CITI seminar – El Hourcine Bergou (INRAE) – 09/04 at 15:00

Title:  Stochastic Three Points Method For Unconstrained Smooth Minimization

Date and Place: 9th April 2021 15:00 – link

Speaker: Dr El Hourcine Bergou (INRAE)

 

Abstract: 

In this work, we consider the unconstrained minimization problem of a smooth function in a setting where only function evaluations are possible. We design a novel randomized derivative-free algorithm—the stochastic three points (STP) method—and analyze its iteration complexity. At each iteration, STP generates a random search direction according to a certain fixed probability law. Our assumptions on this law are very mild: roughly speaking, all laws which do not concentrate all measure on any halfspace passing through the origin will work. Although, our approach is designed to not explicitly use derivatives, it covers some first order methods. For instance, if the probability law is chosen to be the Dirac distribution concentrated on the sign of the gradient, then STP recovers the Signed Gradient Descent method. If the probability law is the uniform distribution on the coordinates of the gradient, then STP recovers the Randomized Coordinate Descent Method.
The complexity of STP depends on the probability law via a simple characteristic closely related to the cosine measure which is used in the analysis of deterministic direct search (DDS) methods. Unlike in DDS, where $O(n)$ ($n$ is the dimension of the problem) function evaluations must be performed in each iteration in the worst case, our method only requires two new function evaluations per iteration. Consequently, while the complexity of DDS depends quadratically on $n$, our method depends linearly on $n$.

 

Biography:

Dr l Hourcine Bergou is a research scientist at INRAE. My research interests are in all areas that intersect with optimization, including algorithms, machine learning, statistics, and operations research. I am particularly interested in algorithms for large scale optimization including randomized and distributed optimization methods.

 


CITI seminar – Michael Barros (University of Essex, UK) – 01/04 at 14:00

Title:  Molecular Communications using Astrocytes for Boolean logic gates implementation in mammalian cells

Date and Place: 1st April 2021 14:00 – link

Speaker: Dr Michael Barros (University of Essex, UK)

 

Abstract: 

In this talk we will show the use of astrocytes to realize Boolean logic gates, through manipulation of the threshold of Ca2+ ion fows between the cells based on the input signals. Through wet-lab experiments that engineer the astrocytes cells with pcDNA3.1-hGPR17 genes as well as chemical compounds, we show that both AND and OR gates can be implemented by controlling Ca2+ signals that fow through the population. A reinforced learning platform is also presented in the paper to optimize the Ca2+ activated level and time slot of input signals Tb into the gate. This design platform caters for any size and connectivity of the cell population, by taking into consideration the delay and noise produced from the signalling between the cells. To validate the efectiveness of the reinforced learning platform, a Ca2+ signalling simulator was used to simulate the signalling between the astrocyte cells. The results from the simulation show that an optimum value for both the Ca2+ activated level and time slot of input signals Tb is required to achieve up to 90% accuracy for both the AND and OR gates. Our method can be used as the basis for future Neural–Molecular Computing chips, constructed from engineered astrocyte cells, which can form the basis for a new generation of brain implants.

 

Biography:

Dr Barros is an Assistant Professor (Lecturer) since June 2020 in the School of Computer Science and Electronic Engineering at the University of Essex, UK. He is also a MSCA-IF Research Fellow (part-time) at the Tampere University, Finland. He received the PhD in Computer Science at the Waterford Institute of Technology in 2016. He previously held multiple academic positions as a Research Fellow in the Waterford Institute of Technology, Ireland.
He has over 70 research peer-reviewed scientific publications in top journals and conferences such as Nature Scientific Reports, IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, in the areas of molecular and unconventional communications, biomedical engineering, bionano science and 6G. Since 2020, he is a review editor for the Frontiers in Communications and Networks journal in the area of unconventional communications. He also served as guest editor for the IEEE Transactions on Molecular, Biological and Multi-Scale Communications and Digital Communications Networks journals. He received the CONNECT Prof. Tom Brazil Excellence in Research Award in 2020. Dr Barros was awarded also the Irish Research Council’s (IRC) Government of Ireland Post-doc Fellow from 2016-2018 and the Enterprise Ireland’s (EI) Commercialization Funding from 2018-2019.

