Equipes Traitement de l'Information et Systèmes

Over the past twenty years, polypyrrole has appeared as the most extensively studied conducting polymer. However, despite the volume of work already done in this area, there has been little focus put on the mechanism of polypyrrole synthesis, especially concerning the most efficient method, electropolymerization. Numerous analytical techniques have been used to study polypyrrole electrodeposition and/or doping. However, the mechanism itself is still a controversial subject as there is not one mechanism which is universally accepted. The mechanism proposed by Diaz is the one most commonly referred to in the literature although several other mechanisms are not lacking in support. The controversy lies in the initiation step as each mechanism proposes a different way of beginning the reaction, varying between electron transfer, proton transfer and direct radical pyrrole formation. Without considering the initiation step, there are many other factors including electrolyte, solvent, temperature and pH which can influence the reaction mechanism during the electropolymerization of pyrrole, thus impacting the characteristics of the polymer formed at the electrode.

In this letter, we address the problem of decoding nonbinary low-density parity-check (LDPC) codes over finite fields GF(q), with reasonable complexity and good performance. In the first part of the letter, we recall the original belief propagation (BP) decoding algorithm and its Fourier domain implementation. We show that the use of tensor notations for the messages is very convenient for the algorithm description and understanding. In the second part of the letter, we introduce a simplified decoder which is inspired by the min-sum decoder for binary LDPC codes. We called this decoder extended min-sum (EMS). We show that it is possible to greatly reduce the computational complexity of the check-node processing by computing approximate reliability measures with a limited number of values in a message. By choosing appropriate correction factors or offsets, we show that the EMS decoder performance is quite good, and in some cases better than the regular BP decoder. The optimal values of the factor and offset correction are obtained asymptotically with simulated density evolution. Our simulations on ultra-sparse codes over very-high-order fields show that nonbinary LDPC codes are promising for applications which require low frame-error rates for small or moderate codeword lengths. The EMS decoder is a good candidate for practical hardware implementations of such codes

The Kalman filter (KF) has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 1970s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal processing, and so on. This paper provides a brief overview of the industrial applications and implementation issues of the KF in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends.

Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we propose a multitask framework for jointly 2D and 3D pose estimation from still images and human action recognition from video sequences. We show that a single architecture can be used to solve the two problems in an efficient way and still achieves state-of-the-art results. Additionally, we demonstrate that optimization from end-to-end leads to significantly higher accuracy than separated learning. The proposed architecture can be trained with data from different categories simultaneously in a seamlessly way. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the effectiveness of our method on the targeted tasks.

We study the energy-critical focusing non-linear wave equation, with data in the energy space, in dimensions 3, 4 and 5. We prove that for Cauchy data of energy smaller than the one of the static solution W which gives the best constant in the Sobolev embedding, the following alternative holds. If the initial data has smaller norm in the homogeneous Sobolev space H1 than the one of W, then we have global well-posedness and scattering. If the norm is larger than the one of W, then we have break-down in finite time.

International audience

Distributed generation (DG) is emerging as a new paradigm to produce on-site highly reliable and good quality electrical power. Thus, the DG systems are presented as a suitable form to offer highly reliable electrical power supply. The concept is particularly interesting when different kinds of energy resources are available, such as photovoltaic (PV) panels, fuel cells (FCs), or wind turbines. The DG of different kinds of energy systems allow for the integration of renewable and nonconventional energy resources. Hence, the DG is becoming a part of the strategic plans of most countries to address current challenges associated with energy management.

Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.

John Rawls's work (1971) has greatly contributed to rehabilitating equality as a basic social value, after decades of utilitarian hegemony,particularly in normative economics, but Rawls also emphasized that full equality of welfare is not an adequate goal either. This thesis was echoed in Dworkin's famous twin papers on equality (Dworkin 1981a,b), and it is now widely accepted that egalitarianism must be selective. The bulk of the debate on ‘Equality of What?’ thus deals with what variables ought to be submitted for selection and how this selection ought to be carried out.

