École Polytechnique Fédérale de Lausanne
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System for control of spasticity
No full textThe present invention relates to a neuromodulation/neurostimulation system (10) for the treatment of spasticity in a mammal, said system (10) comprising: - at least one control unit (12) configured and arranged to provide stimulation data, and - at least one stimulation unit (14), operatively connected to the at least one control unit (12), said at least one stimulation unit (14) being configured and arranged to deliver epidural electrical stimulation to the spinal cord of said mammal, according to said stimulation data, wherein the at least one stimulation unit (14) includes a biocompatible implantable lead (18) configured and arranged to cover at least a portion of the spinal cord of said mammal to deliver epidural electrical stimulation to the dorsal roots innervating the spastic muscles to target neuronal circuitry responsible for spastic episodes.UPCOURTINEAVP-R-TTOAlternative title(s) : (fr) Système de régulation de la spasticit
Big company, Small town. Spatial and Social Capital in a Persistent Company Town.
No full textSomething that is not much discussed when talking about Dalmine is the impact of its production as a supply chain sustaining extractive operations that also serve as a dominance and land exploitation mechanism. For instance, Dalmine supplied various oil and gas operations abroad, contributing to developing and exploiting resources in multiple nations. These pipelines, essential for extracting oil, gas, water, and supporting energy systems, are integral to the infrastructure of extractive sites, particularly in less urbanized countries. Such regions experienced economic neocolonialism throughout the twentieth century, shaping resource exploitation and growth patterns. This dynamic, far from being a relic of the past, continues to drive significant urban and ecological changes. For understanding the company town phenomenon, a critical nexus in understanding global historical processes from the late 19th to the early 20th centuries. The presentation explored the interplay among company towns and colonialism, new imperialism, and its spatial and social capital exploitation, underscoring how they collectively shaped territories and political landscapes worldwide. Company towns, often a byproduct of colonial enterprises, were integral to the framework of new imperialism, underpinning the expansionist policies of major powers.LAB-UHR
On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks
No full textIn supervised learning, the regularization path is sometimes used as a convenient theoretical proxy for the optimization path of gradient descent initialized from zero. In this paper, we study a modification of the regularization path for infinite-width 2-layer ReLU neural networks with nonzero initial distribution of the weights at different scales. By exploiting a link with unbalanced optimal -transport theory, we show that, despite the non-convexity of the 2-layer network training, this problem admits an infinite-dimensional convex counterpart. We formulate the corresponding functional-optimization problem and investigate its main properties. In particular, we show that, as the scale of the initialization ranges between 0 and +infinity, the associated path interpolates continuously between the so-called kernel and rich regimes. Numerical experiments confirm that, in our setting, the scaling path and the final states of the optimization path behave similarly, even beyond these extreme points.DOLALIBLI
Improved extended-range prediction of persistent stratospheric perturbations using machine learning
No full textOn average every 2 years, the stratospheric polar vortex exhibits extreme perturbations known as sudden stratospheric warmings (SSWs). The impact of these events is not limited to the stratosphere: but they can also influence the weather at the surface of the Earth for up to 3 months after their occurrence. This downward effect is observed in particular for SSW events with extended recovery timescales. This long-lasting stratospheric impact on surface weather can be leveraged to significantly improve the performance of weather forecasts on timescales of weeks to months. In this paper, we present a fully data-driven procedure to improve the performance of long-range forecasts of the stratosphere around SSW events with an extended recovery. We first use unsupervised machine learning algorithms to capture the spatio-temporal dynamics of SSWs and to create a continuous scale index measuring both the frequency and the strength of persistent stratospheric perturbations. We then uncover three-dimensional spatial patterns maximizing the correlation with positive index values, allowing us to assess when and where statistically significant early signals of SSW occurrence can be found. Finally, we propose two machine learning (ML) forecasting models as competitors for the state-of-the-art sub-seasonal European Centre for Medium-Range Weather Forecasts (ECMWF) numerical prediction model S2S (sub-seasonal to seasonal): while the numerical model performs better for lead times of up to 25 d, the ML models offer better predictive performance for greater lead times. We leverage our best-performing ML forecasting model to successfully post-process numerical ensemble forecasts and increase their performance by up to 20 % .SDS
Artificial Intelligence for Digital Heritage Innovation: Setting up a R&D Agenda for Europe
