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"Fleury, Eric"
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On absolute war : terrorism and the logic of armed conflict
\"The United States and its allies have long sought to inflict a decisive defeat upon groups such as Al Qaeda and ISIS, while regarding their individual members as malevolent criminals undeserving of combatant status. A clearer understanding of how terrorists define victory, and how their method of fighting relates to conventional military forces, is necessary in order to devise more realistic and effective strategies of counterterrorism. On Absolute War constructs a theoretical framework for the study of terrorism based on Carl von Clausewitz's On War, widely regarded as the greatest analysis of war ever written. Through a review of Clausewitz's work and a set of historical case studies ranging from the Fenian Dynamite Campaign of the 1880s to the wars in Iraq and Afghanistan, Prof. Fleury reveals just how closely terrorism mimics the logic of war\"-- Publisher's description.
Book
MultiAspect Graphs: Algebraic Representation and Algorithms
We present the algebraic representation and basic algorithms for MultiAspect Graphs (MAGs). A MAG is a structure capable of representing multilayer and time-varying networks, as well as higher-order networks, while also having the property of being isomorphic to a directed graph. In particular, we show that, as a consequence of the properties associated with the MAG structure, a MAG can be represented in matrix form. Moreover, we also show that any possible MAG function (algorithm) can be obtained from this matrix-based representation. This is an important theoretical result since it paves the way for adapting well-known graph algorithms for application in MAGs. We present a set of basic MAG algorithms, constructed from well-known graph algorithms, such as degree computing, Breadth First Search (BFS), and Depth First Search (DFS). These algorithms adapted to the MAG context can be used as primitives for building other more sophisticated MAG algorithms. Therefore, such examples can be seen as guidelines on how to properly derive MAG algorithms from basic algorithms on directed graphs. We also make available Python implementations of all the algorithms presented in this paper.
Journal Article
Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings
Antibiotic-resistance of hospital-acquired infections is a major public health issue. The worldwide emergence and diffusion of extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae, including Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP), is of particular concern. Preventing their nosocomial spread requires understanding their transmission. Using Close Proximity Interactions (CPIs), measured by wearable sensors, and weekly ESBL-EC-and ESBL-KP-carriage data, we traced their possible transmission paths among 329 patients in a 200-bed long-term care facility over 4 months. Based on phenotypically defined resistance profiles to 12 antibiotics only, new bacterial acquisitions were tracked. Extending a previously proposed statistical method, the CPI network's ability to support observed incident-colonization episodes of ESBL-EC and ESBL-KP was tested. Finally, mathematical modeling based on our findings assessed the effect of several infection-control measures. A potential infector was identified in the CPI network for 80% (16/20) of ESBL-KP acquisition episodes. The lengths of CPI paths between ESBL-KP incident cases and their potential infectors were shorter than predicted by chance (P = 0.02), indicating that CPI-network relationships were consistent with dissemination. Potential ESBL-EC infectors were identified for 54% (19/35) of the acquisitions, with longer-than-expected lengths of CPI paths. These contrasting results yielded differing impacts of infection control scenarios, with contact reduction interventions proving less effective for ESBL-EC than for ESBL-KP. These results highlight the widely variable transmission patterns among ESBL-producing Enterobacteriaceae species. CPI networks supported ESBL-KP, but not ESBL-EC spread. These outcomes could help design more specific surveillance and control strategies to prevent in-hospital Enterobacteriaceae dissemination.
