Publications by authors named "Roland Bouffanais"

16 Publications

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Author Correction: Interplay between success and patterns of human collaboration: case study of a Thai Research Institute.

Sci Rep 2021 Apr 15;11(1):8634. Epub 2021 Apr 15.

Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Esch-sur-Alzette, Luxembourg.

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http://dx.doi.org/10.1038/s41598-021-88100-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050325PMC
April 2021

Interplay between success and patterns of human collaboration: case study of a Thai Research Institute.

Sci Rep 2021 01 11;11(1):318. Epub 2021 Jan 11.

Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Esch-sur-Alzette, Luxembourg.

Networks of collaboration are notoriously complex and the mechanisms underlying their evolution, although of high interest, are still not fully understood. In particular, collaboration networks can be used to model the interactions between scientists and analyze the circumstances that lead to successful research. This task is not trivial and conventional metrics, based on number of publications and number of citations of individual authors, may not be sufficient to provide a deep insight into the factors driving scientific success. However, network analysis techniques based on centrality measures and measures of the structural properties of the network are promising to that effect. In recent years, it has become evident that most successful research works are achieved by teams rather than individual researchers. Therefore, researchers have developed a keen interest in the dynamics of social groups. In this study, we use real world data from a Thai computer technology research center, where researchers collaborate on different projects and team up to produce a range of artifacts. For each artifact, a score that measures quality of research is available and shared between the researchers that contributed to its creation, according to their percentage of contribution. We identify several measures to quantify productivity and quality of work, as well as centrality measures and structural measures. We find that, at individual level, centrality metrics are linked to high productivity and quality of work, suggesting that researchers who cover strategic positions in the network of collaboration are more successful. At the team level, we show that the evolution in time of structural measures are also linked to high productivity and quality of work. This result suggests that variables such as team size, turnover rate, team compactness and team openness are critical factors that must be taken into account for the success of a team. The key findings of this study indicate that the success of a research institute needs to be assessed in the context of not just researcher or team level, but also on how the researchers engage in collaboration as well as on how teams evolve.
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http://dx.doi.org/10.1038/s41598-020-79447-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7801490PMC
January 2021

On the Challenges and Potential of Using Barometric Sensors to Track Human Activity.

Sensors (Basel) 2020 Nov 27;20(23). Epub 2020 Nov 27.

Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.

Barometers are among the oldest engineered sensors. Historically, they have been primarily used either as environmental sensors to measure the atmospheric pressure for weather forecasts or as altimeters for aircrafts. With the advent of microelectromechanical system (MEMS)-based barometers and their systematic embedding in smartphones and wearable devices, a vast breadth of new applications for the use of barometers has emerged. For instance, it is now possible to use barometers in conjunction with other sensors to track and identify a wide range of human activity classes. However, the effectiveness of barometers in the growing field of human activity recognition critically hinges on our understanding of the numerous factors affecting the atmospheric pressure, as well as on the properties of the sensor itself-sensitivity, accuracy, variability, etc. This review article thoroughly details all these factors and presents a comprehensive report of the numerous studies dealing with one or more of these factors in the particular framework of human activity tracking and recognition. In addition, we specifically collected some experimental data to illustrate the effects of these factors, which we observed to be in good agreement with the findings in the literature. We conclude this review with some suggestions on some possible future uses of barometric sensors for the specific purpose of tracking human activities.
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http://dx.doi.org/10.3390/s20236786DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7731380PMC
November 2020

Spatial super-spreaders and super-susceptibles in human movement networks.

Sci Rep 2020 10 29;10(1):18642. Epub 2020 Oct 29.

Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore.

As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public transportation resuming full capacity, enforcing strict social distancing measures will be extremely challenging, if not practically impossible. Governments are thus paying close attention to particular locations that may become the next cluster of disease spreading. Indeed, certain places, like some people, can be "super-spreaders". Is a bustling train station in a central business district more or less susceptible and vulnerable as compared to teeming bus interchanges in the suburbs? Here, we propose a quantitative and systematic framework to identify spatial super-spreaders and the novel concept of super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it. Our proposed data-analytic framework is based on the daily-aggregated ridership data of public transport in Singapore. By constructing the directed and weighted human movement networks and integrating human flow intensity with two neighborhood diversity metrics, we are able to pinpoint super-spreader and super-susceptible locations. Our results reveal that most super-spreaders are also super-susceptibles and that counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. Our analysis is based on data from Singapore, but can be readily adapted and extended for any other major urban center. It therefore serves as a useful framework for devising targeted and cost-effective preventive measures for urban planning and epidemiological preparedness.
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http://dx.doi.org/10.1038/s41598-020-75697-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596054PMC
October 2020

Asymptotic dynamics of three-dimensional bipolar ultrashort electromagnetic pulses in an array of semiconductor carbon nanotubes.

