All Years Seminars
[INMA] 2019-09-06 (14:00) : Spreading and diffusion on temporal networks
At Euler building (room A.002)
Speaker :
GUEUNING, Martin
Abstract :
Network theory provides a framework to model interacting systems from many different fields. The recent increase in the availability of empirical data has allowed to take into account more realistic characteristics of the networks. In particular, the details of the timing of the interactions have highlighted the non-Markovian nature of the agents in many systems.
In this work, we investigate stochastic processes on temporal networks.
First, we study the impact of non-Markovian activities on Random Walks. We show that memory in the trajectory of the random walker naturally emerges due to bursty behaviours and the presence of short cycles.
Second, we investigate spreading strategies on temporal networks. In particular, we show that the temporal sequence of a cascade of retweets may provide an insight about the way it spread on the network and how to exploit it to provide a list of users to target simultaneously, in order to maximize the final share of a message.
[INMA] 2019-08-27 (15:00) : Koopman Based Control: Bilinearization, Controllability and Optimal Control of Control-Affine Nonlinear Systems
At Euler building (room A.002)
Speaker :
Debdipta Goswami (University of Maryland)
Abstract :
Note the unusual time of this talk (15:00).
Nonlinear systems are ubiquitous in real world applications, but the control design for them is
not an easy task. Hence, methods are sought to transform a control-affine nonlinear system into linear
or bilinear forms to alleviate the problem of nonlinear controllability and control design. While
there are linearization techniques like Carleman linearization for embedding a finite-dimensional
nonlinear system into an infinite-dimensional space, they depend on the analytic property of the
vector fields and work only on polynomial space. The Koopman-based approach described here
utilizes the Koopman Canonical Transform (KCT) to transform the dynamics and ensures bilinearity
from the projection of the Koopman operator associated with the control vector fields on
the eigenspace of the drift Koopman operator. The sufficient conditions for exact bilinearization
are derived. Even if the conditions are not fully met, the approximate bilinearization can also be
posed as an optimization problem. The resulting bilinear system is then subjected to controllability
analysis using the Myhill semigroup method and Lie algebraic structures. Using the same Myhill
semigroup structure, we also seek to prove the existence of an energy-only optimal control.
[INMA] 2019-06-18 (14:00) : LINMA2120 student presentations
At Euler building (room A.002)
Speaker :
Pinon, Brieuc
,
and
Henri Devillez (UCLouvain)
Abstract :
2:00pm: Brieuc Pinon: Encoding Invariance in Deep Learning Architectures
2:40pm: Henri Devillez: Deep reinforcement learning
[INMA] 2019-06-11 (14:00) : Graph generation without isomorphs
At Euler building (room A.002)
Speaker :
McKay Brendan (Australian National University)
Abstract :
We consider the problem of exhaustively generating families of graphs in a way that avoids
isomorphic graphs while at the same time requiring very little memory even for very large families.
Such algorithms are important for listing chemical isomers and for many other applications.
As an example we present several algorithms for generating fullerenes (the "buckyball" molecules
made only of carbon atoms).
[INMA] 2019-06-04 (14:00) : Probabilistic modelling for sensor fusion with inertial measurements
At Euler building (room A.002)
Speaker :
Manon Kok (TU Delft)
Abstract :
In recent years, inertial sensors have undergone major developments. The quality of their measurements has improved while their cost has decreased, leading to an increase in availability. They can be found in stand-alone sensor units, so-called inertial measurement units, but are nowadays also present in for instance any modern smartphone, in Wii controllers and in virtual reality headsets.
The term inertial sensor refers to the combination of accelerometers and gyroscopes. They measure the external specific force and the angular velocity, respectively. Integration of their measurements provides information about the sensor's position and orientation. However, the position and orientation estimates obtained by simple integration suffer from drift and are therefore only accurate on a short time scale. In order to improve these estimates, we combine the inertial sensors with additional sensors and models. To combine these different sources of information, also called sensor fusion, we make use of probabilistic models to take the uncertainty of the different sources of information into account.
In this talk, I will discuss some of the work that I have done on the topic. First, I will discuss our approach to inertial human body motion capture, where we use data from multiple inertial sensors placed on the human body to estimate the body's position and orientation. Second, I'll discuss our work on indoor localisation where we use the magnetic field as a source of position information. We build a magnetic field map using Gaussian processes and solve the problem using simultaneous localisation and mapping (SLAM).
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