All Years Seminars
[INMA] 2021-11-23 (14h) : Implementing spintronic based neuromorphic computing hardware under the reservoir computing approach
At Euler building (room a.002)
Speaker :
Flavio Abreu Araujo (UCLouvain)
Abstract : The brain displays many signatures of non-linear dynamical behavior, including synchronization and complex transient behavior. These observations have inspired a whole class of neuromorphic concepts based on complex networks of interconnected non-linear nodes. Such non-linearity has been identified as one of the main ingredients to achieve excellent performance in cognitive tasks such as spoken digit recognition. In this work, we have quantified in detail the contribution of the acoustic filtering and the neural network, respectively, for spoken digit recognition task using three different frequency decomposition methods: Cochlear, MFCC and Spectrogram. In a first step, we have demonstrated that Cochlear and MFCC are powerful stand-alone features extractors, and they can achieve for themselves very high recognition level: up to 95.8% and 77.2% for cochlear and MFCC, respectively. We have found that such high recognition level is mainly due to the non-linear character of these frequency decomposition methods. First, we have investigated the non- linear dependence of the Spectrogram showing a huge increase of recognition rate from 10% (linear) to 85.6%. In a second step, we have evaluated the gain of the recognition rate provided by the neural network. For simplicity, we have modeled a neural network based on the non- linear dynamics of oscillators in the framework of the reservoir computing approach. The reservoir is generally composed by a large number of fixed and random interconnected non- linear oscillators which generates very complex non-linear dynamics. The key insight behind reservoir computing is that only the external connections (between the reservoir and the output layer) should be trained to obtain the desirable target. Reservoir computing has been identified to be very suitable for different hardware implementations: optical, photonic and spintronic devices. We have found that the contribution of the neural network is dominant for the linear Spectrogram filter but not in the other two cases, i.e., cochlear and MFCC. Finally, we have carried out experiments using non-linear and tunable spin-torque nano-oscillators exhibiting an excellent agreement with our simulations.
[INMA] 2021-11-16 (14h) : Gradient methods with Bregman distances
At Euler building (room a.002)
Speaker :
Radu Dragomir (UCL/INMA)
Abstract : Large-scale optimization problems from signal processing and machine learning are typically solved with gradient methods, because of their low cost per iteration and their simplicity. In this talk, we study a generalization of the standard gradient descent, which consists in replacing the Euclidean distance by a more general Bregman divergence induced by some simple convex reference function. This function is chosen to be adapted to the geometry of the problem at hand through the so-called relative smoothness condition.
We present some advances in this recent line of work, including the study of worst-case complexity through performance estimation problems (PEPs), as well as applications to low-rank matrix optimization.
[INMA] 2021-11-09 (14h) : Graphons for the Estimation of Performance Indices in Large Networks
At Euler building (room a.002)
Speaker :
Renato Vizuete (CentraleSupélec and GIPSA-lab, France )
Abstract : In recent years, the analysis of large networks has received increasing attention in many scientific fields due to the continuous evolution of the world towards a networked environment with a large number of connections. In this type of networks, uncertainties are almost present, and even the graph topology may not be available, which makes the analysis more complicated. One of the most promising tools to address these problems are graphons, defined as the limits of convergent sequences of dense graphs. In this talk, we will present the fundamentals of graphons and their applications in the estimation of performance indices associated with large networks sampled from graphons. In the first part, we will analyze the stability of a SIS epidemic over a network and its robustness to noise using the properties of the graphon operator. For the second application, we will present a study of the spectrum of the Laplacian matrix of a graph using the degree function of the graphon and the estimation of the well known average effective resistance of sampled networks.
[INMA] 2021-11-02 (14h) : Welcome Seminar
At Euler building (room a.002)
Section 1:Using photogrammetry for the objective study of ancient bowed instruments: a machine learning approach
Speaker :
Beghin, Philémon
Abstract : The morphology of today’s violin differs greatly from that of the first instruments of the late 16th century. Indeed, in order to meet the standards suggested by famous orchestras and conservatories, many ancient violins have been recut. It is important for musicologists, violin makers and museum curators to analyse the alterations they have undergone. Specialists agree that the instruments have been reshaped, but have difficulty to prove it rigorously. Moreover, the historical testimonies about this process are imprecise. It is therefore necessary to find an objective way to quantify violin geometry. This project aims to develop a set of algorithmic tools in order to compute an adequate mathematical representation that is able to describe the 3D shape of an object (violin) acquired by photogrammetry. Based on that representation, machine learning techniques will be applied in order to perform clustering and classification with the aim of quantifying their geometric characteristics, their possible anomalies and, if applicable, their original morphology. From an engineering point of view, the development of 3D analyses of objects as complex as violins will allow the growth of methods and knowledge potentially applicable to many fields outside of organology.