Journal Article
Longitudinal data collection to follow social network and language development dynamics at preschool
DyLNet is a large-scale longitudinal social experiment designed to observe the relations between child socialisation and oral language learning at preschool. During three years, a complete preschool in France was followed to record proximity interactions of about 200 children and adults every 5 seconds using autonomous Radio Frequency Identification Wireless Proximity Sensors. Data was collected monthly with one week-long deployments. In parallel, survey campaigns were carried out to record the socio-demographic and language background of children and their families, and to monitor the linguistic skills of the pupils at regular intervals. From data we inferred real social interactions and distinguished inter- and intra-class interactions in different settings. We share ten weeks of cleaned, pre-processed and reconstructed interaction data recorded over a complete school year, together with two sets of survey data providing details about the pupils’ socio-demographic profile and language development level at the beginning and end of this period. Our dataset may stimulate researchers from several fields to study the simultaneous development of language and social interactions of children.Measurement(s)RSSI values (proximity interactions within a preschool) • sociodemographic information about children • children language skills (vocabulary and syntax)Technology Type(s)RFID badges • questionnaire • language testsSample Characteristic - OrganismHomo sapiensSample Characteristic - EnvironmentpreschoolSample Characteristic - LocationFrance
Journal Article
Optimal Proxy Selection for Socioeconomic Status Inference on Twitter
Individual socioeconomic status inference from online traces is a remarkably difficult task. While current methods commonly train predictive models on incomplete data by appending socioeconomic information of residential areas or professional occupation profiles, little attention has been paid to how well this information serves as a proxy for the individual demographic trait of interest when fed to a learning model. Here we address this question by proposing three different data collection and combination methods to first estimate and, in turn, infer the socioeconomic status of French Twitter users from their online semantics. We assess the validity of each proxy measure by analyzing the performance of our prediction pipeline when trained on these datasets. Despite having to rely on different user sets, we find that training our model on professional occupation provides better predictive performance than open census data or remote sensed expert annotation of habitual environments. Furthermore, we release the tools we developed in the hope it will provide a generalizable framework to estimate socioeconomic status of large numbers of Twitter users as well as contribute to the scientific discussion on social stratification and inequalities.
Journal Article
Temporal social network reconstruction using wireless proximity sensors: model selection and consequences
The emerging technologies of wearable wireless devices open entirely new ways to record various aspects of human social interactions in a broad range of settings. Such technologies allow to log the temporal dynamics of face-to-face interactions by detecting the physical proximity of participants. However, despite the wide usage of this technology and the collected datasets, precise reconstruction methods transforming the raw recorded communication data packets to social interactions are still missing.
In this study we analyse a proximity dataset collected during a longitudinal social experiment aiming to understand the co-evolution of children’s language development and social network. Physical proximity and verbal communication of hundreds of pre-school children and their teachers are recorded over three years using autonomous wearable low power wireless devices. The dataset is accompanied with three annotated ground truth datasets, which record the time, distance, relative orientation, and interaction state of interacting children for validation purposes.
We use this dataset to explore several pipelines of dynamical event reconstruction including earlier applied naïve approaches, methods based on Hidden Markov Model, or on Long Short-Term Memory models, some of them combined with supervised pre-classification of interaction packets. We find that while naïve models propose the worst reconstruction, Long Short-Term Memory models provide the most precise way to reconstruct real interactions up to
∼
90
%
accuracy. Finally, we simulate information spreading on the reconstructed networks obtained by the different methods. Results indicate that small improvement of network reconstruction accuracy may lead to significantly different spreading dynamics, while sometimes large differences in accuracy have no obvious effects on the dynamics. This not only demonstrates the importance of precise network reconstruction but also the careful choice of the reconstruction method in relation with the data collected. Missing this initial step in any study may seriously mislead conclusions made about the emerging properties of the observed network or any dynamical process simulated on it.
Journal Article
Structural homogeneity and mass density of bulk metallic glasses revealed by their rough surfaces and ultra-small angle neutron scattering (USANS)
The ultra-small angle neutron scattering (USANS) measures the microscale structure of heterogeneity and the scattering from rough surfaces with small scattering volumes can be neglected. But this is not true in amorphous alloys. The small angle scattering from such surfaces is not negligible, regardless of scattering volume. However, we demonstrate that the unwanted rough surfaces can be utilized to determine the homogeneity and mass density of amorphous metallic glasses using the USANS and surface neutron contrast matching technique. The power law scattering of the homogeneous Cu
50
Zr
50
amorphous alloy disappeared under the surface contrast-matched environment, a mixture of hydrogenated/deuterated ethanol having low surface tension against the metallic alloys, indicating that the scattering originated not from its internal structure but from the rough surface. This confirms the structural homogeneity not only at the atomic level but also on a larger scale of micrometer. On the other hand, the crystallized Cu
50
Zr
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alloy showed strong power-law scattering under the matching environment due to the structural heterogeneity inside the alloy. This technique can apply to the bulk samples when the transmission is high enough not causing multiple scattering that is easily detected with USANS and when the surface roughness is dominant source of scattering.
