arXiv subjects

Roland Bouffanais

Publications and source records attributed to Roland Bouffanais.

At least 37 records · Page 2Linked to original sources

arXiv2019

From Senseless Swarms to Smart Mobs: Tuning Networks for Prosocial Behaviour

Social media have been seen to accelerate the spread of negative content such as disinformation and hate speech, often unleashing reckless herd mentality within networks, further aggravated by malicious entities using bots for amplification. So far, the response to this emerging global crisis has centred around social media platform companies making reactive moves that appear to have greater symbolic value than practical utility. These include taking down patently objectionable content or manually deactivating the accounts of bad actors, while leaving vast troves of negative content to circulate and perpetuate within social networks. Governments worldwide have thus sought to intervene using regulatory tools, with countries such as France, Germany and Singapore introducing laws to compel technology companies to take down or correct erroneous and harmful content. However, the relentless pace of technological progress enfeebles regulatory measures that seem fated for obsolescence.

cs.SI↗

arXiv2019

Experience Replay Using Transition Sequences

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 better 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.

cs.AI↗

arXiv2019

Robust Stabilization of Resource Limited Networked Control Systems Under Denial-of-Service Attack

In this paper, we consider a class of denial-of-service (DoS) attacks, which aims at overloading the communication channel. On top of the security issue, continuous or periodic transmission of information within feedback loop is necessary for the effective control and stabilization of the system. In addition, uncertainty---originating from variation of parameters or unmodeled system dynamics---plays a key role in the system's stability. To address these three critical factors, we solve the joint control and security problem for an uncertain discrete-time Networked Control System (NCS) subject to limited availability of the shared communication channel. An event-triggered-based control and communication strategy is adopted to reduce bandwidth consumption. To tackle the uncertainty in the system dynamics, a robust control law is derived using an optimal control approach based on a virtual nominal dynamics associated with a quadratic cost-functional. The conditions for closed-loop stability and aperiodic transmission rule of feedback information are derived using the discrete-time Input-to-State Stability theory. We show that the proposed control approach withstands a general class of DoS attacks, and the stability analysis rests upon the characteristics of the attack signal. The results are illustrated and validated numerically with a classical NCS batch reactor system.

eess.SY↗

arXiv2019

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

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.

physics.optics↗

arXiv2019

Decentralized Multi-Floor Exploration by a Swarm of Miniature Robots Teaming with Wall-Climbing Units

In this paper, we consider the problem of collectively exploring unknown and dynamic environments with a decentralized heterogeneous multi-robot system consisting of multiple units of two variants of a miniature robot. The first variant-a wheeled ground unit-is at the core of a swarm of floor-mapping robots exhibiting scalability, robustness and flexibility. These properties are systematically tested and quantitatively evaluated in unstructured and dynamic environments, in the absence of any supporting infrastructure. The results of repeated sets of experiments show a consistent performance for all three features, as well as the possibility to inject units into the system while it is operating. Several units of the second variant-a wheg-based wall-climbing unit-are used to support the swarm of mapping robots when simultaneously exploring multiple floors by expanding the distributed communication channel necessary for the coordinated behavior among platforms. Although the occupancy-grid maps obtained can be large, they are fully distributed. Not a single robotic unit possesses the overall map, which is not required by our cooperative path-planning strategy.

cs.RO↗

arXiv2019

Hydrodynamic object identification with artificial neural models

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.