 


CITI seminar – Somantika Datta (Univ. Idaho, USA) – 18/02 at 14:00

Title: Construction and properties of certain real multi-angle tight frames

Date and Place: 18th February 2021 14:00 – link

Speaker: Prof. Somantika Datta (Univ. Idaho, USA)

 

Abstract: 

Frames are now standard tools in signal processing, and have applications ranging from compressed sensing, to communication systems and quantum sensing. Designing frames with some special structure such as equiangularity and tightness is highly desirable in applications. However, constructing equiangular tight frames (ETFs) with a given size in a specific dimension can be difficult or impossible in some cases. This leads one to consider the construction of frames with few distinct angles among pairs of frame vectors. In the special case of d+1 vectors in a d-dimensional space, it is well known that the vertices of a regular simplex will give an ETF. Using this, we will show a specific construction which, for a given dimension d and integer 1 < k ≤ d, gives a real unit norm tight frame such that the number of distinct angles among the vectors is bounded above by k. We will present several properties of this multi-angle tight frame. We also show how one can strategically choose subsets of such a multi-angle tight frame that will be equiangular or orthogonal. This property is meaningful in the context of erasures. We will also discuss a connection between certain unit norm tight frames with three angles and adjacency matrices of regular graphs.

 

Biography:

Somantika Datta is an associate professor of mathematics at the University of Idaho. She received a Ph.D. in mathematics from the University of Maryland, College Park. This was followed by postdoctoral positions at Arizona State University and Princeton University. Her research interests lie in the area of applied harmonic analysis with focus on frame theory and applications in signal processing.

 


CITI seminar – Daryus Chandra (University of Southampton, UK) – 11/02 at 14:00

Title: Quantum Communications over Noisy Entanglement

Date and Place: 11th February 2021 14:00 – link

Speaker: Dr Daryus Chandra (University of Southampton, UK)

 

Abstract: 

Within the Quantum Internet framework, multiple quantum devices are interconnected via pre-shared maximally-entangled quantum states for enabling various applications, including the on-demand classical and quantum communication. Hence, the pre-shared entanglement, which is constituted by the EPR pair, can be viewed as the primary consumable resources within the Quantum Internet. However, the generation and the distribution of the EPR pairs are subject to quantum decoherence imposed by the quantum channels, which will manifest as quantum errors. Similar to the classical domain, the quantum errors imposed by the quantum channels can be mitigated using quantum error-correction codes. In this talk, we will explore two approaches for achieving reliable quantum communication over noisy entanglement by incorporating quantum error-correction codes. More specifically, the first approach is constituted by the consecutive steps of quantum entanglement distillation followed by quantum teleportation, while the second approach can be viewed as the direct quantum communication over noisy entanglement. We will also discuss the pros and the cons of each approach while examining their compatibilities for a broader range of applications for the Quantum Internet framework.

 

Biography:

Daryus Chandra is a research fellow at the Next-Generation Wireless Research Group, School of Electronics and Computer Science, University of Southampton, UK. He received the B.Eng. and M.Eng. degree from the Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Indonesia, in 2013 and 2014, respectively. He obtained his PhD with the Next-Generation Wireless Research Group, School of Electronics and Computer Science, University of Southampton, UK, in 2020. He returned to Southampton in 2021 after completing a one-year postdoctoral research fellowship at Quantum Internet Research Group, University of Naples Federico II, Italy.

 


PhD Defence: “De l’évaluation des performances Wi-Fi à la mobilité contrôlée pour les réseaux de drones”, Remy Grünblatt, 8th of January 2021 at 14:00 PM

The defense will be streamed live here: link

 

Title

De l’évaluation des performances Wi-Fi à la mobilité contrôlée pour les réseaux de drones

 