Section 2:Biomechanics of tactile feedback during dexterous manipulation of objects
Speaker :
Doumont, Donatien
Abstract : The biomechanics of the skin and underlying tissues play a fundamental role in the sense of touch. Indeed, when the finger comes in contact with an object, the deformations induced within these living tissues are translated by the mechanoreceptors (or tactile sensors) into neural signals, which are then interpreted by the brain to generate the appropriate motor response. This project is aimed at better understanding the feedback provided by the tactile afferents during interactions with objects. To that end, we will combine passive stimulation tasks mimicking the mechanical interactions during object manipulation and active manipulation tasks while monitoring the activity of tactile afferents (only in the passive case) and several key biomechanical parameters such as external forces, surface skin deformation, and skin moisture. This combination of techniques will enable us to gain insight into the essential aspects of tactile feedback that enable stable and dexterous control of fingertip forces during manipulation. Such insight will have direct implications for the development of sensorized bionic hands.
Section 3:Simulation of the stride-to-stride variability of patients with Parkinson’s disease
Speaker :
Vandamme, Clémence
Abstract : Gait is a complex mechanism involving several neural structures such as the motor cortex, the cerebellum and basal ganglia. It also requires the integration of sensorial feedback from visual, vestibular and peripheral receptors. The complex coordination of these structures is not yet fully understood. In particular, recent studies revealed that gait fluctuations exhibit Long Range Autocorrelation (LRA) such that fluctuations at any given moment are statistically related to those that occur over many different time scales. Up to now, the physiological origin of LRA remains unclear. Moreover, this specific correlation structure is altered in elderly and in people suffering from neurodegenerative diseases, such as Parkinson’s disease. Consequently, it is thought that an optimal level of LRA is a marker of stable gait and high adaptive capabilities. Clinically, LRA indicators could complement the diagnostic of Parkinson's disease and constitutes a precautious marker of risk of fall. This project aims to provide a comprehensive model that includes a continuous-time control of the biomechanics plant for both a healthy population and Parkinson’s patients, as it is an important challenge for fundamental understanding as well as for clinical applications.
Section 4:Influence of vestibular, visual and somesthesic inputs on dexterous manipulation
Speaker :
Vandergooten, Simon
Abstract : Since birth we evolve in a steady gravitational environment in which our brain has learned to manipulate objects. For example, we are able to unconsciously optimize the force with which we squeeze objects when handling them. In order to better characterize the influence of gravity on upper limb movement kinematics as well as on the dynamics of prehension (i.e. the act of grasping), several parabolic flight campaigns as well as ground-based experiments were performed. These experiments ultimately led to GRIP, an experiment carried out in the International Space Station that focuses on long-term motor adaptation to microgravity, in particular during dexterous object manipulation. On the ground, our current setup comprising a rotating chair, four motion-tracking cameras and the grip-lift manipulandum allows us to perform control experiments that are necessary for the interpretation of and analysis of GRIP data. With this project, we aim to study how visual, vestibular and somatosensory feedbacks interact and affect the planning of movement trajectory, the perception of the verticality and the anticipative force mechanisms underlying finger-arm coordination.
[INMA] 2021-10-26 (14h) : Welcome Seminar
At Euler building (room a.002)
Section 1:Employing neural network based control models to understand how brain generates movements
Speaker :
Kalidindi, Hari Teja
Abstract : Biological agents display impressive abilities to move under dynamic environmental conditions. Distributed regions in the central nervous system coordinate to generate the motor commands suitable for a given behavioral goal. However, the neural computations that underlie even the simplest of the movements are still equivocal. Historically, one of the main sources of the dispute has been due to the emphasis on describing the motor-encoding in isolated brain regions, by ignoring the effect of key control elements such as sensory-feedback, prediction and the physics of the body under control. In this talk, we demonstrate the utility of mechanistic neural networks as normative models to study neural computations underlying movement control. Particularly, we emulate recent observations from the primate motor cortex - the brain region that is implicated for generating movements.
Section 2:Sensing of low-rank plus sparse matrices
Speaker :
Vary, Simon
Abstract : Expressing a matrix as the sum of a low-rank matrix plus a sparse matrix is a flexible model capturing global and local features in data. This model is the foundation of robust principle component analysis, and popularized by dynamic-foreground/static-background separation amongst other applications. In this talk we develop guarantees showing that rank-r plus sparsity-s matrices can be recovered by computationally tractable methods from p=O(r(m+n-r)+s)log(mn/s) linear measurements. We establish that the restricted isometry constants for the low-rank plus sparse matrix set remain bounded independent of the problem size provided p/mn, s/p, and r(m+n-r)/p remain fixed. The developed theory and algorithms also apply to the fully observed case of Robust PCA.
Seminars
Subscribe (ICS)
INMA Contact Info
Mathematical Engineering (INMA)
L4.05.01
Avenue Georges Lemaître, 4
+32 10 47 80 36
secretaire-inma@uclouvain.be
Mon – Fri 9:00A.M. – 5:00P.M.
JOIN US