physics.flu-dyn↗

arXiv2018

Optimal Network Topology for Effective Collective Response

Natural, social, and artificial multi-agent systems usually operate in dynamic environments, where the ability to respond to changing circumstances is a crucial feature. An effective collective response requires suitable information transfer among agents, and thus is critically dependent on the agents' interaction network. In order to investigate the influence of the network topology on collective response, we consider an archetypal model of distributed decision-making---the leader-follower linear consensus---and study the collective capacity of the system to follow a dynamic driving signal (the "leader") for a range of topologies and system sizes. The analysis reveals a nontrivial relationship between optimal topology and frequency of the driving signal. Interestingly, the response is optimal when each individual interacts with a certain number of agents which decreases monotonically with the frequency and, for large enough systems, is independent of the size of the system. This phenomenology is investigated in experiments of collective motion using a swarm of land robots. The emergent collective response to both a slow- and a fast-changing leader is measured and analyzed for a range of interaction topologies. These results have far-reaching practical implications for the design and understanding of distributed systems, since they highlight that a dynamic rewiring of the interaction network is paramount to the effective collective operations of multi-agent systems at different time-scales.

eess.SY↗

arXiv2018

Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement Learning

The idea of reusing or transferring information from previously learned tasks (source tasks) for the learning of new tasks (target tasks) has the potential to significantly improve the sample efficiency of a reinforcement learning agent. In this work, we describe a novel approach for reusing previously acquired knowledge by using it to guide the exploration of an agent while it learns new tasks. In order to do so, we employ a variant of the growing self-organizing map algorithm, which is trained using a measure of similarity that is defined directly in the space of the vectorized representations of the value functions. In addition to enabling transfer across tasks, the resulting map is simultaneously used to enable the efficient storage of previously acquired task knowledge in an adaptive and scalable manner. We empirically validate our approach in a simulated navigation environment, and also demonstrate its utility through simple experiments using a mobile micro-robotics platform. In addition, we demonstrate the scalability of this approach, and analytically examine its relation to the proposed network growth mechanism. Further, we briefly discuss some of the possible improvements and extensions to this approach, as well as its relevance to real world scenarios in the context of continual learning.

cs.AI↗

arXiv2018

A Decentralized Mobile Computing Network for Multi-Robot Systems Operations

Collective animal behaviors are paradigmatic examples of fully decentralized operations involving complex collective computations such as collective turns in flocks of birds or collective harvesting by ants. These systems offer a unique source of inspiration for the development of fault-tolerant and self-healing multi-robot systems capable of operating in dynamic environments. Specifically, swarm robotics emerged and is significantly growing on these premises. However, to date, most swarm robotics systems reported in the literature involve basic computational tasks---averages and other algebraic operations. In this paper, we introduce a novel Collective computing framework based on the swarming paradigm, which exhibits the key innate features of swarms: robustness, scalability and flexibility. Unlike Edge computing, the proposed Collective computing framework is truly decentralized and does not require user intervention or additional servers to sustain its operations. This Collective computing framework is applied to the complex task of collective mapping, in which multiple robots aim at cooperatively map a large area. Our results confirm the effectiveness of the cooperative strategy, its robustness to the loss of multiple units, as well as its scalability. Furthermore, the topology of the interconnecting network is found to greatly influence the performance of the collective action.

cs.RO↗

arXiv2018

Gradual Collective Upgrade of a Swarm of Autonomous Buoys for Dynamic Ocean Monitoring

Swarms of autonomous surface vehicles equipped with environmental sensors and decentralized communications bring a new wave of attractive possibilities for the monitoring of dynamic features in oceans and other waterbodies. However, a key challenge in swarm robotics design is the efficient collective operation of heterogeneous systems. We present both theoretical analysis and field experiments on the responsiveness in dynamic area coverage of a collective of 22 autonomous buoys, where 4 units are upgraded to a new design that allows them to move 80\% faster than the rest. This system is able to react on timescales of the minute to changes in areas on the order of a few thousand square meters. We have observed that this partial upgrade of the system significantly increases its average responsiveness, without necessarily improving the spatial uniformity of the deployment. These experiments show that the autonomous buoy designs and the cooperative control rule described in this work provide an efficient, flexible, and scalable solution for the pervasive and persistent monitoring of water environments.

cs.RO↗

arXiv2018

Self-Organizing Maps as a Storage and Transfer Mechanism in Reinforcement Learning