Opt Express 2019 Sep;27(20):27592-27609

We study the propagation of three-dimensional bipolar ultrashort electromagnetic pulses in an array of semiconductor carbon nanotubes at times much longer than the pulse duration, yet still shorter than the relaxation time in the system. The interaction of the electromagnetic field with the electronic subsystem of the medium is described by means of Maxwell's equations, taking into account the field inhomogeneity along the nanotube axis beyond the approximation of slowly varying amplitudes and phases. A model is proposed for the analysis of the dynamics of an electromagnetic pulse in the form of an effective equation for the vector potential of the field. Our numerical analysis demonstrates the possibility of a satisfactory description of the evolution of the pulse field at large times by means of a three-dimensional generalization of the sine-Gordon and double sine-Gordon equations.
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http://dx.doi.org/10.1364/OE.27.027592DOI Listing
September 2019

Hydrodynamic object identification with artificial neural models.

Sci Rep 2019 08 2;9(1):11242. Epub 2019 Aug 2.

Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore.

The lateral-line system that has evolved in many aquatic animals enables them to navigate murky fluid environments, locate and discriminate obstacles. Here, we present a data-driven model that uses artificial neural networks to process flow data originating from a stationary sensor array located away from an obstacle placed in a potential flow. The ability of neural networks to estimate complex underlying relationships between parameters, in the absence of any explicit mathematical description, is first assessed with two basic potential flow problems: single source/sink identification and doublet detection. Subsequently, we address the inverse problem of identifying an obstacle shape from distant measures of the pressure or velocity field. Using the analytical solution to the forward problem, very large training data sets are generated, allowing us to obtain the synaptic weights by means of a gradient-descent based optimization. The resulting neural network exhibits remarkable effectiveness in predicting unknown obstacle shapes, especially at relatively large distances for which classical linear regression models are completely ineffectual. These results have far-reaching implications for the design and development of artificial passive hydrodynamic sensing technology.
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http://dx.doi.org/10.1038/s41598-019-47747-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6677828PMC
August 2019

Optimal network topology for responsive collective behavior.

Sci Adv 2019 Apr 3;5(4):eaau0999. Epub 2019 Apr 3.

Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.

Animals, humans, and multi-robot systems operate in dynamic environments, where the ability to respond to changing circumstances is paramount. An effective collective response requires suitable information transfer among agents and thus critically depends on the interaction network. To investigate the influence of the network topology on collective response, we consider an archetypal model of distributed decision-making and study the capacity of the system to follow a driving signal for varying topologies and system sizes. Experiments with a swarm of robots reveal a nontrivial relationship between frequency of the driving signal and optimal network topology. The emergent collective response to slow-changing perturbations increases with the degree of the interaction network, but the opposite is true for the response to fast-changing ones. These results have far-reaching implications for the design and understanding of distributed systems: a dynamic rewiring of the interaction network is essential to effective collective operations at different time scales.
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http://dx.doi.org/10.1126/sciadv.aau0999DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447377PMC
April 2019

Experience Replay Using Transition Sequences.

Front Neurorobot 2018 21;12:32. Epub 2018 Jun 21.

Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore.

Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms. In this work, we propose an approach to select and replay sequences of transitions in order to accelerate the learning of a reinforcement learning agent in an off-policy setting. In addition to selecting appropriate sequences, we also artificially construct transition sequences using information gathered from previous agent-environment interactions. These sequences, when replayed, allow value function information to trickle down to larger sections of the state/state-action space, thereby making the most of the agent's experience. We demonstrate our approach on modified versions of standard reinforcement learning tasks such as the mountain car and puddle world problems and empirically show that it enables faster, and more accurate learning of value functions as compared to other forms of experience replay. Further, we briefly discuss some of the possible extensions to this work, as well as applications and situations where this approach could be particularly useful.
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http://dx.doi.org/10.3389/fnbot.2018.00032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022201PMC
June 2018

Effect of Correlations in Swarms on Collective Response.

Sci Rep 2017 09 4;7(1):10388. Epub 2017 Sep 4.

Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore.