Journal Article
Mechanisms of oxide dependent tribological behavior in Ti / Steel sliding and influence of nanostructured surfaces
The tribological behavior of pure titanium having coarse-grained or nanostructured surfaces has been investigated against a steel ball moved with an alternative motion. The nanostructures were obtained by Surface Mechanical Attrition Treatment (SMAT) both at room and at cryogenic temperatures. An unexpected wear behavior was revealed: the hard steel ball was abraded for all cases even if it was several times harder compared to the Ti surface. This was due to the formation of a third body consisting of hard Ti oxides. Interestingly, important variations of the coefficient of friction were also revealed during the rubbing process. These variations could be separated into three successive stages, each with its specific wear mechanisms. The wear regimes were related to changes in the third body layer formed between the Ti and steel surfaces. SMAT changed the formation kinetics of the third body. The temperature at which the SMAT was conducted also introduced different third body formation kinetics. Important variations in the wear resistance were consequently observed between each surface condition.
Journal Article
Electronic Sensors for Assessing Interactions between Healthcare Workers and Patients under Airborne Precautions
Direct observation has been widely used to assess interactions between healthcare workers (HCWs) and patients but is time-consuming and feasible only over short periods. We used a Radio Frequency Identification Device (RFID) system to automatically measure HCW-patient interactions.
We equipped 50 patient rooms with fixed sensors and 111 HCW volunteers with mobile sensors in two clinical wards of two hospitals. For 3 months, we recorded all interactions between HCWs and 54 patients under airborne precautions for suspected (n = 40) or confirmed (n = 14) tuberculosis. Number and duration of HCW entries into patient rooms were collected daily. Concomitantly, we directly observed room entries and interviewed HCWs to evaluate their self-perception of the number and duration of contacts with tuberculosis patients.
After signal reconstruction, 5490 interactions were recorded between 82 HCWs and 54 tuberculosis patients during 404 days of airborne isolation. Median (interquartile range) interaction duration was 2.1 (0.8-4.4) min overall, 2.3 (0.8-5.0) in the mornings, 1.8 (0.8-3.7) in the afternoons, and 2.0 (0.7-4.3) at night (P<10(-4)). Number of interactions/day/HCW was 3.0 (1.0-6.0) and total daily duration was 7.6 (2.4-22.5) min. Durations estimated from 28 direct observations and 26 interviews were not significantly different from those recorded by the network.
The RFID was well accepted by HCWs. This original technique holds promise for accurately and continuously measuring interactions between HCWs and patients, as a less resource-consuming substitute for direct observation. The results could be used to model the transmission of significant pathogens. HCW perceptions of interactions with patients accurately reflected reality.
Journal Article
Host contact dynamics shapes richness and dominance of pathogen strains
The interaction among multiple microbial strains affects the spread of infectious diseases and the efficacy of interventions. Genomic tools have made it increasingly easy to observe pathogenic strains diversity, but the best interpretation of such diversity has remained difficult because of relationships with host and environmental factors. Here, we focus on host-to-host contact behavior and study how it changes populations of pathogens in a minimal model of multi-strain interaction. We simulated a population of identical strains competing by mutual exclusion and spreading on a dynamical network of hosts according to a stochastic susceptible-infectious-susceptible model. We computed ecological indicators of diversity and dominance in strain populations for a collection of networks illustrating various properties found in real-world examples. Heterogeneities in the number of contacts among hosts were found to reduce diversity and increase dominance by making the repartition of strains among infected hosts more uneven, while strong community structure among hosts increased strain diversity. We found that the introduction of strains associated with hosts entering and leaving the system led to the highest pathogenic richness at intermediate turnover levels. These results were finally illustrated using the spread of Staphylococcus aureus in a long-term health-care facility where close proximity interactions and strain carriage were collected simultaneously. We found that network structural and temporal properties could account for a large part of the variability observed in strain diversity. These results show how stochasticity and network structure affect the population ecology of pathogens and warn against interpreting observations as unambiguous evidence of epidemiological differences between strains.
Journal Article