The idea of reusing information from previously learned tasks (source tasks) for the learning of new tasks (target tasks) has the potential to significantly improve the sample efficiency reinforcement learning agents. In this work, we describe an approach to concisely store and represent learned task knowledge, and reuse it by allowing it to guide the exploration of an agent while it learns new tasks. In order to do so, we use a measure of similarity that is defined directly in the space of parameterized representations of the value functions. This similarity measure is also used as a basis for a variant of the growing self-organizing map algorithm, which is simultaneously used to enable the storage of previously acquired task knowledge in an adaptive and scalable manner.We empirically validate our approach in a simulated navigation environment and discuss possible extensions to this approach along with potential applications where it could be particularly useful.

cs.LG↗

arXiv2018

Propagation of three-dimensional bipolar ultrashort electromagnetic pulses in an inhomogeneous array of carbon nanotubes

We study the propagation of three-dimensional (3D) bipolar ultrashort electromagnetic pulses in an inhomogeneous array of semiconductor carbon nanotubes. The heterogeneity is represented by a planar region with an increased concentration of conduction electrons. The evolution of the electromagnetic field and electron concentration in the sample are governed by the Maxwell's equations and continuity equation. In particular, non-uniformity of the electromagnetic field along the axis of the nanotubes is taken into account. We demonstrate that, depending on values of parameters of the electromagnetic pulse approaching the region with the higher electron concentration, the pulse is reflected from the region or passes it. Specifically, our simulations demonstrate that, after interacting with the higher-concentration area, the pulse can propagate steadily, without significant spreading. The possibility of such ultrashort electromagnetic pulses propagating in arrays of carbon nanotubes over distances significantly exceeding characteristic dimensions of the pulses makes it possible to consider them as 3D solitons.

physics.optics↗

arXiv2018

Growth mechanisms of perturbations in boundary layers over a compliant wall

The temporal modal and nonmodal growth of three-dimensional perturbations in the boundary-layer flow over an infinite compliant flat wall is considered. Using a wall-normal velocity/wall-normal vorticity formalism, the dynamic boundary condition at the compliant wall admits a linear dependence on the eigenvalue parameter, as compared to a quadratic one in the canonical formulation of the problem. This greatly simplifies the accurate calculation of the continuous spectrum by means of a spectral method, thereby yielding a very effective filtering of the pseudospectra as well as a clear identification of instability regions. The regime of global instability is found to be matching the regime of the favorable phase of the forcing by the flow on the compliant wall so as to enhance the amplitude of the wall. An energy-budget analysis for the least-decaying hydroelastic (static-divergence, traveling-wave-flutter and near-stationary transitional) and Tollmien--Schlichting modes in the parameter space reveals the primary routes of energy flow. Moreover, the flow exhibits a slower transient growth for the maximum growth rate of a superposition of streamwise-independent modes due to a complex dependence of the wall-boundary condition with the Reynolds number. The initial and optimal perturbations are compared with the boundary-layer flow over a solid wall; differences and similarities are discussed. Unlike the solid-wall case, viscosity plays a pivotal role in the transient growth. A slowdown of the maximum growth rate with the Reynolds number is uncovered and found to originate in the transition of the fluid-solid interaction from a two-way to a one-way coupling. Finally, a term-by-term energy budget analysis is performed to identify the key contributors to the transient growth mechanism.

physics.flu-dyn↗

arXiv2018

Are the different layers of a social network conveying the same information?

Comprehensive and quantitative investigations of social theories and phenomena increasingly benefit from the vast breadth of data describing human social relations, which is now available within the realm of computational social science. Such data are, however, typically proxies for one of the many interaction layers composing social networks, which can be defined in many ways and are typically composed of communication of various types (e.g., phone calls, face-to-face communication, etc.). As a result, many studies focus on one single layer, corresponding to the data at hand. Several studies have, however, shown that these layers are not interchangeable, despite the presence of a certain level of correlations between them. Here, we investigate whether different layers of interactions among individuals lead to similar conclusions with respect to the presence of homophily patterns in a population---homophily represents one of the widest studied phenomenon in social networks. To this aim, we consider a dataset describing interactions and links of various nature in a population of Asian students with diverse nationalities, first language and gender. We study homophily patterns, as well as their temporal evolutions in each layer of the social network. To facilitate our analysis, we put forward a general method to assess whether the homophily patterns observed in one layer inform us about patterns in another layer. For instance, our study reveals that three network layers---cell phone communications, questionnaires about friendship, and trust relations---lead to similar and consistent results despite some minor discrepancies. The homophily patterns of the co-presence network layer, however, does not yield any meaningful information about other network layers.