Social interaction increases significantly the performance of a wide range of cooperative systems. However, evidence that natural swarms limit the number of interactions suggests potentially detrimental consequences of excessive interaction. Using a canonical model of collective motion, we find that the collective response to a dynamic localized perturbation-emulating a predator attack-is hindered when the number of interacting neighbors exceeds a certain threshold. Specifically, the effectiveness in avoiding the predator is enhanced by large integrated correlations, which are known to peak at a given level of interagent interaction. From the network-theoretic perspective, we uncover the same interplay between number of connections and effectiveness in group-level response for two distinct decision-making models of distributed consensus operating over a range of static networks. The effect of the number of connections on the collective response critically depends on the dynamics of the perturbation. While adding more connections improves the response to slow perturbations, the opposite is true for fast ones. These results have far-reaching implications for the design of artificial swarms or interaction networks.
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http://dx.doi.org/10.1038/s41598-017-09830-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5583190PMC
September 2017

Interplay between motility and cell-substratum adhesion in amoeboid cells.

Biomicrofluidics 2015 Sep 29;9(5):054112. Epub 2015 Sep 29.

Massachusetts Institute of Technology , 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.

The effective migration of amoeboid cells requires a fine regulation of cell-substratum adhesion. These entwined processes have been shown to be regulated by a host of biophysical and biochemical cues. Here, we reveal the pivotal role played by calcium-based mechanosensation in the active regulation of adhesion resulting in a high migratory adaptability. Using mechanotactically driven Dictyostelium discoideum amoebae, we uncover the existence of optimal mechanosensitive conditions-corresponding to specific levels of extracellular calcium-for persistent directional migration over physicochemically different substrates. When these optimal mechanosensitive conditions are met, noticeable enhancement in cell migration directionality and speed is achieved, yet with significant differences among the different substrates. In the same narrow range of calcium concentrations that yields optimal cellular mechanosensory activity, we uncovered an absolute minimum in cell-substratum adhesion activity, for all considered substrates, with differences in adhesion strength among them amplified. The blocking of the mechanosensitive ion channels with gadolinium-i.e., the inhibition of the primary mechanosensory apparatus-hampers the active reduction in substrate adhesion, thereby leading to the same undifferentiated and drastically reduced directed migratory response. The adaptive behavioral responses of Dictyostelium cells sensitive to substrates with varying physicochemical properties suggest the possibility of novel surface analyses based on the mechanobiological ability of mechanosensitive and guidable cells to probe substrates at the nanometer-to-micrometer level.
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http://dx.doi.org/10.1063/1.4931762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4592429PMC
September 2015

Persistent cellular motion control and trapping using mechanotactic signaling.

PLoS One 2014 10;9(9):e105406. Epub 2014 Sep 10.

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

Chemotactic signaling and the associated directed cell migration have been extensively studied owing to their importance in emergent processes of cellular aggregation. In contrast, mechanotactic signaling has been relatively overlooked despite its potential for unique ways to artificially signal cells with the aim to effectively gain control over their motile behavior. The possibility of mimicking cellular mechanotactic signals offers a fascinating novel strategy to achieve targeted cell delivery for in vitro tissue growth if proven to be effective with mammalian cells. Using (i) optimal level of extracellular calcium ([Ca(2+)]ext = 3 mM) we found, (ii) controllable fluid shear stress of low magnitude (σ < 0.5 Pa), and (iii) the ability to swiftly reverse flow direction (within one second), we are able to successfully signal Dictyostelium discoideum amoebae and trigger migratory responses with heretofore unreported control and precision. Specifically, we are able to systematically determine the mechanical input signal required to achieve any predetermined sequences of steps including straightforward motion, reversal and trapping. The mechanotactic cellular trapping is achieved for the first time and is associated with a stalling frequency of 0.06 ~ 0.1 Hz for a reversing direction mechanostimulus, above which the cells are effectively trapped while maintaining a high level of directional sensing. The value of this frequency is very close to the stalling frequency recently reported for chemotactic cell trapping [Meier B, et al. (2011) Proc Natl Acad Sci USA 108:11417-11422], suggesting that the limiting factor may be the slowness of the internal chemically-based motility apparatus.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0105406PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4160188PMC
May 2015

Influence of the number of topologically interacting neighbors on swarm dynamics.

Sci Rep 2014 Feb 25;4:4184. Epub 2014 Feb 25.

Singapore University of Technology and Design, 20 Dover Drive, Singapore 138682.