In this paper, the suitability of a class of electric machines for vehicle traction applications is discussed. These machines, which are known as hybrid excitation synchronous machines, combine permanent-magnet (PM) excitation with wound field excitation. The goal behind the principle of hybrid excitation is to combine the advantages of PM excited machines and wound field synchronous machines. It is shown that these machines have good flux weakening capability compared with PM machines, and that they constitute an energy-efficient solution for vehicle propulsion.

This paper considers the problem of communication over a discrete memoryless channel (DMC) or an additive white Gaussian noise (AWGN) channel subject to the constraint that the probability that an adversary who observes the channel outputs can detect the communication is low. In particular, the relative entropy between the output distributions when a codeword is transmitted and when no input is provided to the channel must be sufficiently small. For a DMC whose output distribution induced by the “off” input symbol is not a mixture of the output distributions induced by other input symbols, it is shown that the maximum amount of information that can be transmitted under this criterion scales like the square root of the blocklength. The same is true for the AWGN channel. Exact expressions for the scaling constant are also derived.

This paper provides deterministic approximation results for stochastic processes that arise when ¯nite populations recurrently play ¯nite games.The deterministic approximation is de¯ned in continuous time as a system of ordinary di®erential equations of the type studied in evolutionary game theory.We establish precise connections between the long-run behavior of the stochastic process, for large populations, and its deterministic approximation.In particular, we show that if the deterministic solution through the initial state of the stochastic process at some point in time enters a basin of attraction, then the stochastic process will enter any given neighborhood of that attractor within a ¯nite and deterministic time with a probability that exponentially approaches one as the population size goes to in¯nity.The process will remain in this neighborhood for a random time that almost surely exceeds an exponential function of the population size.During this time interval, the process spends almost all time at a certain subset of the attractor, its so-called Birkho® center.We sharpen this result in the special case of ergodic processes.

In this paper, a method to design regular (2, dc)- LDPC codes over GF(q) with both good waterfall and error floor properties is presented, based on the algebraic properties of their binary image. First, the algebraic properties of rows of the parity check matrix H associated with a code are characterized and optimized to improve the waterfall. Then the algebraic properties of cycles and stopping sets associated with the underlying Tanner graph are studied and linked to the global binary minimum distance of the code. Finally, simulations are presented to illustrate the excellent performance of the designed codes.

The Internet of Things (IoT) market is set to grow rapidly. Although IoT offers new opportunities, it nevertheless raises challenges. The objective of this research is to develop a better understanding of the reasons underlying consumer resistance to smart and connected products. To this end, a quantitative survey was carried out to understand resistance to smartwatch. Structural equation modelling was used to test the conceptual model. The findings show that perceived uselessness, perceived price, intrusiveness, perceived novelty and self-efficacy have an impact on consumer resistance to smart products. Moreover, privacy concerns have an effect on intrusiveness and dependence impacts privacy concerns. To our best knowledge, this is the first research studying smart products through a resistance approach.

There are still unmet needs in finding new technologies for biomedical diagnostic and industrial applications. A technology allowing the analysis of size and sequence of short peptide molecules of only few molecular copies is still challenging. The fast, low-cost and label-free single-molecule nanopore technology could be an alternative for addressing these critical issues. Here, we demonstrate that the wild-type aerolysin nanopore enables the size-discrimination of several short uniformly charged homopeptides, mixed in solution, with a single amino acid resolution. Our system is very sensitive, allowing detecting and characterizing a few dozens of peptide impurities in a high purity commercial peptide sample, while conventional analysis techniques fail to do so.

This book is dedicated to the mathematical study of two-dimensional statistical hydrodynamics and turbulence, described by the 2D Navier–Stokes system with a random force. The authors' main goal is to justify the statistical properties of a fluid's velocity field u(t,x) that physicists assume in their work. They rigorously prove that u(t,x) converges, as time grows, to a statistical equilibrium, independent of initial data. They use this to study ergodic properties of u(t,x) – proving, in particular, that observables f(u(t,.)) satisfy the strong law of large numbers and central limit theorem. They also discuss the inviscid limit when viscosity goes to zero, normalising the force so that the energy of solutions stays constant, while their Reynolds numbers grow to infinity. They show that then the statistical equilibria converge to invariant measures of the 2D Euler equation and study these measures. The methods apply to other nonlinear PDEs perturbed by random forces.