physics.soc-ph↗

arXiv2017

Three-dimensional light bullets in a Bragg medium with carbon nanotubes

We present a theoretical study of the propagation of three-dimensional extremely short electromagnetic pulses (a.k.a. light bullets) through a Bragg medium containing an immersed array of carbon nanotubes. We demonstrate the possible stable propagation of such light bullets. In particular, our results suggest these light bullets can carry information about the Bragg medium itself.

physics.optics↗

arXiv2017

Randomized Constraints Consensus for Distributed Robust Linear Programming

In this paper we consider a network of processors aiming at cooperatively solving linear programming problems subject to uncertainty. Each node only knows a common cost function and its local uncertain constraint set. We propose a randomized, distributed algorithm working under time-varying, asynchronous and directed communication topology. The algorithm is based on a local computation and communication paradigm. At each communication round, nodes perform two updates: (i) a verification in which they check-in a randomized setup-the robust feasibility (and hence optimality) of the candidate optimal point, and (ii) an optimization step in which they exchange their candidate bases (minimal sets of active constraints) with neighbors and locally solve an optimization problem whose constraint set includes: a sampled constraint violating the candidate optimal point (if it exists), agent's current basis and the collection of neighbor's basis. As main result, we show that if a processor successfully performs the verification step for a sufficient number of communication rounds, it can stop the algorithm since a consensus has been reached. The common solution is-with high confidence-feasible (and hence optimal) for the entire set of uncertainty except a subset having arbitrary small probability measure. We show the effectiveness of the proposed distributed algorithm on a multi-core platform in which the nodes communicate asynchronously.

math.OC↗

arXiv2017

Swarming collapse under limited information flow between individuals

The emergence of collective decision in swarms and their coordinated response to complex environments underscore the central role played by social transmission of information. Here, the different possible origins of information flow bottlenecks are identified. Using a combination of network-, control- and information-theoretic elements applied to a group of interacting self-propelled particles, the effect of varying information capacity of the signaling channel on dynamic collective behaviors is revealed. We find a sufficient condition on the information data rate that guarantees the effectiveness of swarming while also highlighting the profound connection with the topology of the underlying interaction network. We also show that when decreasing the data rate, the swarming behavior invariably vanishes following a second-order phase transition irrespective of the intrinsic noise level. The variations along the transition line are found to be in good agreement with information-theoretic predictions.

nlin.AO↗

arXiv2017

Consensus reaching in swarms ruled by a hybrid metric-topological distance

Recent empirical observations of three-dimensional bird flocks and human crowds have challenged the long-prevailing assumption that a metric interaction distance rules swarming behaviors. In some cases, individual agents are found to be engaged in local information exchanges with a fixed number of neighbors, i.e. a topological interaction. However, complex system dynamics based on pure metric or pure topological distances both face physical inconsistencies in low and high density situations. Here, we propose a hybrid metric-topological interaction distance overcoming these issues and enabling a real-life implementation in artificial robotic swarms. We use network- and graph-theoretic approaches combined with a dynamical model of locally interacting self-propelled particles to study the consensus reaching pro- cess for a swarm ruled by this hybrid interaction distance. Specifically, we establish exactly the probability of reaching consensus in the absence of noise. In addition, simulations of swarms of self-propelled particles are carried out to assess the influence of the hybrid distance and noise.

physics.bio-ph↗