is organized as a set of independent subfarms, each connected to one output of the Event Builder (EB) switch fabric. Each subfarm comprises a number of processors analyzing several complete events in parallel. This paper describes the design of the ATLAS EF system, its deployment in the 2004 ATLAS combined test beam together with some examples of integrating selection and monitoring algorithms. Since the processing algorithms are not explicitly designed for EF but are adapted from the offline ones, special emphasis is reserved to system reliability and data security, in particular for the case of failures in the processing algorithms. Other key design elements have been system modularity and scalability. The EF shall be able to follow technology evolution and should allow for using additional processing resources possibly remotely located [ABSTRACT FROM PUBLISHER]

This paper presents an original algorithm that uses a new modeling approach of the interference constraints and uses a probabilistic taboo search algorithm to solve the frequency assignment problem in the field of broadcasting. The results obtained by our algorithm are compared to the operating solutions in the field of FM broadcasting in France and the best known results obtained by hybrid genetic algorithm [2] that uses a classical modeling of the interferences constraints. By analyzing the results obtained by our approach we can observe that we efficiently enhance the quality of the solutions. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) algorithms have shown their capability to complement human analysis in the understanding of complex phenomena. Their advantages essentially rely on the flexibility of their construction that allows capturing complex relationships between inputs and outputs of a problem and also on their ability to adapt themselves via a learning phase to the available information. As a result, AI is widely used in many scientific fields and especially those related to the nuclear industry. This work deals an application of AI based on several classical machine learning approaches for the study of Reactivity Initiated Accidents (RIA) in the CABRI experimental pulse reactor located at the Cadarache research center, southern France. We focus on the interpretation of the fuel rod behaviour during the power pulse using the online fuel motion monitoring system called the hodoscope. The objective of this paper is to investigate how AI algorithms can be used for the automatic detection of a generic fuel delocalization from the signals recorded by the hodoscope. [ABSTRACT FROM AUTHOR]