Recent empirical and theoretical works on collective behaviors based on a topological interaction are beginning to offer some explanations as for the physical reasons behind the selection of a particular number of nearest neighbors locally affecting each individual's dynamics. Recently, flocking starlings have been shown to topologically interact with a very specific number of neighbors, between six to eight, while metric-free interactions were found to govern human crowd dynamics. Here, we use network- and graph-theoretic approaches combined with a dynamical model of locally interacting self-propelled particles to study how the consensus reaching process and its dynamics are influenced by the number k of topological neighbors. Specifically, we prove exactly that, in the absence of noise, consensus is always attained with a speed to consensus strictly increasing with k. The analysis of both speed and time to consensus reveals that, irrespective of the swarm size, a value of k ~ 10 speeds up the rate of convergence to consensus to levels close to the one of the optimal all-to-all interaction signaling. Furthermore, this effect is found to be more pronounced in the presence of environmental noise.
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http://dx.doi.org/10.1038/srep04184DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3933906PMC
February 2014

Resilience and controllability of dynamic collective behaviors.

PLoS One 2013 17;8(12):e82578. Epub 2013 Dec 17.

Singapore University of Technology and Design, Singapore, Singapore.

The network paradigm is used to gain insight into the structural root causes of the resilience of consensus in dynamic collective behaviors, and to analyze the controllability of the swarm dynamics. Here we devise the dynamic signaling network which is the information transfer channel underpinning the swarm dynamics of the directed interagent connectivity based on a topological neighborhood of interactions. The study of the connectedness of the swarm signaling network reveals the profound relationship between group size and number of interacting neighbors, which is found to be in good agreement with field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl. Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic collective behaviors enabling us to uncover that the swarm signaling network is a homogeneous clustered small-world network, thus facilitating emergent outcomes if connectedness is maintained. Resilience of the emergent consensus is tested by introducing exogenous environmental noise, which ultimately stresses how deeply intertwined are the swarm dynamics in the physical and network spaces. The availability of the signaling network allows us to analytically establish for the first time the number of driver agents necessary to fully control the swarm dynamics.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0082578PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866273PMC
October 2014

Physical limits on cellular directional mechanosensing.

Phys Rev E Stat Nonlin Soft Matter Phys 2013 May 29;87(5):052716. Epub 2013 May 29.

Singapore University of Technology and Design, 20 Dover Drive, Singapore 138682.

Many eukaryotic cells are able to perform directional mechanosensing by directly measuring minute spatial differences in the mechanical stress on their membranes. Here, we explore the limits of a single mechanosensitive channel activation using a two-state double-well model for the gating mechanism. We then focus on the physical limits of directional mechanosensing by a single cell having multiple mechanosensors and subjected to a shear flow inducing a nonuniform membrane tension. Our results demonstrate that the accuracy in sensing the mechanostimulus direction not only increases with cell size and exposure to a signal, but also grows for cells with a near-critical membrane prestress. Finally, the existence of a nonlinear threshold effect, fundamentally limiting the cell's ability to effectively perform directional mechanosensing at a low signal-to-noise ratio, is uncovered.
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http://dx.doi.org/10.1103/PhysRevE.87.052716DOI Listing
May 2013

Hydrodynamics of cell-cell mechanical signaling in the initial stages of aggregation.

Phys Rev E Stat Nonlin Soft Matter Phys 2010 Apr 28;81(4 Pt 1):041920. Epub 2010 Apr 28.

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Mechanotactic cell motility has recently been shown to be a key player in the initial aggregation of crawling cells such as leukocytes and amoebae. The effects of mechanotactic signaling in the early aggregation of amoeboid cells are here investigated using a general mathematical model based on known biological evidence. We elucidate the hydrodynamic fundamentals of the direct guiding of a cell through mechanotaxis in the case where one cell transmits a mechanotactic signal through the fluid flow by changing its shape. It is found that any mechanosensing cells placed in the stimulus field of mechanical stress are able to determine the signal transmission direction with a certain angular dispersion which does not preclude the aggregation from happening. The ubiquitous presence of noise is accounted for by the model. Finally, the mesoscopic pattern of aggregation is obtained which constitutes the bridge between, on one hand, the microscopic world where the changes in the cell shape occur and, on the other hand, the cooperative behavior of the cells at the mesoscopic scale.
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http://dx.doi.org/10.1103/PhysRevE.81.041920DOI Listing
April 2010
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