Abstract

La mobilité dans les réseaux de télécommunications est souvent considérée comme un problème qu’il faut résoudre : un appareil mobile sans fil doit adapter ses paramètres de transmission afin de rester connecté à son ou ses homologues, car le canal évolue avec les mouvements de l’appareil. Les drones, qui sont des véhicules aériens sans pilote, ne font pas exception. En raison de leur grande liberté de mouvements, de leur mobilité tridimensionnelle, et ce dans des environnements aussi nombreux que variés, de leur charge utile limitée et de leurs contraintes énergétiques, et en raison du large éventail de leurs applications dans le monde réel, les drones représentent de nouveaux objets d’étude passionnants dont la mobilité est un défi. Pourtant, la mobilité peut aussi être une chance pour les réseaux de drones, surtout lorsque nous pouvons la contrôler. Dans cette thèse, nous explorons comment la mobilité contrôlée peut être utilisée pour augmenter les performances d’un réseau de drones, en mettant l’accent sur les réseaux IEEE 802.11 et les petits drones multi-rotor. Nous décrivons d’abord comment la mobilité est traitée dans les réseaux 802.11, c’est-à-dire en utilisant des mécanismes d’adaptation de débit, puis nous effectuons l’ingénierie inverse de l’algorithme d’adaptation de débit utilisé dans le chipset Wi-Fi du drone Intel Aero. L’étude de cet algorithme d’adaptation de débit, de manière à la fois expérimentale et par simulation, grâce à son implémentation dans le simulateur de réseau NS-3, permet de le comparer à d’autres algorithmes bien connus. Cette étude met en évidence l’importance de ces algorithmes pour les réseaux de drones, en lien avec leur mobilité, et la différence de comportement de chaque nœud en résultant. Par conséquent, une solution de mobilité contrôlée visant à améliorer les performances du réseau ne peut pas supposer beaucoup du comportement des algorithmes d’adaptation de débits. En outre, les applications des réseaux de drones sont diverses, et il est difficile d’imposer des contraintes de mobilité sans devenir incompatible avec un pan complet d’applications. Nous proposons donc une solution de mobilité contrôlée qui exploite le diagramme de rayonnement de l’antenne des drones. Cet algorithme est évalué grâce à outil de simulation développé pour l’occasion, permettant la simulation d’antennes et de drones, basé sur NS-3. Cette solution, qui fonctionne avec n’importe quel algorithme d’adaptation de débit, est distribuée, et ne nécessite aucune coordination globale ou communication spécifique qui pourrait s’avérer coûteuses. Elle ne nécessite pas non plus un contrôle complet de la mobilité du drone comme le requièrent les solutions de mobilité contrôlée existantes, ce qui rend cette solution compatible avec diverses applications.

 

 

Jury

  • Mme. Nathalie MITTON, Directrice de recherche à INRIA Lille Nord – Europe, Rapportrice
  • Mr. Enrico NATALIZIO, Professeur des universités au Technology Innovation Institute – Abu Dhabi, Rapporteur
  • Mme. Laure GONNORD, Maître de conférences à l’université Lyon 1 Claude Bernard – Lyon, Examinatrice
  • Mr. André-Luc BEYLOT, Professeur des universités à l’ENSEEIHT – Toulouse, Examinateur
  • Mr. Franck ROUSSEAU, Maître de conférences à Grenoble INP-Ensimag – Grenoble, Examinateur
  • Mme. Isabelle GUÉRIN-LASSOUS, Professeure des universités à l’université Lyon 1 Claude Bernard – Lyon, Directrice de thèse
  • Mr. Olivier SIMONIN, Professeur des universités à l’INSA de Lyon, Codirecteur de thèse

CITI seminar – Lucien Etienne (IMT Lille-Douai) – 17/12 at 14:00

Title: Self trigger co-design using LASSO regression

Date and Place: 17th December 2020 14:00 – link

Speaker: Lucien Etienne (IMT Lille-Douai)

 

Abstract: 

Networked systems have become more and more pervasive in many modern industrial application. A good justification for their deployment is that they can be cheaper/faster to set in place as well are scalable while also enabling lower maintenance cost. In the past decade a new paradigm has been developed where the controller is not sampled periodically (i.e. with a time–triggered policy), but rather sampled when some condition has been met (Usually a stability or performance criterion being violated). After recalling some general element on classical control scheme ( Linear Quadratic regulator and model predictive control) In this talk, the control of a linear time invariant system with self triggered sampling is considered .
In order to address the controller computation and the future sampling schedule a sparse optimization problem will be considered. A relaxation of the optimal self triggered control can be formulated as a LASSO regression. Using the properties of the solution of the Lasso regression it is shown how to obtain a controller ensuring practical or asymptotic stability while reducing sampling of the control action.