No full textArtificial intelligence (AI) is a game changer in many fields, including cultural heritage. It supports the planning and preservation of heritage sites and cities, enables the creation of virtual experiences to enrich cultural tourism and engagement, supports research, and increases access and understanding of heritage objects. Despite some impressive examples, the full potential of AI for economic, social, and cultural change is not yet fully visible. Against this background, this article aims to (a) highlight the scope of AI in the field of cultural heritage and innovation, (b) highlight the state of the art of AI technologies for cultural heritage, (c) highlight challenges and opportunities, and (d) outline an agenda for AI, cultural heritage, and innovation.SHS-EN
Designing Multi-disciplinary Interactive Virtual Environments for Next-Generation Immersive Learning Experiences: Case Studies and Future Directions in Astrobiology, Anatomy and Cultural Heritage
No full textThis chapter describes the creation and deployment of the Immersive Learning Level Editor (iLLE), a new cross-platform IVE engineered at the University of New South Wales (UNSW) Sydney. It illustrates the design considerations, discipline-specific workflows and pedagogical strategies employed in beginning to formulate a new and coherent pedagogy for immersive learning that is transferable, distributable and scalable across disciplines and platforms. In this way, it provides a number of methodological templates for contemplating discipline-specific immersive learning needs.EMPLU
Sarah Kenderdine + Merete Sanderhoff
No full textThis conversation focuses on the work of Sarah Kenderdine and Merete Sanderhoff, both of whom are based in Europe working on and within museums, as well as playing leadership roles in pan-institutional organisations such as Europeana and the Australasian Association for Digital Humanities. Topics include openness, data sovereignty, ephemeral culture, participation, empowerment and risk - and the technologies that are used to facilitate or express these concepts such as data translation, APIs, virtualisation, visualisation, simulation and immersion. This conversation was recorded on June 21, 2018. At the time, Sarah Kenderdine was Professor of Digital Museology, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland and Merete Sanderhoff was Curator of Digital Museum Practice, National Gallery of Denmark (SMK), Denmark.EMPLU
Less Conservatism, Stronger Robustness: Iterative Robust Gain-Scheduled Path Following Control of Autonomous Bus With Unstructured and Changing Dynamics
No full textPath-following control is a critical technology for autonomous vehicles. However, time-varying parameters, parametric uncertainties, external disturbances, and complicated environments significantly challenge autonomous driving. We propose an iterative robust gain-scheduled control (RGSC) with a finite time horizon based on linear matrix inequality (LMI) approach to address this issue. Firstly, a refined polytopic linear parameter varying (LPV) model is designed to consider inevitable time-varying parameters. Then, using a set of inequalities and constraints derived from Lyapunov asymptotic stability and the minimization of the worst-case objective function, a novel iterative RGSC technique is proposed to address the over-conservatism. Further, an expanded 3D phase plane is applied to define envelope surfaces, elucidating the connection of stable vehicle operation boundaries. Lane change maneuver is performed in TruckMaker/ Xpack4-RapidECU joint HIL platform. Compared with the infinite time horizon method, the tracking accuracy of our finite controller is significantly improved by 18.15%, 16.68%,14.32%, and 35.65% in cornering stiffness, mass, road conditions, and measurement noise, respectively. Simulation results reveal that our method maintains enhanced control accuracy, robustness, and less conservatism despite minor stability deterioration. An experimental test is carried out on an autonomous bus. The results indicate that our finite RGSC method demonstrates efficient computational characteristics and impressive tracking performance and holds the potential for seamless integration into autonomous vehicle systems. The suggested technique provides crucial insight into better trade-offs among robustness-oriented, less-conservatism-oriented, and stability-oriented control for practical application.SHS-EN
Failing to Hash Into Supersingular Isogeny Graphs
No full textAn important open problem in supersingular isogeny-based cryptography is to produce, without a trusted authority, concrete examples of 'hard supersingular curves' that is equations for supersingular curves for which computing the endomorphism ring is as difficult as it is for random supersingular curves. A related open problem is to produce a hash function to the vertices of the supersingular -isogeny graph, which does not reveal the endomorphism ring, or a path to a curve of known endomorphism ring. Such a hash function would open up interesting cryptographic applications. In this paper, we document a number of (thus far) failed attempts to solve this problem, in the hope that we may spur further research, and shed light on the challenges and obstacles to this endeavour. The mathematical approaches contained in this article include: (i) iterative root-finding for the supersingular polynomial; (ii) gcd's of specialized modular polynomials; (iii) using division polynomials to create small systems of equations; (iv) taking random walks in the isogeny graph of abelian surfaces, and applying Kummer surfaces and (v) using quantum random walks.LASE