This paper analyzes the relationship between income inequality and inequality of opportunities for income acquisition in nine developed countries during the 1990s. Equality of opportunity is defined as the situation where income distributions conditional on social origin cannot be ranked according to stochastic dominance criteria. We measure social origin by parental education and occupation and use the database built by Roemer et al. (2003 ). Stochastic dominance is assessed using nonparametric statistical tests. Our results indicate strong disparities in the degree of equality of opportunity across countries and a strong correlation between inequality of outcomes and inequality of opportunity. The U.S. and Italy show up as the most unequal countries in terms of both outcome and opportunity. At the opposite extreme, income distributions conditional on social origin are almost the same in Scandinavian countries even before any redistributive policy. We complement the ordinal comparison by resorting to an original scalar “Gini” index of opportunities, which can be decomposed into a risk and a return component. In our sample, inequality of opportunity is mostly driven by differences in mean income conditional on social origin, and differences in risk compensate the return element in most countries.

The lack of transparency is one of the artificial intelligence (AI)'s fundamental challenges, but the concept of transparency might be even more opaque than AI itself. Researchers in different fields who attempt to provide the solutions to improve AI's transparency articulate different but neighboring concepts that include, besides transparency, explainability and interpretability. Yet, there is no common taxonomy neither within one field (such as data science) nor between different fields (law and data science). In certain areas like healthcare, the requirements of transparency are crucial since the decisions directly affect people's lives. In this paper, we suggest an interdisciplinary vision on how to tackle the issue of AI's transparency in healthcare, and we propose a single point of reference for both legal scholars and data scientists on transparency and related concepts. Based on the analysis of the European Union (EU) legislation and literature in computer science, we submit that transparency shall be considered the "way of thinking" and umbrella concept characterizing the process of AI's development and use. Transparency shall be achieved through a set of measures such as interpretability and explainability, communication, auditability, traceability, information provision, record-keeping, data governance and management, and documentation. This approach to deal with transparency is of general nature, but transparency measures shall be always contextualized. By analyzing transparency in the healthcare context, we submit that it shall be viewed as a system of accountabilities of involved subjects (AI developers, healthcare professionals, and patients) distributed at different layers (insider, internal, and external layers, respectively). The transparency-related accountabilities shall be built-in into the existing accountability picture which justifies the need to investigate the relevant legal frameworks. These frameworks correspond to different layers of the transparency system. The requirement of informed medical consent correlates to the external layer of transparency and the Medical Devices Framework is relevant to the insider and internal layers. We investigate the said frameworks to inform AI developers on what is already expected from them with regards to transparency. We also discover the gaps in the existing legislative frameworks concerning AI's transparency in healthcare and suggest the solutions to fill them in.

In this paper we study two fourth-order cumulant-based techniques for the estimation of the mixing matrix in underdetermined independent component analysis. The first method is based on a simultaneous matrix diagonalization. The second is based on a simultaneous off-diagonalization. The number of sources that can be allowed is roughly quadratic in the number of observations. For both methods, explicit expressions for the maximum number of sources are given. Simulations illustrate the performance of the techniques

The recently developed H.264 video standard achieves efficient encoding over a bandwidth ranging from a few kilobits per second to several megabits per second. Hence, transporting H.264 video is expected to be an important component of many wireless multimedia services, such as video conferencing, real-time network gaming, and TV broadcasting. However, due to wireless channel characteristics and lack of QoS support, the basic 802.11-based channel access procedure is merely sufficient to deliver non-real-time traffic. The delivery should be augmented by appropriate mechanisms to better consider different QoS requirements and ultimately adjust the medium access parameters to the video data content characteristics. In this article we address H.264 wireless video transmission over IEEE 802.11 WLAN by proposing a robust cross-layer architecture that leverages the inherent H.264 error resilience tools (i.e., data partitioning); and the existing QoS-based IEEE 802.11e MAC protocol possibilities. The performances of the proposed architecture are extensively investigated by simulations. Results obtained indicate that compared to 802.11 and 802.11e, our cross-layer architecture allows graceful video degradation while minimizing the mean packet loss and end-to-end delays.