 

Biography:

Dr. Lucien Etienne received a M.Sc. Degree in applied mathematics at the INSA Rouen in 2012 and a joint Ph.D. in automatic control from the university of L’aquila and the university of Cergy-Pontoise in 2016. From 2016 to 2017 he was a post doctoral researcher at Inria Lille-Nord Europe. Since 2017 He is an associate professor at Institut Mines-Télécom Lille Douai. His research interests include switched and hybrid systems, observer synthesis and sampled data systems.

 


CITI seminar – Ignacio Rodriguez (Aalborg University) – 10/12 at 14:00

Title: Experimental Research on Wireless Systems for Industrial Automation

Date and Place: 10th December 2020 14:00 – link to come

Speaker: Ignacio Rodriguez (Aalborg University)

Host: Maracas

 

Abstract: 

The fourth industrial revolution – or Industry 4.0 (I4.0), will introduce major shifts in the way that products will be manufactured in the future. By integrating different cyberphysical systems (CPS), Internet-of-Things (IoT) technologies and cloud computing; the factories of the future will be equipped with highly flexible manufacturing equipment offering also a high reliability, thereby increasing the overall production throughput. One of the key enablers for such revolution is wireless communication. By replacing existing wirelines in the current industrial equipment with wireless technologies, the overall cost of deployment will be reduced, while at the same time a faster re-configuration of the smart production facilities will be enabled. Moreover, the use of wireless technologies will also allow for new industrial use cases requiring full mobility support such as autonomous robots moving items over different workstations in the factory for the sake of manufacturing customized products. During this talk, the AAU Industrial Automation Applied Research Flow will be introduced and illustrated with application examples detailing the different steps from understanding the needs of a factory and the specific communication requirements of industrial use cases; to the final deployment and optimization of the wireless solutions.

 

Biography:

Ignacio Rodriguez received the B.Sc. and M.Sc. degrees in Telecommunication Engineering from University of Oviedo, Spain, and the M.Sc. degree in Mobile Communications and the Ph.D. degree in Wireless Communications from Aalborg University, Denmark. Since December 2016, he has been a Postdoctoral Researcher at the same institution, where he is currently coordinating the Industry 4.0 experimental research activities at the Wireless Communication Networks Section and the AAU 5G Smart Production Lab in collaboration with the Department of Materials and Production. Ignacio is also an External Research Engineer with Nokia Bell Labs, where he is involved in 3GPP and ITU-R standardization activities. His research interests are mainly related to radio propagation, channel modeling, radio network planning and optimization, machine-to-machine communications, ultra-reliable and low-latency communications, 5G and Industrial IoT. He is a co-recipient of the IEEE VTS 2017 Neal Shepherd Memorial Best Propagation Paper Award, and in 2019, he was awarded with the 5G-prize by the Danish Energy Agency and the Danish Society of Telecommunication Engineers.

 


CITI seminar – Howard Yang (Zhejiang University, China) – 26/11 at 14:00

Title: Spatiotemporal Modeling of Wireless Networks

Date and Place: 26 / 11 / 2020 14:00 – https://bbb.tuxlab.net/b/mal-ngv-xm6-qgd

Speaker: Howard Yang (Zhejiang University, China)

Host: Maracas

 

Abstract: 

The rapid growth of wireless applications has brought along new challenges for the next generation network, which is expected to manage a massive number of devices in real-time under a highly dynamic environment. To give an adequate response, it is of necessity to develop an analytical model with which designers can build intuitions, grasp insights, and identify critical issues. In this talk, I will describe a framework for the analysis of large-scale wireless networks in which the transceivers interact with each other through the interference they caused and hence are correlated in both space and time. The analysis straddles stochastic geometry and queueing theory to cope with the issues of spatially interacting queues, and arrive at handy expressions for the SINR distribution. As a result, a wide variety of systems/architecture can be devised based on this theoretical foundation. Specifically, I will demonstrate how to adopt such a mathematical model to the analysis of two particular network metrics, i.e., the packet delay and age of information, and the subsequent network deployment guidelines based on the analytical results.

 

Biography:

Howard Hao Yang received the B.Sc. degree in Communication Engineering from Harbin Institute of Technology (HIT), China, in 2012, and the M.Sc. degree in Electronic Engineering from Hong Kong University of Science and Technology (HKUST), Hong Kong, in 2013. He earned the Ph.D. degree in Electrical Engineering from Singapore University of Technology and Design (SUTD), Singapore, in 2017, and stayed three years as a postdoc. He is now an assistant professor with the ZJU-UIUC Institute, Zhejiang University. Dr. Yang’s background also features appointments at the Princeton University in 2018 – 2019, and the University of Texas at Austin in 2015 – 2016. His research interests cover various aspects of wireless communications, networking, and signal processing, currently focusing on the modeling of next-generation wireless networks, age of information, and federated learning.

 


PhD Defence: “Étalonnage in situ de l’instrumentation bas coût pour la mesure de grandeurs ambiantes : méthode d’évaluation des algorithmes et diagnostic des dérives”, Florentin Delaine, 4th of December 2020 at 10:30AM

The defense will be streamed live here: link

 

Title

Étalonnage in situ de l’instrumentation bas coût pour la mesure de grandeurs ambiantes : méthode d’évaluation des algorithmes et diagnostic des dérives

Abstract

In various fields going from agriculture to public health, ambient quantities have to be monitored in indoors or outdoors areas. For example, temperature, air pollutants, water pollutants, noise and so on have to be tracked. To better understand these various phenomena, an increase of the density of measuring instruments is currently necessary. For instance, this would help to analyse the effective exposure of people to nuisances such as air pollutants. The massive deployment of sensors in the environment is made possible by the decreasing costs of measuring systems, mainly using sensitive elements based on micro or nano technologies. The drawback of this type of instrumentation is a low quality of measurement, consequently lowering the confidence in produced data and/or a drastic increase of the instrumentation costs due to necessary recalibration procedures or periodical replacement of sensors. There are multiple algorithms in the literature offering the possibility to perform the calibration of measuring instruments while leaving them deployed in the field, called in situ calibration techniques.

The objective of this thesis is to contribute to the research effort on the improvement of data quality for low-cost measuring instruments through their in situ calibration. In particular, we aim at 1) facilitating the identification of existing in situ calibration strategies applicable to a sensor network depending on its properties and the characteristics of its instruments; 2) helping to choose the most suitable algorithm depending on the sensor network and its context of deployment; 3) improving the efficiency of in situ calibration strategies through the diagnosis of instruments that have drifted in a sensor network. Three main contributions are made in this work. First, a unified terminology is proposed to classify the existing works on in situ calibration. The review carried out based on this taxonomy showed there are numerous contributions on the subject, covering a wide variety of cases. Nevertheless, the classification of the existing works in terms of performances was difficult as there is no reference case study for the evaluation of these algorithms. Therefore in a second step, a framework for the simulation of sensors networks is introduced. It is aimed at evaluating in situ calibration algorithms. A detailed case study is provided across the evaluation of in situ calibration algorithms for blind static sensor networks. An analysis of the influence of the parameters and of the metrics used to derive the results is also carried out. As the results are case specific, and as most of the algorithms recalibrate instruments without evaluating first if they actually need it, an identification tool enabling to determine the instruments that are actually faulty in terms of drift would be valuable. Consequently, the third contribution of this thesis is a diagnosis algorithm targeting drift faults in sensor networks without making any assumption on the kind of sensor network at stake. Based on the concept of rendez-vous, the algorithm allows to identify faulty instruments as long as one instrument at least can be assumed as non-faulty in the sensor network. Across the investigation of the results of a case study, we propose several means to reduce false results and guidelines to adjust the parameters of the algorithm. Finally, we show that the proposed diagnosis approach, combined with a simple calibration technique, enables to improve the quality of the measurement results. Thus, the diagnosis algorithm opens new perspectives on in situ calibration.

 

Jury

  • M. Jean-Luc Bertrand-Krajewski, Professeur des Universités, Université de Lyon, INSA Lyon, DEEP (Rapporteur)
  • M. Romain Rouvoy, Professeur des Universités, Université de Lille, Spirals (Rapporteur)
  • Mme Nathalie Redon, Maître de conférences, IMT Lille Douai, SAGE (Examinatrice)
  • M. Gilles Roussel, Professeur des Universités, Université du Littoral Côte d’Opale, LISIC (Examinateur)
  • Mme Bérengère Lebental, Directrice de recherche, Institut Polytechnique de Paris, École Polytechnique, LPICM (Directrice de thèse)
  • M. Hervé Rivano, Univeristé de Lyon, INSA Lyon, CITI Lab (Co-directeur de thèse)
  • M. Éric Peirano, Directeur général adjoint en charge de la R&D, Efficacity (Invité)
  • M. Matthieu Puigt, Maître de conférences, Université du Littoral Côte d’Opale, LISIC (Invité)

PhD Defence: “Deep Multi-Agent Reinforcement Learning for Dynamic and Stochastic Vehicle Routing Problems”, Guillaume Bono, 28th of October 2020 at 2PM

The defense will take place in amphitheater Chappe and you are all welcome to attend as long as there are enough place (35 persons max).
It will be also streamed live on youtube at: https://youtu.be/fvCz5ZXYN_I

 

Title

Deep Multi-Agent Reinforcement Learning for Dynamic and Stochastic Vehicle Routing Problems

Abstract

Routing delivery vehicles in dynamic and uncertain environments like dense city centers is a challenging task, which requires robustness and flexibility. Such logistic problems are usually formalized as Dynamic and Stochastic Vehicle Routing Problems (DS-VRPs) with a variety of additional operational constraints, such as Capacitated vehicles or Time Windows (DS-CVRPTWs). Main heuristic approaches to dynamic and stochastic problems simply consist in restarting the optimization process on a frozen (static and deterministic) version of the problem given the new information. Instead, Reinforcement Learning (RL) offers models such as Markov Decision Processes (MDPs) which naturally describe the evolution of stochastic and dynamic systems. Their application to more complex problems has been facilitated by recent progresses in Deep Neural Networks, which can learn to represent a large class of functions in high dimensional spaces to approximate solutions with high performances. Finding a compact and sufficiently expressive state representation is the key challenge in applying RL to VRPs. Recent work exploring this novel approach demonstrated the capabilities of Attention Mechanisms to represent sets of customers and learn policies generalizing to different configurations of customers. However, all existing work using DNNs reframe the VRP as a single-vehicle problem and cannot provide online decision rules for a fleet of vehicles.
In this thesis, we study how to apply Deep RL methods to rich DS-VRPs as multi-agent systems. We first explore the class of policy-based approaches in Multi-Agent RL and Actor-Critic methods for Decentralized, Partially Observable MDPs in the Centralized Training for Decentralized Control (CTDC) paradigm. To address DS-VRPs, we then introduce a new sequential multi-agent model we call sMMDP. This fully observable model is designed to capture the fact that consequences of decisions can be predicted in isolation. Afterwards, we use it to model a rich DS-VRP and propose a new modular policy network to represent the state of the customers and the vehicles in this new model, called MARDAM. It provides online decision rules adapted to the information contained in the state and takes advantage of the structural properties of the model. Finally, we develop a set of artificial benchmarks to evaluate the flexibility, the robustness and the generalization capabilities of MARDAM. We report promising results in the dynamic and stochastic case, which demonstrate the capacity of MARDAM to address varying scenarios with no re-optimization, adapting to new customers and unexpected delays caused by stochastic travel times. We also implement an additional benchmark based on micro-traffic simulation to better capture the dynamics of a real city and its road infrastructures. We report preliminary results as a proof of concept that MARDAM can learn to represent different scenarios, handle varying traffic conditions, and customers configurations.

 

Jury

  • François Charpillet, Research Director at INRIA Nancy Grand Est, Reviewer
  • Romain Billot, Professor at IMT Atlantique, Reviewer
  • René Mandiau, Professor at Université Polytechnique des Hauts de France, Examiner
  • Aurélie Beynier, Associate Professor at Sorbonne Université, Examiner
  • Christian Wolf, Associate Professor at INSA de Lyon, Examiner
  • Olivier Simonin, Professeur à l’INSA de Lyon, Thesis director
  • Jilles Dibangoye, Associate Professor at INSA de Lyon, Co-supervisor
  • Laëtitia Matignon, Associate Professor at Université Lyon 1, Co-supervisor
  • Florian Pereyron, Research Engineer at Volvo Group, Co-supervisor